THESE L`UNIVERSITÉ BORDEAUX 1 Par M. Van Tu DO DOCTEUR

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N0 d’ordre : 4561
THESE
PRÉSENTÉE À
L’UNIVERSITÉ BORDEAUX 1
ECOLE DOCTORALE SCIENCES ET ENVIRONNEMENTS
Par M. Van Tu DO
POUR OBTENIR LE GRADE DE
DOCTEUR
SPÉCIALITÉ : BIOGÉOCHIMIE ET ÉCOSYSTÈMES
EVOLUTION ET SANTÉ DES HERBIERS À Zostera noltii DANS
LE BASSIN D’ARCACHON À TRAVERS LA DYNAMIQUE DE
LA MACROFAUNE BENTHIQUE ASSOCIÉE
EVOLUTION AND HEALTH OF SEAGRASS Zostera noltii IN
ARCACHON BAY THROUGH THE DYNAMICS OF
ASSOCIATED BENTHIC MACROFAUNA
Soutenue le : 17 Septembre 2012
Après avis de :
M. Ángel BORJA, Chercheur, AZTI – Tecnalia, Espagne
M
me
Sarah CULLOTY, Chercheur, Université de Cork, Irelande
Rapporteur
Rapporteur
Devant la commission d’examen formée de :
M. Ángel BORJA, Chercheur, AZTI – Tecnalia, Espagne
Rapporteur
Mme Sarah CULLOTY, Chercheur, Université de Cork, Irelande
Rapporteur
M. Antoine GRÉMARE, Professeur, Université Bordeaux 1
Examinateur
M. Xavier de MONTAUDOUIN, Maître de Conférences, Université Bordeaux 1
Directeur de thèse
Remerciements
Je remercie Antoine Grémare de m’avoir accueilli dans son laboratoire EPOC (UMR 5805) et
d’avoir accepté de participer à mon jury.
Je remercie sincèrement Ángel Borja et Sarah Culloty d’avoir accepté d’être les rapporteurs
de mon travail de thèse et de participer à mon jury.
Cette thèse doit son existence tout d'abord à Xavier de Montaudouin qui m’a fait l’honneur
d’accepter la direction de mes recherches. Ses conseils et remarques pertinents m'ont toujours
été d'une grande aide. Son soutien affectif lors des moments très difficiles de ma vie familiale
est très appréciable. Je tiens à lui exprimer toute ma gratitude.
Je remercie l’ensemble des mes collègues de l’équipe ECOBIOC du laboratoire EPOC, avec
qui j’ai eu des discussions et des échanges scientifiques très fructueux. Mes remerciements
vont particulièrement à Hugues Blanchet, qui avec sa grande disponibilité m’a guidé dans la
classification de la faune benthique et surtout dans l’analyse des données, notamment sous
Primer. Ses suggestions brillantes m’ont beaucoup inspiré. La relecture et les corrections
qu’il a faites rendent mon mémoire plus lisible et plus compréhensible.
Je tiens à remercier chaleureusement Nicolas Lavesque pour ses conseils pertinents, pour son
aide dans la détermination des espèces, la vérification des bases de données et des
synonymies, dans l’analyse des données et la conception des cartes.
J’exprime ma reconnaissance à Cindy Binias qui m’a aidé lors des analyses portant sur les
maladies de la palourde et de la coque (trématodiase, perkinsose, maladie du muscle marron).
Lors de mon travail de recherche, plusieurs collègues m’ont fait découvrir différents outils de
travail. Je remercie Florence Jude-Lemeilleur, Line Bourasseau et Cécile Dang de m’avoir
initié aux méthodes d’analyse de Perkinsus et de la BMD.
La collecte des échantillons sur le terrain a été intense. Je remercie sincèrement Francis
Prince, Henri Bouillard, Pascal Lebleu, Benoît Gouillieux et Laurent Letort pour leur aide
dans ce travail.
Je remercie également Nguyen Van Tien, Le Quang Tuan, Tu Lan Huong, Cao Van Luong de
m’avoir fourni des ressources documentaires appropriées concernant les herbiers et les cartes
du Vietnam.
Je remercie à ce sujet Agnès Massonneau pour son aide à trouver certains articles de
recherche dans mon domaine.
Un grand merci à Sarah Culloty qui s’est montrée une lectrice et une correctrice infatigable
des manuscrits pour la sauvegarde de la langue anglaise ! Ce texte doit beaucoup à son sens
aigu de l’observation et à ses compétences en langue anglaise.
Merci beaucoup à Marie-Claude Duck qui a réservé les billets d’avion lors de mes voyages
de visite familiale au Vietnam et à Florence Daniel qui a rendu mon séjour (hébergement) à la
Station Marine le plus agréable possible.
Ce travail de recherche a été fait dans de très bonnes conditions grâce aux concours financiers
de divers organismes. Je remercie les responsables du Syndicat Intercommunal du Bassin
d’Arcachon, le Ministère de l'Écologie et du Développement Durable (MEDAD) d’avoir
accordé le financement pour la recherche dans le cadre des projets LITEAU2-3, QuaLiF et
REPAMEP, respectivement (Coord. : X. de Montaudouin).
Je remercie le CROUS de Bordeaux qui a assuré toutes les démarches administratives lors de
mon séjour en France.
La réalisation de ce travail de recherche a été possible grâce à une bourse octroyée par le
Ministère de l’Education et de la Formation vietnamien. Je remercie chaleureusement les
responsables de ce programme très innovateur.
Je remercie l’ensemble de mes proches et de mes amis qui m’ont soutenu durant les années
de thèse. Merci enfin à ma famille, mes parents et frères et sœurs de m’avoir accompagné
dans mes choix, assisté quand j’en avais besoin et de m’avoir toujours donné toute latitude.
Résumé
L’objectif général était d’évaluer la réponse du macrobenthos à la dynamique d’un
herbier marin à Zostera noltii (colonisation, maturation, destruction, restauration), dans le
Bassin d’Arcachon, une lagune du sud-ouest de la France.
Colonisation – Quand l’herbier commence à se développer, la structure de la
macrofaune diverge immédiatement entre habitats d’herbier et de sables nus, sans cependant
que les indice biotiques testés (AMBI (AZTI’s Marine Biotic Index), BOPA (Benthic
Opportunistic Polychaetes Amphipods Index), BENTIX) varient. De même, la population du
bivalve dominant, la coque (Cerastoderma edule), souffre du développement de l’herbier. Sa
communauté parasitaire (trématodes) est modifiée, sans que cela n’influe sur la dynamique
des coques.
Maturation – A l’échelle du Bassin, le développement de l’herbier (considéré comme un
signe de “bonne santé”) a été comparé à la santé de deux bivalves endogés dominants, la
palourde japonaise (Ruditapes philippinarum) et la coque (C. edule), évaluée en termes de
prévalence de maladie. Aucune corrélation n’existe entre le taux de recouvrement d’herbier et
la prévalence de trois maladies (trématodiase, perkinsose, maladie du muscle marron).
Destruction - Restauration – Entre 2002 et 2010, la surface d’herbier a diminué de 1/3. En
termes de structure de communautés et d’espèces dominantes, peu de différences ont été
notées au sein de chaque année (entre les 12 stations) et entre années, indépendamment du
déclin de l’herbier. Parmi les indicateurs biotiques, l’indice multivarié MISS est en
adéquation avec la relative similarité de la structure de la macrofaune benthique entre les
groupes discriminés par l’analyse MDS.
En 2005, des activités de dragage dans le Bassin d’Arcachon ont abouti à
l’enfouissement de 0,32 km2 d’herbier à Z. noltii. La structure du macrobenthos a été
immédiatement modifiée sans retour à l’état initial sur les zones couvertes de sable. En
revanche, le macrobenthos (endofaune) s’est rapidement rétabli dans les zones couvertes de
vase alors que l’herbier n’a commencé à se développer qu’au bout de 5 ans après les travaux.
Le dernier chapitre de cette thèse donne un bref aperçu des connaissances actuelles
sur les herbiers vietnamiens et des possibles travaux scientifiques à y mener.
Abstract
The overall objective was to assess macrobenthos response to marine Zostera noltii
seagrass dynamics (colonization, maturation, destruction, restoration), in Arcachon Bay, a
French South-western lagoon.
Colonization – When seagrass starts to develop, the structure of macrofauna community
immediately diverges between sand and seagrass habitats, without however modifying tested
biotic indices (AMBI (AZTI’s Marine Biotic Index), BOPA (Benthic Opportunistic
Polychaetes Amphipods Index), BENTIX). As well, population of the dominant bivalve, the
cockle (Cerastoderma edule), suffers from seagrass development. Their parasite (trematode)
community are impacted, but not sufficiently to explain cockle deficit in seagrass.
Maturation – At the scale of the Bay, seagrass development (considered as a sign of “good
health”) is compared to the fitness of the two dominant infaunal bivalves, the Manila clam
(Ruditapes philippinarum) and the cockle (Cerastoderma edule), measured in terms of
disease prevalence. There was no correlation among seagrass cover rate and the prevalence of
three diseases: trematodiosis, perkinsosis and Brown Muscle Disease.
Destruction - Restoration – Between 2002 and 2010, seagrass cover decreased by 1/3. When
looking at community structure and dominant species, there were moderate differences within
(among 12 stations) and among years, independently of seagrass decline. Among biotic
indicators, multivariate index MISS was in adequation with the relative similarity of
macrofauna structure among groups discriminated by MISS analysis.
In 2005, dredging activities in Arcachon Bay led in burying 0.32 km2 of Z. noltii.
Macrobenthos structure was immediately modified and did not recover in places buried by
sand. Conversely, macrobenthos (infauna) recovered rapidly in areas cover by mud, while
seagrass began to develop again five years after work.
The last chapter of the thesis provides a brief insight of the seagrass in Vietnam, the
actual knowledge and what could be investigated.
Tóm tắt (Résumé en Vietnamien)
Mục đích của luận án là đánh giá các phản ứng của động vật đáy cỡ lớn với những
biến đổi của thảm cỏ biển Zostera noltii (phát triển, suy thoái, phá hủy và phục hồi) ở Vịnh
Arcachon, Tây-Nam nước Pháp.
Phát triển – Khi thảm cỏ biển bắt đầu phát triển, cấu trúc của quần xã động vật đáy lập tức
khác biệt so với quần xã động vật đáy ở khu vực không có cỏ biển (vùng cát). Tuy nhiên, các
chỉ số sinh học (AMBI (AZTI’s Marine Biotic Index), BOPA (Benthic Opportunistic
Polychaetes Amphipods Index), BENTIX) không thay đổi. Chiếm ưu thế trong quần xã động
vật đáy, quần thể Sò (Cerastoderma edule) chịu tác động từ sự phát triển của thảm cỏ biển.
Quần xã kí sinh trùng sán lá (trematode) trong quần thể Sò cũng bị tác động nhưng không đủ
nhiều để giải thích sự suy giảm của quần thể Sò trong thảm cỏ biển.
Suy thoái – Trên quy mô của Vịnh, khi thảm cỏ biển phát triển (dấu hiệu của hệ sinh thái
“khỏe mạnh”), sức khỏe của hai quần thể chiếm ưu thế là Nghêu (Ruditapes philippinarum)
và Sò (Cerastoderma edule) được đánh giá dựa trên tỷ lệ nhiễm bệnh. Không có mối liên hệ
về tỷ lệ bao phủ của thảm cỏ biển với tỷ lệ nhiễm 3 loại bệnh (trematode, perkinsosis, Brown
Muscle Disease).
Phá hủy và phục hồi – Từ năm 2002 đến 2010, thảm cỏ biển bị suy giảm 1/3 diện tích bao
phủ. Tuy nhiên, biến đổi xảy ra trong quần xã động vật đáy và những loài chiếm ưu thế chỉ ở
mức trung bình và độc lập với sự suy giảm của cỏ biển. Trong số các chỉ số sinh học, chỉ có
chỉ số đa biến MISS phản ánh được sự tương đồng của cấu trúc các nhóm quần xã động vật
đáy được tách biệt bởi phân tích đa chiều MDS.
Năm 2005, các hoạt động nạo vét trong vịnh Arcachon dẫn đến việc chôn lấp 0.32
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km cỏ biển Z. noltii. Ở khu vực bị bao phủ bởi cát, cấu trúc quần xã động vật đáy ngay lập
tức biến đổi và không thể phục hồi. Ngược lại, ở khu vực bị bao phủ bởi bùn, quần xã động
vật đáy đã phục hồi trong khi khi thảm cỏ biển bắt đầu phát triển lại sau 5 năm.
Chương cuối cùng của luận án đưa ra một cái nhìn tổng quan về thảm cỏ biển ở Việt
Nam và những vấn đề cần nghiên cứu.
SOMMAIRE/CONTENTS
Chapter 1 - General introduction............................................................................................1 Chapter 2 - Seagrass colonization: knock-on effect on zoobenthic community,
populations and individuals’ health ......................................................................................13 1. Introduction......................................................................................................................14 2. Materials and methods .....................................................................................................16 2.1. Study area..................................................................................................................16 2.2. Sampling procedure ..................................................................................................18 2.3. Data analysis .............................................................................................................19 3. Results..............................................................................................................................20 3.1. Development of the seagrass bed and modifications of sediment characteristics ....20 3.2. Macrobenthic community .........................................................................................21 3.3. Cockle population and related trematodes................................................................28 4. Discussion ........................................................................................................................32 4.1. Kinetic of seagrass development and associated macrofauna ..................................32 4.2. Seagrass development and benthic community health .............................................35 4.3. Seagrass development and cockle population health................................................36 Chapter 3 - Environmental factors contributing to the development of Brown Muscle
Disease and Perkinsosis in Manila clams (Ruditapes philippinarum) and trematodiasis in
cockles (Cerastoderma edule) of Arcachon Bay ....................................................................41 1. Introduction......................................................................................................................42 2. Material and Methods ......................................................................................................44 2.1. Study site...................................................................................................................44 2.2. Sampling procedure ..................................................................................................44 2.3. Environmental factors...............................................................................................46 2.4. Bivalve models and associated pathology ................................................................49 3. Results..............................................................................................................................51 3.1. Environmental factors...............................................................................................51 3.2. Manila clam and Perkinsosis ....................................................................................52 3.4. Cockle and trematodes..............................................................................................56 4. Discussion ........................................................................................................................60 Chapter 4 - Limited consequences of seagrass decline on benthic macrofauna and
associated biotic indicators.....................................................................................................65 1. Introduction......................................................................................................................66 2. Materials and methods .....................................................................................................68 2.1. Study area..................................................................................................................68 2.2. Sampling procedure ..................................................................................................69 2.3. Data analysis .............................................................................................................71 3. Results..............................................................................................................................73 3.1. Macrobenthic community structure ..........................................................................73 3.2. Biotic Indices ............................................................................................................80 4. Discussion ........................................................................................................................80 4.1. Associated macrofauna in seagrass...........................................................................80 4.2. Benthic community in declined seagrass mudflats...................................................82 4.3. Biotic Indices ............................................................................................................84 Chapter 5 - Seagrass destruction: benthic community alteration, secondary production
loss, biotic index reaction and recovery possibility..............................................................89 1. Introduction......................................................................................................................90 2. Material and methods.......................................................................................................92 2.1. Study site...................................................................................................................92 2.2. Macrofauna sampling................................................................................................94 2.3. Sediment and seagrass leaves analysis .....................................................................94 2.4. Estimated loss of secondary production ...................................................................95 2.5. Data analysis .............................................................................................................95 3. Results..............................................................................................................................99 3.1. Seagrass and sediment disposal ................................................................................99 3.2. Main macrozoobenthic assemblages identified in the dataset ................................101 3.3. Trend in the numerical descriptor of the macrofauna assemblages........................102 3.4. Dynamic of impact and recovery of macrobenthic community..............................105 3.5. Loss of secondary production .................................................................................109 3.6. Biotic Indices ..........................................................................................................111 4. Discussion ......................................................................................................................113 4.1. Seagrass destruction and recolonization .................................................................113 4.2. Benthic community alteration and recovery possibility .........................................114 4.3. Secondary production loss ......................................................................................116 4.4. Biotic indices reaction.............................................................................................116 Chapter 6 - Perspective in Vietnam ....................................................................................119 1. Seagrass species in Vietnam ..........................................................................................119 2. Biodiversity in seagrass .................................................................................................122 3. Decline of seagrass in Vietnam......................................................................................122 4. Use of seagrasses in Vietnam ........................................................................................123 5. Threats to seagrass in Vietnam ......................................................................................123 6. Response to threats ........................................................................................................124 7. Researches on seagrass in Vietnam ...............................................................................124 8. Management seagrass beds in Vietnam .........................................................................125 8.1. In terms of science ..................................................................................................126 8.2. In terms of management..........................................................................................127 8.3. In terms of society...................................................................................................127 9. My contribution to Vietnamese seagrass .......................................................................127 Chapter 7 - General discussion - Conclusion .....................................................................129 1. Benthic macrofauna and Zostera noltii seagrass ...........................................................129 2. Seagrass and bivalve health ...........................................................................................131 3. Seagrass and biotic indices ............................................................................................131 References..............................................................................................................................137 Chapter 1- General introduction
Chapter 1 - General introduction
The importance of benthic macrofauna in the functioning of marine ecosystems is
particularly evident in shallow waters (e.g. coastal systems) where its biomass represents a
large proportion of total living mass (Bouma et al., 2009a). Benthic macrofauna largely
participate to food webs and transfers of energy among the different compartments of marine
systems (Reiss and Kroncke, 2005). However, coastal ecosystems are often made of a mosaic
of different habitats. Each habitat has its own characteristics and shelters particular benthic
assemblages (Blanchet et al., 2004; Boström et al., 2006b). There is usually a drastic
difference between hard substrate communities and soft sediment communities, but such
differences also occur among habitats of each of these categories. For soft sediments of
coastal areas, it is often accepted that muddy sediments shelter higher benthic macrofauna
diversity, along with higher abundance and higher biomass than sandy sediments (Bachelet et
al., 1996). Higher organic matter content in and on muddy sediments partly explains this
tendency. Beyond grain-size characteristics of the sediment (often related to organic matter
content), spatial heterogeneity is a major key factor explaining macrofauna distribution, the
general trend being that such “ecosystem engineer” attracts a particularly high diversity of
fauna, with high biomass and abundance, due to niche diversity. In our temperate coastal
waters, many such heterogeneous ecosystems have been described, such as oyster-reefs,
maerl bottoms, Sabellaria reefs, mussel beds, etc., but seagrass beds are the most studied
around the world (Siebert and Branch, 2006; Bouma et al., 2009b; Brun et al., 2009; van
Katwijk et al., 2010). The reason for this interest is certainly related to the multi-functional
role of seagrass. Beyond its architectural complexity that attracts fauna, seagrass is a direct or
indirect (i.e. support to epibionts) source of food for many organisms, participates in
sediment biogeochemistry (roots and rhizomes), traps contaminants, serves as a nursery for
juveniles from the open ocean or for spawning grounds for adults also coming from the open
ocean. Thus, seagrass does shelter important macrofauna and consequently does play an
important role in coastal food webs and other processes (Hemminga and Duarte, 2000;
Larkum et al., 2006).
Seagrass species belong to are angiosperms. They are rhizomatous and clonal plants,
occupying space through the reiteration of shoots. Their leaves and roots are produced as
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Chapter 1- General introduction
result of rhizome extension. This asexual process appears to be the main mechanism for
seagrass proliferation, although species also reproduce sexually. Seagrasses form highly
productive ecosystems, rivalling with the most productive biomes on earth. Their meadows
generally occupy 0-30 m depth littoral fringes off all the continents except Antarctica (Figure
1.1). Seagrass store a large fraction of their substantial production, being responsible for
about 15% of the carbon storage in the ocean. In addition, seagrass exports up to 24% of their
net production to adjacent ecosystems and seaward, acting as important trophic links with
other ecosystems. In addition to their high primary production, seagrass performs many other
functions in the ecosystem such as provision of food for coastal food webs, provision of
oxygen to waters and sediments, carbon sequestration from the atmosphere, organic carbon
export to adjacent ecosystems, sediment stabilization, prevention of sediment resuspension,
improvement of water transparency, shoreline protection, habitat for microbes, invertebrates
and vertebrates (often endangered or commercially important) and trapping and cycling of
nutrients. These functions render seagrass meadows unique, ranking amongst the most
valuable ecosystems in the biosphere, due to the important services they provides (see review
in Duarte, 2002).
On the other hand, seagrass habitats are endangered worldwide. They are vulnerable
ecosystems (Holmer and Marba, 2010), and the services they provide are threatened by the
immediate impacts of coastal development and growing human populations as well as by the
impacts of climate change and ecological degradation (Duffy, 2006; Orth et al., 2006; Airoldi
and Beck, 2007). When assessed globally, seagrass meadows rank among the most threatened
ecosystems on Earth (Duarte et al., 2008). Indeed, seagrass area cover is declining across the
globe and the rate of loss is accelerating (Duarte, 2002; Orth et al., 2006; Hughes et al., 2009;
Waycott et al., 2009; Costello and Kenworthy, 2011) (Figure 1.2). The consequences of
continuing seagrass decline extends far beyond the areas where seagrasses grow (Heck et al.,
2008). Seagrass losses also threaten the future of endangered species such as dugong,
manatee, green turtle (Waycott et al., 2009), salmon (Hughes et al., 2009). Seagrass losses
decrease primary production, carbon sequestration and nutrient cycling in the coastal zone
(Worm et al., 2006). If the current rate of seagrass loss is sustained or continues to accelerate,
the ecological losses will also increase, causing even greater ill-afforded economic losses
(Waycott et al., 2009).
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Figure 1.1. Global seagrass distribution shown as blue points and polygons (data from 2005
UNEP-WCMC) and geographic bioregions: 1. Temperate North Atlantic, 2. Tropical
Atlantic, 3. Mediterranean, 4. Temperate North Pacific, 5. Tropical Indo-Pacific, 6.
Temperate Southern Oceans (source : Short et al., 2007).
Figure 1.2. Global map indicating changes in seagrass area plotted by coastline regions.
Changes in seagrass areal extent at each site are defined as declining (red) or increasing
(green) when areal extent changed by >10%, or no detectable change (yellow) when final
area was within ±10% of the initial area. There were 131 sites in North America, 34 sites in
Europe, and 40 sites in Australia (source Waycott et al., 2009).
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If the relationship between seagrass and epifauna, and particularly motile megafauna,
is well documented, the link between seagrass and infauna is less well understood (Fredriksen
et al., 2010). Indeed, most of the approaches have consisted of comparing areas with and
without seagrass, with a major bias: factors inducing the absence or presence of seagrass
could also be the factors structuring the different benthic assemblages in areas with and
without this vegetation (notion of “confounding factor”) (Fonseca et al., 1990; Orth, 1992;
Edgar et al., 1994; Boström and Bonsdorff, 1997; Connolly, 1997; Sheridan, 1997;
Hemminga and Duarte, 2000; Beck et al., 2001; Crooks, 2002; Heck et al., 2003;
Nagelkerken and van der Velde, 2004; Nakaoka, 2005; Fredriksen et al., 2010). The major
aim of this thesis was to investigate the dynamics of seagrass associated fauna (and
particularly infauna) in the framework of seagrass dynamics: seagrass colonization,
seagrass chronic decline, seagrass brutal destruction and seagrass restoration. In other
words, does seagrass dynamics affect associated infauna and at which scale? Our
primary approach will be through detailed community analyses using modern tools,
developed for example in PRIMER software (Multidimensional scaling (MDS), Similarity
Percentage (SIMPER) …). We consider that this is the most accurate way to describe benthic
communities and their temporal evolution. Then, we will concomitantly measure the
fluctuation of other parameters related to seagrass studies, like: 1) population dynamics and
parasite infection of some dominant infaunal species; and 2) Biotic indices developed or not
in the framework of the European Water Framework Directive (WFD).
1) Population dynamics of marine invertebrates (i.e. proxy of population health) is
controlled by a variety of abiotic and biotic factors, including parasitism. For example, this is
the case for some commercial marine bivalves such as the edible cockle (Cerastoderma
edule) and the Manila clam (Ruditapes philippinarum) that host a number of common marine
parasites which can occasionally be responsible for mass mortalities and economic losses
(Paillard, 2004; Villalba et al., 2004). Among parasites, trematodes occurrence in ecosystems
have an ambiguous status in determining community health: their presence is a sign of good
health, but in return they can badly affect some dominant populations. Indeed, the diversity of
trematodes appears to be a good index of the health of benthic communities. In areas where
the human impact is weak such as in the natural reserves, the parasitic trematode fauna
generally includes a large number of species (Bartoli and Boudouresque, 1997). A diverse
and abundant community of parasites might be reflective of a diverse and abundant
4
community of hosts. Thus, we are left with the apparent quandary that a diverse and healthy
ecosystem should also be one with many parasites (Lafferty, 2003). Conversely, trematodes
may have detrimental effects on population that they parasitize, begetting mass mortality.
For example, parasites can influence the behaviour of the host (Marcogliese, 2002;
Moore, 2002), reduce the impact of herbivores (Thaler et al., 1999) or make hosts more
susceptible to predators (Hudson et al., 1992; Packer et al., 2003); these indirect effects
coupled with the direct effects on abundance of hosts can have an important role in
influencing how energy flows through communities (Hudson et al., 2006). Knowledge of
parasite diversity is thus not only valuable in itself in assessing a neglected part of
biodiversity but it might also serve as a valuable and convenient proxy for ecosystem health
(Hudson et al., 2006). There is now increasing evidence that parasites can be a good proxy
for estimating the health of an ecosystem, not only because they integrate biodiversity over a
period of time, but also because there is growing evidence that some parasites remove
environmental toxins when they are ingested by their hosts (Sures, 2004). As already
mentioned, seagrass bed attracts benthic macrofauna including species that are potential first
intermediate hosts (such as Hydrobia ulvae, Littorina littorea and Nassarius reticulatus) for
trematodes species (de Montaudouin et al., 2012). Moreover, biotic processes which are
affected by ecosystem engineer as seagrass are the major drivers to explain trematode
parasite distribution within a wide range of scales (de Montaudouin and Lanceleur, 2011).
However, the relationship between seagrass status and parasite communities has still received
little attention (Gam et al. 2009a)..
2) Finally, apart from seagrass extent, macrofauna structure and dominant population
dynamics, a way to evaluate and quantify ecological status (ES) of ecosystems consists of
calculating biotic indices (BI) and comparing them to pristine/reference situations. That was
one of the objectives of the European WFD. The WFD establishes a basis for the protection
and improvement of transitional (i.e. estuarine) and coastal waters, amongst other systems. Its
final objective is to achieve not less than ‘good ecological status’ for all waters, by 2015
(EEC,
2000).
Ecological
assessment
is
based
upon
the
status
of
biological,
hydromorphological and physico-chemical quality elements. The biological elements to be
considered are phytoplankton, macroalgae, angiosperms, benthic macroinvertebrates and, in
transitional waters only, fishes. The Ecological Status (ES) of a water body is determined by
5
comparing data obtained from monitoring networks (Ferreira et al., 2007) with reference
(undisturbed) conditions, thus deriving an ecological quality ratio (EQR) (see review in Borja
et al. (2009a)). The EQR is expressed as a numerical value lying between 0 and 1; ‘High
status’ is represented by values close to 1, whilst ‘Bad status’ values lie close to 0. The range
is divided into five ES classes, ‘High’, ‘Good’, ‘Moderate’, ‘Poor’ and ‘Bad’ (Borja et al.,
2009a). Among the elements by the WFD to assess aquatic ecosystems’ ecological status are
the benthic macroinvertebrates. Their location at the sediment/water interface and their life
history traits and characteristics make them highly suitable to assess environmental
conditions. Indeed, most benthic taxa are relatively sedentary, long-lived and thus, unable to
avoid adverse environmental conditions integrating and reflecting the history of local effects
of stress over time. Besides, macrobenthic assemblages comprise species that show different
tolerances to stress and are sensitive indicators of change (Verissimo et al., 2012). Moreover,
macrofauna are dominated by species with different mobility, life-cycles and tolerance to
stress, which covers the WFD demand of integrating differently sensitive species. The
response of macrobenthic communities to several types of stress is well studied, based on
multivariate analyses that takes into account variations in species diversity and their relative
abundance between perturbed and control sites. Based on this knowledge it is possible to
determine a priori the ecological indicator behaviour and thus its appropriateness to detect
changes in the variable of interest (Patricio et al., 2009). Beyond WFD framework, a variety
of indices are available, which measure the status of ecological conditions and trends in
succession in marine benthic systems (Reiss and Kroncke, 2005). Some of these indices will
be tested in this thesis, the idea being to see how they “react” according to seagrass dynamics.
After this introduction (Chapter 1), the thesis will be structured in four major
chapters investigating the relationships between seagrass and associated macrofauna (and
evaluation of ecological quality) at different spatial and temporal scales (Table 1.1).
Comparison of benthic macrofauna and seagrass presence is often biased by the fact
that the occurrence or not of the seagrass (and therefore of the benthic macrofauna) is related
to other factors, like depth, sediment characteristics, etc. In Chapter 2, we monitored the
colonization of a sandflat by seagrass Zostera noltii and surveyed the associated fauna
modifications. Thus, we considered that the only difference between both habitats was the
presence or not of vegetation. In terms of biomass, macrobenthos was dominated by cockles.
6
The edible cockle Cerastoderma edule (L.) is one of the most common intertidal bivalves on
the sandy shores and estuaries of the north-eastern Atlantic. It ranges from the Barents Sea to
the Moroccan coasts (Gam et al., 2009b). Like other suspension feeders, cockles are
parasitized mostly through their ventilatory activity (Wegeberg et al., 1999). We tested the
influence of seagrass colonization on cockle dynamics but also on cockle infection by
trematode parasites. This study was also the occasion to compare the behaviour of different
biotic indices (AMBI (AZTI’s Marine Biotic Index (Borja et al., 2000))), BOPA (Benthic
Opportunistic Polychaetes Amphipods Index (Dauvin and Ruellet, 2007)) and BENTIX
(Simboura and Zenetos, 2002)), in relation with Z. noltii seagrass development.
7
Table 1.1. The structure of the present thesis
CHAPTER
SPATIAL
SCALE
TEMPORAL
SCALE
SEAGRASS
STATUS
STUDIED
VARIABLES
Unvegetated
- Sediment
- Associated macrobenthos
- Cockle dynamics
- Trematode parameters
- AMBI, BOPA, BENTIX
1. Introduction
2. Seagrass colonization: knock-on
effect on zoobenthic community,
populations and individuals’ health
1 station (2 km2)
3. Correlation between bivalves health and
environmental parameters, including Zostera
noltii seagrass bed cover, in Arcachon Bay
Arcachon Bay
(39 stations
– 156 km2)
4. Limited consequences of seagrass
decline on benthic macrofauna and
associated biotic indicators
4 years
Colonization
Vegetatated
Arcachon Bay
(12 stations
– 156 km2)
Winter 2009
Seagrass
distribution &
environment
characteristics
- Trematodes
- Brown Muscle Disease
- Perkinsosis
Two occasions
(spring 2002
& spring 2010)
Healthy seagrass
vs
Declining
seagrass
- Seagrass cover
- Sediment
- Asscociated macrobenthos
- MISS
Seagrass
5. Seagrass destruction: benthic community
alteration, secondary production loss, biotic
index reaction and recovery possibility
0.3 km2
8 years
Burial
Recovery
- Seagrass cover
- Sediment
- Associated macrobenthos
- Secondary production loss
- MISS, d-MISS
6. Perspectives in Vietnam
7. General discussion - Conclusion
8
In Chapter 3, we worked on a mature seagrass bed at the scale of Arcachon Bay. The
aim was to compare the density of seagrass leaves with the health of two dominant bivalves,
the native cockle (Cerastoderma edule) and the exotic Manila clam (Ruditapes philippinarum
Adams & Reeve, 1850). The idea was to compare two different approaches concerning the
concept of health: 1) to evaluate the seagrass leaves density which is a proxy of seagrass
health and which could itself be related to the notion of ecosystem health; 2) to assess bivalve
fitness through the prevalence of potential parasites/diseases which can also be an element to
evaluate ecosystem health. In other words, is seagrass leaves density (negatively) correlated
with disease(s) in dominant bivalves (Manila clams and edible cockles)? The Manila clam is
an endemic species from Indo-Pacific waters. This species was introduced to Europe at the
beginning of the 1970s for culture purposes, initially to France (1972) and later to England,
Spain and Italy (Flassch and Leborgne, 1992). It was introduced to Arcachon Bay (SW
France) in 1980 where it rapidly escaped from parks, colonized seagrass Z. noltii beds and
underwent intensive exploitation by fishermen. In 2010, Arcachon Bay harbored the most
important stock of Manila clams in France (5773 metric tons, t) and ranked first in terms of
national production (713 t yr–1 in 2008) (Sanchez et al., 2010). For the cockle, the health was
measured in terms of trematode load. Depending on the specific trematode species present,
molluscs can serve as first host or second intermediate host. In their first intermediate host,
these parasites reproduce asexually, generally in the gonad and the digestive gland of their
host, leading at the individual scale to growth disturbance (Curtis, 1995; Gorbushin, 1997;
Mouritsen et al., 1999; Probst and Kube, 1999; Curtis et al., 2000), reproduction failure
(Schulte-Oehlmann et al., 1997; Oliva et al., 1999; Krist, 2001; Rice et al., 2006; Lajtner et
al., 2008) and sometimes death (de Montaudouin et al., 2003; Desclaux et al., 2004;
Thieltges, 2006b). Prevalence is generally low (<5%) (Thieltges et al., 2008) and effects on
the host population are not measurable (Kube et al., 2006). However, cases of episodic high
prevalence leading to host mortalities are cited (Jonsson and Andre, 1992; de Montaudouin et
al., 2003; Fredensborg et al., 2006; Thieltges, 2006b). Conversely, trematode parasites using
molluscs as their second intermediate host can display high prevalence (de Montaudouin et
al., 2000; Thieltges and Reise, 2006; Gam et al., 2008). Their larvae remain in the tissues of
their host as metacercariae. This larval stage is often considered as energically inert and
causes little or no immediate physiological or behavioural responses in the adult host
(Lauckner, 1983). They can however impact their host when the number of metacercariae is
9
high (Lauckner, 1987b; Desclaux et al., 2004; Desclaux et al., 2006) or when the host is at the
juvenile stage (Lauckner, 1987a; Wegeberg et al., 1999). Nevertheless few studies have ever
tried to estimate the impact of trematode parasites on the second host bivalve population
dynamics (Gam et al., 2009b). For Manila clams, health status was estimated in terms of
infection by the two major diseases in the lagoon for this bivalve: perkinsosis and brown
muscle disease (BMD). Perkinsosis, caused by the protozoan Perkinsus sp. affects numerous
molluscan species all over the world and can lead to mass mortalities (Azevedo, 1989;
Burreson and Ragone Calvo, 1996; Goggin, 1996; Da Ros et al., 1998; Park and Choi, 2001;
Leite et al., 2004; Cremonte et al., 2005; Villalba et al., 2005). In Korea, this parasite has
been the cause of a severe decrease in clam populations since 1993 (Park and Choi, 2001).
The impact of perkinsosis on molluscs depends especially on infection intensity level but is
also related to environmental conditions (Park and Choi, 2001; Leite et al., 2004; Dang et al.,
2010a). Adverse environmental conditions could increase the impact of Perkinsus sp. on
clams (Dang, 2009). For instance, energy consumption in adult clams with heavy infections
would exceed the energy available for growth (Casas et al., 2002; Villalba et al., 2004). This
would result in a lower condition index and a lower growth rate. Furthermore, to explain the
influence of perkinsosis on clam health, besides the fact that Perkinsus sp. consumes energy
at the expense of the clam, a high concentration of Perkinsus in the gills may decrease
filtration efficiency. This could lead to a decrease of oxygen and food availability for clams
and have direct repercussions on clam metabolism (Dang, 2009).
Seagrass in Arcachon Bay undergoes severe stress at both a global and local scale. At the Bay
scale (Chapter 4) and since 2005, this seagrass has undergone a regression that has been
estimated at -33% of the vegetated surface (Plus et al., 2010). Our aim was to compare
macrobenthos structure before seagrass regression (2002) when Z. noltii covered all our
investigated stations, and eight years later (2010) when seagrass had disappeared in 80% of
the investigated areas. Concomitantly, we compared the results from the three different
univariate biotic indices (AMBI, BOPA, BENTIX) and a multivariate index MISS
(Macrobenthic Index in Sheltered Systems).
Apart from this chronic decline (with no clear reasons to this day), Arcachon Bay
seagrass Z. noltii suffered from a more immediate and rapid aggression: burial due to
sediment disposal (anthropogenic activity). In Chapter 5, we investigated the effects (if any)
10
on associated macrofauna and estimated secondary production loss in relation to time and
sediment grain-size. We compared the different biotic indices (BI) that were already
developed and in use but we also tested another BI that was recently developed (MISS for
Macrobenthic Index in Sheltered Systems). MISS, however, is an uneasy and timeconsuming multivariate BI to be used due to the necessity to obtain biomass. Then, we tested
a derived version (d-MISS) without biomass.
Chapter 6 was the opportunity to give an overview of marine seagrass in my native
country, Vietnam, and to summarize the information available in this region for this topic. I
have evoked, on the basis of what I learned during my PhD, what could be my propositions to
go on working on this subject in my country.
11
12
Chapter 2- Seagrass colonization and macrobenthos
Chapter 2 - Seagrass colonization: knock-on effect on zoobenthic
community, populations and individuals’ health
Published: Do, V.T., de Montaudouin, X., Lavesque, N., Blanchet, H., Guyard, H. (2011).
Estuarine, Coastal and Shelf Science 95: 458-469.
Abstract
This study provided evidence that Zostera noltii presence affects macrofauna
community structure independently from median sediment grain-size and that the notion of
ecosystem health is rather subjective: in the present case, we recorded “good health” in terms
of seagrass development, “no impact” in terms of macrobenthic biotic indices and “negative
effect” for a given key-population. The occurrence and development of a Zostera noltii
seagrass bed was surveyed at Banc d’Arguin, Arcachon Bay (France), to estimate the
modification of the macrozoobenthic community and of the dynamics of a key-population for
the local ecosystem, –the cockle Cerastoderma edule. Even though median grain-size of the
sediment decreased only at the very end of the survey, i.e. when seagrass totally invaded the
area, most of the macrofauna community characteristics (such as abundance and biomass)
increased as soon as Z. noltii patches appeared. The structure of the macrofauna community
also immediately diverged between sand and seagrass habitats, without however modifying
the tested biotic indices (BENTIX, BOPA, AMBI). The health of the cockle population
(growth, abundance, recruitment) was impacted by seagrass development. Related parasite
communities slowly diverged between habitats, with more parasites in the cockles from
seagrass areas. However, the number of parasites per cockle was always insufficient to alter
cockle fitness.
Keywords: Zostera noltii seagrass, WFD, macrozoobenthic community, Cerastoderma
edule, parasite
13
1. Introduction
The presence of seagrass beds is known to enhance species diversity (Orth et al.,
1984; Edgar, 1990; Edgar, 1994; Boström and Bonsdorff, 1997; Hemminga and Duarte,
2000; Fredriksen et al., 2010). The influence of these meadows is both structural in that it
increases the complexity of the habitat, allowing different species to occupy various
ecological niches within an area (Orth et al., 1984); and trophic because it supports epiphytes,
a resource for many grazers (Duffy et al., 2003). Most studies aiming to highlight the effect
of seagrass on diversity have compared species richness in vegetated and non-vegetated areas
(Fonseca et al., 1990; Orth, 1992; Edgar et al., 1994; Boström and Bonsdorff, 1997;
Connolly, 1997; Sheridan, 1997; Hemminga and Duarte, 2000; Beck et al., 2001; Crooks,
2002; Heck et al., 2003; Nagelkerken and van der Velde, 2004; Nakaoka, 2005; Fredriksen et
al., 2010). A difference in zoobenthic community structure between both habitats was always
associated with enhancement of abundance, biomass and species richness in seagrass.
However, these observed differences could also be due to confounding factors, the
presence/absence of seagrass being itself related to contrasted environmental features
(hydrodynamics, depth, grain-size, etc.) (Boström et al., 2006a). Furthermore, how and which
infaunal species responds to the more complex sediment environment created by the seagrass
and how this response may vary across different spatial scales remains unclear (Fredriksen et
al., 2010).
In addition to the development of the seagrass being related to modification of the
associated benthic macrofauna, the present study aimed to discuss the notion of health for a
given ecosystem. In our case study, health of the ecosystem was investigated according to
three approaches: 1) the development of the seagrass which is included in the European
Water Framework Directive (WFD) quality developments (Borja et al., 2000; Simboura and
Zenetos, 2002; Dauvin and Ruellet, 2007); as the WFD considers seagrass as a useful
indicator in terms of depth limit (Krause-Jensen et al., 2005), species composition (the
presence of disturbance-sensitive species), abundance and ecological quality (Foden and
Brazier, 2007); 2) the structure of the associated benthic community which is another quality
element in the WFD. Biotic Indices (BIs) have been developed for assessing the ecology
status (ES); BIs based on the classification of species into ecological groups according to
their level of sensitivity/tolerance to stress; 3) the health of the dominant species in terms of
14
biomass, the cockle Cerastoderma edule. This bivalve is considered as an engineer species
impacting the ecosystem functioning. Indeed, as a suspension feeder, the cockle plays an
important role in benthoplanctonic coupling: putting trophic pressure on phytoplanktonic
supply (Smaal et al., 1986; Smaal, 1997; Widdows and Navarro, 2007), sedimentation of silt
and clay (Widdows et al., 1998; Ciutat et al., 2006) and competition with other filter-feeders.
As an infaunal species, the cockle bioturbates the sediment, modifies bacterial profiles,
structures macrofaunal communities (Flach and Bruin, 1993) and participates in resuspension
processes (Flach and Bruin, 1993; Goñi-Urriza et al., 1999; Ciutat et al., 2006). We
hypothesised that the development of seagrass blades would modify hydrodynamics,
sediment characteristics and shelter availability in such a way that recruitment and predation
would be altered, with a knock-on effect on cockle density and consequently on intraspecific
competition. Cockles fitness was evaluated through three parameters, namely shell growth,
abundance (adults and recruits) and pathogen load. Indeed apart from benthic free living
macrofauna, the study was extended to the parasite fauna (trematodes) of cockles in order to
assess whether or not seagrass presence could facilitate the infection of this key-species (the
cockle) by these potential pathogens.
Cockles are the preferred intermediate hosts for many trematodes (Lauckner, 1983;
Thieltges and Reise, 2006; de Montaudouin et al., 2009). These parasites can exert a pressure
on cockle population dynamics (Blanchet et al., 2003; Gam et al., 2009b). However, these
parasites can be affected by the presence of seagrass, either directly by perturbing propagules
dispersal (Bartoli and Boudouresque, 1997; Gam et al., 2009b) or through their impact on the
dynamics of potential hosts involved in trematode cycles (Hechinger and Lafferty, 2005;
Thieltges and Reise, 2006). The general objective of this study was to monitor the evolution
of a seagrass development in a bare sandflat and to discuss ecosystem quality. Three
approaches have been developed in order to: 1) assess the effect of the colonization of
seagrass beds on the structure of macrofauna in terms of abundance, biomass, species
richness, 2) compare the assessment of ecological status by three biotic indices to evaluate
the “health” quality of the environment, 3) evaluate how the seagrass development may affect
the fitness of a given key-population (cockle).
15
2. Materials and methods
2.1. Study area
Banc d’Arguin (Figure 2.1) is a National Nature Reserve including sand dunes and
semi-sheltered sandflats. It is located at the oceanic entrance of Arcachon Bay – a macrotidal
lagoon situated on the French south-western Atlantic coast (44° 40’ N, 1° 10’ W). A strong
characteristic of this area relates to the large-scale mobility of sand banks and their change of
morphology, due to wind, waves and spring tides. When an area becomes sheltered, a
seagrass bed may develop over a large surface area but may also disappear within a few
months or a few years, buried under sand or eroded by new channels. Consequently, this site
provides a good opportunity to investigate seagrass and its associated macrofauna dynamics.
The studied site consisted of a 2000-m2 intertidal area within the Integral Protection Zone of
the reserve. This area was exempt from direct human activity (e.g. fishing, walking,
anchoring). Salinity remained high year-round (31–34) and surface sediment temperature in
the intertidal fluctuates between -0.2 °C in winter and 30.0 °C in summer (Dang et al.,
2010b).. The benthic macrofauna was described in 1988 (Bachelet and Dauvin, 1993) and in
2002 (Blanchet et al., 2004) when the whole area was free of seagrass. Several marine bird
species winter, nest or migrate, including species that are potential definitive hosts for
trematodes. The surrounding waters are inhabited by many fish species (e.g. bass, mullet,
goby, sole) which are also potential hosts for trematodes.
The cockle population at Banc d’Arguin is located between 0.9 and 1.9 m above chart
datum and has been characterised by highly fluctuating abundances, fast growth rates and
short lifespan (de Montaudouin, 1996; Gam et al., 2009b, 2010).
16
A
1° 10’
N
B
44° 40’
Banc
d’Arguin
C
D
Figure 2.1. Studied site (A: Banc d’Arguin, Arcachon Bay) and pictures of Zostera noltii
seagrass bed expansion over time (B: February 2005, early settlement; C: November 2006,
50% spreading; D: November 2009, 100% spreading).
17
2.2. Sampling procedure
2.2.1. Benthic macrofauna and associated parameters
Five sampling campaigns were carried out in the Zostera noltii patches and the
adjacent sand area over three years (February, June and November 2005, November 2006 and
November 2009) corresponding to different stages of the seagrass development in the studied
area (Figure 2.1). Sampling consisted of collecting the top 20 cm of the sediment with a
0.0225 m² corer, with six replicates per situation (seagrass vs. sand). Sediment was sieved
through a 1-mm mesh; the sieve residue was fixed in 4% buffered formalin and stained with
Rose Bengal. In seagrass beds, Zostera noltii leaves were cut and preserved in formalin. The
top 3-cm sediment layer was also sampled in each replicate for grain-size analysis. In the
laboratory, macrofauna was sorted, identified when possible to the species level, and counted.
Biomass was determined as ash-free dry weight (AFDW) after desiccation (60 °C, 48 h) and
calcination (450 °C, 4 h). Zostera noltii leaves were desiccated (60 °C) until a constant dry
weight was obtained. Sediment grain-size characteristics (median grain-size, percentage of
silt and clays) were determined after sieving pre-weigh dried sediment through a wet column
of sieves with decreasing apertures (1000 μm, 500 μm, 250 μm, 125 μm and 63 μm).
Percentage of organic matter in the sediment was assessed after ignition (450 °C, 4 h) of a
dried aliquot of sediment.
2.2.2. Cockle-Trematode systems
Cockle abundances were determined using quadrates (6 samples × 0.25 m² × 2
habitats) connected to macrofauna cores (that were sampled in the middle of each quadrat).
The top 5 cm depth was sampled with a shovel and sieved on a 1-mm mesh. Individuals were
counted and shell lengths were measured with an electronic calliper to the nearest mm.
Cockle recruitment period was considered as simultaneous in bare sand and seagrass patches.
Consequently, mean shell length of a cohort was an estimate of growth after separating the
different cohorts. Trematode communities in seagrass beds and sand were monitored and
compared when the number of cockles was sufficient (at least 10 individuals) in both
habitats. The study concentrated on the two existing cohorts (2003 and 2004) at the beginning
of the study (February 2005), cohort 2004 until its disappearance after November 2005, and
18
cohort 2008 in November 2009. Five cockles per quadrat and per cohort were dissected for
trematode diagnosis. The flesh was squeezed between two large glass slides and observed
through a binocular microscope with transmitted light. Identification of trematode species
was performed using the key and related references proposed by de Montaudouin et al.
(2009).
2.3. Data analysis
2.3.1. Biotic Indices
Three currently available univariate Biotic Indices (BIs) were tested, namely AMBI
(Borja et al., 2000), BENTIX (Simboura and Zenetos, 2002; Simboura et al., 2005) and
BOPA (Dauvin and Ruellet, 2007). AMBI (AZTI Marine Biotic Index) is based on previous
work from Grall and Glémarec (Grall and Glémarec, 1997). It considers five ecological
groups (available on web page: http://ambi.azti.es) ranging from sensitive species (EGI) to
first-order opportunistic species (EGV) (Borja et al., 2000). BENTIX considers only two
groups: sensitive (GS) and tolerant species (GT), which correspond to ecological groups I and
II, and ecological groups III to V of the AMBI, respectively. The BOPA (Benthic
Opportunistic Polychaetes Amphipods index) is based on the ratio of opportunistic
polychaetes (i.e. polychaetes of ecological groups IV and V of the AMBI) and amphipods
(except Jassa genus).
2.3.2. Statistical analysis
Analysis of variance was applied to assess differences between sand and seagrass in
terms of biomass, abundance, number of species (S), Shannon index (H’), Piélou’s evenness
index (J’) and abundance of parasites in cockles. Prior to ANOVA, homogeneity of variance
was tested by Cochran C test. If significant heterogeneity was identified, data were log10(x+1)
or arcsin√p (for percentages data) transformed, which was sufficient to achieve homogeneity
of variance. Normality of data was assumed. All statistical analyses were performed with
STATISTICA® 7.1 software (StatSoft).
19
2.3.3. Multivariate Analysis
Multivariate analysis was performed to compare macrozoobenthic communities
structure between seagrass and sand areas. Abundances were square-root transformed to
minimize the influence of the most dominant taxa. A non-metric multidimensional scaling
(MDS) based on Bray-Curtis similarity coefficient was used to obtain an ordination plot.
These analyses were performed using PRIMER® – v6 package (Clarke and Warwick, 2001;
Clarke and Gorley, 2006).
3. Results
3.1. Development of the seagrass bed and modifications of sediment characteristics
3.1.1. Seagrass development
In February 2005, Zostera noltii represented small <2 m diameter patches scattered on
a medium sand intertidal flat. In November 2006, seagrass patches occupied half of the flat
and in November 2009, bare sand areas became rare (Figure 2.1). Within the seagrass bed,
biomass of leaves varied according to seasons, but when considering a similar month
(November), an increase was observed from 2005 (55 g DW m-2) to 2006 (99 g DW m-2) and
2009 (290 g DW m-2) (Table 2.1).
3.1.2. Sediment characteristics
Together with seagrass bed extension, the surface sediment characteristics changed
(Table 2.1). In bare sand, the grain-size remained stable (327-357 µm). It also remained
similar in the seagrass (p > 0.05), except at the very end in 2009 (p < 0.05) when the seagrass
covered almost the whole of the flat, allowing finer particles to deposit. However, at that
time, sediments were still medium sands (median = 299 µm). Conversely, silt and clay
content and organic matter content rapidly increased in the seagrass to reach values that were
on average 2.3 fold higher than in bare sand (Table 2.1).
20
Table 2.1. Mean biomass of seagrass leaves (g DW m-2), sediment median particle size (µm),
silt and clay and organic matter content in the sediment (%) (± 1 standard error), at each
sampling date. P-value was calculated from the comparison between bare sand and seagrass.
ns: p > 0.005.
Parameter
Zostera noltii
Sampling date
Feb 2005
Jun 2005
Nov 2005
Nov 2006
Nov 2009
Bare sand
Seagrass bed
50.5 (± 1.2)
181.0 (± 20.9)
55.4 (± 15.6)
99.1 (± 21.9)
290.2 (± 40.9)
P-value
Sediment
Feb 2005
median particle Jun 2005
size
Nov 2005
Nov 2006
Nov 2009
344 (± 4)
358 (± 4)
342 (± 3)
337 (± 1)
327 (± 4)
354 ( ±9)
357 ( ±14)
340 ( ±2)
334 ( ±1)
299 ( ±15)
ns
ns
ns
ns
< 0.05
Silt & Clay
Feb 2005
Jun 2005
Nov 2005
Nov 2006
Nov 2009
1.3 (± 0.1)
0.7 (± 0.1)
0.6 (± 0.0)
2.6 (± 0.6)
0.6 (± 0.0)
2.4 ( ±0.1)
1.4 ( ±0.3)
2.5 ( ±0.5)
5.0 ( ±0.8)
2.3 ( ±0.3)
< 0.001
< 0.05
< 0.01
< 0.05
< 0.01
Organic matter
Feb 2005
Jun 2005
Nov 2005
Nov 2006
Nov 2009
5.7 (± 0.9)
3.2 (± 0.2)
3.9 (± 1.7)
0.5 (± 0.0)
3.2 (± 0.1)
8.0 ( ±0.9)
10.3 ( ±1.6)
8.0 ( ±1.2)
1.7 ( ±0.3)
10.3 ( ±1.5)
ns
< 0.01
ns
< 0.01
< 0.01
3.2. Macrobenthic community
A data matrix of ‘10 stations-dates × 96 species’ was analysed. This matrix was
obtained without removing any species.
3.2.1. Identification of Assemblages
The MDS stress level (< 0.1) corresponded to a good ordination without misleading
interpretation. The ANOSIM test showed significant differences in faunistic composition
between sand and seagrass for each date (R = 0.95, p = 0.002). Based on a 60% similarity
level, the MDS ordination plot (Figure 2.2) allowed the identification of three different
21
groups. There was a distinct separation between bare sand and seagrass bed assemblages.
Within seagrass beds, the benthic assemblages in 2009 were clearly separated from the
assemblage of the seagrass bed during the former sampling dates. When pooling all dates, 34
species out of 96 were found in the seagrass bed only (with Aphelochaeta marioni and
Bittium reticulatum as the main species), 13 species were found in bare sand only (with
Ampelisca brevicornis and Cyclope neritea as dominant species) and the remaining 47
species occurred in both habitats (with Notomastus latericeus and Heteromastus filiformis as
dominant species) (Table 2.2).
Figure 2.2. MDS ordination plots of benthic assemblages (square-root transformed data).
Zn: Zostera noltii; S: bare sand; (year_month).
22
Table 2.2. List of species with mean abundance > 50 ind.m-2 in at least one of three habitats.
(G): gastropod; (B): bivalve; (O): oligochaete; (P): polychaete; (Op): ophiuroid; (A):
amphipod; (D): decapod; (Ma): malacostraca; (N): nemert. Epifauna species are in bold.
Species
Hydrobia ulvae (G)
Notomastus latericeus (P)
Heteromastus filiformis (P)
Cerastoderma edule (B)
Ampelisca brevicornis (A)
Scrobicularia plana (B)
Ruditapes philippinarum (B)
Cyclope neritea (G)
Abra segmentum (B)
Prionospio sp. (P)
Nemertina (N)
Glycera spp. (P)
Euclymene oerstedii (P)
Poecilochaetus serpens (P)
Nassarius reticulatus (G)
Aphelochaeta marioni (P)
Tubificoides benedii (O)
Melinna palmata (P)
Bittium reticulatum (G)
Littorina littorea (G)
Euclymene collaris (P)
Pseudopolydora spp. (P)
Capitella capitata (P)
Mytilus edulis (B)
Gammarus sp. (A)
Microdeutopus gryllotalpa (A)
Platynereis dumerilii (P)
Aonides oxycephala (P)
Gibbula umbilicalis (G)
Melita palmata
Ophiura sp. (Op)
Gammarella fucicola (A)
Nebalia strausi (Ma)
Sand (2005-2009)
Rank
Mean
1
3964
2
397
3
366
4
206
5
74
6
58
7
53
8
50
9
49
10
46
11
28
12
27
13
27
14
15
15
13
16
12
17
12
18
9
19
4
19
4
19
4
19
4
23
3
24
1
24
1
24
1
0
0
0
0
0
0
0
Z. noltii (2005-2006)
Rank
Mean
1
13607
4
607
2
754
5
307
31
6
16
89
12
133
22
31
8
222
13
126
19
59
20
39
20
39
26
19
14
120
3
687
15
115
23
30
7
224
9
187
24
30
25
24
11
139
6
304
18
61
26
19
10
150
17
76
26
19
29
9
30
6
0
0
Z. noltii (2009)
Rank Mean
9
407
3
2400
1
3385
0
0
0
27
7
0
25
37
20
67
13
163
18
96
11
281
19
81
22
59
2
2526
12
222
10
385
5
874
15
133
14
148
20
67
0
0
4
2363
7
652
26
15
17
111
24
52
6
844
15
133
8
511
23
52
3.2.2. Modification of macrofaunal characteristics
A rapid contrast in quantitative parameters (biomass, abundance and species richness)
was observed between the seagrass bed and bare sand. While the seagrass bed consisted of
23
small patches (February 2005), biomass and species richness in bare sand and seagrass
patches were similar (p > 0.05), while abundance was double in the seagrass bed (12363
ind.m-2 against 6400 ind.m-2) (p < 0.01) (Figure 2.3). When seagrass started to expand (June
2005) until the almost entire coverage of the area (in November 2009), macrofauna biomass
(Figure 2.3A), abundance (Figure 2.3B) and species richness (Figure 2.3C) were higher in the
seagrass patches (p < 0.01). Diversity H’ was less or similar for a short time only (until
November 2006), but also became higher in the seagrass (p < 0.01) at the end of the study, in
November 2009 (Figure 2.3D). The Evenness index J’ was always small (<0.8) in both
habitats but higher in the seagrass at the end (Figure 2.3D).
Species were gathered into five trophic groups based on the feeding types (Fauchald
and Jumars, 1979; Hily and Bouteille, 1999): (1) grazers, (2) deposit feeders, (3) scavengers,
(4) predators and (5) suspension feeders. The biomass of each trophic group was compared
between bare sand and seagrass bed (Figure 2.4). At the beginning of seagrass expansion in
February 2005, grazers and scavengers were the groups showing higher biomass (p < 0.05) in
the seagrass bed. In June 2005, grazers, predators and suspension feeders displayed higher
biomass (p < 0.05) in seagrass. In November 2005 and in November 2006, all trophic groups
(except predators) had higher levels (p < 0.05) in the seagrass bed. At the end of the survey,
when the seagrass bed occupied most of the area, all trophic groups (except suspension
feeders) showed higher biomass (p < 0.05) in the seagrass bed.
In terms of position related to substratum, epifauna always displayed higher
abundance, biomass and species richness (p <0.05) in seagrass compared to bare sand, except
abundance in November 2009 (p > 0.05) which was considered low for seagrass epifauna
(Figure 2.5). Hydrobia ulvae explained most of these differences. For infauna, abundance and
biomass remained similar until November 2005 and became higher in seagrass (p < 0.05)
thereafter (Figure 2.5A and B). Species richness was higher in seagrass from June 2005, with
increasing differences until November 2009 (× 1.5 to × 2.5) (Figure 2.5C).
24
160
35000
A
Mean abundance (individuals m-2)
Mean biomass(g AFDW m-2)
180
140
120
100
80
ns
60
40
20
B
30000
25000
20000
Hu
3956/407
Hu
4748/9844
10000
5000
0
Feb.-05
Jun.-05
Nov.-05
Nov.-06
Nov.-09
Feb.-05
3.5
C
D
0.5 0.4
Jun.-05
0.5 0.3
Nov.-05
0.5 0.5
3.0
25
ns
20
2.5
ns
Mean H'
Mean number of species
Hu
Hu
2867/12511 3422/9400
15000
0
30
Hu
4830/22674
15
10
2.0
Nov.-06
0.5 0.5
Nov.-09
0.5 0.7
ns
ns
ns
1.5
1.0
5
0.5
0
0.0
Feb.-05
Jun.-05
Nov.-05
Nov.-06
Nov.-09
Feb.-05
Jun.-05
Nov.-05
Nov.-06
Nov.-09
Figure 2.3. Mean biomass (A), abundance of macrofauna (including Hydrobia ulvae (Hu) density) (B), number of species (C) and Shannon
index with Piélou’s evenness values on top of the figure (D) (+ 1 SE) in bare sand (in white) and in seagrass (in black) at different dates. ns: not
: p < 0.001.
significant, p > 0.05; : p < 0.05; : p < 0.01;
25
80
A
10
deposit feeders (g AFDW m-2)
predators (g AFDW m-2)
12
8
6
ns
4
ns
2
ns
Feb.-05
Jun.-05
Nov.-05
Nov.-06
50
ns
40
30
20
10
Nov.-09
ns
Feb.-05
14
C
scavengers (g AFDW m-2)
35
grazers (g AFDW m-2)
60
0
0
40
B
70
30
25
20
15
10
5
0
Jun.-05
Nov.-05
Nov.-06
Nov.-09
Nov.-05
Nov.-06
Nov.-09
D
12
10
8
6
ns
4
2
0
Feb.-05
Jun.-05
Nov.-05
Nov.-06
suspension feeders (g AFDW m-2)
120
Nov.-09
Feb.-05
Jun.-05
E
100
80
60
ns
ns
40
20
0
Feb.-05
Jun.-05
Nov.-05
Nov.-06
Nov.-09
Figure 2.4. Mean biomass (g AFDW.m-2) of trophic groups (+ 1 SE) in bare sand (in white)
and in the seagrass bed (in black) at different dates. A: predators; B: deposit feeders; C:
grazers; D: scavengers; E: suspension feeders. ns: not significant, p > 0.05; : p < 0.05; : p
: p < 0.001.
< 0.01;
26
30000
A
Mean biomass (g AFDW m-2)
Mean abundance (individuals m-2)
25000
20000
15000
10000
ns
ns
5000
ns
140
B
120
100
80
ns
60
ns
40
20
0
0
epi. inf. epi. inf. epi. inf. epi. inf. epi. inf.
Feb.-05
Jun.-05
Nov.-05
Nov.-06
20
epi.
Nov.-09
inf. epi.
Feb.-05
inf. epi.
Jun.-05
inf. epi.
Nov.-05
inf. epi.
Nov.-06
inf.
Nov.-09
C
18
16
ns
14
12
10
8
6
4
2
0
epi.
inf.
Feb.-05
epi.
inf.
Jun.-05
epi.
inf.
Nov.-05
epi.
inf.
Nov.-06
epi.
inf.
Nov.-09
Figure 2.5. Mean biomass (A), abundance (B), number of species (C) (+ 1 SE) of epifauna
(epi.) and infauna (inf.) in bare sand (in white) and in seagrass (in black) at different dates.
: p < 0.001.
ns: not significant, p > 0.05; : p < 0.05; : p < 0.01;
3.2.3. Assessment of the ecological quality of seagrass and bare sand by biotic indices
Over the years, BOPA index fluctuated without a clear solid trend but corresponded to
a good or high ecological quality. AMBI index classified the quality of both benthic
communities as good during the whole period. The BENTIX index produced a lower
assessment than AMBI and BOPA and classified the ecosystem quality from poor to
moderate (Figure 2.6). From February to June 2005, ANOVA did not indicate any significant
difference in these biotic indices values between seagrass and bare sand. However, in
November 2005, 2006 and 2009, there were significant differences between seagrass and bare
sand alternatively in AMBI, BOPA and BENTIX values (p < 0.05).
27
0.32
0.28
0.24
A
6
moderate
4
AMBI
BOPA
0.04
poor
5
moderate
0.12
0.08
bad
poor
0.2
0.16
B
bad
good
3
2
good
1
high
high
0
0
Feb.-05
Jun.-05
Nov.-05
6
Nov.-06
Nov.-09
Feb.-05
Jun.-05
Nov.-05
Nov.-06
Nov.-09
C
high
BENTIX
5
4
good
3
moderate
poor
2
bad
1
0
Feb.-05
Jun.-05
Nov.-05
Nov.-06
Nov.-09
Figure 2.6. Biotic indices, with thresholds used to classify index values and ecological
quality of the ecosystem (ES) in bare sand and (in white) and in seagrass bed (in black) at
different dates for A: BOPA index; B: AMBI; C: BENTIX.
3.3. Cockle population and related trematodes
3.3.1. Cockle dynamics
Mean shell length of cockles (corresponding to growth if recruitment occurred
simultaneously in the whole area) from the vegetated habitat was smaller (<0.05) than in the
sand habitat in most situations, i.e. different dates and cohorts, and was never larger (Table
2.3). As long as seagrass was present only in patches, from February 2005 to November
2006, the abundance of cockles in seagrass patches was similar to or higher (p < 0.05) than in
bare sand. Between November 2006 and November 2009, the cockle populations completely
collapsed in the seagrass that covered most of the area at that time with cockles remaining
28
only in bare sand close to the seagrass. With the sand/seagrass surface ratio decreasing, the
density of cockles tended to increase in bare sands. At the only occasion when we monitored
recruitment (June 2005), the number of recruits was 4 times higher in the bare sand but
collapsed largely over the following five months (November 2005, Table 2.3).
Table 2.3. Shell length and abundance (± 1 SE) of cockles in bare sand and seagrass. Pvalue was calculated from the comparison between both habitats.
Sampling
date
Feb. 2005
Cockle
cohort
2003
2004
Jun. 2005
2003
2004
2005
Nov. 2005
2003
2004
2005
Nov. 2006
2005
2006
Nov. 2009
2008
2009
Sand
Zostera bed
P- value
Shell length (mm, ± SE)
Abundance (ind.m-2)
Abundance (ind.m-2)
28.3 (± 0.1)
70.0 (± 7.5)
23.0 (± 0.2)
62.7 (± 3.8)
26.9 (± 0.6)
137.3 (± 23.2)
21.0 (± 0.5)
107.3 (± 25.2)
< 0.05
< 0.05
< 0.05
ns
Abundance (ind.m-2)
Abundance (ind.m-2)
Abundance (recruits.m-2)
31.5 (± 0.2)
65.0 (± 8.9)
27.2 (± 0.2)
85.0 (± 9.0)
503.7 (± 79.2)
29.1 (± 0.3)
156.0 (± 30.0)
23.9 (± 0.2)
133.3 (± 31.8)
133.2 (± 50.1)
< 0.001
< 0.01
< 0.001
ns
<0.01
Abundance (ind.m-2)
Abundance (ind.m-2)
Abundance (recruits.m-2)
33.8 (± 0.1)
6.0 (± 2.7)
29.2 (± 0.4)
66.7 (± 22.0)
1.3 (± 0.8)
32.5 (± 0.3)
20.0 (± 5.3)
27.4 (± 0.3)
87.0 (± 18.7)
12.7 (± 4.4)
< 0.05
ns
< 0.05
ns
< 0.05
Abundance (ind.m-2)
Abundance (ind.m-2)
35.4 (± 0.2)
18.7 (± 7.1)
21.3 (± 0.4)
15.3 (± 3.2)
- (± -)
0.0 (± 0.0)
- (± -)
0.0 (± 0.0)
< 0.01
< 0.05
Abundance (ind.m-2)
Abundance (ind.m-2)
32.5 (± 0.7)
4.7 (± 1.9)
17.3 (± 0.4)
102.0 (± 55.1)
31.2 (± 0.5)
3.3 (± 1.6)
- (± -)
0.0 (± 0.0)
ns
ns
< 0.01
29
3.3.2. Parasites community
Cockles were infected by five trematode species. In February 2005, when the seagrass
bed just started to grow, the dominant trematode species were Himasthla interrupta and
Meiogymnophallus minutus. In both cohorts (2003 and 2004), the average parasite abundance
per cockle of these trematode species was similar between habitats (p > 0.05) (Figure 2.5A
and B). In June 2005, the abundance of H. interrupta increased in the bare sand only and
became higher than in seagrass (p < 0.05) (Figure 2.7C). All other trematode species
displayed similar abundance levels in both habitats. In November 2005, H. interrupta
abundance increased 5-fold and became similar in seagrass bed and bare sand. M. minutus
abundance increased and became higher in bare sand (Figure 2.7D). Thereafter, until 2008
cockles disappeared from the seagrass preventing any comparison. In November 2009, the
2008 cockle cohort maintained itself in both habitats, with parasite abundance remaining
higher in the seagrass bed (p < 0.001) for the four dominant trematode species (Figure 2.7E).
30
Nr
30/207
Hq
Hi
Metacercariae number. cockle-1
D
Hu
4748/9844
Ca
Mm
200
180
160
140
120
100
80
60
40
20
0
Sp
22/59
ns
Nr
30/207
500
400
100
Hu
2867/12511
ns
Nr
7/89
Hu
2867/12511
ns
ns
0
Hq
Hi
Ca
Ca
E
Sp
22/119
300
Mm
Pb
Hu
4748/9844
160
Sp
244/163
140
120
100
80
Hu
Nr 4830/22674
7/74
ns
ns
60
40
20
Hu
4830/22674
ns
0
Hi
November 2005: cohort 2004
(shell length: 32-34 mm)
600
200
Hu
4748/9844
nsns
Hq
Pb
June 2005: cohort 2004
C
ns
Hu
4748/9844
ns
February 2005: cohort 2004
B
Sp
22/59
200
180
160
140
120
100
80
60
40
20
0
February 2005: cohort 2003
A
500
450
400
350
300
250
200
150
100
50
0
Mm
Pb
Hq
Hi
Ca
Mm
Pb
November 2009: cohort 2008
Nr
15/59
Hq
Sp
0/0
Hu
3956/407
Hi
Ca
Mm
Hu
3956/407
ns
ns
Pb
Figure 2.7. Parasite abundance (mean ± 1 standard error) and host density (ind m-2) in bare sand (in white) and in seagrass bed (in black). When
available, mean density (ind m-2) of the upper host (=first intermediate host) was determined (sand/seagrass). Nr: Nassarius reticulatus; Hu:
Hydrobia ulvae; Sp: Scrobicularia plana. ns: not significant, p > 0.05; : p < 0.05; : p < 0.01;
: p < 0.001. A (cockle cohort 2003) and B
(cockle cohort 2004): in February 2005; C (cockle cohort 2004): in June 2005; D (cockle cohort 2004): in November 2005; E (cockle cohort
2008): in November 2009.
31
4. Discussion
4.1. Kinetic of seagrass development and associated macrofauna
The structure of benthic communities is largely influenced by sediment grain-size and
by seagrass presence. However, whether sediment characteristics or seagrass is the dominant
driver remains unclear. The originality of this study was to demonstrate that the occurrence of
a seagrass bed rapidly modified the structure of benthic communities, independently from the
median grain-size of the sediment. Three main stages were identified according to the
development of Zostera noltii bed: early settlement, 50% spreading and 100% spreading
(Figure 2.8).
Early settlement
50% spreading
100% spreading
•Silt and clay (×1.9)
•Organic matter (×1.4)
• Median grain-size: ns
• Median grain-size: ns
•Median grain-size (×1.2)
Population (cockles)
•Length (×1.1)
•Abundance (×1.8)
•H. interrupta (equal)
•M. minutus (equal)
•H. quissetensis (equal)
•C. arguinae (equal)
•Length (×1.1)
•H. interrupta (×4.8)
•M. minutus (×4.1)
•H. quissetensis (×3.4)
•C. arguinae (×2.8)
Community
•Length (×1.1)
•H. interrupta
(×1.4)
•M. minutus (×1.1)
•H. quissetensis (×2.5)
•C. arguinae (equal)
•
Community
Community
•Biomass (equal)
•Species richness (equal)
•H’ (1.2)
•BOPA (×0.4)
•AMBI (equal)
•BENTIX (equal)
•Biomass (×4.5)
•Species richness (×2.0)
•H’ (×1.1)
•BOPA (×2.3)
•AMBI (equal)
•BENTIX (equal)
•Biomass (×3.0)
•Species richness (×2.6)
•H’ (×1.8)
•BOPA (×1.4)
•AMBI (× 1.1)
•BENTIX (× 1.4)
Figure 2.8. Summary of seagrass colonization and its main effects on sediments, cockles
population, and macrofauna structure (comparison between bare sand and seagrass bed).
32
In early settlement (February 2005), seagrass was clustered as small patches on a
sheltered sandy beach. Concerning sediment characteristics, only silt and clays and organic
matter content were significantly higher in seagrass compared to bare sand, while median
grain-size remained similar. However, the seagrass macrofauna assemblage was immediately
separated from the sand assemblage. The abundance of macrofauna increased in the seagrass
bed but biomass and species richness remained similar. Most of the abundance increase was
due to the grazing gastropod Hydrobia ulvae (78% of total abundance). This species was one
of the most abundant species in both habitats (bare sand and seagrass) and is reputed to
display higher abundance in seagrass beds (Cardoso et al., 2008). Higher abundance of
macrofauna in vegetated habitats has often been reported (Fonseca et al., 1990; Orth, 1992;
Boström and Bonsdorff, 1997; Fredriksen et al., 2010) and different mechanisms can be
explained, for example 1) decreased predation efficiency due to high habitat complexity
(Orth et al., 1984); 2) habitat preference of dense seagrass by prey as an escape mechanism
from predation (Fonseca and Fisher, 1986; Webster et al., 1998; Boström et al., 2006b); 3)
stabilisation of sediments that accumulate organic material, allowing increased settlement and
growth of infauna (Neckles et al., 1993; Fredriksen et al., 2005); juveniles and adults are also
prevented from being resuspended and transported away (Fonseca et al., 1990); and 4) a high
content of organic matter, which may be common in seagrass meadows, attracts a certain type
of infauna such as deposit-feeding polychaetes (Fredriksen et al., 2010).
When seagrass beds spread over 50% of the available intertidal area (from June 2005
to November 2006), both silt and clays and organic matter contents went on increasing in the
seagrass bed, while median grain-size remained similar between both habitats and compared
to initial values. All macrofauna parameters (biomass, abundance, species richness and H’)
were enhanced in the seagrass beds. Moreover, the structures of both communities were
clearly contrasted. The development of the seagrass bed initially favoured grazers, predators
and suspension feeders in terms of abundance. Seagrass thus appeared as a structuring factor
with a higher strength than sediment. In other words, seagrass development, associated with
increased organic matter, changed the trophic structure of the assemblage in the seagrass bed
independently of the median grain-size. The role of seagrass in structuring communities was
determined in many studies (Stoner, 1980; Orth et al., 1984; Edgar et al., 1994; Webster et
al., 1998; Honkoop et al., 2008; Fredriksen et al., 2010). Stoner (1980) concluded that the
biomass of benthic vegetation, independent of sediment granulometry, exerts a strong
33
influence on the abundance, dominance, diversity, and trophic organization of macrobenthic
infauna and epifauna.
This study also confirmed that vegetation cover is a major factor increasing mobile
epibenthic fauna abundance, biomass and species richness as suggested by several former
studies (Edgar, 1990; Edgar, 1999; Cottet et al., 2007). Greater number of niches and more
food can explain this trend (Nakaoka, 2005). The role of seagrass on infauna is more
controversial. Cottet et al. (2007) suggested that the structuring effect of seagrass is mitigated
in subtidal and high tidal levels, whereas this effect is stronger at low intertidal level (our
study case). At that level, seagrass protects macrofauna against desiccation while rhizomes
and roots create spatial complexity within sediment enable oxygenation (Osenga and Coull,
1983; Hedge and Kriwoken, 2000). At Banc d’Arguin, the discrimination between seagrass
and unvegetated sediments took longer (few months) than for epifauna but was achieved at
all levels (biomass, abundance and species richness). Macrofaunal assemblages modification
primarily concerned deposit-feeders (e.g. capitellid polychaetes, Abra segmentum) hence
rejecting the hypothesis that roots and rhizomes involve loss of burrowing species (Talley
and Levin, 2001).
When the seagrass bed covered approximately the whole flat with only minor sand
patches (November 2009), all sediment characteristics were different between both habitats,
including a significant decrease in the sediment median grain-size in the seagrass bed. This
trend was consistent with previous results highlighting that the seagrass beds accumulate fine
sediments and organic matter (Yang, 1998; Agawin and Duarte, 2002; Leonard et al., 2002;
Bos et al., 2007; van Katwijk et al., 2010). These major changes in the environmental factors
lead to sharp distinctions in community structure between both habitats. Biomass, abundance
of macrofauna, species richness and H’ diversity were significantly higher in the seagrass
bed. Three deposit-feeding polychaetes, Heteromastus filiformis, Aphelochaeta marioni, and
Notomastus latericeus, became the most abundant species. However, the large bivalves
almost disappeared in the seagrass, such as Mytilus edulis, Cerastoderma edule and
Ruditapes philippinarum. The obvious separation of macrofaunal assemblage in 2009 (≈
100% coverage) from those in 2005 (patches) and 2006 (50% coverage) confirmed the
difference between patchiness and continuity in structuring macrofaunal assemblages. In
addition to the generalized idea that macrophytes enhance macrofaunal densities (Heck and
34
Orth, 1980; Borg et al., 2010), a patchy distribution and the presence of edges have been
found to support higher faunal densities than continuous or dense patches (Bouma et al.,
2009b). Our results confirmed the difference in community structure between reticulate
seagrass beds and continuous meadows, but not the decrease of faunal abundance. Indeed, if
Hydrobia ulvae which accounted for nearly 80% of the total macrobenthic abundance is
excluded from the analyses, the faunal density was much higher in the continuous meadows.
4.2. Seagrass development and benthic community health
AMBI, BOPA and BENTIX have been developed and adapted to the objectives of the
WFD. In our study, although the values of AMBI, BOPA and BENTIX alternatively
displayed a difference between seagrass and sand from autumn 2005 to autumn 2009, ES did
not change in both habitats. AMBI and BOPA fairly assessed the quality of the ecosystem in
Banc d’Arguin while BENTIX underestimated it. Comparisons between these indices
revealed that BENTIX did not provide results coherent with the two other indices nor with
what was expected from a seagrass bed in a non-polluted oceanic area. The limitations of the
use of the BENTIX index are met in the case of transitional waters (estuaries and lagoons)
where the natural conditions favour the presence of tolerant species in very high densities. In
this case, undisturbed lagoons or estuaries may appear with low quality status if the BENTIX
index is used. Interpreting different benthic indices developed for different habitats to yield a
common assessment for management purposes is further complicated when the indices are
based on different combinations of metrics (Teixeira et al., 2010). The discrepancy between
indices is mentioned in previous studies (Blanchet et al., 2008).
A comparison of AMBI or BOPA values between habitats or among dates confirmed
that seagrass cover estimation must remain a WFD parameter to be measured. Indeed, BIs
alone did not reveal the transition of habitat from sand to continuous meadow. More exactly,
they were unable to pick-up differences between communities because indices are calculated
according to the sensitivity of a species and not to the identity of the species. Therefore, when
a habitat changes but remain sensitive, as in the present case, BIs cannot detect community
changes. Such lack of reactivity concerning benthic habitat modification was already
demonstrated (Lavesque et al., 2009). However, it is also important to highlight that most BIs
(BOPA, BENTIX) used in this study were originally developed for subtidal communities. For
35
intertidal environments, the thresholds between ES classes should be revised and
‘Acceptable’ (Good and High status) and ‘Not acceptable’ (Moderate or Worse status)
redefined (Blanchet et al., 2008).
4.3. Seagrass development and cockle population health
Cockle population health was estimated through their growth (compared shell length),
their abundance (recruitment, mortality), and their parasite load (trematodes). Mean shell
length was always smaller in seagrass. The vegetation limits hydrodynamics and alters
suspension feeders filtration efficiency (Coen and Heck, 1991; Irlandi and Peterson, 1991;
Irlandi, 1996; Bouma et al., 2009b). However, the changes in the sediment dynamics within
vegetation explain that some investigations have demonstrated increased growth within
seagrass beds while others have not (Irlandi, 1996; Reusch, 1998). The consequence of the
development of seagrass on cockle abundance depended on whether cockles were already
settled or not (a posteriori recruitment). Intraspecific competition can be responsible for a
cockle growth deficit (Jensen, 1992): in the present study, cockle abundance reached values
for which such a process was already observed in this site (de Montaudouin and Bachelet,
1996). When seagrass developed in an established adult cockle population, cockles
abundance remained higher than in bare sand. Seagrass can act as a refuge against predation
(Orth et al., 1984; Boström and Bonsdorff, 1997), and particularly against birds that are the
main predators of adult cockles (Reise, 1985). When seagrass was already present and
recruitment rate was high, the number of recruits was significantly lower in seagrass bed
possibly due to predation that is higher in the seagrass for small prey such as juvenile cockles
(Edgar, 1999). Indeed, predators exhibited higher biomass in the mature seagrass, at the end
of our monitoring. That can also be due to the effect of hydrodynamics and sedimentary
impact of seagrass on recruitment processes. Some species such as mussels that need a hard
substratum to settle are favoured by the presence of seagrass (Reusch, 1998). For an infaunal
bivalve, the presence of high density of leaves, roots and rhizomes can interfere with
settlement (Neira et al., 2006). In addition, increased organic matter deposition in seagrass
can create an unsuitable habitat for buried bivalves with hypoxic conditions and enhanced
sulphide concentrations at the sediment surface (Rosenberg et al., 1991). Considering
parasites, the initial presence of the seagrass bed did not modify trematode infection in
36
cockles between seagrass and bare sand. This may be due to the complex trematode life cycle
that requires time to settle. In June 2005, infection levels differed in cockles from bare sand
but there was no relation with the potential first intermediate host abundance (Hydrobia
ulvae). Four to nine months after seagrass settlement, prevalence of infection of cockles with
two trematode species differed between both habitats. At the completion of seagrass
development, four out of five trematode species exhibited higher infection in the seagrass
bed. Therefore, with time, trematode communities diverged in relation to seagrass presence
as shown in a Moroccan lagoon (Gam et al., 2009a). The occurrence of the ‘upstream’ first
intermediate host is considered as a strong driver to explain ‘downstream’ infection
(Thieltges and Reise, 2007). The correlation between both factors (abundance of first
intermediate host and infection intensity in the second intermediate host) depends on the
scale studied (Wiens, 1989) and can disappear at the small scale (Poulin and Mouritsen,
2004). This is the case in most of our results where a higher infection in one habitat rarely
corresponded to higher first intermediate host abundance. However, we do not have any data
concerning the prevalence of infection of first intermediate hosts in seagrass and bare sands.
As previously mentioned, parasites can alter cockle fitness. In Arguin, parasites
induce mortality when mean Meiogymnophallus minutus abundance in cockles reaches 500
individuals per cockle (Gam et al., 2009b), but this threshold is lower for both Curtuteria
arguinae (∼50 individuals cockle-1) (Desclaux et al., 2006) and Himasthla quissetensis (∼32
individuals cockle-1) (Desclaux et al., 2004). Comparing these thresholds with our results, C.
arguinae (157 individuals cockle-1) would be the main trematode species that could increase
cockle mortality.
Conclusion
The development of the seagrass bed induced a large-scale change in benthic
communities, including trematode assemblages. It is however complex to determine if this
seagrass extension leads to an increase in ecosystem quality. The WFD considered that for
Good Ecological Status “The levels of… angiosperm abundance are consistent with
undisturbed conditions” (EEC, 2000). Our results highlight a rapid modification of benthic
37
community structure, but it is not possible to say whether this is a positive or negative change
remains subjective. The different tested Biotic Indices (BIs) did not reveal any change in
relation to seagrass development. That meant that these BIs are not useful in detecting habitat
modification on these flats. On the other hand, it can also be considered that these BIs
“behaved” independently of benthic habitat change in order to assess the water mass ES as
required by the WFD. Finally, seagrass bed development had a rather negative role on cockle
populations. This negative effect was certainly due to the physical presence of Zostera noltii
(and hydrodynamic consequences) than the parasite’s impact. Indeed, although trematode
parasite can alter their host-population dynamics, infection rate observed in the present study
(i.e. number of metacercariae per cockle) was smaller than what has been observed elsewhere
(Desclaux et al., 2004; Desclaux et al., 2006; Gam et al., 2009b).
38
+++
In Chapter 2, we showed that seagrass presence modify trematode community in
cockles, with an increase of parasite. Three hypotheses can be advanced to explain why
parasite infection was higher in seagrass bed compared to bare sand: 1) The presence of
seagrass may perturb parasite locomotion between both hosts. This hypothesis can be rejected
because parasites displayed higher abundance in cockles from the seagrass. 2) Larger cockles
are more infected because they filtrate more water. This hypothesis can be excluded because
cockles were smaller in the seagrass compared to bare sand. 3) The presence of seagrass
attracts the hosts that will emit the parasites toward the cockles. It is highly plausible but
these data are lacking in the present study.
Nevertheless, seagrass dynamics seems related to host-parasite dynamics. In the next
chapter, we will test correlation among seagrass cover, some environment factors as salinity,
emersion rate, temperature, sediment characteristics and the distribution of different
parasites/pathologies. Then, we will consider parasite in some dominant infaunal species
(Manila clam (Ruditapes philippinarum) and cockle (Cerastoderma edule) as an element
among others to assess seagrass ecosystem fitness. Up to now, the relationship between
seagrass presence/absence and parasite remained poorly documented (Gam et al. 2009a). The
hypothesis is that seagrass, as an engineer habitat, can affect both biotic and abiotic factors
which contribute to the parasite community.
This study was the occasion to develop a tool allowing us to rapidly estimate sea grass
(leaves) biomass from several field campaigns. Indeed, the traditional method of
measurement sea grass biomass requires a lot of time (sorting, dry weighting). Alternative
methods using video camera cannot be applied in Arcachon Bay intertidal mudflats due to
navigation security and/or high turbidity). Here, we proposed a new method based on
simplified photography analysis.
+++
39
40
Chapter 3- Seagrass and bivalve health
Chapter
3
-
Correlation
between
bivalves
health
and
environmental parameters, including Zostera noltii seagrass bed
cover, in Arcachon Bay
In revision in Marine Ecology : An Evolutionary Perpective, with the title “Environmental
factors contributing to the development of Brown Muscle Disease and Perkinsosis in Manila
clams (Ruditapes philippinarum) and trematodiasis in cockles (Cerastoderma edule) of
Arcachon Bay”
Abstract
The aim of the present study was to identify environmental factors that could explain
the distribution of different pathologies of commercially exploited bivalves, in an Atlantic
lagoon, Arcachon Bay. In particular, the role of salinity gradient as a driver was explored.
The Manila clam Ruditapes philippinarum underwent two severe pathologies, i.e. perkinsosis
which is due to a protozoan parasite and brown muscle disease (BMD) the etiological agent
of which remains unknown. Perkinsus olseni infection was very low in a small low-salinity
area but, at the scale of the entire lagoon, was more influenced by organic matter content in
the sediment and by emersion rate. BMD prevalence was also 2.6 times higher in the higher
organic content area but was also negatively correlated with salinity. The sympatric cockle
(Cerastoderma edule) was affected by eight trematodes. These parasites have a complex life
cycle which generally involves three free-living species. The distribution of the different
trematode communities was rather patchy within Arcachon Bay with no clear relationship
with measured environmental factors. The dominance of trematode species could be due to
the presence of the other hosts involved in their life cycle which makes it more difficult to
detect a major environmental driver.
This survey demonstrated that salinity is not the major factor explaining disease
distribution in this temperate lagoon. This result has consequences in the research of “refuge
areas” (free of diseases) or “hot spots” (heavy infection) for high economic value species.
Keywords: bivalves, diseases, distribution, Arcachon Bay, parasites
41
1. Introduction
In transitional ecosystems, salinity is generally a strong driver of community structure
(Blanchet et al., 2005). This is particularly true in systems where the gradient is obvious like
in estuaries (Wolff, 1973; Ysebaert and Herman, 2002; Rybarczyk and Elkaim, 2003).
Typically, three types of faunal communities are encountered, in euryhaline, polyhaline,
mesohaline and oligohaline waters respectively, all of these also having both pelagic
(Herman et al., 1968; Orsi and Mecum, 1986; Baretta and Malschaert, 1988; David et al.,
2006) and benthic components (Attrill and Rundle, 2002; Ysebaert and Herman, 2002).
Parasite species may also display a strong relationship with the haline gradient when their life
cycle involves a free stage that is sensitive to water mass characteristics. This has been
described in protozoans like Perkinsus spp. that parasitize numerous mollusc species (Ahn
and Kim, 2001; La Peyre et al., 2006). When the host dies, the parasite evolves into a stage,
the hypnospore, which spends some time in the water before being inhaled by a new host.
The various different species of Perkinsus genus are known to be limited by low salinity
(Leite et al., 2004). Most trematode species also display free-swimming larval stages in their
life cycles, usually including two such stages. The parasite sexually reproduces in the
definitive host. Eggs are emitted into the water with faeces and develop into miracidium
larvae that infect the first intermediate host, always a mollusc. Asexual reproduction in this
host leads to the formation of a new type of larvae, cercariae, which are shed into the water
and swim or drift before infection of the second intermediate host. There, larvae remain in a
latent stage, the metacercariae, and wait for their host to be predated on by the final host in
order to achieve their life cycle. Many studies demonstrated that these swimming larvae were
sensitive to salinity (Mouritsen, 2002; Koprivnikar et al., 2010), suggesting that this factor
could contribute to explaining the structure of parasite communities.
In lagoons, however, salinity gradients are not always so pronounced and other
structuring factors may interfere in the process such as emersion rate, seagrass occurrence,
and sediment grain-size (Bachelet et al., 1996; Marzano et al., 2003; Blanchet et al., 2004). It
results in a mosaic of communities where the major structuring factors are not always easy to
detect. In relation to parasites, there is little knowledge on the factors that drive their
distribution in these sheltered areas. For fishermen, such knowledge could contribute to
42
identifying ‘refuge areas’ that are more or less exempt from infectious diseases (Hoffmann et
al., 2009).
Arcachon Bay is a good system to illustrate and study these questions. It is a typical
tidal lagoon with a cape sheltering most of the system and with fresh water inputs. Previous
studies performed on free-living intertidal fauna demonstrated that the salinity gradient was
not the only driver of the distribution of pelagic (Vincent, 2002) and benthic communities
(Bachelet and Dauvin, 1993; Blanchet et al., 2004). Arcachon Bay also ranks in first position
in terms of Manila clam Ruditapes philippinarum production in France and periodically
sustains a small cockle Cerastoderma edule fishery also. Both bivalves undergo pathologies.
Perkinsosis (Perkinsus olseni) is very prevalent in Manila clams in the whole bay with no
detected gradient (Dang et al., 2010a). Recently, in 2005, a new disease of the bivalve was
described for the first time, Brown Muscle Disease (BMD) (Dang et al., 2008). The infectious
agent is still unknown, although a virus is suspected (Dang et al., 2009). Finally, trematodes
are abundant, mostly in cockles. A community of 13 species was identified in previous
studies (Desclaux et al., 2002; de Montaudouin et al., 2009) but the global distribution of
these parasites at the lagoon scale remains unknown. Trematodes induce a less severe impact
on their cockle hosts than that observed in Manila clam infected with Perkinsosis and BMD,
except when the intensity (number of parasite individuals per infected host (Bush et al.,
1997)) becomes high in cockles infected as second intermediate hosts (Desclaux et al., 2004)
or when the cockle is the first intermediate host (Jonsson and Andre, 1992; Thieltges, 2006a).
Our specific aims were: 1) to describe a series of biotic and abiotic factors in
Arcachon Bay in order to identify homogeneous entities, i.e. groups of stations that were
defined by environmental characteristics with similar values (of salinity, temperature,
emersion rate, grain-size median, seagrass biomass…); 2) to assess levels of infection in
cockles and clams; 3) to identify environmental factors correlated with the different diseases.
The general aim was to explore whether it is possible to identify ‘hot spots’ (accumulation of
infections) or ‘refuge areas’ (sites free of pathogens) in relation to some biotic and/or abiotic
factors.
43
2. Materials and Methods
2.1. Study site
Arcachon Bay (44°40’N, 1°10W) is a macrotidal (tidal range = 0.9-4.9) coastal
lagoon situated on the South Western coast of France. This 156-km² ecosystem is connected
to the Atlantic Ocean by a 2-3-km wide and 12-km long channel. Channels represent 41 km²
of the lagoon surface and penetrate between large intertidal areas (115 km²). A significant
proportion of these tidal flats (46 km²) are covered by Zostera noltii seagrass beds (Plus et al.,
2010). Arcachon Bay receives freshwater inputs from its North-Eastern and Southern parts
but mainly by a river (Leyre) located in the South-Eastern end of the lagoon. The balance
between marine and continental water inputs and the slow renewal of water by tides induce
salinity and temperature gradients (Robert et al., 1987).
In October and November 2009, a total of 39 stations were sampled along two axes
(i.e. two subareas) drawn between the most seaward part of the lagoon and the most
landward, within the Manila clam and cockle habitat (Figure 3.1). Stations were sampled at
low tide. Six 0.25-m² quadrats were sampled by hand to collect clams and cockles. When the
number of collected individuals was insufficient, they were collected haphazardly in the
immediately surrounding area. When all stations were sampled, we selected 28-36-mm shell
length individuals that corresponded to the length range that was common to all stations.
Sediment was sampled for organic matter analysis and grain-size determination (median).
Pictures were taken for seagrass biomass evaluation (Fujifilm FinePix S9500 camera, 1600 ×
1200 pixels) (see Figure 3.1).
44
Figure 3.1. Principal Component Analysis (PCA) based on 21 environmental factors (A)
from 33 stations (B). Four groups can be separated. Organic Matter content in the sediment
contributed to Axis 1, while salinity and temperature contributed to Axis 2. Group 1: blue;
Group 2: green; Group 3: violet; and Group 4: red. White : No values. Stars represent stations
where bivalves were collected while yellow disks represent stations that were surveyed
within ARCHYD network in order to obtain ground-truth values of salinity and temperature
for model validation. Black disk situates Eyrac tide gauge to calibrate sea surface height with
the model and to deduce emersion time.
45
2.3. Environmental factors
2.3.1. Grain size and organic matter
At each station, the 3 first cm of sediment was collected and subsequently analysed
for grain-size distribution (wet sieving) and organic matter (loss of weight of dry sediment at
450°C for 4 h).
2.3.2. Seagrass leaf biomass
A rapid but reliable method of assessing Zostera noltii’s leaf biomass was developed
for this study, during a preliminary sampling trip.
Fifteen 15 cm × 15 cm quadrats were delicately laid over the sediment surface, at low
tide. These quadrats were visually selected to represent a large range of vegetation cover,
from 0% (bare sediment) to 100%. For each quadrat, a numeric photograph was taken
perpendicularly, one meter above the surface.
Then, leaves were cut at their base with scissors. Back at the lab, each sample was
washed, weighed (fresh weight) and dried at 60°C for 48 hours to obtain a dry weight.
On each photograph, polygons corresponding to bare sediment have been drawn using
image analysis software. The surface of these polygons was automatically calculated and the
seagrass cover was deduced from the quadrate surface. The leaves’ dry weight and the
surface cover were correlated after logarithmic transformation of both variables (and after
removing the picture without grass). The equation being as follows:
Loge(DW) = 1.450 × Loge(S) – 1.733, with R = 0.98 (n=14 pictures)
Where Loge(S) = 0.690 × Loge(DW) + 1.195
DW is Zostera noltii leaves dry weight in g.m-2 and S is the percentage of sediment
covered by Z. noltii. The biomass in fresh weight (FW) could also be obtained
FW = 13.2 × DW, R=0.97 (n=15)
Therefore from a numerically identified picture, it was possible to obtain a biomass
with reliable precision. Of course, the more the vegetation cover approaches 100% cover, the
46
less precise the method becomes, because 100% cover may correspond to many biomass
values, depending on seagrass bed thickness. Then, the drawing of polygons allowed us
obtaining the relationship to calculate leave biomass from seagrass cover but was highly time
consuming for routine survey and not really worth comparing to direct biomass assessment.
Consequently, we used a line-drawing method in order to rapidly estimate foliage biomass.
This method consisted of drawing three equidistant lines across each numerical image
and counting the intersections between lines and leaves. Consequently, the 15 cm × 15 cm
frame must be disposed with leaves perpendicular to one of the square sides and lines have to
be drawn at right angle to leaves. The best correlations were found after logarithmic
transformation of both variables (intersection and biomass).
Loge(DW) = 1.514 × Loge (mean number of intercepts per line) – 1.911, with R =
0.98 (n=14 pictures)
This method was utilized to determine aerial biomass in the 39 investigated stations
(10 frames per station). Less than ten minutes per photograph, including line drawing, were
necessary to estimate aerial biomass.
2.3.3. Temperature, salinity, emersion rate
The high number of sampling sites and the necessity to determine the general
environmental patterns defining the “living conditions” at each sampling site were
incompatible with the setting up of an experimental protocol. Thus, temperature, salinity and
emersion rates for each sampling site, were obtained by means of a mathematical model
(MARS, (Lazure and Dumas, 2008)), which was previously applied and validated on
Arcachon Bay (Plus et al., 2009). This three-dimensional hydrodynamic model calculates free
surface height variations, current speed and direction, water temperature and salinity, at a 235
m resolution on the horizontal plan (10 meshes on the vertical) and at a time step ranging
from 10 to 60 seconds. The model was launched for a three-year period (November 2006 to
November 2009), and the following parameters were recorded for each site:
•
Temperature and salinity minima, maxima and means.
•
Percent of time spent in emersion.
47
•
Frequency histograms for temperature (percent of time spent at temperatures
below the following thresholds: 8, 12, 16 and 20°C). These threshold values cover
the range of values that is found in this area and allow detecting particularly low
and high temperatures from “rare events”.
•
Frequency histograms for salinity (percent of time spent at salinities below the
following thresholds: 13, 28 and 34). These threshold values cover the range of
values that is found in this area and allow detecting particularly low and high
salinities from “rare events”.
Boundary conditions were provided by the global tidal solution FES99 (Lefèvre et al.,
2002) and the atmospheric forcing – air temperature, atmospheric pressure, nebulosity,
relative humidity and surface wind stress – was provided by the ARPEGE model (MétéoFrance).
Comparisons between available ground-truth values and model simulations were
performed in order to validate the mathematical model on the study period (from November
2006 to November 2009). Temperature and salinity observed data were taken from the
ARCHYD database (Ifremer), selecting four stations located along the clam sampling axes
(Figure 3.1). Sea surface height (SSH) observations at the Eyrac tide gauge (Figure 3.1) were
provided by the REFMAR website (refmar.shom.fr) and remain the property of the SHOM
(Naval Hydrographic and Oceanographic Service) and the Gironde DDTM (Sea and Territory
Departmental Directorate). Model evaluation was performed following Piñeiro et al. (2008),
regressing observed vs. predicted values and testing the significance of slope=1 and
intercept=0. This analysis was complemented by RMSD (root mean squared deviation) and
EFF (model efficiency) calculations:
∑ (Yi mod − Yiobs )
n
n
RMSD =
∑ (Yi mod − Yiobs )²
i =1
n
Eff = 1 −
i =1
n
2
(
∑ Yiobs − Y obs
i =1
)
2
where Yimod and Yiobs are respectively the predicted and observed values and n is the
total number of values.
Table 3.1 summarizes the results of the model validation. Observed vs. predicted
values regressions showed that the model behave satisfactorily. Best model performances
48
were obtained for sea surface elevation and temperature, with a very high percentage of
variance in observed values explained by the model (respectively 97% and 98%). Worst
model performance was obtained for salinity but the coefficient of determination still remain
high (85%). All tests for slope=1 and intercept=0 were passed and model efficiency was close
to 1. Theil's partial coefficients show that most of the errors in model predictions were due to
unexplained variance and not to bias or to misleading. Temperature, salinity and SSH root
mean squared deviations are 0.78°C, 0.79 and 19 cm, respectively.
Table 3.1. Regressions parameters (slope a and bias b, for the Yobs = aYmod + b equation),
coefficient of determination (r2), Theil's partial inequality coefficients (Ubias, Uslope and Uerror,,
are the proportions of observed variance not explained by the predicted values but due to
respectively, mean differences between observed and predicted values, slope error and
unexplained variance), root mean squared deviation (RMSD, expressed in the same units as
the variables) and model efficiency (Eff, the closer Eff is to 1, the better is the model), for
observed vs. predicted variables (temperature, TEMP, salinity, SAL and sea surface height,
SSH).
SSH
Temperature
Salinity
a
1.008
0.958
0.910
Significance of test a=1
0.20
0.09
0.18
b
0.016
0.376
3.086
Significance of test b=0
0.33
0.35
0.16
Degree of freedom
915
34
34
r2
0.97
0.98
0.85
Ubias (%)
0.034
0.125
0.039
Uslope (%)
0.002
0.071
0.049
Uerror (%)
0.964
0.804
0.912
RMSD
0.19
0.78
0.79
EFF
0.96
0.98
0.84
49
2.4. Bivalve models and associated pathology
2.4.1. Manila clam and Perkinsus
All collected Manila clams (Ruditapes philippinarum) belonging to shell length class
28-36 mm were opened (34 stations out of 39 investigated stations harboured Manila clams
with adequate shell length) and gills samples were excised. Five gills from clams with
identical lengths were pooled and weighed for analyses of Perkinsus infection levels which
were determined by the FTM (fluid thioglycollate medium) assay (Ray, 1966). Depending on
clam availability, between 2 and 6 pools (except 1 pool in a single station (n°21)) were
obtained. For induction of prezoosporangia (hypnospores), gills samples were placed in
separate 15 mL tubes containing 9.5 mL FTM supplemented with 66 µg mL-1 streptomycin,
32 µg mL-1 penicillin G and, 40 µg mL-1 nystatin (final concentrations), to prevent bacterial
and fungal activity. The tubes were incubated at room temperature for 7 days, in the dark.
After incubation, the samples were stored at 4°C until hypnospore numeration. To lyse
tissues, samples were centrifuged at 2500 rd/min (664 g) for 10 min. Pellets were added with
5 mL NaOH 2N, and incubated at 60°C for at least 1 hour. This step was repeated before
pellets were rinsed twice with 10 mL 0.1 M phosphate-buffered saline (PBS). Final pellets
were resuspended in 1 mL PBS and hypnospores were counted twice using a Malassez
counting chamber.
The concentration of Perkinsus was correlated to the different variables of the
environment (Pearson correlation, after verifying normality of residuals), and was compared
among the four spatial groups (Kruskal Wallis test due to heteroscedasticity) and between the
two axes, i.e. between the two subareas of the lagoon (Student t-test) (Statistica 7 software).
2.4.2. Manila clam and BMD
All collected clams belonging to the 28-36-mm shell length class were opened and an
index of the pathology (Muscle Print Index, MPI) estimated. On the posterior muscle (the
only affected one), the MPI was used to designate the surface colonized by the brown muscle
print on a scale of 0 to 4 as follows: (0 (healthy), 1 (0-25%), 2 (25-50%), 3 (50-75%) and 4
(75-100%)). When both valves displayed different pathology indices, the highest category
50
was selected to characterize the stage of BMD. Prevalence was defined as the percentage of
infected hosts (Bush et al., 1997).
2.4.3. Cockle and trematodes
When possible, five cockles between 13 and 29-mm shell length per station were
opened. The flesh was separated and squeezed between two large glass slides. Trematodes
were identified and counted under a stereomicroscope (de Montaudouin et al., 2009).
Trematode abundance was defined as the mean number of metacercariae per individual host,
and prevalence as the percentage of infected hosts (Bush et al., 1997). A correspondence
analysis was performed on data which consisted of a ‘35 stations × 8 trematode species’
matrix when each species occurred. Data (averaged metacercariae abundance) were
log10(x+1) transformed. In the case of Bucephalus minimus, it was not possible to separate
and count sporocysts and a value of 1 was arbitrarily imposed in the matrix. Particular
attention was devoted to the identification of the ‘contributive’ taxa. A taxa was determined
‘contributive’ when its contribution to the dimension’s inertia was at least twice the mean
theoretical contribution of a taxon. Considering that the 8 taxa of the matrix contributed to
100% inertia, a “contributive” taxon inertia should arbitrarily be over (100/8) × 2 = 25%.
3. Results
3.1. Environmental factors
The Principal Component Analysis separated 4 spatial groups (Figure 3.1A). Group 1
isolated a small number of stations at the mouth of the two freshwater inputs (Canal des
Etangs and Leyre) (Figure 3.1B). This group was characterized by low mean salinity (22.6),
high frequency of T<16°C (50%), high organic matter and silt and clay contents in the
sediment (7.8 and 38.5%, respectively), null emersion time, and low seagrass coverage (7%)
(Table 3.2). Group 2 gathered stations in oceanic position with high median grain size (191
µm), high mean salinity (32.8), mean temperature similar to elsewhere in the lagoon (17°C)
but with low occurrence of cold events, i.e. low frequency of T<8°C (4.2%), low organic
51
matter and silt and clay content in the sediment (3.2 and 16.3%, respectively) (Figure 3.1,
Table 3.2). Groups 3 and 4 displayed medium mean salinity (30.1), medium frequency of
T<8°C (7.7%) (Table 3.2). Axis 2 displayed a higher percentage of silt and clay, organic
matter in the sediment and emersion time and more extreme water temperatures than Axis 1
(Table 3.3).
3.2. Manila clam and Perkinsosis
The mean concentration of Perkinsus in the bay was 62,000 cells.g-1 (gill fresh weight
FW) and could reach 209,000 cells.g-1 (gill FW) (station 33, Axis 2, Figure 3.2). It was
positively correlated to organic matter concentration in the sediment, percentage of emersion,
distance to Leyre river and negatively correlated to frequency of T<16°C and distance to
Canal des Etangs (Table 3.4). There was a significant difference of Perkinsus concentration
among spatial groups (Kruskal Wallis, df=3, H=22.58, p<0.001), Group 1 (7,400 cells.g-1
(gill FW)) being different from Groups 2, 3 and 4 that displayed similar infection (70,193
cells.g-1 (gill FW)). Perkinsus abundance was three times higher in Axis 2 (mean = 95,701
cells.g-1 gill fresh weight) than in Axis 1 (mean = 28,917 cells.g-1 gill fresh weight) (Student
t-test, df=33, t=-4.62, p<0.001).
52
Table 3.2. Different characteristics of the environment averaged for each of the four groups
that were defined by the Principal Component Analysis (Figure 3.2). ANOVA (F value) or
Kruskal Wallis tests (H) were performed to compare values among groups. Superscript letters
gather groups that are similar for a given parameter, values that are different from values of
the three other groups (p<0.05) are in bold.
Median (µm)
Sediment
Silt (%)
Organic matter (%)
Seagrass cover (%)
Mean
F <13‰
Salinity
F <28‰
F <34‰
Mean
Temperature
Group 2
Group 3
Group 4
103 b
191.2 a
96.5 b
108.3 b
±52.8
±85.3
±37.8
±22.5
a
38.5
b
16.3
±11.3
±16.3
bc
a
7.8
±2.6
±3.2
40.1
29.5
6.2 b
±2.5
ab
44.9
±22.7
±23.4
±26.7
22.5 c
32.8 b
30.3 a
29.9 a
±3.7
±0.3
±0.3
1.4
16.4
±8.7
b
0.0
±0
a
66.6
±19.0
a
99.9
±0.0
±0.3
b
1.8
±0.7
75.1
16.7
±0.2
22.7
a
98.8 a
16.7
17.0
±0.1
a
17.0
±0.1
c
±0.5
±0.9
Maximal
34.4
33.1
34.1
32.2
±0.5
±2.2
±2.1
±2.6
F < 8°C
F <16°C
F < 20°C
Current F<0.25m/s
% Emersion time
Clam density (ind/m²)
Leyre
Canal des étangs
Atlantic ocean
9.5
4.2
±0.3
±1.1
a
31.6
±0.7
50.4
±0.7
27.1 b
±0.4
ac
a
48.0
±0.2
a
63.1
59.5
48.3
59.9
±0.7
93.3
95.6
91.3
82.2
±6.9
±4.5
±10.4
±15.7
0.0
30.9
±0
±16.9
ab
16.2
±28.1
5.9
ab
±8.1
9.8
±8.1
±1.9
6.3
13.8
8.2
3.0
ab
b
±1.0
b
±41.7
a
±0.9
a
±11.4
51.4
±7.9
±1.1
ab
12.7
a
32.9
ab
12.4
±1.3
3.3
a
±1.3
9.6
a
±0.4
±15.1
1.36
0.274
23.74
<0.001
23.11
<0.001
12.13
0.006
23.07
<0.001
4.97
0.174
6.26
0.002
ab
23.9
±18.6
8.2 b
±4.3
10.84
0.012
10.66
0.013
9.07
0.028
22.06
<0.001
b
±4.1
9.5
<0.001
a
13.0
8.2
21.92
cb
±0.3
b
<0.001
ab
±1.4
b
22.87
<0.001
28.1 a
±1.3
a
0.002
9.55
±0.3
b
14.64
<0.001
±0.5
b
<0.001
31.80
±0.7
28.0
23.16
a
7.5
a
±0.2
ab
48.4
7.9
1.9
±0.2
±0.2
a
62.7
b
0.002
b
±0.9
a
6.24
b
±0.4
0.8
0.000
b
1.2
3.1
7.91
±1.5
b
0.004
<0.001
±13.1
99.8
13.23
8.32
±0.9
±0.1
a
±0.1
bc
20.1
0.3
p
ab
b
±3.9
b
±7.8
a
0.1
ab
H
ab
±10.8
a
F
a
±8.3
c
±1.3
b
72.2
38.8
9.1
±2.5
a
6.2
3.2
a
Minimal
F <12°C
Distance
(km)
Group 1
a
±1.9
53
Table 3.3. Different characteristics of the environment in both axes (Figure 3.1). Student ttest (t value) was performed to compare values between axes. Values that are different
(p<0.05) are in bold.
Median (µm)
Silt (%)
Sediment
Organic matter (%)
Seagrass cover (%)
Mean
F <13‰
Salinity
F <28‰
F <34‰
Mean
Minimal
Maximal
Temperature
F < 8°C
F <12°C
F <16°C
F< 20°C
Current F<0.25m/s
% Emersion time
Leyre
Distance
Canal des étangs
(km)
Atlantic ocean
Axis 1
Axis 2
t
p
131.9
23.5
4.3
44.6
29.5
2.6
25.2
91.2
16.9
2.5
32.1
6.6
28.2
48.6
61.3
85.3
11.4
17.6
7.2
10.9
8.4
127.4
32.1
7.3
51
30.9
0.8
15.2
92.3
16.9
1.7
33.7
6.9
27.9
48.3
60.9
91.9
28.3
27.7
13.3
4.5
7.4
0.20
-2.12
-3.11
-0.60
-1.38
1.02
1.49
-0.27
0.30
2.28
-2.05
-0.39
0.69
1.33
0.57
-1.53
-3.16
-1.11
-5.10
7.83
0.83
0.838
0.041
0.004
0.550
0.178
0.315
0.144
0.790
0.765
0.029
0.048
0.700
0.489
0.191
0.573
0.135
0.003
0.273
<0.001
<0.001
0.414
54
250 000
Axis 1
Perkinsus cell/g (gill
fresh weight)
200 000
150 000
100 000
50 000
0
1
West
2
3
5
6
7
8
9 10 11 12 13 14 16 17 38 39
Stations
East
250 000
Axis 2
Perkinsus cell/g (gill
fresh weight)
200 000
150 000
100 000
50 000
0
21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
South
Stations
North
Figure 3.2. Abundance of Perkinsus olseni (cells/g (fresh weight) in gills) per station in
Arcachon Bay.
55
3.3. Manila clam and Brown Muscle Disease
The mean Brown Muscle Disease prevalence per station in Manila clams throughout
the bay was 11% (Figure 3.3). BMD prevalence and Muscle Print Index were (MPI)
positively correlated with silt and organic matter contents in the sediment and with frequency
S<34. They were negatively correlated with minimal temperatures. Besides, MPI was
positively correlated with mean water temperature and with Manila clam density (Table 3.4).
However, the influence of freshwater input was not similar between Canal des Etangs and
Leyre. There was no difference of prevalence between spatial groups (one-way ANOVA,
F3,28=2.03, p=0.13). However, Muscle Print Index increased from the most oceanic spatial
group (G2) (MPImean= 0.8) to the more continental ones (G3 and G4) (MPImean= 2.4) groups
(one-way ANOVA, F3,28=11.74, p<0.001). Prevalence was 2.6 times higher along Axis 2
(16.1%) (See Figure 3.1 for localisation of both axes) than along Axis 1 (4.5%) (U-Mann &
Whitney, Z=-2.70, p=0.007) (Figure 3.3). MPI was similar in both axes (1.84) (U-Mann &
Whitney, Z=-1.55, p=0.12).
3.4. Cockle and trematodes
Cockles were present in 35 stations out of the 39 investigated stations. A total of eight
trematode taxa were found: seven species utilize the cockle as second intermediate host. One
of them belongs to Himasthla genus and could be a complex of two species, H. quissetensis
and H. continua. The 8th species, Bucephalus minimus, utilizes the cockle as first intermediate
host with a global prevalence of 13% (N=144 cockles). Dimension 1 of the Correspondence
Analysis (44.6% of inertia) discriminated H. interrupta whereas dimension 2 (23.6% inertia)
discriminated Curtuteria arguinae and H. spp. (Figure 3.4A). Three trematode communities
can be identified in the bay (Figure 3.4B) : 1) H. interrupta was present in the northern part
of the bay only and characterized (mean abundance per station was comprised between 3 and
22 metacercariae.cockle-1) and was accompanied by a high B. minimus prevalence (24% with
N=25 cockles) ; 2) Curtuteria arguinae was the dominant trematode in the south-west end of
the bay, i.e. the most oceanic part (mean abundance per station comprised between 9 and 43
metacercariae.cockle-1, N=34 cockles), often accompanied by Diphterostomum brusinae; 3)
the south-eastern end of the bay was characterized by a higher abundance in H. spp. (mean
56
abundance per station comprised between 9 and 43 metacercariae.cockle-1, N=34 cockles)
that represented the only trematode taxa present here.
Figure 3.3. Brown Muscle Disease (BMD) prevalence per station in Arcachon Bay. Mean
BMD prevalence per axis (i.e. subarea) is mentioned.
57
Table 3.4. Correlation between Perkinsus concentration, Brown Muscle Disease (BMD)
prevalence, BMD’s Muscle Print Index (MPI) and different parameters of the environment
(N=34). Significant correlations (p<0.05) are in bold.
R= Pearson correlation coefficient
Perkinsus concentration
(Cells/g of gills)
Sediment
Salinity
Temperature
BMD
Prevalence (%)
R
p
MPI
R
p
R
p
Median (µm)
-0.04
0.829
-0.27
0.141
-0.28
0.119
Silt (%)
0.25
0.149
0.45
0.010
0.39
0.025
Organic matter (%)
0.35
0.040
0.52
0.003
0.42
0.015
Seagrass cover (%)
0.09
0.594
0.07
0.713
-0.35
0.051
Mean
0.29
0.101
-0.10
0.580
-0.26
0.145
F <13‰
-0.31
0.073
0.08
0.672
-0.03
0.865
F <28‰
-0.27
0.127
0.07
0.681
0.29
0.098
F <34‰
0.03
0.875
0.37
0.039
0.72
<0.001
Minimal
-0.29
0.093
-0.37
0.034
-0.46
0.008
Maximal
0.29
0.090
0.12
0.514
-0.02
0.900
Mean
0.09
0.614
0.12
0.6644
0.66
<0.001
<0.001
F < 8°C
0.00
0.990
0.32
0.071
0.65
F <12°C
-0.26
0.131
0.18
0.330
0.21
0.245
F <16°C
-0.35
0.043
-0.00
0.987
-0.24
0.176
F < 20°C
-0.17
0.344
-0.23
0.213
-0.73
<0.001
Current F<0.25m/s
0.09
0.626
-0.16
0.390
-0.20
0.270
% Emersion time
0.34
0.048
0.13
0.491
0.03
0.875
0.10
0.569
-0.12
0.502
0.37
0.037
Distance
(km)
Leyre
0.43
0.011
0.28
0.114
-0.28
0.113
Canal des étangs
-0.51
0.002
-0.60
<0.001
-0.20
0.260
Atlantic ocean
-0.14
0.422
0.20
0.264
0.62
<0.001
58
A
B
Figure 3.4. Correspondence Analysis discriminating the 34 stations harbouring cockles in
relation to trematode species in cockles (A). Different communities of trematodes in cockles
from Correspondence Analysis (B). Dominant species are indicated and discriminated by
different colours. Stations with no characteristic trematode community are in green.
59
4. Discussion
The aim of the study was to correlate the distribution of three types of bivalve
diseases within a lagoon (Arcachon Bay) in order to identify the main drivers. Salinity is
often cited as a strong contributing factor (Ahn and Kim, 2001; Elandalloussi et al., 2008) but
many confounding factors may intervene along the gradient. The unusual feature of Arcachon
Bay was the presence of two major freshwater inputs that induce a bi-directional salinitytemperature gradient, East-West and North-South. We demonstrated that the effect of salinity
was restricted to a very narrow area around the mouth of the river, for perkinsosis only.
Indeed, Perkinsus concentration was significantly lower than elsewhere in Group 1 only,
corresponding to 3 stations near river mouths. The distribution of all diseases was explained
less within “oceanic vs. continental” axis than between both northwest and southwest
subareas of the lagoon. This suggests that factors other than salinity were contributing.
The Principal Component Analysis discriminated groups of stations in accordance
with what could be expected in such an ecosystem: an oceanic-influenced group (Group 2), a
continent-influenced group (Group 1) and two intermediates groups (Groups 3 and 4)
(Bouchet, 1993).
Concerning perkinsosis, the mean abundance of infection was high compared to
previous studies (Lassalle et al., 2007) but was lower than what was assessed in Arcachon
Bay in 2006 (96,000 cells.g-1 (gill FW) (Dang et al., 2010a). This difference can be explained
by the distribution of sampling stations, which in the present study took into account a
broader area including stations with low infection. Salinity is an important factor structuring
the abundance of Perkinsus spp. (Leite et al., 2004). For P. olseni, the optimal salinity range
is 25-35 (Auzoux-Bordenave et al., 1995) and high infection generally corresponds to high
salinity (Burreson and Ragone Calvo, 1996; Cigarría et al., 1997; Park and Choi, 2001). In
Arcachon Bay a similar gradient was formerly obtained (Dang et al., 2010a) but mainly due
to stations sampled near freshwater inputs. In the present study, the refuge function of lowsalinity areas was evident (Group 1), but concerned less than 5% of the axes length (on the
other hand one should verify whether these low salinity areas are effective for commercially
relevant growth rates in the shellfish?). Consequently, the salinity gradient out of these areas
was not high enough to induce a perkinsosis infection gradient. Unexpectedly, the highest
60
difference in perkinsosis infection was observed between Axes 1 and 2. The environmental
characteristics that were significantly different between axes mainly concerned the sediment
and emersion. The highest content in silt and clay, and organic matter in the sediment
coincided with the highest emersion rate, the highest extreme temperatures and eventually the
highest perkinsosis infection (Axis 2). Higher infection in muddy sediment than in sands was
already reported (Choi et al., 2002). Our hypothesis is also that the highest emersion rate
(higher average hypsometric level) is due to (and/or is the consequence) a smaller input of
oceanic water during flow-tides, a water lower turnover and a higher sedimentation of fine
particles. This lower turn-over is consistent with an unpublished report mentioning that 1/3 of
the water mass transits through Axis 2 against 2/3 through Axis 1 (SOGREAH, 2001). That
would increase the retention of Perkinsus hypnospores and facilitate infection between clams.
A similar observation was made with BMD. Interpretation is more difficult due to the
lack of knowledge concerning the infectious agent which might be a virus (Dang et al., 2009).
Here again, Axis 2 displayed higher prevalence in relation to sediment characteristics (but
similar MPI). This axis was also characterized by higher clam densities (+60%, although
p>0.05) which could facilitate disease transmission.
The situation in relation to trematodes was different because many species were
involved and the distribution corresponded to a community analysis pattern. At the lagoon
scale, three communities were discriminated by one or two dominant trematode species,
certainly in relation to the presence of their other hosts (Sapp and Esch, 1994; Hechinger and
Lafferty, 2005; Byers et al., 2008), i.e. first intermediate and definitive hosts which are also
sensitive to environment characteristics. Concerning the influence of freshwater input,
trematode communities were also different between both areas (Leyre and Canal des Etangs).
This monitoring only dealt with correlations. Relationships between causes and
consequences were not demonstrated but the identification of two different sub-ecosystems
arose, independently of salinity gradient. For Manila clams, the notion of ‘refuge’ concerns a
very small area and is not relevant for BMD. Hence, this is not interesting in terms of fishery
management. However, the difference in infection between both axes is important and gives
arguments to develop fisheries models by subareas (Bald et al., 2009) and not at the whole
the ecosystem. For cockles, trematode communities have a patchy distribution but the level of
61
infection remained low compared to known pathological thresholds (Desclaux et al., 2004;
Gam et al., 2009b) and should, therefore, have a low impact in this system.
62
+++
In Chapter 2, we studied dynamics of macrofauna structure in a growing seagrass
bed. The result displayed that the presence of seagrass beds is correlated with high species
diversity, high biomass, and high abundance. The question is what happens with macrofauna
when seagrass disappears. In the next chapter, we will study the relation between chronic
seagrass decline and associated macrofauna. The main hypothesis is that influence of
seagrass is both structural and trophic. Therefore, if seagrass cover declines, it is expected
that it will be accompanied by a loss of diversity, biomass and abundance, a loss of ecological
services and major modifications of coastal systems functioning. This will be considered by
comparing different situations in terms of benthic macrofauna structures.
Biotic Indices (BI) are usually tested to assess Ecological Status (ES). However, BIs
were performed in this study less to assess ES than to observe their “behaviour” in a changing
habitat. Three of the most utilized univariate BIs, i.e. AMBI (AZTI’s Marine Biotic Index
(Borja et al., (2000)), BOPA (Benthic Opportunistic Polychaetes Amphipods Index (Dauvin
and Ruellet (2007)) and BENTIX (Simboura and Zenetos (2002)), were tested. Besides, the
multivariate index MISS (Macrobenthic Index of Sheltered System (Lavesque et al., 2009))
was also tested. This index was preliminary investigated and successfully applied in
Arcachon Bay (Lavesque et al. 2009). Hence, we wanted to test MISS again with a new set of
data. We also selected these different biotic indices for their potential ability to capture an
ecological situation. Each index may give different information which may be (or may be
not) relevant in our study case. Some of them (AMBI in M-AMBI, BENTIX) are indeed used
within the WFD but others are not (BOPA, MISS).
+++
63
64
Chapter 4 – Seagrass decline and macrobenthos
Chapter 4 - Limited consequences of seagrass decline on benthic
macrofauna and associated biotic indicators
Under Review: V. Tu Do, Hugues Blanchet, Xavier de Montaudouin, Nicolas Lavesque.
Estuaries and Coasts.
Abstract
Marine phanerogams are ecosystem engineers, as their presence induces major
environmental changes that impact on the benthic fauna. Consequently, modifications to the
structure of benthic communities would be expected to be associated with seagrass decline.
Since 2005, Zostera noltii seagrass beds in Arcachon Bay, the largest in Europe, have
undergone a severe decline. Twelve stations distributed throughout the lagoon were sampled
in 2002, and all were found to be densely planted at that time. Subsequently the same stations
were revisited in 2010 and seagrass cover had drastically decreased by that time. Based on
benthic macrofauna, Multidimensional scaling (MDS) analysis identified four groups. Years
were separated. In 2002, two groups were distinct in relation to the water body, since in 2010
separation between the two other groups was related to seagrass occurrence. When looking at
community structure and dominant species there were moderate differences within and
between years, independent of seagrass decline. Seagrass loss did not drastically modify the
species composition as they were preserved in the remaining seagrass patches. However,
there was a drop in macrofauna abundance in unvegetated muddy compared with abundance
in the remaining seagrass areas. Epifauna was particularly affected by seagrass decline.
Among Biotic Indicators (BI) based on macrofauna, multivariate BI MISS (Macrobenthic
Index of Sheltered Systems) was in agreement with the similarity of macrofauna structure
among groups, while other tested BI (AMBI (AZTI’s Marine Biotic Index), BOPA (Benthic
Opportunistic Polychaetes Amphipods), BENTIX) performed badly in relation to seagrass
occurrence. However, no index detected seagrass loss, highlighting the necessity to maintain
a separate survey on seagrass cover.
Keywords: Seagrass, Benthic macrofauna, WFD, Arcachon Bay
65
1. Introduction
Seagrass beds are considered as an important component of coastal ecosystems
(contributing to nutrient cycles, biodiversity, food resource (direct or indirect), sediment
stabilisation, etc.). More particularly they are considered to contribute to structuring
macrofaunal communities and stimulating (alpha)-diversity (Hemminga and Duarte, 2000).
Unfortunately, seagrass meadows are also extremely sensitive to environmental
perturbations, resulting in a global decline at an accelerating rate (Short and WyllieEcheverria, 1996; Hemminga and Duarte, 2000; Orth et al., 2006; Waycott et al., 2009;
Costello and Kenworthy, 2011).
Although natural factors such as ‘wasting disease’ played a role in seagrass
regressions, anthropogenic activities are considered to be primarily responsible (Hemminga
and Duarte, 2000; Waycott et al., 2009). Coastal eutrophication, over-exploitation of
predators such as fish, coastal development such as dredging or harbour construction and
many other anthropogenic activities can lead to the irreversible elimination of seagrasses
from coastal ecosystems (Short and Burdick, 1996; Hemminga and Duarte, 2000; Baden et
al., 2003; Pillay et al., 2010). Waycott et al. (2009) reported that threats to coastal ecosystems
as a result of seagrass losses include loss of habitat and marine biodiversity, sediment
erosion, degradation of water quality, decrease of primary production, carbon sequestration
and nutrient cycling. The decimation of seagrass meadows has also been associated with the
collapse of scallop fisheries, major declines in abundance of waterfowl, the extinction of
some associated invertebrate species (Carlton et al., 1991; Orth et al., 2006), reduction of
abundance of fish and shrimp (Bell et al., 2002), influence of predator–prey dynamics
(Irlandi, 1994; Hovel and Lipcius, 2001; Hovel, 2003) and reduction of faunal growth rates
(Irlandi, 1996; Irlandi et al., 1999), species diversity and abundance (Turner et al., 1999), and
alterations in epifauna community structure (Reed and Hovel, 2006).
In general, despite growing concern regarding the decline of these ecosystems, few
studies directly report on the consequences of such losses, with a shortage of historical data
being a particular hindrance. Most investigations were based on comparisons between areas
with and without the habitat of interest, or on experimental manipulation, which usually
cannot replicate the large spatiotemporal scales characteristic of habitat loss (see review in
Pillay et al., 2010). In Arcachon Bay, where the largest European intertidal seagrass bed of
66
dwarf grass (Zostera noltii) occurs (Auby and Labourg, 1996), a 1/3rd decrease of the
occupied surface has been observed particularly in recent years (since 2005) (Plus et al.,
2010). The exact reason for the loss of seagrass in Arcachon Bay is still unclear, but Plus et
al. (2010) suggested exploring such factors as eutrophication, increased geese grazing,
wasting disease, herbicide contamination or dredging activities that take place permanently in
the Bay for harbour and channel maintenance as well as for supplying beaches with sand and
fighting against coastal erosion. These activities often increase the seawater suspension
matter, thus lowering the available light for the seagrass canopy (Erftemeijer and Robin,
2006). Abundance of macrofauna can be used as a powerful tool to detect even slight
environmental changes (Blanchet et al., 2005). The composition and structure of benthic
macrofauna is one of the indicated biological quality elements to be used in transitional
(estuaries and lagoons) and coastal waters for quality status assessment within the European
Water Framework Directive (WFD 2000/60/EC). Benthic communities may constitute a sort
of memory for the system illustrating stressors that have occurred locally and over a period of
time (Patricio et al., 2009). Within the WFD, benthic invertebrates are one of the biological
elements to be used for the assessment of ecological status (ES) of surface and transitional
water bodies (EEC, 2000). Several biological indices based on the benthic macrofauna
assemblages have been recently developed to assess ES of marine waters within the WFD
(Borja et al., 2000; Simboura and Zenetos, 2002; Rosenberg et al., 2004; Borja et al., 2007;
Muxika et al., 2007; Borja et al., 2009a).
The benthic macrofauna of the seagrass bed in Arcachon Bay was sampled in 2002 at
twelve stations, when the seagrass bed fully extended over the tidal flats (Blanchet et al.,
2004). In order to assess the consequences of seagrass decreases on associated macrofauna,
these twelve stations were re-sampled in 2010. The ratio of selected unvegetated stations
(80% of all stations) corresponded to the ratio of unvegetated surface in the surveyed area of
the lagoon. Our particular objectives were 1) to identify possible changes to the macrofauna
in terms of community structure, biomass, abundance, species richness and trophic groups
and evaluate the concomitant effects of annual variability and seagrass decline; 2) to test
performance of Biotic Indices (BIs) in detecting the changes to ecological status (ES).
67
2.1. Study area
Arcachon Bay is a triangular-shaped macro-tidal lagoon (180 km2), located on the
French Atlantic coast (44° 40’ N, 1° 10’ W) (Figure 4.1). It communicates with the Atlantic
Ocean through a narrow (2-km wide) entrance. The tide is semi-diurnal and the tidal
amplitude varies from 0.8 to 4.6 m. The average water temperatures are 22.5 °C and 6 °C in
summer and winter respectively, and fluctuations in freshwater contributions from rivers and
rainwater influence water salinity between 22 and 35. Many small streams run into the
lagoon, but the two main rivers, the Leyre and Canal des Etangs, contribute 73% and 24%,
respectively, of the total annual freshwater inflow (813 million m3). The total lagoon surface
(180 km2) can be divided into two parts: the subtidal channels (63 km2), and the intertidal
area (117 km2). The main channels have a maximum depth of 25 m and are extended by a
secondary network of shallower channels. The intertidal area comprises sandy to sandy-mud
flats (Plus et al., 2010). Most of these flats (61 km2 in 2005 (Plus et al., 2010)) were still
covered by the largest Zostera noltii seagrass bed in Europe (Auby and Labourg, 1996). The
lower part of the intertidal is generally devoted to Japanese oyster (Crassostrea gigas)
culture, which constitutes a major activity at this site. Adjacent to the mudflats, and lining the
channels, eelgrass (Z. marina) occupies the subtidal sector (Blanchet et al., 2008).
The background chemical pollution in Arcachon Bay is low (Benoit, 2005; de Wit et
al., 2005; Lavesque et al., 2009). Its catchment area is dominated by pine forestry (79%) and
intensive agriculture occupies only 9% of the surface (de Wit et al., 2005). As a consequence,
nutrient inputs to the lagoon are moderate and their concentrations in water remain low
(Castel et al., 1996; Bachelet et al., 2000). Some developments of green macroalgae (mainly
Monostroma obscurum and Enteromorpha spp.) occurred in the early 1990s, but these signs
of moderate eutrophication have not been observed since. The catchment area is poorly
industrialised, and heavy metal contamination is low (Benoit, 2005). Consequently, the
overall water quality of the lagoon can be considered as satisfactory (Lavesque et al., 2009).
68
Group B
Group D
Group C
Group A
2002
2010
Figure 4.1. Studied site (Arcachon Bay) with position of the twelve sampled stations. Station
groups issued from data analysis are indicated and discriminated both years (A and B in
2002, and C and D in 2010).
2.2.1. Macrofauna
Two sampling campaigns were carried out in twelve stations over two years (in spring
2002 and spring 2010, except one station that was sampled in August (see discussion))
corresponding to fully extended Z. noltii seagrass bed in 2002 and after the decline of Z. noltii
bed in 2010 (Blanchet et al., 2004; Plus et al., 2010) (Figure 4.1). These twelve stations were
selected along the widest salinity gradient within the seagrass bed, with 33 (annual average)
in the outer position (station 175) to 22 in the inner position (station 126). Sampling consisted
69
of collecting the top 20 cm of the sediment with a 0.15×0.15 m2 corer, with four replicates
per station (replicates being separated by ca. 10 m). Sediment was sieved through a 1-mm
mesh; the remaining fraction was fixed in 4% buffered formalin and stained with Rose
Bengal. In the laboratory, macrofauna was sorted, identified under the stereomicroscope
when possible to the species level, and counted. Biomass of whole organisms was determined
per species as ash-free dry weight (AFDW) after desiccation (60°C, 48 h) and calcination
(450°C, 4 h). Trophic groups were based on literature description (Fauchald and Jumars,
1979; Bachelet, 1981; Sauriau et al., 1989; Hily and Bouteille, 1999).
2.2.2. Sediment and seagrass leaves analysis
The top 3-cm sediment layer was also sampled in each replicate for granulometric
analysis (median grain-size). Median grain-size was determined after sieving weighed dried
sediment through a wet column of sieves with decreasing apertures (1000 μm, 500 μm, 250
μm, 125 μm and 63 μm).
In 2002, Zostera noltii leaves were cut in each macrofauna sample and desiccated
(60°C) until a constant dry weight was obtained. In 2010, a new method of assessing Zostera
noltii leaf biomass was developed. Fourteen [15 cm × 15 cm]-quadrats were delicately laid
over the sediment surface, at low tide. For each quadrat, a numeric picture was taken
perpendicularly, one meter above the surface. The method consisted of drawing three
equidistant lines across each numerical image and counting the intersections between lines
and leaves. Then biomass and percentage of coverage could be obtained, using the following
relationships:
Loge(DW) = 1.514 × Loge(mean number of intercepts per line) – 1.911, with R = 0.98
(n = 14 pictures)
Loge(S) = 0.690 × Loge(DW) + 1.195 with R = 0.98 (n = 10 pictures)
Where DW is Zostera noltii leaves dry weight in g.m-2 and S is the percentage of
sediment surface covered by Z. noltii. Ten pictures per station were analysed.
70
2.3. Data analysis
Four currently available Biotic Indices (BIs) were tested, namely AMBI (AZTI’s
Marine Biotic Index) (Borja et al., 2000), BENTIX (Simboura and Zenetos, 2002; Simboura
et al., 2005), BOPA (Benthic Opportunistic Polychaetes Amphipods) (Dauvin and Ruellet,
2007) and MISS (Macrobenthic Index in Sheltered Systems) (Lavesque et al., 2009). The
three first indices are based on the classification of species into ecological groups according
to their level of sensitivity/tolerance to pollution. MISS is a multimetric approach using 16
metrics describing the biological integrity of the macrofauna. Ecological status (ES) and
thresholds used to classify index values are reported in Table 4.1.
2.3.2. Multivariate Analysis
Abundances were log10(x+1) transformed to minimize the influence of the most
dominant taxa. A non-metric multidimensional scaling (MDS) based on Bray-Curtis
similarity coefficient was carried out to visually assess differences in macrofaunal
assemblages among stations of the two sampling campaigns (2002 and 2010). Four groups
were identified with the decision that a group should include at least two stations. SIMPER
tests were performed to determine which species contributed to within-group similarity.
These analysis were performed using PRIMER® – v6 (Clarke and Warwick, 2001; Clarke and
Gorley, 2006).
2.3.3. Statistical analysis
Statistical tests were applied to assess the differences between the four groups
identified by MDS in terms of biomass of seagrass leaves, median grain-size and macrofauna
(biomass, abundance, number of species per station (S), trophic groups biomass). ANOVA
test was used when homogeneity of variance (Cochran C test) was achieved. In the case of
variance heterogeneity, data were log10(x+1) transformed. When homogeneity of variances
was not achieved, nonparametric tests (Kruskal-Wallis) were applied. All statistical analyses
were performed with STATISTICA® 7.1 software (StatSoft).
71
Table 4.1. Indices used in this study to assess Ecological Status (ES) and thresholds used to classify index values
Biotic
Indices
Number of ecological
groups
AMBI
5
BENTIX
2
BOPA
2
MISS
Computation of the indices
0 EGI + 1.5 EGII + 3 EGIII + 4.5 EGIV + 6EGV
ES status
Acceptable
0 to 3.3
References
Not acceptable
3.3 to 7
Borja et al. (2000)
3 to 6 (for mud)
0 to 3 (for mud)
Simboura and Zenetos (2002)
0 to 0.13966
0.13966 to 0.30103
Dauvin and Ruellet (2007)
0.4 to 1
0 to 0.4
Lavesque et al. (2009)
based on percentage of ecological groups
6 EGI&II+ 2 EGIII-V
log10 [(fp/fa + 1) + 1]
based on ratio of ecological groups
Sixteen parameters were classified in three categories
describing macrofauna assemblages
EG for AMBI and BENTIX: ecological groups as determined by Borja et al. (2000): EGI: very sensitive to organic enrichment; EGII: indifferent
to enrichment; EGIII: tolerant to excess organic matter enrichment; EGIV: second-order opportunistic species; EGV: first-order opportunistic
species; For BOPA: fp: opportunistic polychaetes frequency; fa: amphipods frequency
72
3. Results
3.1. Macrobenthic community structure
When all dates and stations were combined, 110 taxa were found. At 50% similarity
level, a MDS plot showed a separation among four groups of stations, two in 2002 (A and B)
and two in 2010 (C and D), corresponding to different benthic assemblages (Figure 4.2).
Zostera leaf biomass
(g DW m-2)
Group B
Group A
Group C
Group D
Similarity level 50%
Figure 4.2. Non metric multidimensional scaling (MDS) of stations based on the Bray-Curtis
similarity matrix on Log10(x+1)-transformed abundance data and gathered at 50% similarity
level. Zostera leaves biomass is indicated a bubble plot. For example 02-97 means station 97
(Figure 4.1) sampled in 2002.
Group A clustered 4 stations sampled in 2002 and located closer to the ocean in more
saline water than the other groups (Figure 4.1). Average Z. noltii leaf biomass was 70 g DW
m-2 corresponding to 60% cover of the sediment surface (Table 4.2). Sediment consisted of
mud (grain size median = 45 µm) with 62% of the weight composed of silt and clay. Mean
macrofauna abundance was 28125 ind. m-2 with a mean of 29 species per station (Table 4.2,
Figure 4.3). Group A was characterized by both the numerical dominance and major
contribution (SIMPER test) of the oligochaete Tubificoides benedii (60% of total abundance),
73
the polychaetes Heteromastus filiformis and Melinna palmata and the bivalve Abra
segmentum (Table 4.3, Figure 4.3). The epifauna represented 12.5% of total abundance, half
of it due to Hydrobia ulvae (Figure 4.3). Total biomass was 21.6 g AFDW m-2 distributed
among deposit feeders (27%), grazers (16%), predators (27%) and suspension-feeders (27%)
(Table 4.2). One fourth (5.3 g AFDW m-2) of the total biomass consisted in epifauna. This
epifauna was mostly composed of motile species, Hydrobia ulvae being dominant (21%)
(Figure 4.3) followed by Demospongiae (15%), Idotea chelipes (12%), Nassarius reticulatus
(12%), etc. Infauna biomass (16.4 AFDW m-2) was dominated by Manila clams Ruditapes
philippinarum (45%) (Figure 4.3), followed by T. benedeni (10%), Loripes lacteus (9%) and
Marphysa sansuinea (8%).
Group B clustered 8 stations also sampled in 2002 but located in an inner position in
the lagoon (Figure 4.1), and therefore having a lower average salinity. Mean Z. noltii leaf
biomass (83 g DW m-2), sediment median grain size (36 µm) and silt and clay content (69%)
were similar to that found in Group A (p>0.05) (Table 4.2). Mean macrofauna abundance
(24286 ind. m-2) was also similar to what was observed in Group A but number of species per
station was lower (20) (p<0.001) and species composition was different (Table 4.2). Group B
was particularly characterized by a high abundance of Hydrobia ulvae (63% of total
abundance) (Figure 4.3) explaining that epifauna represented 52% of abundance and that total
biomass (24.2 g AFDW m-2) was dominated by grazers (43%) that were mainly H. ulvae
(88%) (Table 4.2). Suspension-feeders represented the other important fraction of total
biomass (42%) mainly due to Manila clams R. philippinarum that represented 67% of infauna
biomass.
Group C clustered 8 stations sampled in 2010 in oceanic as well as in the inner
position (Figure 4.1). The major difference compared to all other groups was the scarcity of
seagrass leaves (3 g DW m-2 corresponding to a cover of 6% of the surface sediment).
Sediment were slightly more sandy (median = 87 µm with 42% silt and clay content) than in
Group B (Table 4.2). Compared to 2002 (Groups A and B), macrofauna abundance was
divided by 2.7 (Figure 4.3, Table 4.2), but the dominant and contributive species were the
same (Hydrobia ulvae for epifauna, Heteromastus filiformis and Tubificoides benedii for
infauna). The only new dominant species was the polychaete Pygospio elegans with
abundance (899 ind. m-2) 3 to 450 times higher than in other groups (Table 4.3). Biomass was
similar to Groups A and B (Figure 4.2, Table 4.2). Half of it was due to suspension feeders
74
and in particular to the bivalves Ruditapes philippinarum (83% of suspension-feeders
biomass), Musculista senhousia (10%), Loripes lacteus (6%) and Cerastoderma edule (3%).
Infauna biomass was dominated by R. philippinarum (63%) and the large polychaete Melinna
palmate (15%) (Figure 4.3). Epifauna biomass was dominated by the grazer H. ulvae (75%),
followed by the exotic bivalve Musculista senhousia (16%). Species richness per station was
the lowest together with Group B (Figure 4.2, Table 4.2) mainly due to epifauna collapse (4
species only compared with 7-9 in other groups).
Group D clustered only 2 stations corresponding to the only sampled stations where Z.
noltii seagrass continued to flourish in 2010 (biomass leaves = 140 g DW m-2, corresponding
86% cover of the sediment surface) (Table 4.2). Sediments consisted of mud (median = 50
µm, with 54% silt and clay). Mean macrofauna abundance was similar to Group C (13556
ind. m-2) (p>0.05) (Table 4.2, Figure 4.3). Dominant and contributive species were partly
different with the occurrence of Bittium reticulatum (epifauna), and Melinna palmata and
Aphelochaeta marioni (infauna). Biomass was particularly high (128.8 g AFDW m-2) mainly
due to suspension feeders of infauna (in particular Manila clam Ruditapes philippinarum)
(80% of infauna biomass) and epifauna (Mussel Mytilus edulis with 37% of epifauna
biomass; N. reticulatus with 19%) (Table 4.3, Figure 4.3). Mean species richness per station
was relatively high (26) like in Group A.
75
Table 4.2. Average values (min - max) of environmental factors: salinity, tidal level (m), distance to the ocean (km), Zostera noltii leaves
biomass (g DW m-2) and cover (% of sediment surface), median grain-size (µm), silt and clay content (%); quantitative parameters of
macrofauna assemblages: mean abundance (ind. m-2), mean biomass (g AFDW m-2), mean number of species per station, biomass of each
trophic group (g AFDW m-2), abundance (ind. m-2) and biomass (g AFDW m-2) of epifauna and infauna in four groups identified by MDS and
statistic test results for the difference between four groups. Mean comparison: values are ranked and lines gather groups that are not significantly
different (p>0.05).
MDS groups
A
Parameters
B
C
Test, p value
Mean comparison
D
Habitat
Mean annual salinity (range)
32 (30-33)
29 (22-31)
31 (29-33)
30 (19-35)
Tidal level (m, range)
2.1 (1.5-2.4)
2.3 (1.7-2.8)
2.4 (2.2-2.6)
1.6 (1.5-1.7)
Distance to the ocean (km, range)
12 (9-14)
16 (15-17)
14 (9-17)
15 (14-16)
Seagrass biomass (g DW m-2, ±SD)
70 (±18)
83 (±14)
3 (±1)
140 (±52)
Anova, p<0.001
C<A<B<D
Seagrass cover (% of sediment surface, range)
60 (37-62)
67 (37-100)
6 (0-12)
86 (72-100)
Kruskal-Wallis, p<0.005
C<A<B<D
Median Grain-size (µm, ±SD)
45 (±8)
36 (±7)
87 (±22)
50 (±10)
Anova, p<0.05
B<A<D<C
Silt & Clay content (%, ±SD)
62 (±5)
69 (±6)
42 (±7)
54 (±4)
Anova, p<0.05
C<D<A<B
28125 (±3131)
24286 (±4145)
9726 (±1282)
13556 (±1347)
Anova, p<0.001
C<D<B<A
Abundance epifauna (ind. m , ±SD)
3525 (±1865)
16046 (±1908)
5585 (±1308)
4322 (±1749)
Anova, p<0.001
A<D<C<B
Abundance infauna (ind. m-2, ±SD)
24586 (±3771)
8232 (±2804)
4140 (±582)
9222 (±2956)
C<B<D<A
Macrofauna
Mean abundance (ind. m-2 ±SD)
-2
-2
Mean biomass (g AFDW m , ±SD)
21.6 (±2.5)
24.2 (±2.8)
20.5 (±3.6)
128.8 (±46.1)
Anova, p<0.001
C<A<B<D
Biomass epifauna (g AFDW m-2, ±SD)
5.3 (±1.6)
10.8 (±1.2)
6.2 (±1.8)
30.7 (±10.0)
Anova, p<0.001
A<C<B<D
Biomass infauna (g AFDW m-2, ±SD)
16.4 (±2.6)
13.4 (±2.4)
14.3 (±2.9)
98.0 (±36.4)
Anova, p<0.001
B<A<C<D
Mean number of species per station (±SD)
29 (±1)
20 (±1)
20 (±2)
26 (±1)
Anova, p<0.001
C=B<D<A
4.9 (±0.3)
2.3 (±0.5)
4.4 (±0.7)
7.7 (±1.8)
B<C<A<D
Biomass grazers (g AFDW m , ±SD)
3.0 (±0.9)
10.4 (±1.1)
4.9 (±1.1)
8.8 (±4.1)
Anova, p<0.01
A<C<D<B
Biomass predators (g AFDW m-2, ±SD)
2.9 (±1.0)
1.3 (±0.4)
0.8 (±0.2)
3.3 (±1.7)
Kruskal-Wallis, p>0.05
-2
Biomass deposit feeders (g AFDW m , ±SD)
-2
Biomass scavengers (g AFDW m-2, ±SD)
0.7 (±0.5)
0.0
0.0
6.3 (±1.2)
C=B<A<D
Biomass suspension feeders (g AFDW m-2, ±SD)
10.2 (±2.7)
10.2 (±2.4)
10.5 (±3.2)
102.7 (±44.3)
Anova, p<0.01
A=B<C<D
76
2002
16 046 ind.m-2
9 species
3 525
Hu
Nr
Ic
De
Hu
Epifauna
Infauna
Rp
Tb
Tb
20 species
Hf
As
A
GROUP C (2010 – WHOLE LAGOON – BARE MUD)
ind.m-2
16.4 g m-2
5 585 ind.m-2
2010
Ms
4 species
Hu
Hu
Epifauna
Infauna
Hf Tb
Pe Mp
4 140 ind.m-2
Rp
Mp
Ll
14.3 g m-2
Hu
Hu
7 species
Tb
As Hf
Rp
8 232 ind.m-2
13 species
13.4 g m-2
B
24 586 ind.m-2
6.2 g m-2
C
GROUP B (2002 – INNER LAGOON - SEAGRASS)
5.3 g m-2
16 species
GROUP D (2010 – WHOLE LAGOON -SEAGRASS)
GROUP A (2002 – OUTER LAGOON - SEAGRASS)
10.8 g m-2
30.7 g m-2
9 species
4,322 ind.m-2
Br
Hu
Nr
Me
Mp
Am
9 222 ind.m-2
Rp
17 species
Mp
D
98.0 g m-2
Figure 4.3. Average abundance (ind. m-2), biomass (g AFDW m-2) and species richness per
station of epifauna and infauna among four groups (A, B, C and D) discriminated by MDS at
50% similarity (see Figure 4.2). Am: Aphelochaeta marioni; As: Abra segmentum; Br:
Bittium reticulatum; De: Demospongiae; Hu: Hydrobia ulvae; Hf: Heteromastus filiformis;
Ic: Idotea chelipes; Ll: Loripes lacteus; Me: Mytilus edulis; Mp: Melinna palmata; Ms:
Musculista senhousia; Nr: Nassarius reticulatus; Pe: Pygospio elegans; Tb: Tubificoides
benedii. Scale for biomass is different in group D.
77
Table 4.3. List of the main species contributing to the within-station groups similarity (SIMPER procedure). Contribution to similarity is given
as percentage and rank. Top four dominant species per Group are in bold. Species are only ranked when mean abundance > 50 ind.m-2 in at least
one group. Blank cell = absent species. Ecological groups (I to V) are ascribed according to AZTI data base (http://ambi.azti.es), see Table 1.
A
Zoological group
Cnidaria
Nemertea
Mollusca Polyplacophora
Mollusca Gastropoda
Mollusca Bivalvia
Annelida Polychaeta
Position
epifauna
infauna
epifauna
epifauna
epifauna
Trophic group
predator
predator
grazer
grazer
scavenger
suspension feeder
infauna
deposit feeder
suspension feeder
infauna
deposit feeder
(continued on the next page)
Taxon
Anthozoa (II)
Nemertea (III)
Polyplacophora (II)
Bittium reticulatum (I)
Hydrobia ulvae (III)
Littorina littorea (II)
Rissoa membranacea (I)
Nassarius reticulatus (II)
Modiolus modiolus (I)
Mytilus edulis (III)
Abra segmentum (III)
Loripes lacteus (I)
Ruditapes philippinarum
(III)
Ampharete acutifrons (I)
Aonides oxycephala (III)
Aphelochaeta marioni (IV)
Clymenura clypeata (III)
Euclymene oerstedi (I)
Heteromastus filiformis
(IV)
Manayunkia aestuarina (II)
Melinna palmata (III)
Notomastus latericeus (III)
Paraonidae (NA)
Pseudopolydora antennata
(IV)
Pseudopolydora spp. (IV)
Pygospio elegans (III)
Abundance
Rank
Mean
42
9
350
3
17
147
3
1844
3
B
Contribution
Rank
%
11
1.47
7
4.3
8
6
2.99
4.44
Abundance
Rank
Mean
42
14
83
3
7
211
1
15264
10
131
8
161
C
Contribution
Rank
%
11
1.27
7
3.23
1
9
6
49.84
2.32
3.64
Abundance
Rank Mean
15
33
7
36
1
5461
3
6
D
Contribution
Rank
%
13 1.32
1
10
22
286
4
13
1236
178
3
17
9.73
0.79
3
-
603
29
4
12
5.8
1.18
7
-
107
6
7
24
7
15
-
50
489
44
158
36
11
16
0.8
9
144
8
3.13
8
74
14
10
0.9
2.46
12
104
13
1.09
6
-
7
424
18
4
2
5
2414
14
1119
2
12.89
5.95
4
8.24
12
183
13
1.13
514
458
1
17
1
3
4
4
5
-
-
3
-
24
-
6
6
313
27.5
Abundance
Rank Mean
22
44
20
56
4
1600
3
1756
7
344
Contribution
Rank
%
19
2.14
14
1
8
2.14
13.7
3.71
15
111
6
4.28
4.8
13
9
-
156
233
44
18
5
2.14
6.77
8
3
5
633
7
4.28
5
7.03
2
16
11
11
3000
78
189
3
10
706
3
10.9
6
622
2
10.7
3
-
860
7
14
6
5.91
1
-
3122
44
4
9.33
11
1.69
2
29
1
899
12
1.47
2
12
8
-
278
44
7
78
Table 4.3. (continued)
A
Zoological group
Position
Trophic group
predator
Annelida Oligochaeta
Phoronida
Crustacea Amphipoda
infauna
infauna
epifauna
infauna
deposit feeder
suspension feeder
deposit feeder
deposit feeder
Crustacea Cumacea
Crustacea Decapoda
epifauna
epifauna
grazer
suspension feeder
deposit feeder
predator
Crustacea Isopoda
epifauna
infauna
infauna
grazer
deposit feeder
grazer
Insecta Diptera
Taxon
Streblospio shrubsolii (III)
Diopatra sp. (I)
Glycera spp. (II)
Nephtys hombergii (II)
Phyllodocidae (II)
Syllidae (NA)
Tubificoides benedii (V)
Phoronis psammophila (II)
Ampithoe sp. (I)
Ericthonius difformis (I)
Siphonoecetes sp. (I)
Melita palmata (I)
Perioculodes longimanus
(I)
Microdeutopus gryllotalpa
(III)
Corophium insidiosum (III)
Iphinoe trispinosa i (I)
Carcinus maenas (III)
Hippolyte sp. (I)
Idotea chelipes (II)
Cyathura carinata (III)
Chironomidae (III)
Dolichopodidae (IV)
Abundance
Rank
Mean
25
20
23
1
18
67
3
17
53
16844
142
22
64
21
64
11
19
16
6
14
8
-
222
42
81
25
153
706
161
369
33
B
Contribution
Rank
%
Abundance
Rank
Mean
15
64
12
1.36
-
10
18
0.76
-
1
1
28.73
2
-
5431
3
6
8
-
35
-
24
15
C
Contribution
Rank
%
10
1.35
2
7.7
Abundance
Rank Mean
22
3
10
58
18
1
5
9
-
582
69
4
-
4
1
D
Contribution
Rank
%
9
14
2.88
1.23
4
8.1
Abundance
Rank Mean
Contribution
Rank
%
16
-
44
78
33
12
17
13
3.03
2.14
3.03
14
12
19
133
156
67
10
3.03
16
10
78
222
9
3.71
-
11
-
11
-
22
15
2.14
-
33
-
44
11
16
11
2.14
3.03
0.81
-
5
5.89
9
2.48
11
13
-
8
17
6
125
3
92
25
5
3.78
-
13
-
18
24
4
29
10
15
1.73
1.18
79
3.2. Biotic Indices
The four investigated indices were compared among groups and provided different
results in terms of ecological quality status but also in terms of temporal trend with year.
AMBI considered that the 2010 groups (C and D) had a better status than in 2002 (Groups A
and B) without any relationship to seagrass presence (Table 4.4). BOPA was rather optimistic
but with an opposite trend (Group B with a better ES than group D) and, in 2010, with a
better ES when seagrass was absent (Group C). BENTIX provided poor ES status in all
groups except in group D where it was good. MISS maintained its ES as high in the four
groups.
Table 4.4. Biotic indices and ecological status (ES) in the four groups A, B, C and D
discriminated by MDS (Figure 4.2)
AMBI
Groups
A
B
C
D
ES
poor
moderate
good
good
BENTIX
Value ES
Value
4.57
poor
2.41
3.33
poor
2.26
3.32
poor
2.12
2.73
good
3.05
BOPA
ES
Value
good
0.061401
high
0.025416
good
0.134524
moderate 0.165953
ES
high
high
high
high
MISS
Value
0.84
0.88
0.80
0.80
4. Discussion
The present study showed that the biomass of seagrass dramatically declined in 2010
in ten sampled stations out of twelve, as described by Plus et al. (2010) on larger scale. The
concomitant effects on associated benthic macrofauna were rather moderate, with a
significant decrease of abundance and diversity (measured as species richness per station),
while biomass remained stable.
4.1. Associated macrofauna in seagrass
Groups A, B and D allowed comparison of macrofauna communities in seagrass with
different conditions. Benthic communities of Groups A and B were sampled simultaneously
(in spring 2002), in similar sediments (mud), both with dense Z. noltii seagrass but they
80
differed in terms of the surrounding water bodies. Both groups of stations were situated in
two different water bodies, the external neritic waters (A) and the intermediate neritic water
(B) (Bouchet, 1993). Species composition was also similar between Groups A and B and
differences were due to contrasted dominance in terms of abundance and biomass of
Hydrobia ulvae and Tubificoides benedii, and occurrence of some polychaete species. Indeed,
Hydrobia ulvae was present and abundant in both groups, taking advantage of the presence of
Z. noltii (and probably of associated periphyton (Cardoso et al., 2005)). However, Hydrobia
ulvae abundance was 8 times lower in oceanic Group A. A possible explanation is stronger
hydrodynamic conditions in the outer lagoon washing out these mudsnails (Zühlke and Reise,
1994). Abundance of Tubificoides benedii in Group A is usual (Bachelet et al., 2000) while it
is surprisingly low in Group B, without any explanation. Finally, species that were restricted
to a single group are characterized by the absence or reduction of the larval pelagic phase
(Ampharete acutifrons, Clymenura clypeata, Manayunkia aestuarina). In contrast, species
with widespread spatial distribution display a longer pelagic phase (e.g. Heteromastus
filiformis, Glycera spp.) (Cazaux, 1973; Wolff, 1973; Marcano and Cazaux, 1994; Blanchet
et al., 2004).
Group D encompassed the last two stations of our survey that still harboured dense
seagrass in 2010. One of these stations was not sampled in spring like the other ones but in
summer. This may have biased results but previous 2-year monitoring in the same area
determined that biomass and abundance of macrofauna sieved with a 1-mm mesh was
relatively constant among seasons (Bachelet et al., 2000). Compared to Groups A and B
(seagrass in 2002), macrofauna abundance in Group D was half. This could be due to
interannual fluctuation, but this abundance (13556 ind. m-2) is also low compared with those
observed in other studies in Arcachon Bay (50000-200000 in Castel et al. 1989; Bachelet et
al. 2000). An alternative hypothesis is that Group D characterized a relictual fragmented
seagrass within a large unvegetated mudflat (see Group C). Group D was also characterized
by the largest biomass of all groups mainly due to the settlement of a population of Manila
clams Ruditapes philippinarum. This exotic bivalve was introduced in Arcachon Bay in 1980
and rapidly naturalized in the intertidal Z. noltii seagrass beds (Goulletquer et al., 1987).
81
4.2. The benthic community in reduced seagrass mudflats
Group C corresponded in 2010 to stations where seagrass leaf biomass was
dramatically low, i.e. between 0 and 7 g DW m-2 depending on the station. Most stations in
Group C were located at few meters from stations sampled in 2002 (Groups A and B).
Structurally complex habitats strongly shape the physical environment, e.g. by modifying
light conditions, hydrodynamics, sedimentation, providing shelter and refuges, and buffering
the effects of disturbances. When these habitats are lost, many of these functions are also lost
(Airoldi et al., 2008). Blanchet et al. (2005) hypothesized that Zostera noltii meadows had a
structuring effect on benthic communities when leaf biomass exceeds 28 g DW m-2. In the
present study, seagrass leaves declined from 79.5 g DW m-2 in 2002 to 2.2 g DW m-2 in 2010.
Consequently, the benthic communities should have been altered by these changes. However,
the difficulty is in distinguishing natural year-to-year variations (Boström et al., 2002) from
effects of seagrass decline on macrofauna. Therefore, our approach consisted of a comparison
between changes in macrofauna between 2002 and 2010 in stations where the seagrass had
almost disappeared (Groups A and B vs. Group C) with stations where the seagrass remained
(Groups A and B vs. Group D).
Lower abundance of macrofauna in unvegetated habitats compared to vegetated
habitats has often been reported (Fonseca et al., 1990; Orth, 1992; Boström and Bonsdorff,
1997; Cottet et al., 2007; Fredriksen et al., 2010; Do et al., 2011) and different mechanisms
for this were proposed, such as: 1) decreased predation efficiency due to high habitat
complexity (Orth et al., 1984); 2) habitat preference of dense seagrass by prey as an escape
mechanism from predation (Fonseca and Fisher, 1986; Webster et al., 1998; Boström et al.,
2006b); 3) stabilisation of sediments that accumulate organic material, allowing increased
settlement and growth of infauna (Neckles et al., 1993; Fredriksen et al., 2005); juveniles and
adults are also prevented from being resuspended and transported away (Fonseca et al.,
1990); and 4) a high content of organic matter, which may be common in seagrass meadows,
attracts a certain type of infauna such as deposit-feeding polychaetes (Fredriksen et al., 2010;
Do et al., 2011). Consequently, our results in Group C are consistent with the literature, as we
observed a 2.4 magnitude drop in abundance. However, the same decline in magnitude was
observed in Group D (same year but in seagrass) (i.e. a loss of 11000-15000 ind. m-2), but the
82
species involved were different and the final macrofauna community structure discriminated
two groups (C and D) in relation to Z. noltii presence or absence.
Thus, the scenario that we propose and seems the most plausible is: 1) In 2002, the
vast Z. noltii seagrass bed sheltered a relatively homogeneous associated macrofauna
dominated in terms of abundance by grazers (and especially Hydrobia ulvae) and several
opportunistic deposit feeders. Biomass was dominated by the Manila clam Ruditapes
philippinarum (infauna) and the mudsnail Hydrobia ulvae (epifauna). A few species, by their
abundance, allowed discrimination of two subcommunities, one related to oceanic water
(Group A) and the other to more sheltered water (Group B). 2). In 2010, seagrass beds had
almost disappeared in 83% of the investigated area. Thus the ecosystem engineer role of the
seagrass (Jones et al., 1994) has also disappeared. Then, as expected by numerous studies
comparing vegetated and unvegetated sediments (Fonseca et al., 1990; Boström and
Bonsdorff, 1997; Fredriksen et al., 2010; Do et al., 2011), abundance and species richness
(here calculated by station in order to minimize the unbalanced design of the groups with
different number of stations) decreased. This decrease is sufficient to discriminate Group C,
but however the species were quite similar to those observed in 2002 (Groups A and B). The
deposit-feeder Tubificoides benedii has particularly declined. There is no explanation, since
this oligochaete is abundant in all marine stressed habitats, often characterized by hypoxic
condition (Giere et al., 1999) and is not dependent on seagrass presence. Another logical
consequence of seagrass disappearance is the scarcity of grazing epifauna. 3) Group C
represented a dominant habitat in 2010 and the loss of macrofauna abundance had
consequences on the remaining small areas where seagrass remained. Group D clustered
stations considered as belonging to “fragmented seagrass”, i.e. matching the general deficit of
macrofauna abundance but containing some characteristic epibenthic species that could graze
on Z. noltii blades (Bittium reticulatum, Littorina littorea, Rissoa membranacea, etc.). Group
D was also characterized by a very high suspension-feeders biomass, dominated by R.
philippinarum and Mytilus edulis. However, at a the lagoon scale, there is no attended
modification of ecosystem functioning because Group D represents a small percentage of the
total investigated area (<20%).
83
4.3. Biotic Indices
In terms of seagrass cover, the Water Framework Directive (WFD) considers that
Groups A, B and D situations corresponded to the conditions of a ‘Good’ Ecological Status
(ES) and C should be considered as ‘moderate’ or ‘poor’ (Foden and Brazier, 2007).
Consequently and when extrapolating to benthic fauna indicators, MISS was rather more
efficient than other indices (AMBI, BOPA, BENTIX) to assess ES of the investigated area
although it did not detect seagrass loss. Indeed, AMBI showed an improvement in ES while
seagrass were in decline in 2010. This was due to the decrease in abundance of highly
dominant species belonging to ecological groups III to V (rather opportunistic) such as
Hydrobia ulvae (group III), Tubificoides benedii (group IV), Heteromastus filiformis (group
V) (Table 4.1 for ecological groups definition). BENTIX assessed incorrectly and failed to
detect the change of ES in almost all stations. The limitation of the BENTIX index is in
estuaries or lagoons where the natural conditions favour the presence of tolerant species in
very high densities like in the present study. BOPA remained optimistic in all situations (high
ES) except in group D that was, however, characterized by a flourishing seagrass. Generally,
most of these Biotic Indices (BENTIX and BOPA) perform badly in semi-enclosed
ecosystems where the natural benthic habitat consists of muddy, organic matter enriched
sediments (Blanchet et al., 2008; Lavesque et al., 2009).
MISS with 16 metrics describing community, trophic composition and pollution
indicators is supposed to be more efficient than other indices in detecting perturbations in this
kind of ecosystem (Lavesque et al., 2009). However, it failed to detect any seagrass changes.
On the other hand, it has been previously mentioned that macrofauna changes were moderate
among groups. In this context, MISS was consistent with benthic community similarities
among the four groups.
Conclusions
We expected more significant changes to have occurred due to seagrass decline
because Z. noltii is an engineer species. In fact the changes were not so important either
because the seagrass did not entirely disappear at the lagoon scale (25% loss between 2005
and 2007) and/or because the benthic system is resistant to this change. Of course, this study
did not take into account motile epifauna that are the species most affected by seagrass
84
regression (Boström et al., 2006a). Our results emphasized the necessity to integrate
numerous parameters in order to correctly describe ecosystem trajectories. Evolution with
time, seagrass development/regression, or benthic fauna structure do not always evolve in the
same way and so cannot always be described by the biotic indicators present.
85
86
+++
The main objective of this thesis was to correlate seagrass evolution with associated
macrofauna community changes. After analysing the changes in benthic macrofauna
structures in relation with seagrass colonization (Chapter 2) or seagrass destruction
(Chapter 4), the next chapter will focus on the response of macrofauna to severe seagrass
damage and to seagrass recover.
In chapters 2 and 4, we showed that changes of benthic community structures are
often subtle and cannot be detected by most biotic indices. In such situation (i.e. seagrass
changes), different Biotic Indices (BIs) are not accurate enough to detect changes of benthic
communities in this situation and should not be utilized in this sense. In the next chapter, we
will monitor community structure changes and will also test BIs in the case of seagrass buried
by sediment disposal at a large spatial scale (0.32 km²).
This survey was the occasion to test again a multivariate BI that was experimented in
Arcachon Bay, few years ago, MISS (Lavesque et al. 2009). However, we also tried to
simplify MISS.
Eventually, due to the difficulty that we had in the previous chapter to discriminate
differences in macrofauna structures or BIs between vegetated and unvegetated substrates, we
also had a new approach in comparing secondary production among these different situations.
+++
87
88
Chapter 5- Seagrass burial, macrobenthos and Biotic Indices
Chapter 5 - Seagrass burial by dredged sediments: benthic
community alteration, secondary production loss, biotic index
reaction and recovery possibility
Published: V. Tu Do, Xavier de Montaudouin, Hugues Blanchet, Nicolas Lavesque (2012).
Marine Pollution Bulletin 64: 2340-2350.
Abstract
In 2005, dredging activities in Arcachon Bay led to the burying of 320,000 m2 of Zostera
noltii intertidal seagrass. Recovery by macrobenthos and seagrass was monitored. Six months
after the work, seagrass was absent and macrobenthos drastically different from surrounding
vegetated stations was observed. Due to sediment dispersal, the disposal area was rapidly
divided into a sandflat with a specific benthic community maintained until the end of the
survey in 2010, and a mudflat where associated fauna became similar to those in adjacent
seagrass beds. The macrobenthic community needed 3 years to recover while seagrass needed
5 years to recover in the station impacted by mud. The secondary production loss due to this
dredging activity was low. In this naturally carbon enriched system, univariate biotic indices
did not perform well in detecting seagrass destruction and recovery. MISS (Macrobenthic
Index of Sheltered Systems) gave more relevant conclusions and a simplified version was
tested with success, at this local scale.
Keywords: Seagrass, macrobenthos, sediment disposal, secondary production, WFD
89
1. Introduction
The disposal of dredged material constitutes one of the most important problems in
coastal zone management (Van Dolah et al., 1984; Bolam and Rees, 2003; Bolam and
Whomersley, 2005). Coastal works (e.g. harbors, docks, breakwaters), beach stabilization,
dredging and excess siltation from changes in land catchments, are examples of
anthropogenic activities that result in changes of the sedimentary dynamics and consequent
seagrass loss. Frequently, such human-induced activities result in complete, perhaps
irreversible, disappearance of seagrass meadows from coastal areas (Cabaco et al., 2008).
Many studies concerning the effects of dredged material deposition on benthic
macroinvertebrates and physical environment have been carried out. The effects of dredge
material relocation are smothering (Stronkhorst et al., 2003), chemical contamination (Bolam
et al., 2006), changes in sedimentology (Harvey et al., 1998; Essink, 1999), increased levels
of organic carbon, reduction in abundance, number of species and diversity (Van Dolah et al.,
1984; Wildish and Thomas, 1985; Cruz-Motta and Collins, 2004), and increased dominance
of tolerant and opportunistic species (Rees et al., 1992). Impacts of burial are the most
obvious effects of dredged material placement on benthic organisms in the short term, both at
intertidal and subtidal placement sites (Roberts et al., 1998; Powilleit et al., 2006; Bolam,
2011). Since different types of effects were identified in these studies, it is impossible to draw
a general conclusion about the impact of dredged material deposition on the benthic
community structure (Harvey et al., 1998). In addition, benthic community recovery after
dredged material deposition has not been well studied, particularly with reference to seagrass
habitats (Sheridan, 2004).
A long term and large-scale spatial study on effects of seagrass bed burial was
initiated in Arcachon Bay, following sediment disposal. Arcachon Bay harbours the largest
Zostera noltii seagrass bed in Europe (Auby and Labourg, 1996), which occupies most
intertidal areas. Arcachon Bay is also an important site for oyster farming which implies
regular cleaning of oyster parks that are rapidly invaded by non-exploited, “wild” oysters
(Crassostrea gigas). Empty shells and live animals are traditionally buried in large holes
(“souilles”) dug in remote areas, within the lagoon. In 2002, these “souilles” were full and
stakeholders decided to dig another one, near the previous one, in a seagrass area. It consisted
90
of a 5000-m2 pit able to receive 100,000 m3 of shells. Works were implemented during the
winter of 2004 and extracted sediments were disposed over a 20,000 m2 seagrass bed, rapidly
dispersing within a total impacted area of 300,000 m2 (×10 cm thickness). Between 2002 and
2010, a benthic survey was performed in the primary disposal area, in the secondary sediment
spreading area and in controlled stations. Our aim was: (1) to monitor macrobenthic
communities in and out of the impacted area in terms of biomass, abundance, diversity,
structure and trophic groups. (2) To assess the loss of secondary production related to
seagrass destruction, Z. noltii biomass being a major component influencing the overall
macrobenthic production (Dolbeth et al., 2011). Besides, secondary production is one of the
most comprehensive measurements of ecosystem health (Dolbeth et al., 2005). It may reveal
greater insights into ecosystem change than static parameters such as diversity, density or
biomass. Combining production with long-term datasets could increase our level of
understating system functioning (see for instance, Dolbeth et al., 2007; Pranovi et al., 2008).
To compare different biological indicators implemented or not in the Water Framework
Directive (WFD) and based on macrofauna communities structure and to observe how they
responded to this physical stress and potential seagrass recovery. Indeed, benthic macrofauna
are a powerful tool to detect even slight environment changes (Blanchet et al., 2005). It may
locally detect the level of stress and integrate the recent history of stress, constituting a sort of
memory for the system (Patricio et al., 2009). The composition and structure of benthic
macrofauna is one of the indicated biological quality elements to be used in transitional
(estuaries and lagoons) and coastal waters for ecological status assessment. Several biological
indices (AMBI (AZTI’s Marine Biotic Index), BOPA (Benthic Opportunistic Polychaetes
Amphipods Index), BENTIX and MISS (Macrobenthic Index of Sheltered System)) based on
the benthic macrofauna assemblages have been recently developed to assess ecological status
(ES) of marine waters (Borja et al., 2000; Simboura and Zenetos, 2002; Dauvin and Ruellet,
2007; Lavesque et al., 2009).
Particular attention will be devoted to a newly developed multi-metric index, MISS
(Lavesque et al., 2009). The implementation of this index in routine monitoring exposes two
major problems. Firstly, two out of 16 metrics are based on biomass (total biomass and the W
statistic). Assessing biomass is time consuming and requires sample destruction. Secondly,
five metrics are related to the identification of trophic groups which is often a hazardous task,
91
except maybe for suspension feeders. As a result, we tested MISS in different derived
versions to check whether we can simplify its calculation without degrading the information.
2.1. Study site
The study site was an intertidal mudflat called "Dispute” in the middle of Arcachon
Bay (Figure 5.1). Arcachon Bay is a triangular-shaped macro-tidal lagoon (180 km2), located
on the southwest French Atlantic coast (44°40’N, 1°10’W). It communicates with the
Atlantic Ocean through a narrow (2-km wide) entrance. The tide is semi-diurnal and the tidal
amplitude varies from 0.8 to 4.6 m. The average temperatures vary seasonally between 6 °C
and 22.5 °C. Fluctuations in freshwater contributions from rivers and rainwater influence
water salinity, ranging between 22 and 35. Many small streams run into the lagoon, but the
two main rivers, the Leyre and Canal des Etangs, contribute 73% and 24%, respectively, of
the total annual freshwater inflow (813 million m3 yr-1). The total lagoon surface (180 km2)
can be divided in two parts: the subtidal channels (63 km2), and the intertidal areas (117 km2)
(Plus et al., 2010). The main channels have a maximum depth of 25 m and are extended by a
secondary network of shallower channels. The intertidal area comprises sandy to sandy-mud
flat (Plus et al., 2010). Most of these flats (61 km2 in 2005 are covered a Z. noltii seagrass bed
(Plus et al., 2010). The lower part of the intertidal is generally devoted to Japanese oyster (C.
gigas) culture, which constitutes a major activity at this site. Adjacent to the mudflats, and
lining the channels, eelgrass (Zostera marina) occupies the subtidal sector. Sediment
temperatures vary annually between -1 and 35.4 °C (average = 15.8 °C) and salinity varies
between 18.5 and 34.5 (average = 30).
Around Dispute, four stations were monitored. Two “impacted” stations (IS: impacted
by sand and IM: impacted by mud) located in the impacted area and two “un-impacted” Z
noltii stations, with one situated near the impacted stations (PS: proximate seagrass) and one
situated far from the impacted stations (RS: remote seagrass), were monitored after the
92
operations in August 2005, 2006, 2008 and 2010 (Figure 5.1). Before the works, in August
2002, two stations corresponding to RS and PS were sampled.
A
B
Atlantic
Ocean
44°40 N
Arcachon
Leyre river
C
1°10 W
Dispute site
Figure 5.1. Location of Arcachon Bay on the southwest French coast (A), the study site in
the lagoon (B), different stations: RS: remote seagrass; PS: proximate seagrass; IM: impacted
by mud; IS: impacted by sand (C).
93
2.2. Macrofauna sampling
At low tide, the top 20 cm of the sediment was collected with a 0.0225-m2 corer, with
four replicates per station. Sediment was sieved through a 1-mm mesh; the remaining fraction
was fixed in 4% buffered formalin and stained with Rose Bengal. In the laboratory,
macrofauna was sorted, identified when possible to the species level, and counted. Biomass
was determined as ash-free dry weight (AFDW) after desiccation (60 °C, 48 h) and
calcination (450 °C, 4 h).
2.3. Sediment and seagrass leaf analysis
The top 3-cm sediment layer was also sampled for granulometric analysis. Sediment
grain-size characteristics (median grain-size, percentage of silt and clays) were determined
after sieving weighed dried sediment through a wet column of sieves with decreasing
apertures (1000, 500, 250, 125 and 63 μm). Percentage of organic matter in the sediment was
assessed after ignition (450 °C, 4 h) of a dried aliquot of sediment.
In 2002 and 2006, Z. noltii leaves were cut in each macrofauna sample and desiccated
(60 °C) until a constant dry weight was obtained. In 2010, a new method of assessing Z. noltii
leaf biomass was developed. Fourteen [15 cm × 15 cm]-quadrats were delicately laid over the
sediment surface, at low tide. For each quadrat, a numeric photograph was taken
perpendicularly, one meter above the surface. The method consisted of drawing three
equidistant lines across each numerical image and counting the intersections between lines
and leaves. Then biomass and percentage of coverage could be obtained, using the following
relationships:
Loge(DW) = 1.514 × Loge (mean number of intercepts per line) – 1.911, with R = 0.98
(n = 14 pictures)
Loge(S) = 0.690 × Loge(DW) + 1.195 with R = 0.98 (n = 10 pictures)
where DW is Z. noltii leaf dry weight in g m-2 and S is the percentage of sediment
surface covered by Z. noltii. Ten pictures per station were analysed.
94
2.4. Estimated loss of secondary production
Macrofauna species were gathered in five trophic groups based on the feeding types
(Fauchald and Jumars, 1979; Bachelet, 1981; Sauriau et al., 1989; Hily and Bouteille, 1999):
(1) deposit feeders, (2) grazers, (3) predators, (4) scavengers and (5) suspension feeders.
Then, biomass of each group was calculated. A P/B ratio was assigned for each trophic group
using values calculated by Blanchet (2004). Then at each date we subtracted the secondary
production in the impacted area from the secondary production in the seagrass to obtain a
gross estimation of secondary production loss. This value was multiplied by the surface of
destroyed seagrass and the time elapsed from the previous sampling date.
2.5. Data analysis
2.5.1. Multivariate analysis
Multivariate analysis was performed to compare macrozoobenthic communities
structure between areas. Abundances were square root-transformed to minimize the influence
of the most dominant taxa. A non-metric multidimensional scaling (n-MDS) based on BrayCurtis similarity coefficient was carried out to obtain an ordination plot. A Cluster Analysis
was used to determine groups of stations × dates that were homogeneous in terms of benthic
community. SIMPER analysis was performed to determine which species contributed to
between-group dissimilarity. These analyses were performed using PRIMER® – v6 package
(Clarke and Warwick, 2001; Clarke and Gorley, 2006).
In order to investigate the pattern of change of numerical descriptor of the benthic
assemblage such as total biomass, total number of species, total abundance, number of
species, abundance and biomass of epi- and infauna, biomass of the different trophic groups,
a Principal Coordinate Analysis (PCO) was performed on the matrix of Euclidean distances
among stations based on fourth-root transformed and normalized data. This analysis was
performed using PRIMER PERMANOVA package (Anderson et al., 2008).
95
Three currently available univariate Biotic Indices (BIs), namely AMBI (Borja et al.,
2000), BENTIX (Simboura and Zenetos, 2002), BOPA (Dauvin and Ruellet, 2007) and one
multimetric approach called MISS (Lavesque et al., 2009) were tested. Ecological quality
status and thresholds used to classify index values were reported in Table 5.1.
AMBI is based on previous work from Grall and Glémarec (1997). It considers five
ecological groups (available on web page: http://ambi.azti.es) ranging from sensitive species
(EGI) to first-order opportunistic species (EGV) (Borja et al., 2000) (Table 5.1).
BENTIX considers only two groups: sensitive (GS) and tolerant species (GT), which
correspond to ecological groups I and II, and ecological groups III to V of the AMBI,
respectively (Table 5.1).
BOPA is based on the ratio of opportunistic polychaetes (i.e. polychaetes of
ecological groups IV and V of the AMBI) and amphipods (except Jassa genus) (Dauvin and
Ruellet, 2007) (Table 5.1).
96
Table 5.1. Indices used in this study to assess Ecological Status (ES) and thresholds used to classify index values.
Number of
ecological groups
5
BENTIX
2
6 EGI&II+ 2 EGIII-V
3.5 to 6 (for
sand)
3.0 to 6 (for
mud)
0 to 3.5 (for
sand)
0 to 3.0 (for
mud)
Simboura and Zenetos
(2002)
BOPA
2
log10 [(fp/fa + 1) + 1]
based on ratio of ecological groups
0 to 0.13966
0.13966 to
0.30103
Dauvin and Ruellet
(2007)
Sixteen parameters were classified in three
categories describing the macrofauna
assemblages
0.6 to 1
0 to 0.6
Lavesque et al. (2009)
MISS
Computation of the indices
Ecological Status (ES)
Acceptable
Not acceptable
0 to 3.3
3.3 to 7
Biotic
Indices
AMBI
0 EGI + 1.5 EGII + 3 EGIII + 4.5 EGIV + 6EGV
References
Borja et al. (2000)
EG: ecological groups as determined by Borja et al. (2000); fp: opportunistic polychaetes frequency; fa: amphipods frequency (except Jassa sp.).
97
MISS basically consisted of including a selection of existing BIs with additional
metrics describing the community (five parameters): abundance per m2, biomass g AFDW
per m2, number of species (per 0.045 m2), Shannon Index H', and Piélou's evenness J'; trophic
composition (five parameters): grazers per m2, selective deposit feeders per m2, non-selective
deposit feeders per m2, suspension feeders per m2, carrion feeders per m2 (e.g. carnivorous,
omnivorous and scavengers); and pollution indicators (six parameters): values of two Biotic
Indices were used (AMBI and BOPA), abundance per m2 of sensitive species (e.g. EGI and
EGII), abundance per m2 of tolerant species (e.g. EGIII), abundance per m2 of opportunistic
species (e.g. EGIV and EGV). Finally, the W statistic, referring to Abundance-Biomass
Comparison (ABC curves) was computed with PRIMER® - v6 package. W measured the
extent to which biomass curves lays above the abundance curves (positive values were
expected for the undisturbed conditions, negative values for impacted samples) (Warwick,
1986). The BOPA and the AMBI indices were included as indicators of, respectively,
pollution by hydrocarbons and organic matter inputs (Lavesque et al., 2009). MISS was
inspired by the development of Indices of Biological Integrity conducted in North America
(Weisberg et al., 1997; Engle and Summers, 1999; Van Dolah et al., 1999; Llanso et al.,
2002a; Llanso et al., 2002b). Monitoring results were compared with reference conditions, in
order to derive an Ecological Status (ES) (Table 1).
The reference condition for a water body type is a description of the biological
elements, which corresponds totally, or almost totally, to undisturbed (pristine) conditions,
i.e. with no, or only a very minimal, impact from human activities (Borja and Muxika, 2005;
Muxika et al., 2007). In this study, reference conditions were derived by the software from
the data collected in the 38 stations located on normally vegetated Z. noltii beds in 2002
(Blanchet, 2004; Lavesque et al., 2009).
In addition, derived calculations of MISS were investigated. Firstly, we deleted
biomass related parameter (biomass and W values) from calculation because biomass
assessment is time consuming and it can be useful to keep samples in laboratory. Secondly,
we did not consider trophic groups because definition is often controversial (except for
suspension feeders), and many species have mixed trophic habits. Thirdly, different
combinations of the two first attempts (without ‘biomass + W + trophic groups except
suspension feeders’) were performed.
98
3. Results
3.1. Seagrass and sediment disposal
Before sediment disposal (in 2002), seagrass covered 62-68% of the sediments
surface with biomass ranging from 70 g DW m-2 to 80 g DW m-2 (Table 5.2). Six months
after this work, in August 2005, seagrass was completely destroyed in both impacted stations
(IS and IM) and remained absent up to and including 2008, included. In 2010, seagrass only
recovered in IM stations (41 g DW m-2 or 43% coverage total surface) while it was still
absent in IS station (Table 5.2). In un-impacted stations PS and RS, seagrass were always
present though its cover varied according to year (Table 5.2).
Sediments consisted of sandy muds to muddy sands in un-impacted stations with
median grain-size varying from 20 to 100 according to date and small-scale heterogeneity
(Table 5.2). In both stations submitted to sediments deposit (IM and IS), sediments in 2005
(i.e. just after the works) were not still sorted and consisted of muddy sands (median: 100120 µm; silt and clay: 26-28%; organic matter: 5%). With time, finer sediments were washed
out. Near the discharge place (IS) only larger material remained while fine sediments
accumulated around IM station. Hence, sediment evolved toward sands with low silt and clay
content at IS station (median: 150-190 µm; silt and clay fraction ≤23%; organic matter ≤2%)
whereas sediments consisted of muddy sand to sandy mud at station IM (median: 40-70 µm;
silt and clay: ≤60%; organic matter: 6-9%) (Table 5.2).
99
Table 5.2. Seagrass leaves biomass (g DW m-2), seagrass cover (% of sediment surface), sediment median particle size (µm), silt and clay and
organic matter content in the sediment (%) before dredging (August 2002) and after (August 2005-2006-2008-2010), “-”: missing data.
Seagrass leaves
biomass (g DW m-2)
80
29
82
87
Seagrass
coverage (%)
68
33
70
72
PS
70
103
9
192
Aug. 2005
Aug. 2006
Aug. 2008
Aug. 2010
IM
Aug. 2005
Aug. 2006
Aug. 2008
Aug. 2010
IS
Status
Remote seagrass
Date
Aug. 2002
Aug. 2005
Aug. 2006
Aug. 2008
Aug. 2010
Station
RS
Proximate seagrass
Aug. 2002
Aug. 2005
Aug. 2006
Aug. 2008
Aug. 2010
Impacted by mud
Impacted by sand
Median grain-size (µm)
20 (sandy mud)
90 (muddy sand)
100 (muddy sand)
30 (sandy mud)
60 (sandy mud)
Silt and clay
content (%)
82
37
26
73
50
Organic matter
content (%)
5
8
10
9
7
62
52
15
100
20 (sandy mud)
100 (muddy sand)
90 (muddy sand)
20 (sandy mud)
40 (sandy mud)
73
31
36
83
57
7
9
8
10
8
0
0
0
41
0
0
0
43
100 (muddy sand)
70 (muddy sand)
40 (sandy mud)
40 (sandy mud)
26
47
59
60
5
9
6
7
0
0
0
0
0
0
0
0
120 (muddy sand)
190 (sand)
150 (muddy sand)
180 (sand)
28
5
23
6
5
2
2
1
100
3.2. Main macrozoobenthic assemblages identified in the dataset
Multivariate analysis performed on the ‘18 stations-dates × 110 species’ data matrix
showed, at a 35% similarity level, that three different groups of stations-dates could be
identified (Cluster Analysis) (Figure 5.2). The first main group (Group 1) included all unimpacted stations (PS and RS) as well as two IM stations from the last two dates of the
monitoring, 2008 and 2010. The second group of stations (Group 2) gathered all stations
which were impacted by sand (IS) as well as the IM station just after the sediment deposits
(in 2005) (Figure 5.2). Finally, the latter station (IM station in 2006) displayed a benthic
community differing from that of all other situations the first year after sediment disposal
(Group 3, Figure 5.2).
Similarity
35%
Figure 5.2. Non metric multidimensional scaling (n-MDS) of stations based on Bray-Curtis
similarity matrix after square root-transformed abundance data. RS: remote seagrass; PS:
proximate seagrass; IM: impacted by mud; IS: impacted by sand. Groups of stations
identified by the Cluster Analysis at a 35% similarity level are identified (Group 1: ×; Group
2: ; Group 3: ).
101
According to the SIMPER analysis, the dissimilarity between groups 1 and 2 was
mainly due to: (1) a much lower abundance or disappearance of several taxa of grazing
epifauna such as Hydrobia ulvae (4193 ±951 ind. m-2 vs. 444 ±208 ind. m-2), Bittium
reticulatum, Idotea chelipes or Littorina littorea in Group 2 (Table 5.3); (2) a decrease of the
abundance level of many infaunal polychaete taxa such as Melinna palmata, Aphelochaeta
marioni, Heteromastus filiformis or Clymenura clypeata as well as other infaunal taxa such as
the bivalves Abra segmentum and Loripes lacteus (which disappeared from stations of Group
2); (3) nevertheless, few species were found at higher abundance levels in Group 2 such as
the infaunal polychaetes Nephtys hombergii and Streblospio shrubsolii, the bivalve
Cerastoderma edule and the amphipod Ampelisca sp. (Table 5.3).
3.3. Trend in the numerical descriptor of the macrofauna assemblages
The first axis of the principal coordinates analysis (PCO) extracted more than 58% of
total variation. Together with axis two, 72.5% of total variation was represented (Figure 5.3).
The ordination of samples along the first axis of the PCO clearly separated samples retrieved
within un-impacted seagrass beds (positive values) from samples collected in both impacted
areas (negative values). Nevertheless there was a noteworthy exception with samples from
2010 collected at the IM station which gathered within the data cloud corresponding to unimpacted seagrass (Figure 5.3).
The ordination of samples obtained through the first PCO axis was positively
correlated with all numerical descriptors showing that samples collected in impacted sites
(except at station IM in 2010) displayed lower values for almost all of these descriptors
(Table 5.4). However the best correlations (Spearman coefficient of correlation >0.8) were
obtained with total, epifauna and infauna biomass, total number of species and abundance of
epifauna (Table 5.4). This analysis shows that, on a purely numeric point of view, only
station IM in 2010 displayed values comparable to un-impacted seagrass beds.
102
Table 5.3. List of the main species contributing to contribution to dissimilarity (SIMPER
analysis) between groups identified by Cluster Analysis (Group 1: RS and PS stations from
2002 to 2010 and IM stations from 2008 to 2010; Group 2: IS stations from 2005 to 2010 and
IM station in 2005; Group 3: IM station in 2006, see Figure 5.2). Top five dominant species
in each group and top five contributed species for dissimilarity between groups are in bold.
Mean abundance ind. m-2
± standard deviation
% contribution to
dissimilarity between groups
Zoological group
Position
Trophic group
Taxon
Group 1
Group 2
Group 3
1 vs 2
1 vs 3
Nemertea
infauna
predators
Nemertinea
69±20
4±3
11±11
1.6
1.2
0.9
Mollusca Gastropoda
epifauna
grazers
Hydrobia ulvae
4193±951
444±208
0
13.5
14.1
7.1
Bittium reticulatum
405±130
0
11±11
3.7
2.6
1.4
Littorina littorea
53±16
2±2
0
1.4
1.3
0.3
scavengers
Nassarius reticulatus
37±8
2±2
0
1.2
1.1
0.3
deposit feeders
Abra segmentum
219±62
22±20
56±21
3.6
1.6
2.8
Abra tenuis
3±2
11±9
22±13
0.6
1.2
1.5
Cerastoderma edule
45±20
53±18
0
1.5
1.2
2.6
Loripes lacteus
97±26
0
0
1.9
1.7
0
Mollusca Bivalvia
infauna
suspension feeders
Annelida Polychaeta
infauna
2 vs 3
Ruditapes philippinarum
104±27
7±4
0
2.0
1.9
0.8
suspension feeders
Mytilus edulis
67±42
0
0
1.3
1.1
0
deposit feeders
Aonides oxycephala
76±29
0
0
1.2
1.1
0
Aphelochaeta marioni
682±173
147±93
0
4.7
4.8
3.2
Clymenura clypeata
136±41
9±5
0
1.8
1.6
0.9
Euclymene oerstedi
47±13
2±2
0
1.2
1.0
0.2
Heteromastus filiformis
609±78
191±91
700±204
4.7
1.9
7.3
Melinna palmata
1099±220
9±9
78±21
6.7
5.2
3.2
Polydora spp.
0
2±2
22±13
0.2
1.2
1.7
Pseudopolydora spp.
67±21
38±22
33±11
1.6
1.4
1.7
Pygospio elegans
46±31
89±45
100±71
2.1
2.0
1.8
Streblospio shrubsolii
27±18
73±37
0
1.6
0.6
1.8
Glycera spp.
33±8
27±7
0
1.0
1.0
2.1
Nereis diversicolor
0
4±3
733±456
0.4
7.1
10.8
predators
Nephtys hombergii
38±9
51±12
0
1.0
1.3
2.6
Annelida Oligochaeta
infauna
deposit feeders
Tubificoides benedii
412±110
31±16
44±44
4.4
3.2
2.1
Phoronida
Crustacea
Amphipoda
infauna
suspension feeders
Phoronis psammophila
35±12
0
0
1.2
1.1
0
infauna
deposit feeders
Ampelisca sp.
7±4
42±23
0
1.4
0.4
2.3
Corophium urdaibaiense
1±1
0
22±13
0.1
1.1
2.0
Melita palmata
40±17
33±15
22±22
1.4
1.1
1.4
Microdeutopus gryllotalpa
57±35
11±8
167±38
1.2
3.0
4.5
10.5
grazers
Crustacea Isopoda
Insecta Diptera
infauna
deposit feeders
Cyathura carinata
40±14
7±4
744±286
1.2
6.2
epifauna
deposit feeders
Lekanesphaera spp.
2±1
24±10
0
1.2
0.1
1.6
grazers
Idotea chelipes
105±33
0
0
2.1
1.9
0
grazers
Dolichopodidae
15±6
2±2
189±90
0.7
3.1
5.5
infauna
103
Figure 5.3. Ordination of samples obtained by the principal coordinates analysis (PCO)
performed on the matrix of numerical descriptors of macrofauna assemblages. Sb: samples
retrieved in un-impacted seagrass beds (RS and PS from all dates from 2002 to 2010); IM:
samples retrieved in the stations with mud deposits; IS: samples retrieved in the station with
sand deposits. The number indicates the year of collection from 2005 (05) to 2010 (10).
Table 5.4. Correlations (Spearman Rank Correlation Coefficient) of each numeric descriptor
values with the first two axes of the principal coordinates analysis. In bold: R >0.8.
Variables
Total biomass
Total number of species
Biomass of epifauna
Abundance of epifauna
Biomass of infauna
Total abundance
Deposit feeders biomass
Abundance of infauna
Biomass of infauna
Grazers biomass
Suspension feeders biomass
Scavengers biomass
Abundance of epifauna
Carnivores biomass
PCO axis 1
(58.2% of total variation)
0.91
0.90
0.88
0.85
0.82
0.80
0.79
0.78
0.78
0.77
0.70
0.64
0.63
0.50
PCO axis 2
(14.3% of total variation)
-0.11
-0.24
0.27
0.22
-0.32
0.27
-0.29
-0.33
-0.45
0.50
-0.14
-0.27
0.68
-0.42
104
3.4. Dynamic of impact and recovery of macrobenthic community
Six months after sediments disposal (in August 2005), both impacted areas were
covered by a mixture of sand and mud. Seagrasses were destroyed and the macrofauna was
dramatically changed in the same way at both impacted stations (IM and IS) (Figures 5.2 &
5.4). Compared to un-impacted seagrass beds, benthic macrofauna assemblages were
characterized by very low biomass (1.3 ±0.4 g AFDW m-2 vs 15.4 ±2.9 g AFDW m-2) of both
epifauna and infauna in impacted stations (Table 5.5). The number of species was also
drastically reduced with 11 ±2 taxa per station in impacted sites and 17 ±2 taxa per station in
un-impacted areas (Table 5.5).
With time, the perturbed area divided into two distinct habitats. A 2.104 m2-sandflat
(IS) near the place where sediments were initially disposed in 2005 and a larger bare mudflat
(IM, 28.104 m2), due to fine sediment migration. In August 2006, benthic communities at both
impacted sites were still very different from that of seagrass beds. In the mudflat (IM),
biomass, abundance and species richness were half those of un-impacted seagrass bed (Table
5.5). In the sandflat, the benthic community was drastically different with a particularly low
abundance (400 ind. m-2 i.e. 13% of mudflat abundance and 5% of seagrass abundance), low
biomass (0.2 g AFDW m-2 i.e. 2% of biomass in bare mudflat and 1% of biomass in seagrass
bed), and a very low number of species (5 species).
More than 3 years after the perturbation, in August 2008, the macrozoobenthic
assemblage from the mudflat (IM) was similar to that of seagrass beds according to a Cluster
Analysis (Figure 5.2) even though no seagrass recovery was observed at that time. Though
benthic assemblage was similar in terms of species composition and dominance pattern, the
benthic assemblage from station IM still displayed reduced biomass and rather low
abundance and diversity compared to seagrass beds assemblage (Figure 5.4). In the
meantime, the macrobenthic assemblage within the sandflat remained very different from that
of all other studied stations (Figure 5.2). In this station, the benthic assemblage was still
characterized by reduced diversity, abundance and biomass (Figure 5.4).
In 2010, the full recovery of the benthic assemblage in terms of species composition,
dominance pattern, abundance, biomass and diversity patterns was observed at the IM station
(Figures 5.2 & 5.4). In the meantime, Z. noltii shoots had returned at this site, 4-5 years after
105
its destruction. However, the IS site remained devoid of any seagrass cover and still displayed
a clearly different benthic community (Figure 5.2-5.4).
106
Table 5.5. Mean values (± standard deviation) of abundance, biomass, number species richness of macrofauna, biomass of epifauna and infauna
and biomass of each trophic groups in un-impacted (RS, PS), IM and IS stations from 2005 to 2010.
August 2005
August 2006
August 2008
August 2010
Parameters
RS, PS
IM, IS
RS, PS
IM
IS
RS, PS
IM
IS
RS, PS
IM
IS
Abundance (ind. m-2, ±SD)
4111±744
2700±817
7594±974
3011±648
400±151
9350±806
6011±3450
567±150
6777±580
17844±1844
1022±243
-2
Abundance epifauna (ind. m , ±SD)
1611±408
933±494
1239±502
56±33
67±43
6183±1490
4589±3430
133±91
2161±664
13289±2382
489±252
Abundance infauna (ind. m-2, ±SD)
2483±460
1750±360
6339±795
2956±630
333±143
3156±1082
1422±489
411±102
4611±993
4556±1016
533±189
Biomass (g AFDW m , ±SD)
15.4±2.9
1.3±0.4
21.7±5.5
10.3±5.4
0.2±0.1
41.8±24.0
3.3±1.8
4.2±1.7
64.4±16.5
13.9±5.8
4.4±3.9
Biomass epifauna (g AFDW m-2, ±SD)
5.8±2.2
0.6±0.3
7.3±2.2
0.4±0.4
0.05±0.03
30.6±23.0
2.4±1.8
2.4±2.0
15.4±3.8
5.6±2.3
0.2±0.1
Biomass infauna (g AFDW m-2, ±SD)
9.0±2.3
0.7±0.1
14.2±3.8
9.9±5.4
0.1±0.1
11.2±3.3
0.9±0.3
1.6±1.1
49.0±13.0
8.3±4.5
4.2±3.9
-2
Number species (±SD)
17±2
11±2
23±2
12±2
5±1
13±2
9±2
7±1
18±1
16±2
7±2
Deposit feeders (g AFDW m-2, ±SD)
2.4±0.6
0.4±0.1
4.9±0.7
0.8±0.2
0.05±0.0
4.2±1.7
0.8±0.2
0.4±0.2
3.9±0.8
7.3±4.1
0.9±0.8
-2
Grazers (g AFDW m , ±SD)
2.9±1.9
0.6±0.3
5.1±2.3
0.3±0.1
0.03±0.02
4.6±0.9
2.2±1.7
0.1±0.1
4.4±1.4
4.4±1.4
0.1±0.1
Predators (g AFDW m-2, ±SD)
1.7±1.0
0.3±0.1
1.9±0.7
9.2±5.5
0.1±0.0
0.4±0.2
0.3±0.1
0.4±0.4
1.6±0.6
1.8±0.8
0.05±0.02
Scavengers (g AFDW m , ±SD)
2.5±1.2
0
1.6±0.4
0
0
1.9±1.0
0
2.0±2.0
3.2±1.0
0
0
Suspension feeders (g AFDW m-2, ±SD)
5.5±1.8
0.05±0.05
8.1±3.1
0
0.03±0.02
30.7±23.1
0.01±0.01
1.3±1.0
51.3±15.7
0.4±0.2
3.3±3.2
-2
107
A
140
120
100
80
60
40
20
0
2002
2005
2006
2008
2010
2002
2005
2006
2008
2010
2005
2006
2008
2010
2005
2006
2008
2010
Mean biomass (g AFDW m-2)
RS
Mean abundance (ind. m-2)
30000
PS
IM
IS
B
25000
20000
15000
10000
5000
2002
2005
2006
2008
2010
2002
2005
2006
2008
2010
2005
2006
2008
2010
2005
2006
2008
2010
0
RS
40
PS
IM
IS
C
35
30
25
20
15
10
5
RS
PS
IM
2010
2008
2006
2005
2010
2008
2006
2005
2010
2008
2006
2005
2002
2010
2008
2006
2005
2002
0
IS
Figure 5.4. A) Mean biomass (g AFDW m-2), (B) abundance (ind. m-2), and (C) species
richness of the benthic macrofauna. RS: remote seagrass; PS: proximate seagrass; IM:
impacted by mud; IS: impacted by sand (Figure 5.1).
108
3.5. Loss of secondary production
The loss of secondary production increased between 2005 and 2010 in the impacted
sites, from 20 to 51 g AFDW m-2 y-1 and from 20 to 75 g AFDW m-2 y-1 in mud and in sand
respectively (Table 5.6). This increase was mostly due to suspension feeders (mussels, clams)
that progressively settled in un-impacted stations and not in impacted stations (see Table 5.3).
Indeed, the part of secondary production loss due to suspension feeders was of the order of
30% in 2005, against more than 70% in 2010. The global loss of production was 10-16 times
the higher in the area impacted by mud than area impacted by sand, mainly due to the
difference in the surface (× 14 times). Over 5.5 years and a total surface of 30 × 104 m2,
secondary production loss reached 74 t AFDW.
109
Table 5.6. Loss of secondary production by trophic group (g AFDW m-2 y-1). The total secondary production loss (total area and total duration
from the previous date. For 2005, elapsed time from the works was 0.5 year is given on last line. RS, PS, IM and IS: see Figure 5.1.
Trophic group
P/B y-1
2005
IM, IS
g AFDW m-2 y-1
2006
IM
g AFDW m-2 y-1
IS
g AFDW m-2 y-1
2008
IM
g AFDW m-2 y-1
IS
g AFDW m-2 y-1
2010
IM
g AFDW m-2 y-1
IS
g AFDW m-2 y-1
Deposit feeders
2.4
4.8
9.8
11.8
8.2
9.1
-8.2
7.2
Grazers
2.4
5.5
11.5
12.2
10.8
5.8
0.0
10.3
Predators
0.9
1.3
-6.6
1.6
0.0
-0.1
-0.2
1.4
Scavengers
0.9
2.3
1.4
1.4
1.7
-0.1
2.9
2.9
Suspension feeders
1.1
6.0
8.9
8.9
33.8
32.3
56.0
52.8
Total production
19.9
25.0
35.9
54.5
47.0
50.5
74.6
Total production loss g AFDW
2.99×106
7.04×106
0.72×106
30.49×106
1.90×106
28.30×106
2.99×106
110
3.6. Biotic Indices
3.6.1. Assessment perturbation by univariate Biotic Indices
AMBI classified all stations as “good” or “moderate” without any recognition
sediment disposal (Table 5.7). As well, BENTIX classified almost all stations as “moderate”
and “poor” without any relation to sediment disposal (Table 5.6). BOPA achieved more
contrasted results, from “poor” to “high”. This index increased the quality status of IM with
time. Conversely, BOPA gave fluctuating ecological status (ES) to control stations (RS, PS)
and observed strong amelioration of station IS that was however considered as the most
impacted during the whole monitoring (Table 5.7). In general, there was a disagreement
between the classification of univariate Biotic Indices (AMBI, BENTIX, BOPA) and they did
not detect the perturbation related to sediment disposal.
3.6.2. Assessment perturbation by multimetric approach (MISS) and derived-MISS
In seagrass, ES from MISS varied between “moderate” and “high” according to year
and position (RS, PS). In the site impacted by mud (IM), ES remained at a “moderate” level
until 2010 when it achieved “good”. In the site impacted by sand (IS), ES was “poor” until
2006, and reached moderate between 2008 and 2010 (Table 5.7). When removing biomassrelated parameters (i.e. total biomass and W), ES from MISS changed in one situation out of
18 (IS in 2005). Conversely, when MISS was calculated without trophic groups parameters, it
modified 1/3 of ES estimations, mostly by degrading them (Table 5.7).
111
Table 5.7. Biotic indices (AMBI, BENTIX, BOPA, MISS and d-MISS) and ecological status (ES) according to Table 5.1. For d-MISS: values
different from MISS are in bold. RS, PS, IM and IS: see Figure 5.1.
AMBI
Station
RS
PS
IM
IS
MISS (with 16
indices)
d-MISS (without
biomass+W)
d-MISS (without
trophic groups except
suspension feeders)
d-MISS (without
W+biomass+trophic groups
except suspension-feeders)
BENTIX
BOPA
ES
Score
ES
Score
ES
Score
ES
Score
ES
Score
ES
Score
good
2.53
moderate
0.00662
high
0.9
high
0.90
high
1.00
high
1.00
high
3.7
moderate
2.78
moderate
0.09607
good
0.54
moderate
0.50
moderate
0.57
moderate
0.53
moderate
2006
3.7
moderate
2.46
poor
0.24761
poor
0.76
good
0.66
good
0.51
moderate
0.51
moderate
2008
3.0
good
2.46
poor
0.00619
high
0.60
moderate
0.56
moderate
0.43
moderate
0.39
poor
2010
3.5
moderate
2.66
moderate
0.25719
poor
0.69
good
0.69
good
0.52
moderate
0.52
moderate
2002
3.2
good
2.48
poor
0.08911
good
0.66
good
0.62
good
0.49
moderate
0.45
moderate
2005
2.4
good
2.51
moderate
0.18368
moderate
0.49
moderate
0.47
moderate
0.36
poor
0.44
poor
2006
2.5
good
2.54
moderate
0.18309
moderate
0.56
moderate
0.57
moderate
0.56
moderate
0.57
moderate
2008
3.1
good
2.54
moderate
0.20392
poor
0.73
good
0.74
good
0.53
moderate
0.54
moderate
2010
1.9
good
2.90
moderate
0.07912
good
0.52
moderate
0.60
moderate
0.42
moderate
0.50
moderate
2005
3.8
moderate
2.98
moderate
0.21241
poor
0.47
moderate
0.50
moderate
0.42
moderate
0.45
moderate
2006
3.5
moderate
3.09
good
0.17760
moderate
0.52
moderate
0.55
moderate
0.52
moderate
0.55
moderate
2008
3.1
good
3.01
good
0.08949
good
0.54
moderate
0.52
moderate
0.67
good
0.66
good
good
Year
Score
2002
3.1
2005
ES
2010
3.0
good
2.87
moderate
0.08095
good
0.72
good
0.77
good
0.62
good
0.67
2005
3.0
good
2.69
moderate
0.15609
moderate
0.38
poor
0.42
moderate
0.36
poor
0.40
moderate
2006
3.0
good
2.48
poor
0.19562
poor
0.34
poor
0.35
poor
0.37
poor
0.38
poor
2008
1.9
good
2.63
moderate
0.10574
good
0.42
moderate
0.43
moderate
0.42
moderate
0.43
moderate
2010
2.6
good
2.72
moderate
0.09314
good
0.44
moderate
0.42
moderate
0.38
poor
0.35
poor
112
4. Discussion
4.1. Seagrass destruction and recolonization
Six months after the sediments disposal, seagrass totally disappeared in the sites that
were covered by sediment (sand and mud). The responses of seagrass to sediment burial have
been assessed in many studies (Marba and Duarte, 1994; Duarte et al., 1997; Cabaco and
Santos, 2007; Cabaco et al., 2008; Han et al., 2012) and is species specific and strongly sizedependent (Cabaco et al., 2008). According to Cabaco and Santos (2007), Z. noltii is highly
sensitive to burial and erosion disturbances due to the small size of this species and the lack
of vertical rhizomes. Hence, most Z. noltii plants under complete experimental burial died
between the 1st and the 2nd week (Cabaco and Santos, 2007). Whereas the mortality caused
by burial increased with decreasing seagrass size, the potential to recover from disturbances
by growth is enhanced with decreasing seagrass size (Duarte et al., 1997; Peralta et al., 2005).
A trade-off related to seagrass size exists, in terms of recovery time versus resistance to
stressors, such as sediment disturbance (Han et al., 2012).
Duarte et al. (1997) found that small seagrass species, such as Halophila ovalis and
Halodule uninervis were able to recover within 4 months of burial disturbance, while Cabaco
and Santos (2007) did not observe any recovery of Z. noltii within 2 months of experimental
burial. In fact, Zostera noltii is well adapted to cope with sediment disturbances of limited
amplitude (i.e. ±6 cm) and with continuous events by rapidly relocating their rhizomes to the
preferential depth (Han et al., 2012). However, in our study, seagrass partly recovered in the
IM station only 5 years (from 2005 to 2010) after burial. This long delay could be related to
the thick layer of sediment (≥10 cm) discharged on a single occasion (Han et al., 2012).
Characteristics of sediment were also an important factor, since we observed that areas
covered by sand remained free of seagrass after 5 years. The reason is certainly not directly
linked to the sediment grain-size since Do et al. (2011) showed that Z. noltii could colonize a
sand flat within 4 years.
113
4.2. Benthic community alteration and recovery possibility
Seagrass destruction and the changes due to sediment disposal altered benthic
community. The impact depends on the amount of discharged sediment, disposal time, water
depth, currents, particle size, and other abiotic parameters (see review in Witt et al., 2004).
One of the main effects of dumping of dredged sediments relates to burial of benthos at dump
sites (Essink, 1999). Local benthos has to cope with deposition of sediments which are in
many cases strongly anaerobic. Sensitivity of benthos to being covered by dredged sediments
is strongly dependent on the thickness of sediments and their ability to restore contact with
the overlying water (Essink, 1999). The mortalities generally increase with increased
sediment depth, exotic sediment and burial time (Maurer et al., 1981a, b, 1982; Harvey et al.,
1998). Decreases in macrofaunal abundances, biomasses and species richness as a
consequence of the disposal have been reported in several studies (Harvey et al., 1998; CruzMotta and Collins, 2004; Witt et al., 2004; Ware et al., 2010). Our study confirmed that
benthic macrofauna community structure at the disposal sites had changed substantially
following deposition. Indeed, 6 months after work (in August 2005), macrofauna
assemblages showed a decrease of biomass, abundance and species number in impacted (IM,
IS) stations. After 18 months (in August 2006), the macrofauna assemblages displayed a clear
difference between impacted (IM, IS) and un-impacted stations (PS, RS). Biomass,
abundance and diversity were lower in the station affected by sand disposal. Faunal
differences between the disposal sites and the reference areas were indeed correlated with
changes in the sediment composition depending on impact types. The disposal sites had a
higher proportion of mud or sand, which influenced species composition (Witt et al., 2004).
Sediment disposal affected differently benthic macrofaunal species according to their specific
feeding behavior, mobility or morphology (Pearson and Rosenberg, 1978; Van Dolah et al.,
1984; Witt et al., 2004).
Following burial, macrobenthic invertebrate recovery can occur by a combination of
three main mechanisms: planktonic recruitment of larvae, lateral migration of juveniles/adults
from adjacent un-impacted areas and/or vertical migration through the deposited material
(Bolam and Whomersley, 2005; Bolam et al., 2011). The relative importance of these
mechanisms will depend on a number of factors such as spatial scale timing, rate and depth of
placement (Bolam et al., 2006). Which mechanism ultimately predominates has important
114
implications for the rate and successional sequences of invertebrate recovery (Bolam et al.,
2010). For example, in cases where material is deposited thinly over a large area, a relatively
rapid recovery through vertical migration may occur. If the sediments are deposited at a depth
which exceeds the organisms’ burrowing and migration ability, total elimination of the
ambient community will occur (although some species may successfully be transported
within the dredged material) in the short term due to smothering; a slower recovery will then
ensue
through
lateral
migration
(days/weeks)
and/or
planktonic
settlement
(weeks/months/years) (Bolam, 2011). Because of the thick disposal sediment layer, in our
case, few or no species were able to recolonize the disposal sites by vertical migration. On the
other hand, migration of adults from undisturbed areas and reproduction and larval
recruitment from undisturbed areas could explain the gradual re-establishment of macrofauna
(Harvey et al., 1998).
Invertebrate recovery following dredged material disposal in relatively unstressed
marine environments generally takes between 1 and 4 years, while in more naturally stressed
areas, recovery is generally achieved within 9 months, although deeper polyhaline habitats
can take up to 2 years to recover (Bolam and Rees, 2003). Differences in recovery times are
attributed to the number of successional stages required to regain the original community
composition that depends on their life-history traits (Bolam and Rees, 2003). In Upper
Laguna Madre, mollusc and polychaete species composition and densities in seagrasses that
had colonized dredging deposits required at least 10 years to become similar to communities
in adjacent natural seagrass beds (see review in Sheridan, 2004). In our study, benthic
community showed a recovery three years after sediment disposal. At that time, sediment
consisted of mud, like in 2002, but seagrass had not yet recovered. This observation
highlights the importance of sediment type for benthic organisms, especially concerning
infauna which is more independent to seagrass presence (Cottet et al., 2007).
On other hand, the recovery of seagrass after 6 years in IM station also explained the
increase of species richness, abundance and biomass. The benefits of seagrass habitats for
ecosystems’ diversity, health and functioning were broadly documented (Orth et al., 1984;
Edgar, 1990; Blanchet et al., 2004; Do et al., 2011). Sheridan (2004) reported that, once
seagrass start to cover dredged sediments, increases in densities of the associated mobile
macrofauna would be expected. The presence of intertidal seagrass potentially increases food
115
availability for both infauna and epibenthic organisms by acting as a sink for organic matter
(Asmus and Asmus, 2000). Seagrass meadows reduce water movement and increase
sedimentation rates of fine particles and detritus. The above-ground vegetation provides
habitats and substrates for free-living animals and epiphytic animals and algae also, the
below-ground rhizome network offers sediment stability, creating favorable living conditions,
including shelter from predation, for a wide range of infaunal organisms (Fredriksen et al.,
2010).
4.3. Secondary production loss
Increasing percentages of plant burial significantly increase mortality and
consequently decrease secondary production (Mills and Fonseca, 2003). Since secondary
production responds quickly following the disposal of dredged material, the response of
benthic production to disposal is more predictable than community (Wilber et al., 2008;
Bolam, 2011). In fact, our results showed that both approaches differ. While benthic
macrofauna tended to recover in terms of structure since 2008 (at least in mud), secondary
production loss reached the highest values in 2008 and kept similar in 2010.
A gross calculation however, tends to show that this production loss has small
consequence on higher trophic level. Indeed, we calculated a total loss of 74 t AFDW, over
the whole area and in 5.5 years. With a production/consumption rate of 15%, this would
consist in a loss of predator production of 11 t AFDW over 5.5 years, i.e. 1.5 t AFDW per
year (i.e. 15.2 t Fresh Weight per year) which is insignificant at the scale of the lagoon.
However this calculation is only based on trophic pathways and does not take in
consideration the effect of seagrass destruction as an habitat loss for potential predators
(Summerson and Peterson, 1984; Irlandi, 1994; Boström et al., 2006a).
4.4. Biotic indices reaction
Previous studies in Arcachon Bay already stated that some Biotic Indices (AMBI,
BENTIX, BOPA) may perform badly in semi-enclosed ecosystems that are naturally enriched
in organic carbon (Blanchet et al., 2008; Lavesque et al., 2009). The present study confirmed
that these BIs did not detect both the seagrass burial and its recovery.
116
Some previous studies have attributed a poor performance of AMBI to highlight
anthropogenic pressures (Albayrak et al., 2006; Labrune et al., 2006; Quintino et al., 2006;
Zettler et al., 2007; Simonini et al., 2009), especially when the disturbance agent is not
related to organic enrichment (Sampaio et al., 2011). AMBI has ability to detect different
anthropogenic impacts worldwide, including anoxia and hypoxia, eutrophication, nutrient
loads, sediment toxicity (metals, PAH), and aquaculture (see review in Borja and Tunberg
(2011)). However, when using AMBI, reference conditions must be assessed independently
for each habitat (Muxika et al., 2007; Borja et al., 2012). In addition, our results showed that
the AMBI classified “Good” ES whereas the BENTIX assigned ‘Moderate’ ES for most
times/sites. The disagreements between both indices were already reported in some previous
studies (Simonini et al., 2009; Do et al., 2011). It was suggested that the discrepancy in the
AMBI and BENTIX results could be ascribed to differences in: (i) the weighting of sensitive
and tolerant groups of species in the formulae; (ii) the scaling of boundary limits among
classes; (iii) the arrangement of the ‘tolerant’ species, which are weighted separately in the
AMBI, whereas the BENTIX method required all tolerant species to be weighted equally; and
(iv) the attribution of the species to the ecological groups (see reviewed in Simonini et al.,
2009).
BIs constitute an extreme in terms of data reduction from the species × abundance
tables to a single numerical value. As a consequence, they are unable to assess the drastic
changes that occur following sediment disposal. Our result showed that MISS (Macrobenthic
Index for Sheltered Systems), that includes some of the existing BIs, namely BOPA and
AMBI, together with an additional set of metrics showed better results in assessing the
seagrass burial and its recovery. However, MISS requires biomass which is destructive and
time-consuming. We showed that it was possible to calculate a derived MISS (d-MISS)
without biomass that provided very similar conclusions. This BI should be now tested in
other conditions. Conversely, we recommend to keep considering trophic group separation,
even though it is often uneasy to classify species according to a clear trophic regime.
117
118
Chapter 6 - Perspective in Vietnam
1. Seagrass species in Vietnam
Seagrasses are distributed across the globe but unlike other taxonomic groups with
worldwide distribution, they exhibit low taxonomic diversity, with approximately 60 species
worldwide (Orth et al., 2006). In Vietnam, there are 14 species of seagrass reported (Nguyen
et al., 2010) (Table 6.1). Compared to other countries in the East Asian region (China,
Vietnam, Cambodia, Thailand, Malaysia, Indonesia and Philippines), seagrass species
diversity in Vietnam ranked third after the Philippines (16 species) and Malaysia (15 species)
(UNEP, 2004).
Table 6.1. The list of seagrass species in Vietnam
N0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Taxon
Family Hydrocharitaceae
Halophila beccarii Asch., 1871
Halophila decipiens Ostenf., 1901
Halophila ovalis (R.Br.) Hook.f., 1858
Halophila minor (Zoll.) den Hartog, 1957
Thalassia hemprichii (Ehrenb. ex Solms) Asch., 1871
Enhalus acoroides (L.f.) Royle, 1839
Family Ruppiaceae
Ruppia maritima L., 1753
Family Cymodoceaceae
Halodule pinifolia (Isobe et al.) den Hartog, 1964
Halodule uninervis (Forsk.) Boiss., 1882
Syringodium isoetifolium (Asch.) Dandy, 1939
Cymodocea rotundata Ehr.. & Hempr. ex Aschers., 1870
Cymodocea serrulata (R. Br.) Aschers. & Magnus, 1870
Thalassodendron ciliatum (Forsskål) den Hartog, 1970
Family Zosteraceae
Zostera japonica Ascherson & Graebner, 1907
Total
In the North
In the South
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
8
+
14
119
Vietnam is at the overlap of temperate and tropical seagrass species with Zostera
japonica growing intertidally in the north and mixing with Halophila ovalis, while in the
south the species composition is similar to the Philippines and Malaysia (Edmund and
Frederick, 2003). Ruppia maritima is common in coastal zones in northern Vietnam,
Halophila ovalis is widespread in coastal zones and around islands in north and central
Vietnam, while Halodule pinifolia is particularly prevalent in southern areas of Vietnam.
Seagrass beds are distributed along the entire coastline with meadows occurring from
Vietnam’s northern border with China, through to the south-western border with Cambodia
(Figure 6.1), but mostly from the middle of the southern sections. Species diversity tends to
increase gradually from the north to the south. In particular, in the north, seagrass beds such
as in Quang Ninh and Hai Phong provinces have the lowest number of species (5 species) and
the smallest coverage area (100 ha). Moreover, the meadows in this area are usually single
species. In the central part, seagrass beds in Tam Giang-Cau Hai and Lap An (Thua ThienHue province) have 6 species with 1100 ha coverage area. In the South, seagrass beds in Phu
Quoc Island (Kien Giang province) are the most diverse with 9 species and coverage area of
over 10000 ha. It is usual to find 3 to 4 seagrass species per square meter in Phu Quoc Island
(Tu, 2009).
120
Dam Ha
Tran island
Lien Vi
Ba Mun island
Co To island
Gia Luan
Cat Hai
Bach Long Vi island
Kim Trung
Thanh Long
Xuan Hoi
Vung Ang
Cua Gianh
Nhat Le
Cua Tung
Cua Viet
Tam Giang – Cau Hai lagoon
Lang Co lagoon
Thu Bon
Ly Son island
Thi Nai
Cu Mong
O Loan
Van Phong bay
Nha Phu
Thuy Trieu
My Hoa
Vinh Hao
Phu Quoc island
Phu Quy island
Con Dao island
Dugong recorded
Dugong sighting Record of seagrass
Figure 6.1. Map of seagrass distribution in Vietnam (source Nguyen (2004))
121
2. Biodiversity in seagrass
Very few studies have been undertaken to identify seagrass associated biota in
Vietnam as well as in East Asia (UNEP, 2004). However, the demonstration site proposals
from the seven countries of East Asia (China, Vietnam, Cambodia, Thailand, Malaysia,
Indonesia, Philippines) participating in the Project “Reversing Environmental Degradation
Trends in the South China Sea and Gulf of Thailand”, indicate that, along with 17 seagrass
species, there are at least 25 species of epiphytic algae, 21 macrobenthic algae, 10 penaeid
shrimps, 100 gastropods, 5 siganid fishes, 7 sea urchins, and 7 seahorses (UNEP, 2004). Most
of the major commercial fisheries of the region occur immediately adjacent to seagrass beds
(UNEP, 2004).
In Vietnam, according to Nguyen (2010) density of macrobenthos inside seagrass is
1.5-5.2 times higher than in sediment without seagrass. This study also found 323
macrobenthos, 219 seaweeds, 214 fishes, 178 juvenile species (including fishes, crabs and
shrimps), 60 gastropods, 10 sea cucumbers, 5 seahorses, 8 prawns (Penaeidae), 4 urchin
species in the seagrass beds (Nguyen et al., 2010).
Seagrass in Vietnam have another importance as it feeds endangered species such as
dugongs (Dugong dugon) and green turtles (Chelonia mydas). Until recently, it was widely
considered that the only remaining population of dugongs in Vietnam inhabited areas of Con
Dao National Park, an archipelago of 14 islands in the southern province of Ba Ria-Vung
Tau. The major threats to dugongs in Vietnam are hunting (not widespread), gill nets and
starvation through habitat destruction.
3. Decline of seagrass in Vietnam
Vietnam has at least 440 km2 of seagrass beds as determined from remote sensing and
ground-truth surveys. Vietnam coastal zone has been heavily impacted by sedimentation and
domestic and agricultural pollution (Edmund and Frederick, 2003). Seagrass meadows in
Vietnam have suffered serious degradation, with approximately 40 to 50% of their areas lost
over the past 2 decades (UNEP, 2004). Especially in the North Vietnam, some seagrass areas
decreased by 85% as in Ha Coi (Quang Ninh province) and some completely disappeared as
in Tien Yen (Quang Ninh province) by construction activities and coastal development. In
122
general, very little information on seagrass loss is available from Vietnam (Nguyen et al.,
2010).
4. Use of seagrasses in Vietnam
Seagrass in Vietnam are important either for direct use or as habitat. The majority of
exploited seagrasses (particularly Zostera and Ruppia genus) are used as food for livestock
and fertilizer. The main importance of seagrass, however, is for the use of associated biota,
such as algae (e.g. Gelidiella acerosa, Gracilaria spp., Hypnea spp., Sargassum spp., and
Turbinaria spp. are of commercial value), harvesting of the swimming crabs (Portunus
pelagicus and P. sanguinolentus), sea cucumbers (commercially important species
Holothuria scabra and Halodeima atra), finfish (at least 34 commercially important species),
and seahorses (in particular Hippocampus kuda) (Tu, 2009).
5. Threats to seagrass in Vietnam
Threats to seagrasses in Vietnam include both natural and human impact. However,
the main threat is anthropogenic activities such as destructive fishing practices (e.g. explosive
fishing, trawling, electric fishing, poisons), intensive aquaculture (rapid expansion of fish
farming, shellfish culture, etc.), land reclamation (using tidal flats for agricultural purposes),
and coastal development (e.g. construction of roads, bridges, houses and ports, dredging
activities), which have resulted in sedimentation and land-based pollution. Pressure on
seagrass beds stems from the lack of public awareness of their importance (UNEP, 2004).
Natural threats include typhoons (Tonkin Gulf in northern Vietnam experiences an
annual average of 35 typhoons), turbidity and sedimentation (river runoff from agriculture,
forestry, and urban development) and freshwater runoff (particularly during the rainy season),
climate change, sea-level rise. Tropical hurricanes are associated with heavy rainfall and
runoff, which may have negative impact on seagrass distribution as a result of deteriorated
light conditions due to suspended material and increased abundance of algae blooms induced
by enhanced nutrient discharges (Carlson et al., 2010). As an example of the hurricanes effect
on seagrass, Thalassodendron ciliatum species completely disappeared in Con Dao island
after the Linda storm in 1997 (Tu, 2009).
123
6. Response to threats
To date very little work has been done in response to the specific threats to seagrass in
Vietnam as well as in the South China Sea Region. UNEP/GEF South China Sea Project
(Seagrass monitoring in Vietnam was conducted as a part of the UNEP/GEF project
“Reversing environmental degradation trends in the South China Sea and Gulf of Thailand”
(2002-2008)), two Europe-funded projects (Prediction of the Recovery and Resilience of
Disturbed Coastal Communities in the tropics (South East Asian) (code: IC18CT980292)
(1998-2003) and Biodiversity assessment of the Ha Long Bay Heritage Area and proposition
of management plans (2002-2003)) and one project funded by United States of America
(National Oceanic and Atmospheric Administration) (Monitoring of seagrass in Bai Tu Long,
Quang Ninh Province (2003-2004) have recently been completed. The other projects
attempted to predict the resilience and recovery of disturbed seagrass at two sites as Bai Bon
(Maintaining seagrass beds for biodiversity, particularly endangered species) and Thuy Trieu
(Community Based Management) (UNEP, 2004). In addition, indirectly a number of projects
and programs that deal with coastal protection in Vietnam likewise promote the protection
and conservation of seagrass habitats such as the establishment of fifteen marine protected
areas (MPA) in Vietnam.
7. Research on seagrass in Vietnam
There have been only 62 ISI-cited publications on the seagrasses of Southeast Asia in
the last three decades and most work has been done on few sites such as Northwest Luzon in
the Philippines and South Sulawesi in Indonesia (Ooi et al., 2011). There is very little
quantitative data, especially long time series, from the tropical Indo-Pacific, in particular
Southeast Asia and eastern Africa. Our understanding of the processes driving spatial and
temporal distributions of seagrass species in these regions is rudimentary and has focused
primarily on estuarine and back reef/lagoonal seagrass meadows, with little work on fore reef
systems (see review in Ooi et al. (2011)). In Vietnam, there is also little research that has
been undertaken on seagrass. Therefore, the seagrass is poorly understood in all terms of
biodiversity, ecology and the natural and human impacts upon it. From 1999 up to now, only
about 60 works have been published on seagrass ecosystems in Vietnam (see Annex 1). What
is known is most often contained in reports and workshop and conference documents that are
124
not available to the wider scientific community. Most results of research are published in the
Vietnamese language and are rarely referenced by other academics. The total number of ISI
Web of Science publications on Vietnam seagrass until now are only three (Gacia et al.,
2003; Huong et al., 2003; Marba et al., 2010). There are also a few works on Southeast Asia
mentioning Vietnam seagrass (Morton and Blackmore, 2001; Kennedy et al., 2004; Ooi et al.,
2011). In addition, the researches mainly refer to diversity studies while further studies on
ecological processes in seagrass have been poorly studied. In general, seagrass ecosystems
receive the least attention compared to other marine ecosystems such as coral reefs,
mangrove forests. Knowledge of seagrass ecosystems is incomplete and the seagrass is
gradually lost in Vietnam.
8. Management seagrass beds in Vietnam
Managing seagrass meadows requires an integrated approach (Borum et al., 2004),
including efforts to avoid excessive nutrient and organic inputs from agricultural,
aquaculture, and urban sources and to prevent sediment loading, which causes a deterioration
in the submarine light climate so critical for seagrass growth (Waycott et al., 2009). With
reference from Duarte (2002) we propose some actions for management of seagrass beds in
Vietnam (Figure 6.2).
Society
Managerial
Scientific
- Improved education
- Monitoring programs
- Increased knowledge
Figure 6.2. Cooperative elements required to prevent present trend towards seagrass decline
and efficiently conserve seagrass ecosystems (source: Duarte (2002))
125
8.1. In terms of science
More research effort and monitoring programs must be conducted. The research could
be done towards:
- Research on ecological drivers of seagrasses such as disturbance events, sediment
characteristics, herbivory and light aims to understand the processes driving spatial and
temporal distribution of seagrass species.
- Research on habitat suitability models, habitat fragmentation and habitat loss in
seagrass areas; particularly study the changes in the number, shape, size, quality, and species
composition of seagrass.
- Assess the future of seagrasses under the exponentially increasing pressure of
population growth and development in coastal zones.
- Research on genetic diversity and ecological seagrass including physical and
biological characteristics such as the growth pattern, transfer of material, environmental
regulation. A focused research effort on seed bank, seedling survival and various factors
affecting the restoration process required.
- Research on transplantation to restore seagrass beds in the areas where they totally
disappeared.
- Development of a set of criteria and indicators with an associated numerical scoring
system, encompassing biological, environmental and socio-economic characteristics for
assessment ecoquality of seagrass beds.
The results of researches will be used for maintaining existing biodiversity or
restoring degraded biodiversity to former levels, removing or reducing the causes, hence
reducing the existing rates of degradation, or preventive actions that prevent the adoption of
unsustainable patterns of use, before they commence.
126
8.2. In terms of management
In Vietnam, marine and coastal resources law and policy have been ineffective in
achieving goals for sustainable development. Marine policy has mostly focused on national
security and sovereignty issues, with little emphasis on marine environmental protection
(Nguyen, 2008). Vietnam needs to issue effective policies for conservation of seagrass in
coastal management. Besides, it is urgent to establish seagrass protected areas in order to (1)
to prevent remaining seagrass beds from negative impacts and threatened species living in
this ecosystem from extinction; (2) to restore declining meadows; (3) to rationally use
resources in seagrass beds.
8.3. In terms of society
The lack of public awareness of seagrass losses contributes to continued decline of
this ecosystem. Raising awareness of people living in the coastal communities in the field of
protection, restoration and sustainable development of seagrass ecosystems is very important.
More effective communication of scientific knowledge about seagrass ecosystem is required.
Scientific understanding of the causes and consequences of ecosystem loss will be most
effective in reversing the negative trajectories of coastal ecosystems if science is converted to
public awareness, which is essential to ensure ecosystem conservation (Inglehart, 1995).
Hence effective use of formal (e.g., school programs, media) and informal (e.g., web)
education avenues and an effective partnership between scientists and media communicators
are essential to raise public awareness of issues, concerns, and solutions for seagrass
ecosystem. Only increased public understanding can ultimately inform and result in effective
management of this ecologically important ecosystem (Duarte et al., 2008).
9. My contribution to Vietnamese seagrass
My knowledge acquired during the implementation of my thesis would be helpful for
understanding seagrass in Vietnam. The new method of determining seagrass biomass by
photo will be applied in Vietnam. Besides, the study of parasites in marine organisms with
new approaches such as population dynamics, health of benthic communities will be
proposed and implemented.
127
In Vietnam, for the moment, there is not any study on the application of biotic indices
in the assessment of ecological quality of coastal environment. Although some multimetric
indices such as M-AMBI, BAT (Benthic assessment tool) and at a much lesser extent MISS
have been developed in many countries, testing these indices applicability now in Vietnam is
necessary. Of course, though some modifications must be done (reference conditions,
determination of tolerant and sensitive species, etc.), the successful application of these
indices will bring great significance in assessing the quality of coastal ecosystems in
Vietnam.
128
Chapter 7- General discussion - Conclusion
Chapter 7 - General discussion - Conclusion
1. Benthic macrofauna and Zostera noltii seagrass
Seagrasses are habitually used as biological indicators of ecosystem health
(Montefalcone, 2009). Our study confirmed a higher abundance (Fonseca et al., 1990; Orth,
1992; Boström and Bonsdorff, 1997; Cottet et al., 2007; Fredriksen et al., 2010), biomass
(Stoner, 1980) and diversity of macrofauna (Fonseca et al., 1990; Edgar et al., 1994; Boström
and Bonsdorff, 1997; Patricio et al., 2009; Fredriksen et al., 2010) in the seagrass beds
compared to unvegetated areas. Seagrass occupation induces rapid modification of benthic
community structure, but it would be subjective to conclude whether this is a positive or a
negative change. The present study highlighted the complexity in determining if seagrass
presence leads to an increase in ecosystem quality. In chapter 2, for example, we concluded
that the presence of seagrass considered as the sign of “good health” of the ecosystem had a
“moderate impact” in terms of macrobenthic biotic indices and a “rather negative effect” on
cockle population fitness.
Seagrasses are vulnerable ecosystems (Holmer and Marba, 2010). Even though, water
quality of Arcachon Bay can be considered as satisfactory (Lavesque et al., 2009), seagrass in
the lagoon have declined since 2005 (Plus et al., 2010). The observed variations in ammonia
(N-NH4) in the inner part of the lagoon are a symptom of the seagrasses disappearance and
thus, a first sign indicating a change in the Arcachon Bay ecosystem towards more instability
and vulnerability (Plus et al., 2010). Our study also detected the decrease of seagrass biomass
in ten out of twelve sampled stations in 2010 compared to in 2002. Seagrass absence is
usually related to low values of macrobenthos abundance, biomass and species richness
(Hemminga and Duarte, 2000; Deegan et al., 2002; Hughes et al., 2002; Airoldi et al., 2008).
Our results showed that seagrass decline was correlated to a moderate change of macrofauna,
with lower abundance and species richness. Seagrass cover was reduced by 25% over the
whole lagoon (Plus et al., 2010), resulting in seagrass fragmentation but still at levels below
the thresholds at which benthic communities decline dramatically. The effects of habitat loss
129
are mainly strong and negative, whereas habitat fragmentation results in weaker effects on
diversity that can be both positive and negative (Fahrig, 2003; Arponen and Boström, 2012).
The benthic macroinvertebrate fauna in Arcachon Bay in 2002 differed from that in
2010 in certain ways that would be expected in a system that had been subjected to increased
environmental stress. Seagrass loss was associated with other features that would indicate
that environmental stress had increased, such as a greater variability in composition among
samples and a reduction in diversity at all sites (Wildsmith et al., 2011). It is proposed that
the changes undergone by the benthic environment in Arcachon Bay are also due to a
combination of anthropogenic effects rather than to a single factor (seagrass loss). However,
it is difficult to unambiguously distinguish the anthropogenic disturbance effects from the
natural temporal variability (Elliott and Quintino, 2007; Patricio et al., 2009; Prato et al.,
2009; Borja and Tunberg, 2011), especially in estuarine and lagoon systems where
eurytolerant organisms are adapted to dynamic conditions and naturally high organic loading
(Rakocinski, 2012). Therefore, methods for discerning effects of anthropogenic from natural
stressors using macrobenthic process indicators will need to be developed (Rakocinski,
2012). However, it appears that only long-term study can determine whether the putative
anthropogenic impact is ‘‘real” or merely part of a long-term natural cycle. Many short-term
pollution monitoring surveys are of limited value since they fail to address natural temporal
variability. It is always critical to isolate the natural from the artificial (see review in Dauvin
(2010)). In this study, explaining the changes of macrobenthic communities was limited
because the temporal scale is only eight years (from 2002 to 2010).
Z. noltii is very sensitive to sediment disposal (Han et al., 2012) and it will take years
or decade for seagrass to recover to its former state after such an event (Bryars and
Neverauskas, 2004; Cardoso et al., 2005). In our study, seagrass had totally disappeared six
months (or less) after this destruction. After that, seagrass needed five years to recover in the
site covered by mud while it was still absent in the site covered by sand. As a result of
seagrass destruction, abundance, biomass and diversity of the benthic community in the
disposal sites have decreased after deposition. However, the macrofauna needed only three
years to show substantial recovery. According to Verissimo et al. (2012), although benthic
communities tend to respond rapidly to environmental change, recovery processes are
generally slower, and may require from two to more than twenty five years. In general, it is
130
impossible to draw a general conclusion about the impact of dredged material deposition on
the benthic community structure (Harvey et al., 1998).
While there appears little generality in the response of benthic community structure to
dredged material disposal, perhaps functional attributes such as productivity show a more
predictable response; however only by an increased number of comparable studies which
include production estimates can we begin to attempt to address this (Bolam et al., 2011).
Crisp (1984) also pointed out that the most integrative parameters of population health are
certainly the secondary production. In fact, our results showed both the structure and function
need to be analyzed in order to further understand the responses of macrofauna to sediment
disposal. However, the function features of macrofauna perhaps respond more slowly than
structure features with recovery events.
2. Seagrass and bivalve health
Parasites have a major impact on ecosystem health through their impact on driving
biodiversity and ecosystem organization (Hudson et al., 2006). Our study confirmed that
level of trematode infection in cockle population in Arcachon Bay remained low compared to
known thresholds and should have a low impact. Indeed, cockles in the lagoon were
considered healthy and lightly stressed (de Montaudouin et al., 2010). Another exploited
bivalve species in the lagoon, Manila clams seemed moderately stressed (de Montaudouin et
al., 2010). Arcachon Bay does not provide the best conditions for Manila clams, like
prolonged emersion time (Dang et al., 2010b), but the local population seemed to develop
resistance patterns, at least against metal aggression (de Montaudouin et al., 2010). Our result
also confirmed that environmental factors such as organic matter, temperature, and salinity
contribute to spatially heterogeneous distribution of the parasite and that salinity is not the
major factor explaining disease (perkinsosis, Brown Muscle Disease, trematodiosis)
distribution in this lagoon.
3. Seagrass and biotic indices
Our study demonstrated that the single indices as AMBI (AZTI's Marine Biotic
Index), BOPA (Benthic Opportunistic Polychaetes Amphipods Index) and BENTIX could
not exhaustively assess ecology quality status (ES).
131
One disadvantage of AMBI in such ecosystems (lagoons, estuaries) is that mistakes
can occur during the grouping of the species into different groups according to their response
to pollution situations. Once it draws on the response of organisms to organic inputs in the
ecosystem, it does not detect the effects caused by other types of pollution (Marin-Guirao et
al., 2005; Pinto et al., 2009).
The use of a single sensitivity/tolerance list in different geographical areas (such as in
AMBI Ecological Groups (EG)) is not appropriate (Gremare et al., 2009). The species
sensitivity/tolerance levels change with geographical location (Smith et al., 2001; Prato et al.,
2009). Therefore, AMBI classifications and local expert classifications should be the focal
point for additional investigation, both locally and internationally (Teixeira et al., 2012).
Further autoecological studies are nevertheless clearly needed to make clearer the actual
sensitivity/tolerance levels of the species. Moreover, it is important to establish the degree of
sensitivity of each species to different sources of pollution (de-la-Ossa-Carretero et al., 2012).
In addition, AMBI cannot clearly distinguish locals with high trophic diversity but
composed of a relatively high percentage of detritivore species, typical of places where an
accumulation of fine sediments and organic matter occur. This is the case in seagrass beds,
which are usually places presenting high species richness and high trophic diversity (Gamito,
2008; Gamito and Furtado, 2009; Gamito et al., 2012b). Faunal composition of healthy
benthic communities from naturally organic enriched sediments, and especially at stabilized
seagrass beds such as Zostera spp., do not reflect the theoretical model for the expected
distribution of ecological groups at unpolluted situations as described by Borja et al. (2000).
At these habitats, the relative proportion of abundance of ecological groups is more evenly
distributed, with no clear dominance of sensitive species (EG I) over the remaining groups,
and also with a typical presence of opportunistic species (EG IV and V) (Gamito et al.,
2012a).
The weaknesses of AMBI could be improved with adjustments to the index
calibration by (1) improving species classification (Teixeira et al., 2012); (2) applying a data
transformation (Warwick et al., 2010; Muxika et al., 2012; Teixeira et al., 2012); (3)
adjusting thresholds for condition categories (Teixeira et al., 2012); (4) using different types
of input data, specifically numerical abundances (NAMBI), biomass (BAMBI) and
production (PAMBI) (Warwick et al., 2010); (5) using a multivariate AMBI approach (MAMBI) (Teixeira et al., 2012).
132
Warwick et al. (2010) suggested that pre-treating data prior to calculating the indices,
using a spectrum of power transformations (square root, fourth root, logarithm,
presence/absence) such as are routinely used in nonparametric multivariate analyses, might
usefully down-weigh the influence of dominant species and give a better overview of the
status of assemblages (see review in Muxika et al., 2012). Ecological indices based on
relative abundances of species are often over-sensitive to the superabundance of one or a few
dominants. Moreover, it is usual to find high variation in numerical abundance, even between
replicate counts of the same species, which can affect the robustness of the index (Warwick et
al., 2010). As a result, some authors support the transformation of the data, especially prior to
multivariate analyses, in order to down-weigh the dominant species (Clarke and Warwick,
2001; Warwick et al., 2010). However, Muxika et al. (2012) stated that it is not yet feasible to
determine if it is better to calculate AMBI from abundance data, from biomass data or from
production data, or if the subsequent index will be more sensitive to pressures or impacts if
the input data are pre-treated or not. In fact, all the indices react to changes in pressures in a
similar way and follow similar improvement or degradation paths after those changes.
As similar with AMBI, BENTIX was based on the pollution resulting from organic
enrichment, their application in other pollution cases may not be successful (Marin-Guirao et
al., 2005; Dauvin, 2007; Elliott and Quintino, 2007; Dauvin et al., 2010; Muxika et al., 2012).
In addition, this index also showed some limitations when applied to estuaries and lagoons
where the natural conditions favour the presence of tolerant species in very high densities
(Simboura and Zenetos, 2002; Borja et al., 2004; Blanchet et al., 2008; Prato et al., 2009).
Indeed, the present study showed that BENTIX always give a low quality for the undisturbed
sites such as Banc d’Arguin.
BOPA is actually widely criticized because it takes into account only three categories
of organisms – opportunistic polychaetes, amphipods (except Jassa) and other species – but
only the first two have a direct effect on the index calculation (Pinto et al., 2009) and notably,
the sensitivity to pollution for the same taxonomic group differs from one species to another
(Afli et al., 2008). Another point is that it does not consider the oligochaete influence, which
may also include opportunistic species (Pinto et al., 2009).
Our results also proved that AMBI, BOPA and BENTIX rarely agree in assessing
ecological status (ES). Differences in ES assessment with AMBI and BENTIX were proven
by some previous studies (Prato et al., 2009; Simonini et al., 2009). Prato et al. (2009) gave
133
some explanations: (1) Discordances on assignment of ecological groups to many species.
Despite the recent efforts of the indices authors to revise the libraries of the species list, some
taxa resulted as ‘sensitive’ according to AMBI, while are ascribed as ‘tolerant’ according to
BENTIX. The assignment of ecological group to species is often arguable since it is based on
field remarks rather than correct knowledge of their autoecology and this may vary between
scientist and geographical area. Moreover, species react differently depending on interspecies interaction and environmental conditions. (2) The incompleteness of check-lists. This
difficulty could lead to an exclusion of large number of individuals in applying biotic indices.
(3) The misclassification of species to EG leads to differences between AMBI and BENTIX
owing to the differential weight each index puts in the different ecological groups. BENTIX
tends to reveal extreme values in the ES because species are ascribed only to 2 EG rather than
5 EG of AMBI. In the case where communities are dominated by tolerant species, the
BENTIX index assesses a lower ES rather than AMBI. The difference was found in a
transitional system where communities were dominated by ‘EG III’ by AMBI and ‘2’ by
BENTIX and therefore the ES final assessments were always lower for BENTIX index. (4)
Features of indices pertaining to the boundary limits among quality classes. The indices
AMBI and BENTIX are widely utilized in assessing the ES in marine environments, but their
correct application in transitional systems would depend on a resettlement. As a matter of
fact, thresholds settled in the biotic index scale values need to be modified according to the
natural variability of transitional waters referring to abiotic conditions and the abundance of
opportunistic species (Prato et al., 2009).
Although many benthic indices were successfully validated during the last decade,
most indices and assessment scales were developed for local geographic regions, and often
only for specific habitats within the region. Using benthic indices for assessment over large
geographic areas can be problematic because species composition and reference conditions
change naturally with ecoregion and habitat (Borja et al., 2009b). BIs (BENTIX, BOPA) used
in this study were originally developed for subtidal communities. For intertidal environments,
the thresholds between ES classes should be revised, and ‘Acceptable’ and ‘Not acceptable’
redefined (Blanchet et al., 2008). The establishment of reference conditions is a key process
and should be habitat-specific in order to properly reflect natural benthic gradients (Dauvin et
al., 2007; Blanchet et al., 2008; de Paz et al., 2008; Puente et al., 2008; Teixeira et al., 2008a;
Gamito et al., 2012a). Thresholds of benthic indices used to define ecological status should
134
be calibrated for hydrographically and/or biogeographically different estuarine or transitional
ecosystems (Chainho et al., 2007). However, it is also difficult to determine a reference status
for estuarine (transition) and coastal waters (Dauvin, 2007; Elliott and Quintino, 2007;
Teixeira et al., 2008b). In the future validation of the reference conditions, by using data from
similar systems, both degraded and healthy conditions should be undertaken (Borja and
Tunberg, 2011). Our result also confirmed that setting the reference condition is an important
step in assessing ES. Moreover, these indices must be tested for different ecosystems,
geographies, hydrological regimes and diverse pressure types.
Several studies across the world demonstrate that none of the available indices should
be considered ideal to measure biological effects of pollution, because every index was
originally developed for one or a few stressors (Quintino et al., 2006; Salas et al., 2006;
Chainho et al., 2007). Therefore, biotic indices must be validated for other stressors such as
physical disturbance and chemical pollution (Labrune et al., 2006; Patricio et al., 2009).
Moreover, the use of single univariate indicators to assess ecological quality is a too reduced
perspective of environmental complexity (Van Hoey et al., 2010). Complex ecosystems, such
as estuaries and lagoons, can also show more complex responses in some indicators. Our
study demonstrated that MISS or d-MISS including several indices would give a better,
although far from perfect, result than univariate indices in ES assessment. Combining several
metrics, each of them providing information on a biological attribute, in such a way that,
when integrated, determines the systems’ overall status and condition. This is the main
strength of biotic indices, since they allow the integration of the ecosystem’s information and
parameters, providing a broader understanding of the system’s processes (Pinto et al., 2009).
In addition, the use of multimetric tools allows overcoming the sensitivity of single metrics
by combining several indices (Buckland et al., 2005; Teixeira et al., 2008b). Consequently,
the application of multi-metric methods (compared to single metrics) increases the
probability of a correct evaluation of the ecological conditions of the system (Quintino et al.,
2006; Borja et al., 2007; Afli et al., 2008; Lavesque et al., 2009; Van Hoey et al., 2010;
Gamito et al., 2012b).
Although benthic invertebrate fauna presents a lot of advantages for assessing
ecological quality (Dauvin, 2007; Borja and Dauer, 2008; Dauvin et al., 2012), we agree that
there are several disadvantages of the existing benthic indices based on benthic organisms:
(1) they represent a static expression of an ecological condition, (2) they are not explicitly
135
linked to changes in ecological function, (3) they may not be specific with respect to different
kinds of stressors, (4) they are subject to underlying taxonomic changes across estuarine
gradients, (5) their use can be labour intensive, and they are not applied consistently across
bio-geographic provinces (Dauvin et al., 2012). Many previous studies confirmed that benthic
macrofauna are good indicators of ecosystem health (Dauer et al., 1993; Warwick, 1993;
Reiss and Kroncke, 2005; Dauvin et al., 2010; Verissimo et al., 2012). Our study showed that
although seagrass has been decreased by 1/3rd of the occupied surface since 2005 (Plus et al.,
2010), macrofauna only changed a little in this period. Seagrass development/regression and
benthic fauna structure do not always evolve in the same way.
4. Conclusion
In fact, only one group the macrofauna is not enough to explain all of the changes in
environmental status. Some investigations have demonstrated the fundamental advantage of a
multi-species approach, with the inclusion of many taxonomic and functional groups that
have a broad range of sensitivities to any given environmental regime (Attrill and Depledge,
1997; Patricio et al., 2012). For example, macrofauna and meiobenthic nematodes may
provide different but complementary types of information, depending on the indices used and
the different “response-to-stress” times of each benthic group. Optimally, both groups should
be used in marine pollution monitoring programs (Patricio et al., 2012). Coastal lagoons are
complex systems, with considerable habitat heterogeneity and often subject to high temporal
dynamics, which constitutes a great challenge for ecological assessment programs (Gamito et
al., 2012a). The present study suggests that the necessity to integrate numerous parameters
(macrofauna, motile megafauna, meiofauna, fishes, etc.) to assess this ecosystem.
Finally, we agree that the next studies undertaken should continue to establish
ecological thresholds in order to forecast the ecosystem trajectory. The critical ecological
thresholds exist in the structural patterning of biogenic ecosystems that, when exceeded,
cause abrupt shifts in the distribution and abundance of organisms (Boström et al., 2011). A
quantitative analysis of seagrass trajectories will be critically important to forecast the likely
cumulative effects of the known and emerging stressors of seagrasses (Orth et al., 2006).
136
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Annex 1. The literatures on Seagrass in Vietnam
Aryuthaka C., M. Fortes, Nguyen Van Tien, S. Satumanatpan, H. Malikuswovo, K. Sour,
X.P. Huang , K. Passfield, 2004: Seagrass in the South China Sea. UNEP /GEF/SCS
Technical publication No 3, 12 pages.
Chu The Cuong, Lang Van Keng, Nguyen Mai Luu, Tran Manh Ha, 2008. Macrobenthos
resource in seagrass beds in Phu Quoc district, Kien Giang province, UNEP/
GEF/SCS report (in Vietnamese: Nguồn lợi động vật đáy trong thảm cỏ biển Phú
Quốc, tỉnh Kiên Giang).
Do Cong Thung, 1998. Macrobenthos in seagrass beds in Quang Ninh. Annual collection of
works on Marine Environment and Resources. Publishing House for Science and
Technology, Hanoi, V, p. 103- 198 (in Vietnamese: Động vật đáy trong thảm cỏ biển
khu vực Quảng Ninh. Tuyển tập Tài nguyên và Môi trường Biển).
Do Cong Thung, 1999. Macrobenthos in seagrass beds in Lap An lagoon (Thua Thien Hue). .
Annual collection of works on Marine Environment and Resources. Publishing House
for Science and Technology, Hanoi, VI, p. 293-250 (in Vietnamese: Động vật đáy
trong thảm cỏ biển đầm Lập An (Thừa Thiên - Huế). Tuyển tập Tài nguyên và Môi
trường Biển).
Fortes M., Nguyen Van Tien, S. Satumanatpan, H. Malikuswovo, K. Sour, X.P. Huang, The
UNEP/GEF Seagrass demonstration sites in South China Sea: Milestones in Seagrass
research and coastal resource management in Southeast Asia. Selected papers of the
NaGISA World Congress. Kyoto Univ. Japan, May, 2007: p.125-137.
Gacia, E., Duarte, C.M., Marba, N., Terrados, J., Kennedy, H., Fortes, M.D., Tri, N.H., 2003.
Sediment deposition and production in SE-Asia seagrass meadows. Estuarine, Coastal
171
Huong, T.T.L., Vermaat, J.E., Terrados, J., Van Tien, N., Duarte, C.M., Borum, J., Tri, N.H.,
2003. Seasonality and depth zonation of intertidal Halophila ovalis and Zostera
japonica in Ha Long Bay (northern Vietnam). Aquatic Botany 75, 147-157.
Mai Ky Vinh, Tran Chinh Khuong and Symington, K., 2005. Application of GIS and remote
sensing for mapping seagrass distribution in Kien Giang province. National
Workshop on Protection Environment and Aquatic Resources, Hai Phong, 1415/1/2005 (in Vietnamese: Ứng dụng công nghệ GIS và ảnh viễn thám để xây dựng
bản đồ phân bố cỏ biển ở vùng ven biển tỉnh Kiên Giang. Hội thảo toàn quốc Bảo vệ
Môi trường và nguồn lợi thủy sản).
Marba, N., Duarte, C.M., Terrados, J., Halun, Z., Gacia, E., Fortes, M.D., 2010. Effects of
seagrass rhizospheres on sediment redox conditions in SE Asian coastal ecosystems.
Nguyen Huu Dai, 2005. Restoration and protection of seagrass - model of management and
sustainable development in coastal areas. National Workshop on Protection
Environment and Aquatic Resources, Hai Phong, 14-15/1/2005 (in Vietnamese: Phục
hồi và bảo vệ các thảm cỏ biển – Mô hình quản lý và phát triển bền vững vùng biển
ven bờ. Hội nghị toàn quốc về Môi trường và Bảo vệ nguồn lợi Thủy sản).
Nguyen Huu Dai, Pham Huu Tri, Nguyen Thi Linh, Nguyen Xuan Vy, 2002. The decline of
seagrass beds in Khanh Hoa and their resilience. The collection papers of National
Workshop on South China Sea, Nha Trang 16-19/9/2002, p. 359-368 (in Vietnamese:
Sự suy giảm các thảm cỏ biển ở Khánh Hòa và khả năng phục hồi chúng. Tuyển tập
Báo cáo Khoa học hội nghị Khoa học toàn quốc về Biển Đông).
Nguyen Huu Dai, Pham Huu Tri, Nguyen Thi Linh, Nguyen Xuan Vy, 2006. Chapter VIII
Solutions to protect and restore seagrass ecosystems. Summary of the Project:
"Research solutions to protect and restore coral reef ecosystems, seagrass and
pollution environment" in the Collection of the main results of the sea survey program
and technology application. Vol II. Code KC.09, p. 149-168 (in Vietnamese: Các giải
pháp bảo vệ và phục hồi hệ sinh thái cỏ biển. Tóm tắt Báo cáo Đề tài: “Nghiên cứu
giải pháp bảo vệ, phục hồi hệ sinh thái rạn san hô, cỏ biển và khắc phục ô nhiễm môi
172
trường tự sinh” trong tuyển tập các kết quả chủ yếu của chương trình Điều tra cơ bản
và nghiên cứu ứng dụng công nghệ Biển. Quyển II. Mã số KC.09).
Nguyen Thi Thu và Nguyen Huu Phung, 2002. Young-of-the-year fish species composition
in Tam Giang – Cau Hai lagoon. Annual collection of works on Marine Environment
and Resources. Publishing House for Science and Technology, Hanoi, IX, p. 283-294
(in Vietnamese: Thành phần nguồn giống cá trong đầm phá Tam Giang-Cầu Hai.
Tuyển tập Tài nguyên và Môi trường Biển).
Nguyen Thi Thu và Nguyen Manh Ha, 2008. Young-of-the-year fish, crab and shrimp in
seagrass beds in Phu Quoc Island. Report of UNEP/GEF/SCS project (in Vietnamese:
Nguồn giống tôm, cua, cá trong thảm cỏ biển Phú Quốc. Báo cáo chuyên đề - Điểm
trình diễn San hô, Cỏ biển đảo Phú Quốc thuộc Dự án UNEP/GEF/SCS).
Nguyen Thi Thu, 2001. Young-of-the-year fish, crab and shrimp in seagrass beds in Lang Co.
for Science and Technology, Hanoi, VIII, p. 211-219 (in Vietnamese: Nguồn giống
tôm, cua, cá trong thảm cỏ biển Lăng Cô. Tuyển tập Tài nguyên và Môi trường Biển).
Nguyen Van Quan, 2006. Preliminary studies on fishery resources in the seagrass beds in Phu
Quoc Island, Kien Giang province. Report Workshop on Science, Technology and
Maritime Economy for the industrialization and modernization of the country, Do
Son, Hai Phong, 25-26/10/2006, p. 126-135 (in Vietnamese: Bước đầu nghiên cứu
nguồn lợi cá trong thảm cỏ biển đảo Phú Quốc, tỉnh Kiên Giang. Báo cáo Hội thảo
Khoa học, công nghệ và kinh tế biển phục vụ sự nghiệp công nghiệp hóa và hiện đại
hóa đất nước).
Nguyen Van Tien, Le Thanh Binh, Nguyen Huu Dai, Tran Hong Ha, Tu Thi Lan Huong, Do
Nam, Dam Duc Tien, 2004. Towards managing seagrass ecosystems Vietnam.
Publishing House for Science and Technology, Hanoi, 132 p. (in Vietnamese: Tiến tới
quản lí hệ sinh thái cỏ biển Việt Nam).
Nguyen Van Tien, 1996. Data on species composition and distribution of seagrass in Thua
Thien-Hue-Da Nang coastal. Annual collection of works on Marine Environment and
Resources. Publishing House for Science and Technology, Hanoi, III, p. 263-270 (in
173
Vietnamese: Dẫn liệu về thành phần loài và phân bố của cỏ biển vùng ven biển Thừa
Thiên Huế-Đà Nẵng. Tuyển tập Tài nguyên và Môi trường biển).
Nguyen Van Tien, 1998. Preliminary studies in seagrass ecosystems Journal of Vietnamese
Environment, Association for the Protection of Nature and Environment of Vietnam.
Publishing House for Science and Technology, Hanoi, II, p. 41-50 (in Vietnamese:
Bước đầu nghiên cứu hệ sinh thái cỏ biển ở Việt Nam Tạp chí Môi trường, Hội Bảo
vệ thiên nhiên và Môi trường Việt Nam).
Nguyen Van Tien, 1998. Species composition and distribution of seagrass in Viet Nam.
Abstracts 3-rd International seagrass biology Workshop (ISBW). Quezon city,
Philippines, 19-26 Apr. 1998: p.88.
Nguyen Van Tien, 1998. Species composition and distribution of seagrasses in Quang Ninh.
for Science and Technology, Hanoi, V, p. 183-190 (in Vietnamese: Thành phần loài
và phân bố của cỏ biển ở Quảng Ninh. Tuyển tập Tài nguyên và Môi trường biển).
Nguyen Van Tien, 1998. Seagrass ecosystem management approach in Vietnam. Annual
collection of works on Marine Environment and Resources. Publishing House for
Science and Technology, Hanoi, V, p.: 220-229 (in Vietnamese: Tiếp cận quản lý hệ
sinh thái cỏ biển ở Việt Nam. Tuyển tập Tài nguyên và Môi trường biển).
Nguyen Van Tien, 1999. Data on species composition and distribution of seagrass in
Vietnam. Annual collection of works on Marine Environment and Resources.
Publishing House for Science and Technology, Hanoi, VI, p. 192-207 (in Vietnamese:
Dẫn liệu về thành phần loài và phân bố của cỏ biển Việt Nam. Tuyển tập Tài nguyên
và Môi trường biển).
Nguyen Van Tien, 1999. Seaweed-seagrass research situation in Vietnam. Publishing House
for Science and Technology, Hanoi, VI, p. 169-181 (in Vietnamese: Tình hình nghiên
cứu rong-cỏ biển ở Việt Nam. Tuyển tập Tuyển tập Tài nguyên và Môi trường biển).
174
Nguyen Van Tien, 2000. Seagrass of Vietnam. Journal of Biology Today's the biology
Vietnam, VI, 3(21)/2000, p. 44-45 (in Vietnamese: Cỏ biển Việt Nam. Tạp chí Sinh
học Ngày nay của Hội các ngành sinh học Việt Nam)
Nguyen Van Tien, 2001. Seagrass of Hai Phong. Hai Phong Journal of Science and
Economy, 5, p. 4-5 (in Vietnamese: Cỏ biển Hải Phòng. Tạp chí Khoa học & Kinh tế
Hải Phòng).
Nguyen Van Tien, 2003. Seagrass survey methods (Chapter XI). In the book "Handbook of
investigation and monitoring of biodiversity." WWF Indochina Programme. Transport
Publishing House Company Limited, p. 333-352 (in Vietnamese: Phương pháp điều
tra Cỏ Biển (chương XI). Trong sách Sổ tay hướng dẫn điều tra và giám sát đa dạng
sinh học. Chương trình WWF Đông dương. Nhà in Giao thông Vận tải).
Nguyen Van Tien, 2003. Researches in distribution of Vietnamese seagrasses. Publishing
House for Science and Technology, Hanoi, X, p. 66-88.
Nguyen Van Tien, 2004. Data on seagrasses in Quang Ninh and Thua Thien Hue province
waters. Proceedings of the Vietnam-Italy scientific conference: Biodiversity
conservation in the coastal zone of Vietnam. Publishing House of Vietnam National
University, Hanoi, p.107-112.
Nguyen Van Tien, 2005. Propose measures to protect seagrass resources in Tam Giang-Cau
Hai lagoon. Proceedings National Conference of Thua Thien-Hue, 24-26/12/2005 (in
Vietnamese: Đề xuất các giải pháp bảo vệ nguồn lợi cỏ biển đầm phá Tam Giang-Cầu
Hai. Kỉ yếu Hội thảo quốc gia về đầm phá Thừa Thiên-Huế).
Nguyen Van Tien, 2005. Seagrass ecosystems management in Phu Quy Island, Binh Thuan
province. Proceedings National Conference on Research in Life Sciences. Hanoi,
3/11/2005, Publishing House for Science and Technology, p. 1098-1101 (in
Vietnamese: Quản lí hệ sinh thái cỏ biển đảo Phú Quí (Bình Thuận). Kỉ yếu Hội nghị
toàn quốc về nghiên cứu Cơ bản trong Khoa học Sự sống).
Nguyen Van Tien, 2005. Reasonable using of seagrass in Vietnam. Proceedings National
Conference on Environmental Protection and Aquatic Resources, Hai Phong 14-
175
15/01/2005. Agriculture Publishing House, Hanoi: p. 426-433 (in Vietnamese: Sử
dụng hợp lí hệ sinh thái cỏ biển Việt Nam. Kỉ yếu Hội nghị toàn quốc về Bảo vệ Môi
trường và Nguồn lợi Thuỷ sản).
Nguyen Van Tien, 2005. Degradation of seagrass ecosystems in Vietnam and propose
mitigation measures. Proceedings National Conference on Environment. Hanoi, 2122/4/2005: p. 840-844 (in Vietnamese: Suy thoái hệ sinh thái cỏ biển ở Việt Nam và
đề xuất các giải pháp giảm thiểu. Kỉ yếu Hội nghị Môi trường toàn quốc. Hà Nội).
Nguyen Van Tien, 2005. Seagrass ecosystem degradation and propose a number of
management tasks. Journal of Environmental Protection, Issue 1+2/2005, p. 41-43 (in
Vietnamese: Suy thoái hệ sinh thái cỏ biển và đề xuất một số nhiệm vụ quản lí. Tạp
chí Bảo vệ Môi trường).
Nguyen Van Tien, 2006. Vietnam seagrass ecosystems. Journal of Fisheries, 11, p. 33-36 (in
Vietnamese: Hệ sinh thái cỏ biển Việt Nam. Tạp chí thuỷ sản).
Nguyen Van Tien, 2009. Some issues in marine ecosystem management in Vietnam.
Proceedings National Conference on Marine and sustainable development. Do Son,
Hai Phong on 28 - 29/11/2009. Publishing House for Science and Technology, Hanoi,
p. 152 – 157 (in Vietnamese: Một số vấn đề về quản lí hệ sinh thái Cỏ biển Việt Nam.
Kỉ yếu Hội nghị toàn quốc về Sinh vật biển và phát triển bền vững).
Nguyen Van Tien, Dam Duc Tien, 1996. The seagrass beds from some remote islands in
Vietnam. Abstracts 3th international congress on the Marine Biology of the South
China Sea (PACON). 8 Oct.-1 Nov. 1996, the University of Hong Kong.
Nguyen Van Tien, Dam Duc Tien, 1997. Species composition and distribution of seagrass in
Con Dao. Publishing House for Science and Technology, Hanoi, IV, p. 263-269 (in
Vietnamese: Thành phần loài và phân bố của cỏ biển Côn Đảo. Tuyển tập Tài nguyên
và Môi trường biển).
Nguyen Van Tien, Dam Duc Tien, 2000. Preliminary studies on seagrass in the Spratly
Islands. National Conference Proceedings biology: basic research problems in
biology. Hanoi, 7-8/10/2000, p. 293-296 (in Vietnamese: Bước đầu nghiên cứu cỏ
176
biển ở quần đảo Trường Sa. Kỉ yếu Hội nghị Sinh học Quốc gia: Những vấn đề
nghiên cứu cơ bản trong Sinh học).
Nguyen Van Tien Dang Ngoc Thanh, 2003. Ecological characteristics of seagrass beds
(Chapter 12). In the book "East Sea", Volume IV, Marine Biology and Ecology,
Vietnam National University Publishing House, Hanoi, p. 254-267 (in Vietnamese:
Đặc trưng Sinh thái các bãi cỏ biển (chương 12). Trong sách “Biển Đông”, tập IV,
Sinh vật và Sinh thái Biển).
Nguyen Van Tien Dang Ngoc Thanh, Nguyen Huu Dai, 2002. Vietnam Seagrass. Species
composition, distribution, ecology, biology. Publishing House for Science and
Technology, Hanoi, 165 p (in Vietnamese: Cỏ Biển Việt Nam. Thành phần loài, phân
bố, sinh thái, sinh học).
Nguyen Van Tien, Le Thi Thanh, 2001. Data on red algae (Hypnea) in west coast of Tonkin
Bay. Annual collection of works on Marine Environment and Resources. Publishing
House for Science and Technology, Hanoi, VIII, p. 197-210 (in Vietnamese: Dẫn liệu
về rong đông (Hypnea) bờ Tây vịnh Bắc Bộ. Tuyển tập Tài nguyên và Môi trường
biển).
Nguyen Van Tien, Le Thi Thanh, 2003. Some research results of seagrass recovery by
growing in Ha Long Bay, Quang Ninh province Annual collection of works on
Marine Environment and Resources. Publishing House for Science and Technology,
Hanoi, X, p. 262-268 (in Vietnamese: Một số kết quả nghiên cứu trồng phục hồi cỏ
biển ở vịnh Hạ Long (Quảng Ninh). Tuyển tập Tài nguyên và Môi trường biển).
Nguyen Van Tien, Le Thi Thanh, 2007. Seagrass ecosystems management in Phu Quoc
Island, Kien Giang province. Proceedings National Conference on Research in Life
Sciences, Qui Nhon in 10/8/2007. Publishing House for Science and Technology,
Hanoi, p. 603 – 606 (in Vietnamese: Quản lí hệ sinh thái Cỏ biển ở đảo Phú Quốc,
tỉnh Kiên Giang. Kỉ yếu Hội nghị toàn quốc Nghiên cứu cơ bản trong Khoa học sự
sống).
Nguyen Van Tien, Le Thi Thanh, 2008. Firstly propose on protect areas of seagrass in
Vietnam. Journal of Agriculture and Rural Development, March/2008, p. 12-18 (in
177
Vietnamese: Bước đầu đề xuất các khu bảo tồn cỏ biển ở Việt Nam. Tạp chí Nông
nghiệp và Phát triển Nông thôn).
Nguyen Van Tien, Le Thi Thanh, Tu Thi Lan Huong, 2007. Seagrass ecosystems
management in Quang Nam province. Proceedings National Conference on Ecology
and Biological Resources, Hanoi in 26/10/2007, Agriculture Publishing House, p.
141-147 (in Vietnamese: Quản lí hệ sinh thái Cỏ biển ở tỉnh Quảng Nam. Kỉ yếu Hội
nghi quốc gia về Sinh thái và Tài nguyên Sinh vật).
Nguyen Van Tien, Le Thi Thanh, Tu Thi Lan Huong, 2002. Seaweed in Phu Quoc Island,
Kien Giang. Annual collection of works on Marine Environment and Resources.
Publishing House for Science and Technology, Hanoi, IX, p. 189-194 (in Vietnamese:
Cỏ biển đảo Phú Quốc, tỉnh Kiên Giang. Tuyển tập Tài nguyên và Môi trường biển).
Nguyen Van Tien, Nguyen Huu Dai, 2002. Studies on seaweeds in Viet Nam. Annual
collection of works on Marine Environment and Resources. Publishing House for
Science and Technology, Hanoi, p. 67-75.
Nguyen Van Tien, Nguyen Huy Yet, Le Thi Thanh, 2003. First data on resource of
pharmaceuticals from marine life in Vietnam. Conference Proceedings First National
Medicine. Hanoi 11-12/3/2003, p. 95-99 (in Vietnamese: Dẫn liệu bước đầu về nguồn
dược liệu từ sinh vật biển Việt Nam. Kỉ yếu Hội nghị Dược liệu toàn quốc lần thứ
nhất).
Nguyen Van Tien, Nguyen Xuan Hoa, 2008. Seagrass resources in Thi Nai lagoon, Binh
Dinh province. Annual collection of works on Marine Environment and Resources.
Publishing House for Science and Technology, Hanoi, XIII, p. 194-203 (in
Vietnamese: Nguồn lợi thảm cỏ biển đầm Thị Nại, tỉnh Bình Định. Tuyển tập Tài
nguyên và Môi trường biển).
Nguyen Van Tien, Tu Thi Lan Huong, 2005. Ecology and management measures to restore
seagrass in Lap An lagoon, Thua Thien-Hue. Proceedings National Conference on
Environmental Protection and Aquatic Resources. Hai Phong 14-15/01/2005.
Agriculture Publishing House, p. 547-554 (in Vietnamese: Giải pháp sinh thái và quản
178
lí nhằm phục hồi thảm cỏ biển đầm Lập An, Thừa Thiên Huế. Kỉ yếu Hội nghị toàn
quốc về Bảo vệ Môi trường và Nguồn lợi thuỷ sản).
Nguyen Van Tien, Tu Thi Lan Huong, 2008. Seagrass research methods. Publishing House
for Science and Technology, Hanoi, 102 pages (in Vietnamese: Phương pháp nghiên
cứu cỏ biển).
Nguyen Van Tien, Vo Si Tuan, Nguyen Huy Yet, 1997. The results of seaweeds and seagrass
study in Spratly Archipelago during RP-VN. JOMSRE-SCS96. Proceeding of
Scientific Conference on RP-VN JOMSRE-SCS 96. Ha Noi, Vietnam 2223/April/1997: p.102-113.
Nguyen Xuan Hoa and Tran Cong Binh, 2002. Monitoring seagrass and dungongs in the Con
Dao 1998-2002. The collection of works in South China Sea Science Conference
Report-2000, Nha Trang, 16-19/9/2002. Agriculture Publishing House, p. 626-637 (in
Vietnamese: Giám sát thảm cỏ và dungongs ở Côn Đảo giai đoạn 1998-2002. Tuyển
tập Báo cáo Hội nghị Khoa học Biển Đông-2000).
Nguyen Xuan Hoa, Nguyen Huu Dai, Pham Huu Tri, Nguyen Thi Linh, 1999. The seagrass
beds in South of Vietnam. Anthology of Science Report of 4th Science and
Technology Conference on National Sea, p. 967-974 (in Vietnamese: Các thảm cỏ
biển phía nam Việt Nam. Tuyển tập Báo cáo Khoa học, Hội nghị Khoa học Công
nghệ Biển toàn quốc lần thứ IV. Trang: 967-974.)
Nguyen Xuan Hoa, Nguyen Huu Dai, Pham Huu Tri, Nguyen Thi Linh, 2000. Study the
variation of seagrass Enhalus acoroides (L.f.) Royle, Thalassia hemprichii (Ehrenb.)
Asch., Cymodocea serrulata (R.Brown) Asch. and Magn. in Khanh Hoa province.
Anthology of Science Report of Science and Technology Conference on National Sea,
2000, p. 179-180 (in Vietnamese: Nghiên cứu sự biến động các thảm cỏ biển Enhalus
acoroides (L.f.) Royle, Thalassia hemprichii (Ehrenb.) Asch., Cymodocea serrulata
(R.Brown) Asch. and Magn. ở vùng biển ven bờ tỉnh Khánh Hòa. Tuyển tập Báo cáo
Khoa học Hội nghị Khoa học Biển Đông).
Tran Duc Thanh Dinh Van Huy, Nguyen Van Tien, Nguyen Huy Yet, the 1998. First results
using satellite imagines in study distribution of seagrass, coral and seaweed in central
179
Vietnam. Annual collection of works on Marine Environment and Resources.
Publishing House for Science and Technology, Hanoi, V, p. 94-102 (in Vietnamese:
Kết quả bước đầu sử dụng tài liệu ảnh vệ tinh nghiên cứu phân bố cỏ biển, rong biển
và san hô ở miền Trung Việt Nam. Tuyển tập Tài nguyên và Môi trường Biển).
Tu Thi Lan Huong and Nguyen Van Tien, 2005. Some ecological characteristics of the
seagrass beds of Kien Giang province. 1st National Workshop on Biology and
Ecology in Hanoi, p. 758-763 (in Vietnamese: Một số đặc điểm sinh thái của các
thảm cỏ biển tỉnh Kiên Giang. Hội nghị Sinh học- Sinh thái Quốc gia lần I tại Hà
Nội).
Tu Thi Lan Huong and Nguyen Van Tien, 2008. Monitoring of seagrass resources in Bai Bon
Beach, Phu Quoc Island. Report of the project: "Prevent the trend of environmental
degradation in South China Sea and Gulf of Thailand" (in Vietnamese: Giám sát
nguồn lợi cỏ biển ở điểm trình diễn Bãi Bổn, đảo Phú Quốc. Báo cáo thuộc dự án:
“Ngăn chặn xu thế suy thoái môi trường Biển Đông và vịnh Thái Lan).
Tu Thi Lan Huong, 2005. SeagrassNet: some results of seagrass monitoring in The Vang
Island, Quang Ninh province. Annual collection of works on Marine Environment and
Resources. Publishing House for Science and Technology, Hanoi, IX, p. 189-195 (in
Vietnamese: SeagrassNet: một số kết quả giám sát cỏ biển đảo Thẻ Vàng, tỉnh Quảng
Ninh. Tuyển tập Tài nguyên và Môi trường Biển).
Tu Thi Lan Huong, 2006. An overview for seagrass managers. Journal of Environmental
Protection Agency, 10, p. 19-20 (in Vietnamese: Một cái nhìn tổng quan về cỏ biển
cho các nhà quản lý. Tạp chí của Cục Bảo vệ Môi trường).
Tu Thi Lan Huong, 2007. The role and value of seagrass ecosystems. Vietnam Sea Magazine,
3, p. 31-32 (in Vietnamese: Vai trò và giá trị hệ sinh thái cỏ biển. Tạp chí Biển Việt
Nam).
Tu Thi Lan Huong, 2008. Comparing the seasonal fluctuations of the populations of seagrass
Halophila ovalis in Vietnam coastal. 2nd National Scientific Conference on ecological
and biological resources, Hanoi 26/10/2007, p. 437-441 (in Vietnamese: So sánh sự
biến động theo mùa của các quần thể cỏ biển Halophila ovalis ở vùng triều ven biển
180
Việt Nam. Hội nghị Khoa học Toàn quốc lần thứ 2 về sinh thái và tài nguyên sinh
vật).
Tu Thi Lan Huong, Nguyen Van Tien, 2010. Seagrass in Vietnam with the challenges of
climate change. (www.kienviet.net:11/12/2010) (in Vietnamese: Thảm cỏ biển Việt
Nam với những thách thức trong điều kiện biến đổi khí hậu).
181

THESE L`UNIVERSITÉ BORDEAUX 1 Par M. Van Tu DO DOCTEUR

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