Restauração ecológica: ligando prática e teoria

Transcription

Instituto de Pesquisas Jardim Botânico do Rio de Janeiro
Escola Nacional de Botânica Tropical
Programa de Pós-graduação Stricto Sensu
Tese de Doutorado
Danielle Justino Capossoli
Rio de Janeiro
2013
Instituto de Pesquisas Jardim Botânico do Rio de Janeiro
Escola Nacional de Botânica Tropical
Programa de Pós-graduação Stricto Sensu
Tese apresentada ao Programa de PósGraduação em Botânica, Escola Nacional
de Botânica Tropical, do Instituto de
Pesquisas Jardim Botânico do Rio de
Janeiro, como parte dos requisitos
necessários para a obtenção do título de
Doutor em Botânica.
Orientador: Fabio Rubio Scarano
Coorientadora: Marinez Ferreira de
Siqueira
Rio de Janeiro
2013
i
Tese submetida ao corpo docente da Escola Nacional de Botânica Tropical,
Instituto de Pesquisas Jardim Botânico do Rio de Janeiro - JBRJ, como parte
dos requisitos necessários para a obtenção do grau de Doutor.
Aprovada por:
Prof. Dr. Fabio Rubio Scarano (Orientador) _________________________
Prof. Dra. Giselda Durigan
_________________________
Prof. Dr. Reinaldo Bozelli
_________________________
Prof. Dr. Luiz Fernando Duarte de Moraes
_________________________
Prof. Dr. Pablo J. F. Pena Rodrigues
_________________________
em 28/02/2013
Rio de Janeiro
2013
ii
C345r
Capossoli, Danielle Justino.
Restauração ecológica: ligando prática e teoria / Danielle Justino
Capossoli. – Rio de Janeiro, 2013.
xi, 182 f. : il. ; 28 cm.
Tese (doutorado) – Instituto de Pesquisas Jardim Botânico do Rio de
Janeiro / Escola Nacional de Botânica Tropical, 2013.
Orientador: Fabio Rubio Scarano
Coorientadora: Marinez Ferreira de Siqueira.
Bibliografia.
1.Ecologia da restauração. 2.Modelagem ecológica. 3.Trajetória
ecológica. I. Título. II. Escola Nacional de Botânica Tropical.
CDD 333.72
iii
RESUMO
A descrição ou previsão de trajetórias ecológicas de iniciativas de restauração ecológica é
um exemplo de como a teoria ecológica pode solucionar problemas práticos. Tais
trajetórias descrevem a via de desenvolvimento de um dado sistema ecológico ao longo do
tempo, e englobam uma ampla, porém limitada, potencialidade de expressões ecológicas,
descritas através de atributos estruturais e funcionais. Trajetórias ecológicas podem ser
investigadas através do emprego de ferramentas preditivas, como por exemplo, modelos
ecológicos. O uso de modelos ecológicos é recorrente na literatura, porém, são mais
observados no contexto da Ecologia do que em estudos de caráter aplicado à restauração de
ecossistemas. Esta tese revisa a aplicação de modelos ecológicos à previsão de trajetórias
ecológicas em iniciativas de restauração ecológica, examina atributos biológicos
habitualmente utilizados por pesquisadores brasileiros para previsões intuitivas de cenários
futuros e, por fim, integra essas duas abordagens ao aplicar um modelo preditivo de
trajetória ecológica a um projeto de reabilitação ecológica em igapó amazônico. Assim,
esse trabalho visa construir um continuum que integra a base teórica ecológica com a
vertente aplicada da restauração de ecossistemas. Os principais resultados encontrados
foram: (a) os esforços de restauração de ecossistemas utilizaram prioritariamente análises
estatísticas ao invés da modelagem ecológica; ademais, foram poucos os estudos que
projetaram estados futuros destes esforços, (b) os atributos biológicos freqüentemente
utilizados por pesquisadores brasileiros para avaliar o sucesso de esforços de restauração
em diferentes biomas apresentam uma sobreposição com atributos biológicos utilizados
para descrever e prever a dinâmica da vegetação no contexto dos estudos ecológicos, (c) o
processo de reabilitação da floresta inundável amazônica obteve sucesso em termos de
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composição florística, estrutura e diversidade. Porém, a reabilitação ecológica do igapó
amazônico ainda não atingiu o objetivo de estabelecer uma floresta auto-sustentável, (d) o
processo de reabilitação não necessáriamente conduzirá as florestas artificiais a um estado
similar àquele encontrado nas florestas naturais de igapó.
Palavras-chave: Modelagem ecológica, Ecologia da Restauração, trajetória ecológica,
previsão de estados futuros, continuum prática-teoria.
v
ABSTRACT
The description and prediction of ecological trajectories in ecological restoration initiatives
is an example of how ecological theory can assist the solution of practical problems. These
trajectories describe the development of a given ecosystem over time, and encompass a
wide, but limited, ecological expressions, described by structural and functional attributes.
Ecological trajectories can be investigated through the use of predictive tools, such as
ecological models. The use of ecological models is recurrent in the literature, however, they
are mostly observed in the context of ecology than in the context of ecosystem restoration
studies. This thesis reviews the application of ecological models to predict ecological
trajectories in ecological restoration initiatives, examines biological attributes commonly
used by Brazilian researchers to intuitively predict future scenarios, and finally integrates
these two approaches by applying a predictive model of ecological trajectory on a
rehabilitation project of an Amazonian flooded forest. Thus, this study aims to build a
continuum that integrates the theoretical aspects with applied practices of ecological
restoration. The principal results found here were: (a) ecological restoration projects
applied mainly statistical analysis instead of ecological modelling; few studies projected
future states of these projects, (b) biological attributes frequently used by Brazilian
researchers to assess restoration success in different biomes showed a overlap with
biological attributes used to describe and predict vegetation dynamics in the context of the
ecological studies, (c) the rehabilitation process of the flooded Amazon forest has been
successful in terms of floristic composition, structure and diversity. However, the
ecological rehabilitation has not reached yet the goal of establishing a self-sustainable
vi
forest, (d) not necessarily the rehabilitation process will lead artificial forests to a state
similar of the natural igapó forests.
Key words: Ecological modeling, Ecological Restoration, ecological trajectory, future
states prediction, continuum between theory and practice.
vii
AGRADECIMENTOS
Agradeço ao meu orientador Fabio Rubio Scarano pela generosidade, apoio,
dedicação, e acompanhamento do meu processo de crescimento e o amadurecimento
profissional. Agradeço também à Marinez Siqueira por aceitar o convite da minha
coorientação. Seu olhar crítico foi fundamental na estruturação desta tese. Obrigada pela
confiança, incentivo, paciência e apoio, principalmente nos momentos em que fraquejei.
Agradeço ao corpo docente e administrativo do Programa de Pós-Graduação Stricto
Sensu em Botânica Diversidade Vegetal: Conhecer e Conservar - Escola Nacional de
Botânica Tropical, e em especial à Hevelise Peregrino pelas orientações, lembranças de
prazos e palavras de suporte em momentos críticos do doutorado.
Agradeço o apoio financeiro do Núcleo de Tecnologias de Recuperação de
Ecossistemas (NUTRE) durante os dois primeiros anos da pesquisa para o desenvolvimento
das atividades acadêmicas e para a realização da “I Oficina sobre Trajetórias Sucessionais
de Ecossistemas em Restauração”, e agradeço a bolsa concedida nos dois últimos anos do
doutorado pelo Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).
Agradeço à Mineração Rio do Norte pelo apoio logístico durante as saídas de campo
realizadas em Porto Trombetas, e aos pesquisadores Francisco Esteves, Reinaldo Bozelli,
Luiz Roberto Zamith e Marcos Paulo Barros pelo trabalho em conjunto e agradável
convívio. Agradeço também ao Jerônimo e Eraldo pela ajuda nas atividades de campo,
pelas sugestões, e pelos momentos de descontração.
Agradeço o incentivo, as sugestões, e o apoio dos amigos Mário Garbin, Andrea
Sánches-Tapia. Em especial agradeço a Rodolfo de Abreu pela ajuda nas análises
viii
estatísticas e a Jerônimo por ter tido a paciência e generosidade de rodar o modelo
matemático comigo passo-a-passo.
Agradeço aos amigos que me ajudaram a manter a calma durante a minha
caminhada: Tarsila Menezes (in memorian) e todos os Quixotescos e Quixotescas; à “galera
da faculdade”; à Débora Aranha, Marina Freire e Gustavo Barreto; aos amigos da “Equipe
Fixa” e todos os colegas de trabalho; e todos aqueles que levaram a sério a missão “Dani:
sai um pouco do casulo”.
Por fim, agradeço o carinho, o apoio, o incentivo, a paciência e as orações da minha
querida mãe, da Tia Maria, e do meu pai.
ix
SUMÁRIO
Introduction
12
Capítulo I - The application of ecological models to predict vegetation future states
Abstract
23
Resumo
25
Introduction
27
Methods
33
Results
35
Discussion
52
References
56
Capítulo II – Biological attributes used to assess the success of Brazilian restoration
projects
Abstract
73
Resumo
75
Introduction
77
Methods
79
Results
80
Discussion
86
Appendix 1
88
Appendix 2
89
x
References
90
Capítulo III – Previsão de estados futuros de florestas artificiais de igapó (Porto
Trombetas, Pará, Brasil)
Resumo
93
Abstract
95
Introdução
97
Materiais e métodos
100
Resultados
120
Discussão
169
Apêndice 3
172
Apêndice 4
173
Referências Bibliográficas
174
Final remarks
179
Anexo 1 - Tropical Artificial Forests
183
Anexo 2 - Modeling the Success of Restoration in Tropical Ecosystems
210
xi
INTRODUCTION
12
INTRODUCTION1
Land use changes, and consequently natural habitat loss, represent the main
driver of biodiversity loss worldwide (Alkemade et al. 2009). It is propelled by the
increasing human population size and by the accelerating need for resources, which
demands agricultural lands, pastures, plantations, built areas and infrastructure (Hanski
2011). Besides creating novel ecosystems which have their own structural and
functional characteristic, biodiversity loss impacts the goods and services provided by
ecosystems, such as food, fuel, climate control, water cycling, erosion and sediment
retention, nutrient cycling, and soil formation (Secretariat of the Convention on
Biological Diversity 2000).
In face of the global threat to species and ecosystems, in 1992 the first global
agreement on the conservation and sustainable use of biological diversity - the
Convention on Biological Diversity (CBD) - was created. In its Strategic Plan the CBD
adopted the target “to achieve by 2010 a significant reduction of the current rate of
biodiversity loss at the global, regional and national level as a contribution to poverty
alleviation and to the benefit of all life on Earth” (Secretariat of the Convention on
Biological Diversity 2000). The 2010 Biodiversity target was endorsed by the World
Summit on Sustainable Development in 2004, and incorporated into the Millennium
Development Goals (MDGs) (United Nations 2010). However, little progress toward
the 2010 target has been made. Studies showed that indicators of biodiversity status
continue to decline, and that the drivers of biodiversity loss continue to increase despite
international efforts (Alkemade et al. 2009, Butchart et al. 2010, United Nations 2010).
1
Partes do texto estão publicadas em: Capossoli, D.J., Sansevero, J.B.B., Garbin, M.L. & Scarano, F.R. 2009.
Tropical Artificial Forests, in Tropical Biology, edited by Fabio Rubio Scarano and Ulrich Luttge, in Encyclopaedia
of Life Support Systems (EOLSS), Developed under the Auspices of the UNESCO, Eolss Publishers, Oxford, UK.
[http://www.eolss.net] (vide Anexo 1 desta tese).
13
Those facts can undermine the achievement of the 2015 MDGs, since any reduction in
extreme poverty will be attained only if environmental sustainability is also achieved
(Sachs et al. 2009).
Considering that social and economic sustainability are underpinned by
ecological sustainability, and that the actual trend in biodiversity loss must be reversed
to ensure human survival on Earth, the parties to the Convention on Biological Diversity
adopted the “2011-2020 Strategic Plan for Biodiversity”. One of the 20 goals of this
plan is to restore at least 15% of degraded land in the world by 2020 (Secretariat of the
Convention on Biological Diversity 2010, Mittermeier et al. 2010). Ecological
restoration is imperative for the reestablishment of biodiversity and the provision of
ecosystem services, and it is estimated that over 1 billion hectares of previously forested
areas could be restored, corresponding to about 6% of the total area of the planet
(Global Partnership on Forest Landscape Restoration 2011). Restoration projects, in
general, are successful when the goal is the conservation of biodiversity (Lindenmayer
et al. 2010) and the improvement of ecosystem services (Benayas et al. 2009).
Nevertheless, if properly done they can also increase economic opportunities and
benefits, while enhancing the social, cultural, psychological and spiritual aspects of
human well-being (Aronson et al. 2006).
Ambitious efforts are being made to restore forests, ecosystem services, and
biodiversity around the world. An example of a global scale effort to promote ecological
restoration on regional and local scales is the “New York Declaration” adopted by
Botanic Gardens around the world. The actions support Target 15 of the CBD’s
Strategic Plan, which is the target relating to ecological restoration (Botanic Gardens
Conservation International 2011). National efforts are emerging too. For instance, in
Brazil different stakeholders such as NGOs, governments, corporations, universities,
14
research centers and farmers are joining forces to promote the restoration of 15 million
hectares of Atlantic forest by 2050 through The Atlantic Forest Restoration Pact (The
Atlantic Forest Restoration Pact 2011).
The growing demand for ecological restoration is a major challenge for
Restoration Ecology, the science that develops and tests its theoretical framework
(Palmer et al. 1997). This young science has two major demands: the expansion of the
conceptual basis that guides restoration efforts, and the development of better predictive
tools (Suding et al. 2004). One path to achieve epistemological maturity of Restoration
Ecology is through a better understanding of ecological trajectories of restoration
projects and the capacity in predicting the time required to attain pre-established
conditions based on reference systems. The ecological trajectory describes the path of
development of an ecosystem over the time. In the field of Restoration Ecology this
course begins with the degraded ecosystem and culminates in the desired state,
expressed as goals that include reference values. It consists of all biotic and abiotic
ecological attributes, and can be monitored by sequential measurements of different
ecological variables. When plotted, these information can show trends that confirm or
not if the restoration effort follows the desired trajectory (SER 2004).
Ecological modelling is a useful tool for understanding and predicting vegetation
dynamics, and thus, ecological trajectories. Numerous models have been published for
these purposes (Liu & Ashton 1995). Models are verbal, visual (e.g. diagram) or
mathematical (e.g. expressions, equations, coefficients) representations of a given
object, idea, or condition. They may be conceptual or quantitative. The latter include
mathematical expressions, and therefore are used to formulate predictions. In this case,
they are called predictive models (Jackson 2000). Ecological models have the ability to
capture the reality of recovery systems, and so, its use is promising in the study of
15
ecological trajectories, and can provide critical assistance in the decision-making
(Anand & Desrochers 2004). Nevertheless, models can be considered an integral part of
Restoration Ecology (Urban 2006). The application of modelling in Restoration
Ecology can help to predict the development pathway and can provide information
about changes or adjustments on restoration activities (Twilley et al. 1998).
Artificial forests - used here as synonym of the terms ‘planted’ and ‘humanmade forests’, are defined as forests that are not produced by nature spontaneously,
constitute a major challenge to the scientific community, environmental agencies and
private initiative due to the high variation among such types of forests. Artificial forests
differ from native forests in that they comprise both non-native and native tree species
and differ in structure, composition and intensity of management and because of the
orderliness and uniformity that they often show. Actually, they cover globally about 2
% of land area, which represents 7 % of global forest area (about 300 million hectares).
There is a handful of reasons to implement artificial forests: (i) to compensate
ecological and economic losses as well as social impoverishment caused by
deforestation; (ii) to supply raw materials for industry such as pulp, paper and highquality products for both, domestic uses and exportation; (iii) to restore, recover and
rehabilitate degraded sites in order to increase biological diversity and/or ecosystem
services as well as genetic diversity; (iv) the higher wood productivity of planted forest
when compared to native forests; and (v) other purposes such as rural development, to
provide firewood, windbreaks, protection of water sources for irrigation, and also be
carbon sequestration and storage.
Restoration projects generally involve different stakeholders, and rarely are fully
subsidized by governments. In this sense, the private sector becomes an important
player. The sector faces the challenge of introducing the most appropriate operational
16
practices that ensure productivity to the company, but at the same time conserve
biodiversity. Companies committed to the environment can support conservation and
restoration initiatives. Thus, the implementation of protective forests can be seen as a
business opportunity, able to promote competitive advantage, differentiation strategy, at
the same time that it aggregates environmental responsibility to the company and
contributes to long-term sustainability (Secretariat of the Convention on Biological
Diversity 2006).
The objective of this thesis is to evaluate tools used to predict future scenarios of
ecosystems restoration initiatives. This thesis aims to strengthen the bridge between
theory and practice related to the ecological restoration of ecosystems. Therefore, it is
structured in the following way:
(i)
The first chapter reviews tools and biological variables more often
applied to the description and prediction of ecological trajectories and vegetation
dynamics;
(ii)
The second chapter compares biological indicators frequently used to
predict ecological trajectories with those used by Brazilian researchers to
evaluate the development of restoration efforts in the country;
(iii)
The third chapter applies an ecological model developed by our working
group to predict future states of an artificial igapó forest, planted to rehabilitate
an impacted Amazonian lake;
(iv)
Finally, in the conclusion chapter, a discussion is made about the
applicability and generality of ecological models and its biological variables in
tropical ecosystems.
17
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Alkemade, R., van Oorschot, M., Miles, L., Nellemann, C., Bakkenes, M. & Brink, B.
2009. GLOBIO3: A Framework to Investigate Options for Reducing Global
Terrestrial Biodiversity Loss. Ecosystems, 12: 374–390.
Anand, M. & Desrochers, R.E. 2004. Quantification of restoration success using
complex systems concepts and models. Restoration Ecology, 12 (1): 117-123.
Aronson, J., Clewell, A.F., Blignaut, J.N. & Milton, S.J. 2006. Ecological restoration: A
new frontier for nature conservation and economics. Journal for Nature
Conservation, 14, 135–139.
Benayas, J.M. R, et al. 2009. Enhancement of biodiversity and ecosystem services by
ecological restoration: a meta-analysis. Science, 325: 121-124.
Botanic Gardens Conservation International
(BGCI).
2011.
The Ecological
Restoration on a Global Scale: Harnessing the Power of the World’s Botanic
Gardens: The New York Declaration. New York, USA. Available in:
http://www.bgci.org/resources/news/0790/. Access in: 30/05/2011.
Butchart, S.H.M., Walpole, M.,
Collen, B., van Strien, A., Scharlemann, J.P.W.,
Almond, R.E.A., Baillie, J.E.M., Bomhard, B., Brown, C., Bruno, J., Carpenter,
K.E., Carr, G.M., Chanson, J., Chenery, A.M., Csirke, J., Davidson, N.C.,
Dentener, F., Foster, M., Galli, A., Galloway, J.N., Genovesi, P., Gregory, R.D.,
Hockings, M., Kapos, V., Lamarque, J-F., Leverington, F., Loh, J., McGeoch, M.A.,
McRae, L., Minasyan, A., Morcillo,M.H., Oldfield, T.E., Pauly, D., Quader, S.,
Revenga, C., Sauer, J.R., Skolnik, B., Spear, D., Stanwell-Smith, D., Stuart,S.N.,
Symes, A., Tierney, M., Tyrrell,T.D., Vié, J-C., Watson, R. 2010. Global
Biodiversity: Indicators of Recent Declines. Science, 328, 1164-1168.
18
Global Partnership on Forest Landscape Restoration (GPFLR). 2011. A world of
opportunity. Available in: http://pdf.wri.org/world_of_opportunity_brochure_201109.pdf . Acess in: 30/05/2011.
Hanski, I. 2011. Habitat Loss, the Dynamics of Biodiversity, and a Perspective on
Conservation. AMBIO, 40: 248–255.
Jackson, L.J., Trebitz, A.S & Cottingham, K.L. 2000. An Introduction to the Practice of
Ecological Modeling. BioScience, 50: 694-706.
Lindenmayer, D.B., Steffen, W., Burbidge, A.A., Hughes, L., Kitching, R.L., Musgrave,
W., Smith, M.S. & Werner, P.A. 2010. Conservation strategies in response to rapid
climate change: Australia as a case study. Biological Conservation, 143 (7): 15871593.
Liu, J. & P.S. Ashton. 1995. Individual-based simulation models for forest succession
and management. Forest Ecology and Management, 73 (1-3): 157–175.
Mittermeier, R.A. Baião, P.C., Barrera, L., Buppert, T., McCullough, J., Langrand, O.,
Larsen, F.W. & Scarano, F.R. 2010. O Protagonismo do Brasil no Histórico Acordo
Global de Proteção à Biodiversidade. Natureza & Conservação, 8 (2): 1-4.
Palmer, M.A., Ambrose, R.F., & Poff, N.L. 1997. Ecological Theory and Community
Restoration Ecology. Restoration Ecology, 5 (4): 291-300.
Sachs, J.D., Baillie, J.E.M., Sutherland, W.J., Armsworth, P.R., Ash, N., Beddington, J.,
Blackburn, T.M., Collen, B., Gardiner, B., Gastonm K.J., Godfray, H.C.J., Green,
R.E., Harvey, P.H., House, B., Knapp, S., Kumpel, N.F., Macdonald, D.W., Mace,
G.M., Mallet, J., Matthews, A., May, R.M., Petchey, O., Purvis, A., Roe, D., Safi,
K., Turner, K., Walpole, M., Watson, R. & Jones, K.E. 2009. Biodiversity
conservation and the Millennium Development Goals. Science, 325: 1502-1503.
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Secretariat of the Convention on Biological Diversity. 2000. Sustaining life on earth:
how the Convention on Biological Diversity promotes nature and human wellbeing. Available in: http://www.cbd.int/convention/guide. Access in: 30/05/2011.
Secretariat of the Convention on Biological Diversity. 2006. Global Biodiversity
Outlook
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pages
Available
in:
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Biodiversity 2011 – 2020 and the Aichi Targets: living in harmony with nature.
Available
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http://www.cbd.int/doc/strategic-plan/2011-2020/Aichi-Targets-
EN.pdf. Access in: 30/05/2011.
SER (Society for Ecological Restoration). 2004. The SER International Primer on
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Restoration.
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04/10/2010.
Suding, K.N., Gross, K.L. & Houseman, G.R. 2004. Alternative states and positive
feedbacks in restoration ecology. Trends in Ecology and Evolution, 19 (1): 46-53.
The Atlantic Forest Restoration Pact. 2011. Mission and objectives. Available in:
http://www.pactomataatlantica.org.br. Access in: 30/05/2011.
Twilley, R.R., Rivera-Monroy, V.H., Chen, R. & Botero, L. 1998. Adapting an
Ecological Mangrove Model to Simulate Trajectories in Restoration Ecology.
Marine Pollution Bulletin, 37 (8-12): 404-419.
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20
Urban, D.L. 2006. A modeling framework for Restoration Ecology. In: Foundations of
Restoration Ecology. Eds: Falk, D.A., Palmer, M.A. & Zedler, J.B. Island Press.
21
CAPÍTULO I
THE APPLICATION OF ECOLOGICAL
MODELS TO PREDICT VEGETATION
FUTURE STATES
22
ABSTRACT
(The application of ecological models to predict vegetation future states) The
extension of the conceptual basis to guide restoration efforts and the development of
better predictive tools – for exemple, ecological models, are current demands of
Restoration Ecology. Ecological models when applied in the context of Restoration
Ecology can capture the reality of the restored system and predict the future states of
these efforts. They can also assist the evaluation of the success of such initiatives and
simulate more realistic endpoints. However, the use of ecological models is frequent in
the domains of Ecology and Forestry sciences. The pressing need to monitor, assess,
and quantify the success of restoration efforts requires the development and application
of new methods to embrace the complexity of ecological ecological trajectories.
Therefore, this study aimed to review ecological models, statistical analysis and
biological attributes frequently used in the study of vegetation dynamics and ecological
trajectories, responding the following questions: (1) how similar are the set of
ecological models, statistical analysis, and biological variables used in Ecology with
those used in Restoration Ecology? and (2) Among ecological models, statistical
analysis, and biological variables compiled in this review, which are the most suitable
for application in Restoration Ecology? To identify relevant papers ScienceDirect and
SCOPUS databases were consulted. Inclusion and exclusion criteria were applied, and
resulted in an analysis of 72 articles. This review showed that ecological modelling has
more often focused on native vegetation dynamics from temperate zones of the
Northern Hemisphere, than on artificial forests in tropical zones; studies conducted in
the perspective of “Restoration” and that dealt with artificial forests frequently used
statistical analyses rather than ecological modelling; few restoration efforts projected
successional trajectories and future scenarios; and multiple biological attributes have
23
been used in the reviewed papers. The results suggests that the ecological literature
owns a variety of appealing ecological models and input biological variables that could
be used to guide restoration efforts in the sense of a more robust prediction of
vegetation future states. This great variation in the models and attributes suggests that
cases should be analyzed one by one. Consequently, different models should be applied
in different cases.
Key words: Prediction, vegetation dynamics, ecological trajectory, Restoration
Ecology.
24
RESUMO
(A aplicação de modelos ecológicos para prever estados vegetacionais futuros) A
ampliação da base conceitual que guia os esforços de restauração e o desenvolvimento
de melhores ferramentas preditivas - por exemplo, modelos ecológicos, são demandas
atuais da Ecologia da Restauração. Modelos ecológicos quando aplicados no contexto
da Ecologia da Restauração podem capturar a realidade dos sistemas restaurados e
prever os estados futuros destes esforços. Eles também podem auxiliar a avaliação do
sucesso destas iniciativas e simular pontos finais mais realísticos. Contudo, o uso de
modelos ecológicos é mais freqüente no domínio da Ecologia e Silvicultura. A
necessidade atual de monitorar, avaliar e quantificar o sucesso dos esforços de
restauração requer o desenvolvimento e aplicação de novos métodos que compreendam
a complexidade das trajetórias ecológicas. Sendo assim, este estudo teve como objetivo
revisar modelos ecológicos, análises estatísticas e atributos biológicos frequentemente
utilizados nos estudos de dinâmica vegetacional e trajetórias ecológicas, respondendo as
seguintes respostas: (1) os modelos ecológicos, análises estatísticas e atributos
biológicos utilizados no contexto da Ecologia são similares aos utilizados no âmbito da
Ecologia da Restauração? e (2) Dentre os modelos ecológicos, análises estatísticas e
atributos biológicos analisados, quais são mais adequados à Ecologia da Restauração?
Para identificar os artigos relevantes as bases de dados ScienceDirect e SCOPUS foram
consultadas. Critérios de inclusão e exclusão foram aplicados, resultando em 72 artigos
considerados nesta revisão. Esta revisão mostrou que a modelagem ecológica focou com
maoir frequência a dinâmica de vegetações nativas das zonas temperadas do hemisfério
norte, do que as florestas artificiais tropicais; estudos conduzidos na perspectica da
“Restauração” e que lidaram com florestas artificiais utilizaram frequentemente análises
estatísitcas ao invés da modelagem ecológica; poucos esforços de restauração
25
projetaram as trajetórias ecológicas e cenários futuros; e múltiplos atributos biológicos
foram utilizados nos artigos revisados. Os resultados sugerem que a literatura ecológica
possui uma ampla variedade de modelos ecológicos e atributos biológicos que poderiam
ser utilizados para guiar os esforços de restauração em busca de predições de estados
vegetacionais futuros mais robustos. Esta grande variedade de modelos e atributos deve
ser analisada caso a caso. Consequentemente, diferentes modelos devem ser aplicados a
diferentes situações.
Palavras-chave: Predição, dinâmica vegetacional, trajetória ecológica, Ecologia da
Restauração.
26
INTRODUCTION2
Almost two-thirds of the world’s ecosystems have been directly converted by
human activities, or have been degraded to some extent. Thus, major improvements and
efforts are needed to restore and manage ecosystems (Millennium Ecosystem Assess
2005). Restoration costs are high, and can range from hundreds to thousands, or even
hundreds of thousands of USD per restored hectare (UNEP 2010). Difficulties and
expenses are mainly associated to restoration goals (Hobbs 2007). In fact, costs of
restoration are almost 10-fold that of effectively managed protected areas. Nonetheless,
these numbers are small compared to the estimated costs of losing these ecosystems
services in the long-term. Restoration when well-planned can represent a profitable
public investment, and can act as an engine of economy and a source of green
employment (UNEP 2010).
Ecological restoration is the process of assisting the recovery of an ecosystem
that has been degraded, damaged, or destroyed (SER 2004). Different goals can guide
restoration efforts. According to Ehrenfeld (2001) restoration projects can be speciesbased (recovery of target species populations), ecosystem-based (recovery ecosystem
functions and proccess) or ecosystem service based (recovery of ecosystem services that
benefit humankind). However, one common goal among those initiatives is the recovery
of autogenic processes to the point where anthropogenic assistance is no longer needed
(SER 2004).
Considering the established goals, restoration efforts may produce different
outcomes. In some cases, recovery can be rapid and complete; in others, recovery may
be partial, or may not occur (Suding 2011). According to Maron et al. (2012) restoration
hole in compensating for losses of biodiversity still requires evidence support.
2
Capítulo formatado de acordo com as regras de publicação do periódico “Natureza e Conservação”.
27
Restoration efforts success evaluation process is hampered by limited monitoring data,
limited access to monitoring data, and lack of consensus regarding the standardization
of evaluation criteria (Suding 2011).
The process involves four important tasks: (a) develop a conceptual model about
the system ecological behavior, (b) analyze how far from the target or desired condition
the system is through an assessment of the system’s current state, (c) to conduct
management experiments to guide the system in the desired condition; and (d) to assess
the success of the experimental intervention through monitoring (Urban 2006). The
established ecosystem should be capable to respond in a dynamic way to environmental
changes (Walker & del Moral 2008).
However, when ecological restoration is made impossible, the alternative is to
rehabilitate the degraded ecosystem. In this case, new functions and conditions can be
established, emphasizing the recovery of ecosystem processes, productivity and services
without an explicit intention to restore the composition and structure of the original
ecosystem (SER 2004, Costa et al. 2010, de Moraes et al. 2010).
Restoration Ecology is the field that develops and tests the theoretical
framework of restoration efforts (Palmer et al. 1997). It provides concepts, models,
methodologies and tools for practitioners in support of their practice. Because it is a
young science, restoration literature is highly fragmented and disjointed. Many
conceptual approaches appear to offer little value to practitioners, as well as many
practical approaches are characterized by minimal conceptual content (Lunt 2001).
According to Sudding et al. (2004), Restoration Ecology has two important demands the extension of the conceptual basis to guide restoration efforts and the development of
better predictive tools.
28
The extension of the conceptual basis to guide restoration efforts can be
achieved by including the knowledge generated in restoration projects about ecosystem
functioning (Lunt 2001), and by the application of some concepts of other sciences,
such as Mathematics (Anand & Desrochers 2004) and Ecology (Palmer et al. 1997).
The theoretical structure of Mathematics helps restoration scientists and practitioners to
understand the behavior of complex systems, expands the range of approaches that
attempt to understand the implications of non-linearity and emergence of unexpected
behavior in dynamic systems (Anand & Desrochers 2004), increases the possibilities of
observation and interpretation of biological data enabling closer to the reality of
ecological systems (Souza & Buckeridge 2004), and assists the description of ecological
trajectories (SER 2004).
The diverse theoretical body of Ecology can be used to support restoration
projects as well (Palmer et al. 2006). The understanding of assemblage rules is an
essential step to ensure the future success of restoration projects (Halle & Fattorini
2004, Temperton & Hobbs 2004, Young et al. 2005). The application of the knowledge
about ecological succession can accelerate the response of a degraded area to
restorations efforts, thus, maximizing economy and efficiency (Walker & del Moral
2008).
The second demand of Restoration Ecology, the development of better
predictive tools, can be achieved through the application of models (either exploratory
or predictive sense; Perry & Millington 2008) to understand and predict the ecological
trajectories of recovered systems. In fact, the common view among restoration
ecologists to look to the future (Lunt 2001) may benefit from this promising approach.
Core subjects to Restoration Ecology – such as the determination of the final destination
of the restoration effort, how this could be affected by initial conditions and stochastic
29
environmental disturbances (Anand & Desrochers 2004), and the description and
prediction of ecological trajectories - could be addressed and developed under such
perspective.
Models represent real-world phenomena in terms of mathematical equations, and
from them, useful information for understanding and predicting real systems can emerge
(Avula 2003). They have been successfully used to generate and test hypotheses, to
analyze complex systems, to synthesize multidisciplinary knowledge, and to guide
assessment or optimization of decision making processes (Wu 1994). They are also
applied to reveal hidden assumptions, and to recognize gaps of knowledge (Jackson
2000).
Models are important tools for Restoration Ecology (Palmer et al. 2006). They
can capture the reality of the recovered system more accurately and demonstrate how
predictable are the results of a restoration effort (Palmer et al. 2006), assist the
evaluation of the success of such initiatives, simulate more realistic endpoints (Twilley
et al. 1999), and infer the necessary conditions needed to prompt the ecological system
to follow its natural trajectory (Anand & Desrochers 2004). However, this approach has
received little attention from restoration ecologists (Anand & Desrochers 2004). There
are few examples available in the literature that applies specific modelling techniques to
restoration efforts (Michener 1997). Instead, this kind of approach is more common
within Ecology and Forestry sciences, in topics not necessarily involving directly
practical applications such as restoration.
Vegetation modelling studies place great emphasis on the vegetation component,
since it provides the basic energy for biological activity as a whole, and also provides
the mechanical structure of biological environment in that other organisms live (Jeffers
30
1988). In fact, forest ecosystems harbor two-thirds of terrestrial biodiversity
(Millennium Ecosystem Assessment 2005).
Modelling vegetation dynamics involves dealing with complexity, and other
challenges, such as scale, representativeness, evaluation, validation procedures, use of
adequate datasets to validate model processes, linking statistical approaches and
modelling approaches, and linking theoretical and applied approaches (Perry & Enright
2006, Scheller & Mladenoff 2007, Larocque et al. 2011), and plant responses to
disturbances (Purves & Pacala 2008).
However, these difficulties have not prevented the development and refinement
of these tools over the last three decades (Shugart et al. 1988). The advent of
mathematical models on vegetation dynamics in the early 1960s, provided a precious
and manipulatively formalization about successional mechanisms (Shugart 1984), and
over the past three decades there has been a remarkable development of those computer
models (Shugart et al. 1988). Prediction has become an important way of testing the fit
between theory and observed phenomena (Pickett et al.1989), and nowadays, much of
the advance in the theory of vegetation dynamics are related to forest modelling
(Terradas 2005).
A wide range of models have been developed to describe vegetation dynamics,
hence, there is no ‘‘best modelling approach’’ (Urban & Shugart 1992). Each modelling
approach has its pros and cons, therefore the context of its purpose should be considered
(Carmel et al. 2001). Reviews about vegetation dynamics usually handle subsets of
models (Busing & Mailly 2004, Peng 2000, Scheller & Mladenoff 2007) and its
implementation in the context of forestry management (Taylor et al. 2009).
The pressing need to monitor, assess, and quantify the success of restoration
efforts (Holl & Howarth 2000) requires the development and application of new
31
methods to embrace the complexity of ecological recovery trajectories. Additionally, it
is important to emphasize the lack and the need for a summary on the application of
modelling in the context of Ecological Restoration. Since ecological modelling has
become a global activity, there is a need for nations to publish their experiences in
relation to modelling.
The goal of the present study is to evaluate ecological models, statistical analysis
and biological variables (species traits and community attributes) used in the study of
vegetation dynamics and ecological trajectories, responding the following questions:
(i)
Is there similarity between the set of ecological models, statistical analysis,
and biological variables used in Ecology and those used in Restoration
Ecology?
(ii)
Among ecological models, statistical analysis, and biological variables
compiled in this review, which are the most suitable for application in
Restoration Ecology?
32
METHODS
To identify papers relevant to this study, the ScienceDirect and SCOPUS
databases were consulted. Search terms used were divided into three blocks of words:
(a) model*, predict*, and simulat*, (b) forest*, vegetation*, and stand, and (c)
dynamics, trajectory, and pathway. The search was carried out by the combination of
the words of each block (for example: model* + vegetation* + dynamics). The operator
(*) allowed the access to both the word root and its derivations. Recent papers (2000today) were selected if they contained in the title, abstract and keywords at least one of
the terms of each block of words. The search resulted in about 900 papers. Articles that
dealt with non-vegetation focal systems, population models, ecosystem models,
ecophysiological models, dynamic vegetation global models, distribution models,
growth-and-yield models, gap distribution models, and landscape dynamics models
were excluded. The remaining articles (n=72) were analyzed according to Table 1.
Therefore, this review is not exhaustive, but representative of modeling of future
vegetation scenarios – either at natural settings or restoration settings.
33
Table 1. Criteria used in the characterization of the articles included in the literature review.
Geographic region
Africa, Central America, North America, South America, Asia, Europe, Oceania
Boreal forest, Grassland, Mediterranean vegetation, Temperate forest, Tropical forest, Wetlands
Biome
>1 biome
General characteristics
Vegetation origin
Native (Natural, Disturbed, Managed), Artificial, Hypothetic, Mixed
Research line
Ecology, Restoration
Application purpose
Exploratory, Predictive
Ecological model
Transition models, Gap models, Hybrid models, Others
Linear comparison (regression, correlation, time trajectories), Group comparison (analysis of
Methodological tool
Statistical analysis
variance, t test, Kruskall-Wallis), Ordination (Detrended correspondence analysis (DCA),
canonical correspondence analysis (CCA), cluster analysis), Hybrid Analysis
Biological variables
Species-specific traits
Dispersion, establishment, persistence
Community attributes
Structure, diversity
Environmental drivers
Climatic data, edaphic data, disturbance
34
RESULTS
1.
General characteristics
Of the 72 papers included in this review, 36% (n=26) were conducted in North
America, 33% (n=24) in Europe, 13% (n=9) in Asia, 7% (n=5) in Oceania, 6% (n=4) in
South America, 4% (n=3) in Africa and 1% (n=1) in Central America. The geographic
regions that presented a greater number of studied biomes were Oceania, North
America, and Europe (Table 2). Most studies took place in Temperate Forest (n=28,
39%). Tropical Forests were addressed in 14% (n=10) (Figure 1).
Native forests were addressed in 76% (n=55) of the papers, whereas artificial
forests were focused in only 8% (n=6) of the sampled studies. While native forests were
the focus of studies in all biomes, artificial forests were targeted only in studies
conducted in Temperate Forests, Boreal Forests and Grasslands (Figure 2).
Studies designed in the context of Ecology were more frequent (n=64; 89%) than
those designed in the framework of Restoration Ecology (n=8; 11%). Vegetation
dynamics were predicted in 68% (n=49) of articles and described in 32% (n=23) of the
cases. Tropical Forests were the target of Ecology-oriented studies only (Figure 3).
35
Figure 1: Proportion of papers included in this review per biome types.
36
Table 2: Diversity of biomes types considered in
papers included in this review.
Geographic region
Number of biomes
Africa
2
Asia
4
Central America
1
Europe
6
North America
6
Oceania
4
South America
1
37
Figure 2: Number of papers included in this review according to biome type and vegetation
origins.
38
(a)
(b)
Figure 3: Proportion of papers included in this review according to research line and biome
type: (a) Restoration ecology and (b) Ecology.
39
2.
Methodological tool
In 81% (n=58) of the sampled articles, ecological models were applied to the
study of vegetation dynamics, while statistical analyses were employed in 19% (n=14)
of the cases. The use of ecological models was ten times greater in the framework of
Ecology when compared to Restoration. The latter used more frequently statistical
analyses to study restoration processes (Figure 4).
Ecological models and statistical analyses were recurrent in the study of native
vegetation dynamics. Among papers conducted in artificial forests, statistical analyses
were more frequently used to describe or predict the successional trajectory (Figure 5).
Among papers performed in Tropical forests (N=10) 90% applied ecological models
(Figure 6).
Among ecological models, GAP models (n=33; 57%) and transition models
(n=17; 29%) were more abundant in the reviewed literature. Meanwhile, hybrid analysis
(n=8; 57%) was the most common statistical analyses employed in the study of
vegetation dynamics (Figure 7).
The identity of ecological models used by Restoration differed from those used
in Ecology, but the model CANOPY was used in both research lines. Ecological models
more often used in Ecology were: ZELIG, FORCLIM, and SORTIE (Table 3).
Among statistical analysis applied in the context of Restoration group
comparison and ordination were predominant in the study of ecological trajectories
(Table 4).
40
Figure 4: Number of papers included in this review according to methodological tool
(Ecological model and Statistical analysis) and research line (Ecology and Restoration).
41
(a)
(b)
Figure 5: Proportion of papers included in this review according to methodological tool and
vegetation origins: (a) ecological model types and (b) statistical analysis types.
42
Figure 6: Number of papers included in this review according to methodological tool
(Ecological model and Statistical analysis) and biome type.
43
(a)
(b)
Figure 7: Proportion of papers included in this review according to methodological tool. (a)
Ecological model types and (b) Statistical analisys types.
44
Table 3: Ecological models applied in articles compiled in this review to study vegetation
dynamics and ecological trajectory.
Ecology
Restoration ecology
ASTROMOD1
EDS20
CANOPY2
CANOPY21
DRYADES3
YAFSIM22
FAREAST4
FORCLIM5
FORGRA6
FORMIX7
FORRUS8
FORSPACE9
Gap models
GREFOS10
JABOWA11
LINKNZ12
MBI13
MOUNTAIN14
PPA15
SELVA16
SORTIE17
ZELIG18
No name19
Cellular automata23
Transition models
Markov chain24
State-and-Transition25
45
Table 3: Contined…
GAP model + Transition model26
Hybrid models
ZELIG + Frost27
Compartment model28
Distribution model29
Other models
Grid-based model30
Rule-based model31
Size-structure model32
GAP MODELS: 1 – Berardi 2002; 2 – Choi et al. 2001; 3 - Mailly et al. 2000; 4 - Xiaodong & Shugart 2005; 5 –
Busing et al. 2007, Risch et al. 2005, Weber et al. 2008, Wehrli et al. 2005; 6 - van der Meer et al. 2002; 7 - Huth &
Ditzer 2000, Huth & Tietjen 2007; 8 - Chumachenko et al. 2003; 9 - Kramer et al. 2003; 10 - Fyllas et al. 2007; 11 Ehman et al. 2002; 12 - Hall & Hollinger 2000; 13 - Picard & Franc 2001; 14 - Cordonnier et al. 2008; 15 - Purves et al.
2008; 16 - Verzelen et al. 2006; 17 - Coates et al. 2003, Tremblay et al. 2005; 18 - Larocque et al. 2006, Larocque et al.
2011, Pabst et al. 2008, Seagle & Liang 2001; 19 - Fyllas et al. 2010, Moraive & Robert 2003, Robert 2003, Wallentin et
al. 2008; 20 - Ngugi et al. 2011; 21 - Choi et al. 2007; 22 - Nuttle & Haefner 2007; TRANSITION MODELS: 23 Aassine & El Jah 2002, Alonso & Sole 2000, Colasanti et al. 2007, Favier et al. 2004, Vega & Montana 2011; 24 Augustin et al. 2001, Baltzer et al. 2000, Risch et al. 2009, Spathelf & Durlo 2001; 25 – Bashari et al. 2009, Bar
Massada et al. 2009, Gambiza et al. 2000, Joubert et al. 2008, Koniak & Noy-Meir 2009, Liang 2010, Perry & Enright
2007, Strand et al. 2009, Tahvonen et al. 2010; HYBRID MODELS: 26 - Acevedo et al. 2001; 27 - Ranson et al. 2001;
OTHER MODELS: 28 - Gillet 2008; 29 - Picard & Franc 2001; 30 - Birch et al. 2000; 31 - Glenz et al. 2008; 32 Umeki et al. 2008.
46
Table 4: Statistical analyses applied in articles compiled in this review to study
vegetation dynamics and ecological trajectory.
Ecology
Variance analysis6
Group comparison
Linear comparison
Restoration ecology
Regression analysis1
PCA7
Ordination
Linear comparison + Group
comparison2
Group comparison +
Linear comparison + Group
Ordination4
Hybrid analyses
comparison + Ordination3
Group comparison + Ordination4
Linear comparison + Ordination5
LINEAR COMPARISON: 1 - Carmel et al. 2001, Keith et al. 2007, Kurkowski et al. 2008; HYBRID
ANALYSIS: 2 - Anderson et al. 2005; 3 - Capitanio & Carcaillet 2008; 4 - Drury & Runkle 2006, Måren
et al. 2008, Rydgren et al. 2011; 5 - Fulton & Harcombe 2002, Lebrija-Trejos et al. 2010, Tzanopoulos et
al. 2007; GROUP COMPARISON: 6 – Gutrich et al. 2009; ORDINATION: 7 - Anand & Desrochers
2004.
47
3.
Of the 72 papers included in this review, 38% (n=27) combined 3 classes of
biological variables to study vegetation in flux, 26% (n=19) combined 2 classes, 21%
(n=15) used only 1 class, and 15% (n=11) combined 4 classes of biological variables.
While articles conceived through the lens of Ecology used frequently 3 classes
of biological variables to describe or predict vegetation dynamics, those conceived
through the lens of Restoration applied 1 to 3 classes of biological variables (Figure 8).
Regarding the identity of the biological variables used, “Persistence” and
“Establishment” were predominant as species-specific traits, while “Structure” was
predominant as community-level attribute in both research lines. In Statistical analysis,
Diversity was predominant in Restoration, and Structure was predominant in the context
of Ecology (Table 5). The main biological variables used in the studies of vegetation
dynamics and ecological trajectories can be visualized in Table 6.
48
(a)
(b)
Figure 8: Proportion of paper included in this review according to the number of biological
variable classes used: (a) Ecology and (b) Restoration ecology.
49
Table 5: Frequencies of type of biological variables included in papers compiled in this
review.
Biological variable class
Ecological
models
Restoration
Ecology
Dispersion
2
11
Establishment
1
19
Persistence
2
42
Structure
2
17
Diversity
0
5
1
36
Dispersion
0
1
Establishment
0
3
Persistence
0
2
Structure
3
5
Diversity
4
1
2
2
Statistical
analysis
50
Table 6. Main species traits and community attributes used in ecological
models and statistical analysis to describe and predict vegetation dynamics
and ecological trajectories from papers compiled in this review.
Biological variable class
Seed dispersal distance, seed production, dispersal
Dispersion
syndrome, diaspore size, seed size, seed mass, seed
longevity
Seedling establishment rate, seedling growth rate,
seedling survival rate, seedling mortality rate,
Establishment
sprouting capacity, seed fecundity rate, seedling
density, ability to fix nitrogen
Longevity, habitat, size, growth rate, survival rate,
mortality rate, stress tolerance, biomass, density, leaf
Persistence
parameters,
allometric
parameters,
phenology,
pollination syndrome, reproduction strategy, wood
density, deciduousness
Biomass, basal area, cover, dominance, density, size
Structure
distribution, vertical stratification
Diversity
Species composition, species richness, diversity index
Edaphic conditions, climatic conditions, topography,
disturbance type, growing season
51
DISCUSSION
This review showed that: (1) ecological modelling has more often focused on native
vegetation dynamics from temperate zones of the Northern Hemisphere, than on artificial
forests in tropical zones; (2) studies conducted in the perspective of “Restoration” and that
dealt with artificial forests frequently used statistical analyses rather than ecological
modelling; (3) few restoration efforts projected successional trajectories and future
scenarios; (4) multiple biological variables have been used in the reviewed papers, although
they can all be easily grouped into a few categories only. Moreover, the identity of these
variables did not differ between “Ecology” and “Restoration” (see Table 5 and Table 6).
With respect to the first item mentioned above, only 9% of the studies compiled in
this review were conducted in tropical countries. According to Stocks et al. (2008) this
geographical bias arises from the under representation of research emerging from tropical
countries, and can be related to the following factors: limited investments in education and
research, limited financial resources, limited infrastructure to research, political unrest, and
policy of attraction and retention of foreign researchers. The analysis of bibliometric
indicators, used to evaluate the results of investments in research and the publication of
scientific articles, demonstrates that China, India, Australia and Brazil – considering
tropical megadiversity countries alone, were ranked among the first 20 in scientific
production volume worldwide. The other megadiverse countries account for only 2% of
world scientific production (SCImago Journal & Country Rank 2011).
Another point that must be emphasized is that none of the articles elaborated
through the lens of “Restoration” compiled in this review were conducted in tropical
countries. Reviewing the use of ecosystem attribute to determine restoration success, Ruiz52
Jaen & Aide (2005) found that most studies were conducted in the US and Europe. The
authors suggest that this geographical bias was more related to environmental legislation
demands and available financial resources than the actual degradation state. Legal
instruments may motivate restoration efforts. Countries such as Brazil, USA, Australia and
Canada possess legal instruments that may induce restorations efforts. However,
specifically in Brazil, there is a debate within the academic and professional community
about what should be the best practice for a given ecosystem or ecosystem type (Aronson
2010, Aronson et al. 2011). Despite the debate, this limited geographical perspective can
negatively affect the task of generalizations and accurately assessing conservation priorities
(Stocks et al. 2008).
There is an untapped potential related to the application of ecological modelling in
restoration efforts. Usually, the two fundamental questions involving the scientific
assessment of restoration projects – determination of project goal and the evaluation of its
success, are analyzed through statistical tools to make inferences (Osenberg et al. 2006),
considering three approaches: (1) direct comparison between restored ecosystem
parameters and reference systems parameters, (2) evaluation of attributes of restored
ecosystems against a list of nine attributes that provide a basis for determining when the
restoration is complete, and (3) plotting restored ecosystems information to analyze if they
follow the desired trajectory (SER 2004). Restoration efforts should attempt to look to the
future, since restored ecosystems should be sustainable in the future and should have
multiple alternative goals and trajectories for unpredictable endpoints (Choi 2007).
However, predictions derived from predictive models require a deliberate causal structuring
which is based on ecological theory and must include a validation procedure (Legendre &
53
Legendre 2003). The lack of hypothesis testing and experimental design in restoration
efforts, the short temporal monitoring of restoration efforts, and the choice of reference
ecosystems are factors that impose difficulties in the application of ecological models in
restoration efforts, mainly in the tropical zone.
Nevertheless, no such difficulties prevented the application of ecological models in
restoration efforts. For example, a GAP model (CANOPY) was applied to simulate effects
of thinning on growth rates and development of old-growth structural features in secondgrowth northern hardwoods and investigate the effects of different restoration treatments
(Choi et al. 2007). The authors concluded that if a heavy thinning treatment were applied in
a forest stand, after 45 years, it would attain structural features of later stages of old-growth
forests. More recently, Ngugi et al. (2011) applied another GAP model (EDS) to model
vegetation growth dynamics in order to assist long-term planning and assess recovery
success. They found that the projected species composition closely approximated (90%
correspondence) the observed composition after 39-year period simulated. Other attribute
simulated were tree density, basal area and biomass. They showed different relative bias
compared to species composition projections.
Finally, as regards to the use of multiple biological variables in the reviewed papers,
structural communities attributes (basal area and biomass) were more frequent in the
studies conducted under the perspective of Ecology and Restoration Ecology. In fact, RuizJaen & Aide (2005) reviewing the use of ecosystem attributes for determining the
restoration success observed that the majority of studies compiled also used vegetational
structural measurements. In this case, the authors credited three factors: (1) legislative
demand, (2) the premise that fauna recovery follows vegetation structuring, and (3) facility
54
in measuring vegetation attributes. According to Ruiz-Jaen & Aide (2005), most of the
work used multiple indicators to measure the success of restoration, corroborating the result
found in this study.
It is worth noting that information about input biological variables used in
ecological models are often known only for a few species (Knevel et al. 2003), and remains
scattered over many sources. Currently, there are initiatives to compile and systematize that
information, mainly for temperate species. Those sources are available in various
languages, and they are collected and stored in different ways, but not always integrated
(Kleyer et al. 2008).
Although this review is not an exhaustive literature verification, the results found
here suggest that the ecological literature owns a variety of appealing ecological models
and input biological variables that could be used to guide restoration efforts in the sense of
a more robust prediction of vegetation future states. Depending on the original question or
objective, the studies revised used different ecological models to describe or predict the
dynamics of vegetation. This great variation in the models and their input biological
variables suggests that cases should be analyzed one by one. Consequently, different
models should be applied in different cases. However, in the search for generalization,
some features should be considered in the choice of the most suitable ecological model to
predict restored vegetation future states, such as, accessibility of inputs, capacity to include
alternative scenarios, form of visualization of outputs, generality, and validation (Turner et
al. 1995). Another important feature to be observed in the choice of models to be applied in
restoration ecology is the flexibility and ability include plant functional types.
55
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71
CAPÍTULO II
BIOLOGICAL ATTRIBUTES USED TO ASSESS
THE SUCCESS OF BRAZILIAN RESTORATION
PROJECTS
72
ABSTRACT
(Biological attributes used to assess the success of Brazilian restoration projects)
Brazilian researchers have a considerable experience in ecological restoration, especially in
the Atlantic Forest biome. However, one of the greatest challenges of ecological restoration
worldwide is to ensure long-term success of those initiatives. In order to measure
restoration success, a large number of qualitative and quantitative biological attributes have
been proposed and applied. In acknowledgement of the actual demand for ecosystem
restoration, the necessity of applying predictive modeling to strengthen the theoretical body
of Restoration Ecology, and the notorious Brazilian experience in ecological restoration of
tropical environment, the Center for Technologies and Recovery of Ecosystems conceived
the 1st Workshop about Successional Trajectories of Restored Ecosystems (August 18-22,
2008). The event gathered several Brazilian researchers to discuss, based on their practical
experiences and expertise, which would be the best biological attributes to assess the
success of Brazilian restoration projects, and outline a general ecological model to describe
and predict ecological trajectories of tropical restored environments. A list of 36 biological
attributes was proposed to assess the success of Brazilian restoration initiatives in different
biomes. These attribute were classified in species-specific, functional, structural, and
diversity, and then, compared to the set of attributes compiled in the previous chapter. The
results found here showed an overlap between biological attributes proposed by Brazilian
scientists and those those used in vegetation dynamics studies. This overlap can be seen as
an opportunity to integrate datasets originated from monitoring of ecological studies and
restoration initiatives outputs. This fact contributes to a better understanding of restoration
success and better support management actions.
73
Key words: Brazililian expertise, restoration ecology, biological attributes, restoration
success.
74
RESUMO
(Atributos biológicos utilizados para acessar o sucesso de projetos de restauração
brazileiros) Pesquisadores brasileiros possuem considerável experiência em restauração
ecológica, especialmente no domínio do bioma Floresta Atlântica. Porém, um dos maiores
desafios da restauração ecológica no mundo é assegurar o sucesso em longo prazo destas
iniciativas. Para medir o sucesso da restauração uma ampla gama de atributos biológicos,
qualitativos e quantitativos foram propostos e aplicados. Tendo em vista a demanda atual
pela restauração ecológica, a necessidade de aplicação da modelagem preditiva para
fortalecer o arcabouço teórico da Ecologia da Restauração, e a notória experiência
brasileira na Restauração de ambientes tropicais, o Núcleo de Técnicas de Recuperação de
Ecossistemas concebeu a I Oficina sobre trajetórias sucessionais de ecossistemas
restaurados (18 a 22 de agosto de 2008). O evento reuniu pesquisadores brasileiros para
discutir, com base nas suas experiências e expertise, quais seriam os atributos biológicos
mais indicados para medir o sucesso de iniciativas de restauração brasileiras, e desenvolver
um modelo ecológico geral para descrever e prever trajetórias ecológicas de ambientes
tropicais restaurados. Uma lista com 36 atributos biológicos foi proposta para acessar o
sucesso de iniciativas brasileiras de restauração em diferentes biomas. Estes atributos foram
classificados em espécie-específicos, funcionais, estruturais e de diversidade, e comparados
com o conjunto de atributos compilados no capítulo anterior. Os resultados encontrados
aqui mostraram uma sobreposição entre os atributos biológicos propostos pelos
pesquisadores brasileiros e aqueles utilizados nos estudos sobre dinâmica vegetacional.
Esta sobreposição pode ser considerada uma oportunidade de integrar os dados originados
de estudos de monitoramento ecológico e resultados de iniciativas de restauração ecológica.
75
Este fato contribui para o melhor entendimento do sucesso da restauração e melhor suporte
às ações de manejo.
Palavras-chave: Conhecimento brasileiro, restauração ecológica, atributos biológicos,
sucesso da restauração.
76
INTRODUCTION3
The previous chapter demonstrated that multiple biological attributes (speciesspecific traits, community attributes, ecosystem attributes and environmental variables) are
applied in studies of vegetation dynamics modelling, as input and output data. It was also
demonstrated that there are no differences between biological attributes employed in
models used in the context of Restoration Ecology and Ecology. It is noteworthy that the
application of modelling techniques to predict future states of restoration initiatives is
infrequent, mainly in tropical countries. In fact, despite recent efforts (Siqueira et al. 2009,
Giannini et al. 2012), Brazilian scientific productivity in the field of ecological modelling is
still insignificant compared to the productivity of countries like United States, Germany
and Canada (Jorgensen 2000).
On the other hand, Brazil has a considerable experience in ecological restoration,
especially in the Atlantic Forest biome. According to Rodrigues et al. (2009) although
some past experiences did not result in self-perpetuating forests, restoration of high
diversity forests is feasible and depends on the strategies applied and on the surrounding
landscape. However, one of the greatest challenges of ecological restoration worldwide is
to ensure long-term success of those initiatives (Kentula 2000, Doren et al. 2009, Le et al.
2012). In order to assess restoration success, a large number of qualitative and quantitative
indicators and biological attributes have been proposed and applied (Le et al. 2012).
In acknowledgement of the actual demand for ecosystem restoration, the necessity
of applying predictive modeling to strengthen the theoretical body of Restoration Ecology,
3
Researchers cited in Appendix 1 of this chapter are co-authors. This chapter has been formatted according to the journal
“Natureza e Conservação”.
77
and the notorious Brazilian experience in ecological restoration of tropical environments
(Rodrigues et al. 2009), the Center for Technologies and Recovery of Ecosystems
(NUTRE) at the Federal University of Rio de Janeiro conceived the 1st Workshop about
Successional Trajectories of Restored Ecosystems (August 18-22, 2008), an event that
gathered several Brazilian researchers to discuss, based on their practical experiences and
expertise, which would be the best biological attributes to assess the success of Brazilian
projects, and outlined a general ecological model to describe and predict ecological
trajectories of tropical restored areas. The aim of the workshop was: (1) elaborate a list of
biological attributes frequently used to evaluate success in restoration efforts in Brazil, and
(2) develop an ecological model to describe and predict ecological trajectories of tropical
restored areas.
In this chapter, the biological attributes proposed by Brazilian researchers to
measure the success of restoration projects will be compared with those attributes often
used to describe and predict vegetation dynamics and ecological trajectories of restored
systems compiled in the previous chapter. It is expected that there will be a large overlap
between the two data sets.
The description of the mathematical model proposed by the workgroup was not the
target of this thesis, but can be found in the Annex 2 of this thesis.
78
METHODS
The 1st Workshop about successional trajectories of restored ecosystems was
organized by the Center for Technologies and Recovery of Ecosystems (NUTRE), a project
supported by the Brazilian Oil Company (PETROBRAS) through its research center
(CENPES) in partnership with the Federal University of Rio de Janeiro. The project was
conceived in 2004 and the main goal was to diagnose environmental damage and to
structure recovery actions through an articulated expert network.
The workshop was held in Rio de Janeiro Botanical Gardens and NUTRE, both
located in the city of Rio de Janeiro. It gathered ten Brazilian leading experts in the fields of
ecological restoration, ecological modelling and vegetation dynamics. It also had the
presence of Petrobras’s and NUTRE’s technical staff (Appendix 1 of this chapter).
The event was designed to allow brainstorming sessions followed by synthesis
meetings. During the brainstorming sessions were discussed issues related to the nature and
scope of predictive models, their applicability in different biomes, and possible biotic and
abiotic variables of responses (Appendix 2 of this chapter).
The results of the 1st Workshop about successional trajectories of restored
ecosystems were compiled and reported firstly as a scientific report, and secondarily as
scientific papers. One paper addresses the ecological model proposed by Brazilian
researchers (see Annex 2 of this thesis) and the other addresses the biological variables (this
chapter).
79
RESULTS
A list of 36 biological attributes was proposed during the 1st Workshop about
successional trajectories of restored ecosystems to assess Brazilian restoration
initiatives in different biomes (Table 1). Among those, 59% (n=21) were speciesspecific parameters mainly associated with Persistence, 22% (n=8) were functional
attributes, 14% (n=5) were structure community attributes, and 6% were diversity
attributes (n=2). Regarding the data sources, more than half of species-specific
attributes were obtained from relevant literature (n=12, 57%). Structural attributes and
diversity attributes were essentially from empirical origins. Functional attributes were
obtained from empirical measurements or bibliographical consultation.
Biological attributes proposed by Brazilian scientists in restoration efforts were
included among those attributes used in pure and applied studies to describe and predict
vegetation dynamics and ecological trajectories (Table 2). However, with respect to
species-specific attributes the overlap was low, 11% (n=9) and 30% (n=17) for
“Establishment” and “Persistence” attributes respectively. Among “Structural”,
“Diversity” and “Environmental drives” attributes, the overlap was 31% (n=16), 33%
(n=3) and 20% (n=5) respectively.
80
Table 1: Biological attributes frequently used by Brazilian researchers to evaluate the success of
restoration projects implemented in different biomes. This list is the result of the 1st Workshop about
successional trajectories of restored ecosystems (August 18-22, 2008), Rio de Janeiro, Brazil.
Legend: (P) – Persistence; (D) – Dispersion, (E) – Establishment.
Species-specific parameter
Source
Data type
Potential height (P)
Herbarium/bibliographic
Continuous
Longevity (P)
Bibliographic
Discrete
Maximum diameter at breast height (P)
Empirical
Continuous
Dispersion syndrome (D)
Bibliographic
Categorical
Growth rate (P)
Empirical/bibliographic
Categorical
Wood density (P)
Bibliographic
Continuous
Nitrogen fixation (P)
Bibliographic
Categorical
Deciduousness (P)
Empirical
Categorical
Leaf width (P)
Empirical
Continuous
Leaf length (P)
Empirical
Continuous
Leaf shape (P)
Bibliographic
Categorical
Pollination syndrome (E)
Bibliographic
Categorical
Diaspore size (E)
Bibliographic
Continuous/categorical
Seed size (E)
Bibliographic
Continuous/categorical
Seed weight in grams (E)
Empirical
Continuous
Habit (P)
Categorical
Seed longevity (E)
Bibliographic
Categorical
Regeneration strategy (P)
Categorical
Tolerance to adverse conditions (P)
Bibliographic
Categorical
Shade tolerance (P)
Bibliographic
Categorical
81
Table 1. continued…
Reproductive system (P)
Bibliographic
Categorical
Total basal area
Empirical
Continuous
Total density
Empirical
Discret
Structure sizes
Empirical
Categorical
Total leaf area index
Empirical
Continuous
Invasive grassland coverage
Empirical
Continuous
Richness
Empirical
Categorical
Equity
Continuous
Proportion of native seedlings
Categorical
Presence of nodulation
Empirical
Categorical
Inventory of litter
Continuous
Litter deposition
Continuous
Litter moisture
Empirical
Continuous
Percentage of soil exposed
Empirical
Continuous
pH
Continuous
Soil organic matter
Continuous
Structure attributes
Diversity attributes
Functional attributes
82
Table 2: Biological attributes frequently used as input and output of ecological models
applied to describe and predict vegetation dynamics and ecological trajectories. This
list is the result of the previous chapter article review. Attributes followed by asterisk
are frequently used by Brazilian researchers to evaluate the success of restoration
projects implemented in different biomes. Legend: (P) – Persistence; (D) – Dispersion,
(E) – Establishment.
Species-specific parameters (n=34)
Seed dispersal distance (D)
Seed production (D)
Dispersal period (D)
Dispersal syndrome (D)*
Seed mass (E)*
Seed longevity (E)*
Diaspore size (E)*
Seed size (E)*
Germination rate (E)
Seedling establishment rate (E)
Seedling growth rate (E)
Seedling survival rate (E)
Seedling mortality rate (E)
Sprouting capacity (E)
Seed fecundity rate (E)
Seedling density (E)
Pollination syndrome (E)*
Longevity (P)
Size (P)*
83
Table 2: continued...
Age (P)
Basal area (P)
Growth rate (P)*
Survival rate (P)
Photosynthetic productivity (P)
Ability to fix nitrogen (P)*
Mortality rate (P)
Stress tolerance (P)*
Biomass (P)
Density (P)
Leaf parameters (P)*
Allometric parameters (P)
Wood density (P)
Phenology (P)
Crown parameters (P)
Structure attributes (n=16)
Biomass
Crown class
Crown radii
Diameter
Height
Canopy diameter
Basal area*
Cover
Dominance
84
Table 2: continued...
Density*
Wood density
Age structure
Size structure*
Leaf area index*
Invasive species cover*
Vertical stratification
Diversity attributes (=3)
Species composition*
Species richness
Diversity index
Environmental drivers (n=5)
Edaphic conditions*
Climatic conditions
Topography
Disturbance type
Growing season
85
DISCUSSION
A wide range of methodologies for the establishment of criteria and indicators of
restoration success can be found in the literature. Kearns & Barnett (1998) proposed a
field procedure called “Ecosystem Function Analysis” based on easy to measure and
scientifically credible ecosystem indicators. Silveira (2012) proposed a methodology for
assessment and monitoring restoration success based on low cost, legal criteria and
literature recommendations. Fonseca (2011) proposed the development of indicators on
the perspective of the paradigm of ecological integrity, considering three attributes
ecosystem: composition, structure and ecological processes. Herrick et al. (2006)
elaborated an approach based on the combination of soil and vegetation indicators for
monitoring restoration success of arid and semi-arid upland ecosystems.
Nevertheless, despite efforts aiming to evaluate success, the lack of appropriated
monitoring data and its reporting, and the uncertainty regarding the definition about
successful restoration represents a hindrance to the evaluation of Ecological Restoration
effectiveness as an applied science. There is also a need to compile data about when and
why projects have succeeded (Suding 2011). Besides that, there is another important gap
knowledge that must be considered. Some especies-specifics attributes are known for a
few species, and even if this information exists, it is not always included in any
published ecological research or data base (Knevel et al. 2003). According to Kleyer et
al. 2008 knowledge about species traits is growing, especially in Europe. However, this
information remains scattered over many sources, not always integrated. This kind of
gap constitute an important point of fragility of sciences that deal with vegetation
dynamics and the description of ecological trajectories, associated to the difficulty to
choose the adequate ones to simulate, predict and project vegetation states. In fact,
results found here showed that species trait, mainly associated to persistence and
86
establishment, were not generally applied by Brazilian researches. This can be a
reflection of the lack of tropical species-specific information.
Although not exhaustive, the proposed list of biological attributes frequently used
by Brazilian researchers to assess restoration success in different biomes shows a overlap
with biological attributes used to describe and predict vegetation dynamics in the context
of the ecological studies. This overlap can be seen as an opportunity to integrate dataset
bases originated from monitoring of ecological studies and restoration initiatives outputs.
This fact contributes to a better understanding of restoration success and better support
management actions, and can also contribute to the generalization of predictive tools
used to guide restored systems trajectories. However, there is an important need for
further test and to validate the sensitivity of those attributes to changes in the trajectory
of the ecosystem. According to Vallauri et al. (2005) the set indicators used to assess
restoration success should be agreed upon and then tested to reflect their evolution.
87
Appendix 1. Participants of the 1st Workshop about successional trajectories of restored ecosystems (August 18-22, 2008), Rio de Janeiro,
Brazil.
General Coordination Fabio Rubio Scarano
Botanical Garden Research Institute of Rio de Janeiro, Rio de Janeiro
Antônio C.G. Melo
Forestry Institute of São Paulo (IF), São Paulo
Enio Egon Sosinski Junior
State University of Campinas (UNICAMP), São Paulo
Giselda Durigan
Forestry Institute of São Paulo (IF), São Paulo
Gislene Maria da Silva Ganade
University of the Sinos Valley (UNISINOS), Rio Grande do Sul
Luiz Roberto Zamith Coelho Leal
Parks and Gardens Foundation, (FPJ), RJ
Guest Researchers
Marcia Cristina Mendes Marques
Federal University of Paraná (UFPR), Paraná
Pablo J. F. Pena Rodrigues
Sergio Miana de Faria
The Brazilian Agricultural Research Corporation (EMBRAPA), Rio de Janeiro
Tania Sampaio Pereira
Valério De Patta Pillar
Federal University of Rio Grande do Sul (UFRGS), Rio Grande do Sul
Vera Lex Engel
State University of Júlio de Mesquita Filho (UNESP), São Paulo
National School of Tropical Botany (ENBT), Rio de Janeiro
Postgraduate students Jerônimo B. Barreto Sansevero
National School of Tropical Botany (ENBT), Rio de Janeiro
Mário Luís Garbin
Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro
88
Appendix 2. Schedule of the 1st Workshop about successional trajectories of restored ecosystems (August 18-22, 2008), Rio de Janeiro, Brazil.
Monday
Tuesday
Wednesday
Thursday
Friday
Time
18th august
19th august
20th august
21th august
22th august
(NUTRE)
(JBRJ)
(JBRJ)
(JBRJ)
(NUTRE)
Entrepreneurs: experiences
Group A and B:
09:00 – 10:30
Suggestions, criticisms
and expectations
Biotic variables
Guided visit to Rio de
10:30 – 10:50
Arrivals
Coffee break
Coffee break
Janeiro Botanical
Coffee break
Garden
Researchers: experiences and
Future directions, work
10:50 – 12:00
Plenary session
expectations
schedule
12:00 – 13:30
Lunch
Lunch
Lunch
Lunch
Lunch
13:30 – 14:00
NUTRE project
Synthesis
Synthesis
Final session
History and
perspectives of
Group A:
14:00 – 15:00
ecological
Nature
Group A and B:
restoration in
and scope
ecological model
Group A and B:
Brazil
of
Gruop B: Biomes
development
Abiotic variables
Use of predictive ecological
models
models in
Departures
15:00 – 16:00
Restoration
Ecology
16:00 – 16:20
Coffee break
Coffee break
Coffee break
Coffee break
Presentation of
Group A and B:
16:20 – 18:00
the workshop
Plenary session
Plenary session
ecological model
agenda
development
NUTRE: Federal University of Rio de Janeiro (UFRJ) - campus Cidade Universitária – Rio de Janeiro;
JBRJ: Research Institute of Rio de Janeiro Botanical Garden – Rio de Janeiro.
89
REFERENCES
Doren, R.F., Trexler, J.C., Gottlieb, A.D. & Harwell, M.C. 2009. Ecological indicators
for system-wide assessment of the greater everglades ecosystem restoration
program. Ecological Indicators, 9s: s2-s16.
Fonseca, V.H.C. 2011. Seleção de indicadores ecológicos para avaliação de planos de
restauração de áreas degradadas. Universidade Federal de São Carlo. 86p.
Giannini, T.C., Siqueira, M.F., Acosta, A.L., Barreto, F.C.C., Saraiva, A.M. & Alves
dos Santos, I. 2012. Desafios atuais da modelagem preditiva de distribuição de
espécies. Rodriguésia 63: 733-749.
Herrick, J.E., Schuman, G.E. & Rango, A. 2006. Monitoring ecological processes for
restoration projects. Journal for Nature Conservation, 14: 161-171.
Jorgensen, S.E. & Bendoricchio, G. 2001. Fundamentals of Ecological Modelling.
Elsevier. 530 p.
Kearns, A. & Barnett, G. 1998. Use of ecosystem function analysis in the mining
industry. In: Proceedings of Workshop on Indicators of Ecosystem Rehabilitation
Success. Eds. Asher, C.J. & Bell, L.C. pp. 31-46.
Kentula, M.E. 2000. Perspectives on setting success criteria for wetland restoration.
Ecological Engineering, 15: 199–209.
Kleyer, M., Bekker, R.M., Knevel, I.C., Bakker, J.P., Thompson, K., Sonnenschein, M.,
Poschlod, P., van Groenendael, J.M., Klimes, L., Klimesova, J., Klotz, S., Rusch,
G. M., Hermy, M., Adriaens, D., Boedeltje, Beatrijs Bossuyt, G., Gent, U.,
Dannemann, A., Endels, P., Goetzenberger, L., Hodgson, J.G., Jackel, A-K.,
Kuehn, I., Kunzmann, D., Ozinga, W.A., Roemermann, C., Stadler, M.,
Schlegelmilch, J., Steendam, H.J., Tackenberg, O., Wilmann, B., Cornelissen,
J.H.C., Eriksson, O., Garnier, E. & Peco, B. 2008. The LEDA Traitbase: a database
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of life-history traits of the Northwest European flora. Journal of Ecology, 96 (6):
1266-1274.
Knevel, I.C., Bekker, R.M., Bakker, J.P. & Kleyer, M. 2003. Life-history traits of the
Northwest European flora: The LEDA database. Journal of Vegetation Science, 14:
611-614.
Le, H.D., Smith, C., Herbohn, J. & Harrison, S. 2012. More than just trees: Assessing
reforestation success in tropical developing countries. Journal of Rural Studies, 28:
5-19.
Vallauri, D., Aronson, J., Dudley, N. & Vallejo, R. 2005. Monitoring and Evaluating
Forest Restoration Success. In: Forest Restoration in Landscapes. Ed. Mansourian,
S., Vallauri, D., Dudley, N. Springer, New York. Pg. 150-158.
Rodrigues, R.R., Lima, R.A.F., Gandolfi, S. & Nave, A.G. 2009. On the restoration of
high diversity forests: 30 years of experience in the Brazilian Atlantic Forest.
Biological Conservation, 142 (6): 1242-1251.
Silveira, C.J.A. 2012. Proposta de indicadores para a avaliação de projetos de
restauração de ecossistemas no Alto Jequitinhonha. Dissertação de Mestrado.
Universidade Federal dos Vales do Jequitinhonha e Mucuri. 130 p.
Siqueira, M.F.; Durigan, G., de Marco Júnior, P., Townsend, P.A. 2009. Something
from nothing: Using landscape similarity and ecological niche modeling to find
rare plant species. Journal for Nature Conservation 17: 25-32.
Suding, K.N. 2011. Toward an Era of Restoration in Ecology: Successes, Failures, and
Opportunities Ahead. Annual Review of Ecology, Evolution, and Systematics, 42:
465–487.
91
CAPÍTULO III
PREVISÃO DE ESTADOS FUTUROS DE
FLORESTAS ARTIFICIAIS DE IGAPÓ
(PORTO TROMBETAS, PARÁ, BRASIL)
92
RESUMO
(Previsão de estados futuros de florestas artificiais de igapó - Porto Trombetas,
Pará, Brasil) Entre os anos de 1979 e 1989 a Mineração Rio do Norte lançou cerca de
24 milhões de toneladas de rejeito oriundo do beneficiamento do minério de bauxita na
região noroeste do lago Batata, o que afetou aproximadamente 30% da sua superfície
causando a morte da vegetação local, e, conseqüente a desestruturação dos habitats para
a fauna e flora. A partir de 1993, a empresa deu início ao programa de reabilitação do
lago Batata, com o intuito de sistematizar o plantio de espécies típicas de igapó sobre o
rejeito de bauxita. Este estudo teve como objetivo analisar a similaridade florística e o
processo de reabilitação das florestas artificiais de igapó plantadas nas áreas marginais
do lago Batata (Porto Trombetas, Pará), e prever, através de análise funcional e de
cadeia de Markov, seus potenciais estados futuros. Para tanto, foram selecionadas áreas
com florestas artificiais de diferentes idades (três áreas com plantios de cinco anos, três
áreas com plantios de dez anos e três áreas com plantios de quinze anos) e três áreas de
florestas naturais de igapó, consideradas controles. Em cada uma das áreas selecionadas
a vegetação foi amostrada através de dez quadrados de 25 m2 distribuídos ao longo de
quatro de transectos. Dentro de cada quadrado, todas as plantas foram medidas (altura,
diâmetro da base e cobertura da copa). Para a análise da similaridade florística entre as
florestas artificiais e florestas naturais de igapó foi construída uma matriz de
similaridade considerando a abundância das espécies amostradas. A avaliação do
processo de reabilitação se deu através da comparação dos indicadores ecológicos de
sucessos por análise de variância hierarquizada. A Análise de Componentes Principais
foi empregada para ordenar as áreas com florestas artificiais de distintas idades e
florestas naturais de igapó em relação as variáveis biológicas de diversidade (riqueza de
espécies), de estrutura vegetacional (altura, diâmetro, área basal, cobertura de copa e
93
densidade) e de processos ecossistêmicos (densidade e riqueza de regenerantes). A
modelagem dos estados futuros dos plantios foi realizada com base na identificação de
tipos funcionais de plantas ótimos, e a projeção futura da composição de tipos
funcionais foi realizada segundo a cadeia Markoviana. Os resultados demonstraram
similaridade florística entre as florestas artificiais e as florestas naturais de igapó e a
existência de uma tendência à convergência de valores da floresta plantada em relação à
floresta natural de igapó, com exceção da área basal média que apresentou valores
menores e estacionários quando comparados aos das florestas de igapó naturais. A
análise funcional identificou três tipos funcionais. A projeção da composição de tipos
funcionais futura mostrou que a atividade de reabilitação no lago Batata não
necessariamente conduzirá as florestas artificiais a estados similares àqueles
encontrados nas florestas naturais de igapó após 175 anos.
Palavras-chave: Trajetória ecológica, rejeito de bauxita, tipos funcionais de plantas.
94
ABSTRACT
(Artificial wetland forests future states prediction - Porto Trombetas, Pará, Brasil)
Between 1979 and 1989, the Mineração Rio do Norte disposed about 24 million tons of
bauxite tailings in the northwestern portion of Batata Lake, which affected
approximately 30% of its surface, and caused the supression of part of the local
vegetation and consequent disruption of habitats for fauna and flora. Since 1993, the
company initiated a rehabilitation program in Batata Lake, which systematized the
planting of native wetland species on bauxite tailings. This study aimed to analize the
floristic similarity and the rehabilitation process of artificial wetland forests planted in
marginal areas of Lake Batata (Porto Trumpets, Pará), and, through functional analysis
and Markov chain, predict its potential future states. For that, artificial forests with
different ages were selected (three plantations with five years, three with 10 years and
three with 15 years), and compared with three natural forest areas igapó, considered
controls. Vegetation was sampled in ten 25 m2 plots distributed along four transects in
each artificial and natural forest. Within each plot, all plants were measured (height,
diameter of the base and canopy cover). For the floristic similarity analysis a similarity
matrix considering the abundance of the species was constructed. The rehabilitation
process evaluation was made through the comparison of ecological indicators through
hierarchical analysis of variance. The Principal Component Analysis was used to sort
the areas with artificial forests of diferent ages and natural igapó forests in relation to
biological variables of diversity (richness), structure (height, diameter, basal area,
canopy and density) and ecosystem process (density and richness of seedlings). The
modeling of future states of plantations was based on the identification of optimal plant
functional types, and future projection of functional types was performed according to
the Markov chain. The results showed floristic similarity between the artificial forests
95
and natural igapó forests and the existence of a trend towards convergence of the
planted forest in relation to natural forest igapó, with the exception of average basal area
which showed lower values when compared to stationary and natural forests. Functional
analysis identified three functional types. The projection of future functional types
showed that activity of rehabilitation in Lake Batata not necessarily lead artificial
forests to a state similar to that found in natural forests igapó after 175 years.
Key words: Ecological trajectory, bauxite tailings, plant functional type.
96
INTRODUÇÃO4
A modelagem ecológica é uma ferramenta útil para o entendimento e projeção
da dinâmica vegetacional, e inúmeros modelos já foram publicados para estes fins (Liu
& Ashton 1995). Modelos ecológicos possuem a habilidade em capturar a realidade de
sistemas em vias de recuperação, e neste sentido, seu uso no estudo de trajetórias
ecológicas é promissor e pode gerar informações importantes no auxílio a tomadas de
decisões (Anand & Desrochers 2004).
A trajetória ecológica é o caminho de desenvolvimento do ecossistema ao longo
do tempo, e no campo da Ecologia da Restauração, ela tem início com o ecossistema
degradado, e culmina em um estado vegetacional desejável, que pode ser expresso em
valores de referência. Na ausência de dados originados de monitoramentos de unidades
amostrais permanentes, a trajetória ecológica pode ser avaliada através da substituição
do espaço pelo tempo, ou seja, fazendo uso de sítios restaurados com diferentes idades,
que representam diferentes estágios ao longo da trajetória sucessional de recuperação
(Twilley et al. 1998). Ela é monitorada através de medidas seqüenciadas de diferentes
variáveis ecológicas (SER 2004). Tais variáveis podem ser consideradas indicadores
ecológicos quando são perfeitamente identificáveis, fáceis de medir e compreender, e
representativas da condição do ambiente ou das tendências de mudança destas
condições ao longo do tempo (Dale & Beyeler 2001).
A aplicação da modelagem no estudo das trajetórias ecológicas pode gerar
informações sobre o tempo necessário para obtenção de condições previamente
estabelecidas, embasadas em valores dos sistemas de referência (Twilley et al. 1998). A
4
Capítulo formatado de acordo com as regras de publicação do periódico Restoration Ecology. Este capítulo testa o
modelo matemático descrito no artigo Modeling the Success of Restoration in Tropical Ecosystems - Mário L.
Garbin, Danielle J. Capossoli, Giselda Durigan, Vera L. Engel, Sergio M. de Faria, Gislene Ganade, Carla Madureira,
Marcia C. M. Marques, Antônio C.G. Melo, Tania S. Pereira, Pablo J. F. P. Rodrigues, Jerônimo B. B. Sansevero,
Fabio R. Scarano, Enio E. Sosinski Jr., Luiz R. Zamith & Valério P. Pillar, que encontra-se em etapa final de redação.
(vide Anexo 2 desta tese).
97
aplicação de modelos ecológicos também contribui para o entendimento das trajetórias
ecológicas de associadas ao sucesso dos projetos de reabilitação, principalmente
naquelas situações em que a recuperação ambiental se torna particularmente difícil por
lidar com ecossistemas sujeitos a condições ambientais severas (longos períodos de
inundação e baixa disponibilidade de nutrientes). Este é o caso do esforço de
rehabilitação de uma floresta artificial de igapó, plantada sobre áreas marginais do lago
Batata que foram soterradas por rejeito de bauxita, e que permanecem expostas apenas
durante o período de águas baixas (setembro-dezembro) (Dias et al. 2012).
Sendo assim, a aplicação de algoritmos e modelos matemáticos constitui uma
importante oportunidade para o fortalecimento do continuum que integra a base teórica
ecológica com a vertente aplicada da restauração de ecossistemas. A expectativa é que
haja uma retro-alimentação entre a teoria e a prática.
Objetivos
Este estudo tem como objetivos realizar o diagnóstico fitossociológico, avaliar o
desempenho e prever através de análise funcional e cadeia de Markov os estados futuros
das florestas artificiais plantadas nas áreas marginais do lago Batata (Porto Trombetas,
Pará). Pretende-se responder às seguintes perguntas:

A composição florística dos plantios é similar à composição florística dos
regenerantes das áreas de plantios?

A composição florística dos regenerantes presentes nas áreas de plantios é
similar à composição florística das florestas naturais de igapó?

Os atributos estruturais e de diversidade nas florestas artificiais de igapó
alcançaram os valores dos atributos das florestas naturais de igapó?

As florestas artificiais apresentarão atributos de estrutura similares às
98
florestas naturais de igapó?
99
MATERIAIS E MÉTODOS
1.
Área de estudo
Este estudo foi conduzido em áreas marginais do lago Batata (1º25’ e 1º35’S,
56º 15’ e 56º25’W), localizado no distrito de Porto Trombetas, município de Oriximiná,
região oeste do Estado do Pára (Figura 1). O clima da região corresponde ao Am de
Koppen, isto é tropical úmido de monções com precipitação excessiva durante alguns
meses. A precipitação anual varia entre 2.500 a 3.000 mm e a temperatura média anual é
de 26ºC. A estação chuvosa (precipitação média de 265 mm/m3) ocorre entre dezembro
a maio, e a estação seca (precipitação média de 73 mm/m3) entre julho a outubro (Reis
2006). De acordo com a Mineração Rio do Norte, localizada no distrito de Porto
Trombetas, a temperatura média anual da região onde está inserido o lago Batata é de
27ºC (mínimo média de 22ºC e máxima média de 33ºC), e a umidade relativa do ar
cerca de 82%.
O lago Batata está localizado à margem direita do rio Trombetas (Figura 1),
bacia do médio rio Amazonas. Sua bacia de drenagem possui área aproximada de 271,6
km2 e perímetro de 72 km, e nela escoam 87 canais, regionalmente denominados
igarapés. O complexo formado pelo rio Amazonas e seus tributários pertence à
categoria dos rios de águas claras, que tipicamente possuem alta transparência, baixa
quantidade de partículas em suspensão (matéria orgânica e inorgânica), pH variando de
baixo a neutro e baixa disponibilidade de nutrientes (Dias et al. 2012). Ele está sujeito a
grandes variações sazonais do nível da água. O pulso hidrológico determina uma intensa
variação no nível fluviométrico do lago Batata ao longo do ano, o que lhe confere
grande oscilação entre o caráter lêntico (durante a época de águas baixas) e lótico
(durante a época de águas altas) (Bozelli et al. 2000). Na fase de águas altas, o volume
do lago Batata e do rio Trombetas se expande sobre suas planícies de inundação,
100
promovendo grande interação entre o ambiente terrestre e o ambiente aquático, uma vez
que este dois corpos hídricos encontram-se constantemente conectados.
101
(a)
(b)
Figura 1: Bacia Hidrográfica Amazônica. (a) Localização do rio Trombetas (distrito de Porto
Trombetas, município de Oriximiná, Estado do Pará) na bacia hidrográfica do médio Amazonas;
(b) Localização do lago Batata no sistema rio-planície de inundação do rio Trombetas (1º25’ e
1º35’S, 56º 15’ e 56º25’W). Os pontos vermelhos indicam a região impactada pelo rejeito da
bauxita. A seta indica o local onde este estudo foi conduzido (Fontes: Estrada 2007).
102
2.
Processo de degradação e recuperação ecológica do lago Batata
Entre os anos de 1979 e 1989, a Mineração Rio do Norte lançou cerca de 24
milhões de toneladas do rejeito oriundo do beneficiamento do minério de bauxita sobre
o sedimento natural da região noroeste do lago Batata. O fato ocasionou o assoreamento
de cerca de 30% da sua superfície, o que corresponde à 630 ha (Lapa 2000), e
desencadeou várias alterações nos processos ecológicos do sistema, que persistem até os
dias atuais. A espessa camada de rejeito, com 4-6 metros de profundidade, causou a
assoreamento de áreas de floresta inundável, a formação de novas áreas de igapó que
surgiram a partir do assoreamento das áreas permanentemente inundadas, formação de
novo substrato sobre o sedimento natural em cerca de 30% do lago, e aumento dos
valores de sólidos em suspensão na coluna d’água.
O rejeito de bauxita consiste em 75% de argila, 21% de lama, 3% de areia fina, e
1% de areia grossa. Já o solo do igapó não impactado é composto por 49% de argila,
37% de lama, e 13% areia fina. A exposição freqüente e prolongada do rejeito de
bauxita à luz solar promove a desidratação e consolidação do substrato (Dias et al.
2012).
A presença de partículas em suspensão na coluna d’água provoca intenso efeito
físico de bloqueio da passagem da luz com aumento na turbidez. O assoreamento
ocasionou também a expansão da área marginal do lago sobre uma área originalmente
inundável, criando um novo ambiente a ser colonizado. Estas áreas se caracterizam por
apresentar substrato pobre em matéria orgânica, resultante da presença das argilas e,
portanto, potencialmente desfavoráveis ao estabelecimento de comunidades vegetais.
Esta situação é única no Brasil e no mundo, e representa uma oportunidade importante
para o estudo da sucessão ecológica em resposta às alterações ambientais (Esteves
2000).
103
A partir de 1993 a Mineração Rio do Norte deu início a um programa de
recuperação ambiental do lago Batata, cuja principal ação foi o plantio de espécies
típicas de igapó sobre o rejeito de bauxita. Os plantios foram realizados em diferentes
porções da área impactada do lago de forma aleatória. Espécies de igapó foram
plantadas em linhas com espaçamento de 1m x 1m, 1,5m x 1,5m, 1,6m x 1,6m, 2,0m x
2,0m (Figura 2). A composição de espécies e freqüência variou entre os plantios em
função da disponibilidade de sementes e produção de mudas no Horto da MRN.
Atualmente, os plantios já cobrem praticamente toda a área impactada e apenas ações
complementares para substituição dos indivíduos que morrem são implementadas.
Além do impacto ambiental, a imagem da empresa também foi comprometida
pela presença do passivo ambiental. Atualmente a companhia possui um dos maiores
custos ambientais dentre as empresas que atuam no ramo da mineração no Estado do
Pará, e a recuperação do lago Batata e de suas áreas marginais representa uma grande
parcela destes gastos (Enríquez 2009). A partir deste episódio a empresa modernizou
seus métodos de descarte, sendo considerada atualmente uma referência na operação de
mineração sustentável (MNR 2009).
104
(a)
(b)
(a)
(c)
(a)
Figura 2: Processo de reflorestamento realizado em 2004 em área marginal do Lago Batata Porto Trombetas, Pará. (a) Abertura de covas; (b) Plantio de mudas; (c) Mudas plantadas.
105
3.
Áreas de estudos
Para a realização deste estudo foram selecionadas áreas reflorestadas de
diferentes idades e áreas de florestas naturais. Foram consideradas três áreas com
reflorestamentos de cinco anos (plantio de 1998 mensurado em 2003, plantio de 2001
mensurado em 2006, e plantio de 2003 mensurado em 2008), três áreas com
reflorestamentos de dez anos (plantio de 1994 mensurado em 2003, plantio de 1996
mensurado em 2006, e plantio de 1998 mensurado em 2008), três áreas de
reflorestamentos de quinze anos (plantio de 1994 mensurado em 2009, plantio de 1995
mensurado em 2010 e plantio de 1997 mensurado em 2010), e três áreas de florestas
naturais de igapó (mensuração em 2003, 2006 e 2010) (Tabela 1).
É importante ressaltar que os plantios estudados permanecem inundados durante
o período de águas altas. A inundação pode durar de quatro a oito meses dependendo do
ano e da topografia da área onde estão localizados.
106
Tabela 1. Descrição das áreas de estudos localizadas nas áreas marginais do lago Batata - Porto Trombetas, Pará.
Área
Ano da
Nº inicial de
Área amostrada
Tratamento
Código
Espaçamento
aforestada
mensuração
indivíduos
(ha)
Plantio de 1994
2003
10 anos
DI
1x1 e 1,5x1,5
25 e 11
3,5
Plantio de 1994
2009
15 anos
QI
1x1 e 1,5x1,5
25 e 11
3,5
Plantio de 1995
2010
15 anos
QII
2x2
6
7,0
Plantio de 1996
2006
10 anos
DII
1,5x1,5
11
10,0
Plantio de 1997
2010
15 anos
QIII
1,6x1,6
10
7,0
Plantio de 1998
2003
5 anos
CI
1,6x1,6
10
8,5
Plantio de 1998
2008
10 anos
DIII
1,6x1,6
10
8,5
Plantio de 2001
2006
5 anos
CII
1,5x1,5
11
10,5
Plantio de 2003
2008
5 anos
CIII
1,5x1,5
11
10,0
Igapó natural
2003
Referência
RI
Igapó natural
2006
Referência
RII
Igapó natural
2010
Referência
RIII
-
Nº de mudas
17,660
17,660
21,400
36,530
27,956
33,229
33,229
46,749
42,718
-
Nº de
espécies
18
18
25
28
24
22
22
22
30
-
107
4.
Amostragem da vegetação
A amostragem da vegetação foi realizada através de transectos (quatro por área)
cujos comprimentos variaram entre 50 m a 100 m de acordo com a declividade e
distância em relação a áreas permanentemente inundadas. A distância entre transectos
em cada área variou de 5 a 10 metros. Ao longo de cada transecto foram implantados
dez parcelas de 25 m2, totalizando uma área amostrada de 0,1 ha por plantio. Devido à
inclinação natural do terreno, os transectos foram posicionados perpendicularmente à
linha de vegetação natural, permitindo a amostragem da vegetação em diferentes zonas
topográficas.
Dentro de cada parcela, todos os indivíduos plantados foram medidos (altura,
diâmetro da base e cobertura da copa) (Figura 3). Os indivíduos estabelecidos através de
replantios foram excluídos das análises.
A regeneração natural foi quantificada em dois quadrados de 1 m2 instalados em
extremidades opostas das parcelas de 25 m2. Os regenerantes foram identificados e
mensurados (altura e diâmetro da base).
Nas áreas de igapó natural, o mesmo delineamento amostral foi utilizado. Nas
parcelas de 25 m2 foram mensurados os indivíduos com altura igual ou superior a 1,50
m. Nos quadrados de 1 m2 foram mensurados todos os indivíduos regenerantes.
A cobertura da copa de cada indivíduo foi obtida através do cálculo da área da
elipse, que teve como base a medida do maior e menor diâmetro da copa. Para o cálculo
da sobrevivência das mudas plantadas, foi estimado um número inicial de indivíduos
nas parcelas de acordo com os espaçamentos utilizados.
Para o cálculo da área foliar, área foliar específica, massa foliar e massa foliar
específica foram selecionados cinco indivíduos das espécies mais abundantes nos
plantios. Para cada indivíduo foram coletadas três folhas totalmente expandidas. Para
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cada folha foi medido o peso fresco e tomada a medida de área com o auxílio de um
scanner. O material foi depositado em uma estufa a 75°C por três dias. Após esse
período foram realizadas as medidas de peso seco.
109
(a)
(b)
(c)
(d)
Figura 3: Equipe de campo realizando as medições dos indivíduos amostrados em plantios com
10 e 15 anos, localizados em áreas marginais do Lago Batata - Porto Trombetas, Pará. (a)
Mensuração do maior e menor diâmetro da copa de indivíduo em plantio com 15 anos; (b)
Mensuração do diâmetro de regenerante em parcela de regeneração em plantio com 15 anos; (c)
Mensuração de altura de indivíduo em plantio com 10 anos; (d) Vista parcial do plantio com 10
anos.
110
5.
Análise dos dados
5.1.
Composição florística e estrutura fitossociológica das florestas
artificiais e florestas naturais de igapó
Os parâmetros fitossociológicos foram calculados com o software Mata Nativa 2
de acordo com Mueller-Dumbois & Ellenberg (1974) (densidade, dominância, valor de
cobertura, valor de importância), Magurran (1988) (Indice de Shannon-Weaver) e
Brower & Zar (1988) (Índice de Simpson).
5.2.
Similaridade florística entre as florestas artificiais e florestas naturais
de igapó
A similaridade florística entre as florestas artificiais e florestas naturais de igapó
foi determinada através da análise de agrupamentos. Primeiramente foi considerada a
abundância total das espécies amostradas por áreas com plantios da mesma idade.
Posteriormente a abundância das espécies amostradas em cada área de plantio e em cada
floresta natural de igapó foi considerada separadamente. Em ambas análises foram
construídas matrizes de similaridades considerando a abundância das espécies
amostradas (n=79). A medida de similaridade empregada foi a média de agrupamentos
por médias não-ponderadas (UPGMA), e o coeficiente de distância considerado foi o de
Bray-Curtis. Essas análises foram realizadas por meio do software PAST versão 2.17c
(Hammer et al. 2001), e os seus resultados foram expressos na forma de dendrogramas.
5.3.
Avaliação do processo de reabilitação do lago Batata através de
indicadores ecológicos
O processo de reabilitação das áreas marginais do lago Batata foi conduzido
através do acompanhamento temporal de indicadores ecológicos. Segundo Dale &
111
Beyeler (2001) indicadores ecológicos compreendem variáveis de fácil identificação,
mensuração e compreensão, e quando aplicados à ecologia da restauração, permitem o
monitoramento de alterações na biodiversidade e processos ecológicos do ecossistema
em vias de restauração, tendo como referência um estado desejável (de Moraes et al.
2010, de Oliveira 2011). Neste trabalho, foram considerados como indicadores
ecológicos as variávies biológicas relacionadas à estrutura vegetacional (altura,
diâmetro, área basal, cobertura de copa e densidade), diversidade de espécies (riqueza
de espécies) e processos ecológicos (densidade e riqueza de regenerantes). As florestas
naturais de igapó foram consideradas sistemas de referências (Ruiz-Jaen & Aide 2005,
de Moraes et al. 2010).
A análise de variância hierarquizada foi aplicada para comparar os valores das
variáveis biológicas supracitadas entre as florestas artificiais e naturais de igapó,
considerando as variações entre as diferentes idades dos plantios (hipótese nula: os
valores médios dos variáveis biológicas não variam entre as distintas idades) e entre as
diferentes áreas amostradas (hipótese nula: os valores médios dos variáveis biológicas
não variam entre as áreas amostradas) (“Área” aninhada à “Idade”). O Teste de Tukey
modificado para amostras com números diferentes foi utilizado para comparar as
múltiplas médias, a 5% de significância. Todos os dados foram transformados (Log
[x+1]) para obtenção da normalidade (Zar 1999). A análise foi realizada no pacote
estatístico Statistica 6.0. Foram excluídas da análise as parcelas vazias, isto é, aquelas
cuja mortalidade de indivíduos plantados foi de 100% no caso dos plantios, ou que não
apresentaram nenhum indivíduo no caso das florestas naturais de igapó. Para a análise
da cobertura de copa foram excluídos os indivíduos cujas projeções de copas não
puderam ser mensuradas devido ao alto grau de intercepção das copas, fato que ocorreu
predominantemente nas florestas naturais de igapó. Para a análise da sobrevivência, as
112
áreas de igapó natural também foram excluídas. Os indivíduos regenerantes foram
analisados separadamente.
A análise de componentes principais, com base na matriz de correlação, foi
empregada para ordenar as áreas com florestas artificiais de distintas idades e florestas
naturais de igapó em relação às variáveis biológicas de estrutura vegetacional (altura,
diâmetro, área basal, cobertura de copa e densidade), de diversidade de espécies
(riqueza de espécies), e de processos ecológicos (densidade e riqueza de regenerantes).
Os dados foram transformados (Log [x+1]). A análise foi realizada no software PAST
versão 2.17c (Hammer et al. 2001).
5.4.
Modelagem de estados futuros com base em tipos funcionais de plantas
A modelagem dos estados futuros dos reflorestamentos conduzidos nas margens
do lago Batata foi realizada com base no modelo matemático proposto por Garbin et al.
(em preparação – vide Anexo 2). Esta ferramenta preditiva foi concebida com o intuito
de descrever e prever as trajetórias sucessionais de esforços de restauração ecológica de
ecossitemas tropicais com base em tipos funcionais de plantas (Garbin et al. em
preparação). De acordo com Pillar & Sosinsky (2003), os tipos funcionais ótimos são
grupos de plantas que, independente das relações filogenéticas, apresentam um conjunto
de características similares. Sendo assim, tais organismos respondem de forma similar à
determinadas variáveis ambientais. A alta diversidade de espécies encontrada nos
ambientes tropicais pode representar uma limitação à modelagem matemática por
requerer a construção de modelos complexos. Desta forma, o emprego da abordagem
baseada em tipos funcionais é uma maneira de simplificar o sistema modelado, fato que
facilita o entendimento sobre a estrutura e funcionamento do mesmo.
113
O modelo matemático proposto é composto por seis passos independentes. No
entanto, nesta tese, apenas os dois primeiros passos serão aplicados. Eles serão descritos
de forma sucinta. Informações mais detalhadas podem ser obtidas em Pillar (1999),
Pillar & Sosinski (2003), Pillar et al. (2009), Carlucci et al. (2012).
5.4.1. Primeiro passo: seleção dos atributos ótimos e definição dos tipos
funcionais de plantas das florestas artificiais
O primeiro passo envolveu três etapas: a delimitação de critérios para a inclusão
de espécies, a escolha dos atributos a serem mensurados, e a elaboração de três matrizes
que contemplam os dados de entrada do modelo (Fonseca & Ganade 2001, Pillar 1999).
Os critérios utilizados para a inclusão de espécies na modelagem dos estados
futuros foram respectivamente o valor de importância e a disponibilidade de dados
acerca dos atributos espécie-específicos. Com base nos dados fitossociológicos, foram
selecionadas as cinco espécies com os maiores valores de importância nas florestas
artificiais e naturais de igapó. Do total de doze espécimes, quatro foram exclusos por
ausência de dados secundários ou dados foliares (Gladonia Griseb., Leopoldina pulchra
Mart., Ouratea Aubl. e Parkia pendula (Willd.) Benth. ex Walp.). As oito espécies
remanescentes podem ser observadas na Tabela 2.
A escolha dos atributos espécie-específicos foi baseada nos atributos listados
durante o 1º Workshop sobre trajetórias sucessionais de ecossistemas restaurados
(vide capítulo anterior desta tese). Estes atributos foram compilados através de consultas
ao Banco de Dados da Flora Brasileira (JABOT) e bibliografia especializada, com
exceção dos atributos foliares (largura foliar, altura foliar, área foliar específica e massa
foliar específica), obtidos através de medidas empíricas (Tabela 2).
Após a seleção das espécies e dos atributos espécies-específicos, as três matrizes
114
de dados de entrada foram elaboradas. A Matriz B descreve as espécies por atributos
espécie-específicos (Tabela 2). A Matriz W descreve a densidade dos indivíduos por
áreas (Tabela 3). A Matriz E descreve as comunidades vegetais por áreas (Tabela 4).
As análises foram realizadas no software SYNCSA for Windows - Version 2.6.9
(©V.Pillar 1992-2010). Os tipos funcionais de plantas foram definidos pelo método de
agrupamento UPGMA baseado no Índice de Similaridade de Gower. A Distância de
Cordas foi utilizada como função de semelhança entre as comunidades. Durante a
seleção dos atributos ótimos, tanto os padrões de convergência como os de divergência
foram utilizados como critérios de classificação.
115
Tabela 2: Atributos espécie-específicos compilados para as oito espécies consideradas na modelagem dos estados futuros dos reflorestamentos
localizados em áreas marginais do Lago Batata - Porto Trombetas, Pará. Estes dados foram utilizados na elaboração da Matriz B.
Al
Di
Lf
Af
Ae
Me
Ts
An
Zo
Hi
Ti
Fn
(m)
(cm)
(cm)
(cm) (cm2g-1) (g-1cm2)
(cm2)
Acosmium nitens
30
35
77,06 36,51 939,32
0,0010
1
0
0
*
1
1
Couepia paraensis
20
50
10,43
5,62
72,83
0,0171
0
1
0
12
1
0
Couepia paraensis subsp. glaucescens
19
46
22,32 11,49 125,27
0,0084
0
1
0
6
1
0
Dalbergia inundata
7
15
53,86 19,21 3499,99 0,0000
0
0
1
1
1
1
Eschweilera blanchetiana
33
32
13,86
7,23
103,02
0,0101
0
1
0
3
*
1
Genipa spruceana
30
50
16,05
6,56
96,70
0,0107
0
1
0
4
1
*
Macrolobium acaciifolium
40
60
59,50 22,82 921,28
0,0011
0
1
0
15
1
0
Swartzia polyphylla
30
60
73,87 31,75 681,59
0,0010
0
0
1
25
0
1
Legenda: Al – Altura máxima, Di – Diâmetro máximo, Lf – Largura foliar, Af – Altura foliar, Ae – Área foliar específica, Me – Massa foliar específica, An - Anemocoria, Zo Zoocoria, Hi - Hidrocoria, Ts – Tamanho da semente, Ti - Tolerância a inundação e Fn - Capacidade de fixar nitrogênio. An, Zo, Hi, Ti e Fn são dados dicotômicos (0 = ausência do
atributo; 1 = presença do atributo). (*) Dados não encontrados na bibliografia consultada.
116
Tabela 3: Abundância das oito espécies consideradas na modelagem dos estados
futuros dos reflorestamentos localizados em áreas marginais do Lago Batata - Porto
Trombetas, Pará. Estes dados foram utilizados na elaboração da Matriz W.
Igapó
5 anos
10 anos
15 anos
natural
Acosmium nitens
77
128
36
21
Couepia paraensis
82
114
25
84
5
48
19
0
Dalbergia inundata
109
30
6
10
Eschweilera blanchetiana
67
84
18
8
Genipa spruceana
115
62
26
0
0
3
67
4
Swartzia polyphylla
3
27
9
50
117
Tabela 4: Variáveis biológicas (estrutura vegetacional, diversidade de espécies e processos ecológicos) utilizadas na modelagem dos estados futuros dos
reflorestamentos localizados em áreas marginais do Lago Batata - Porto Trombetas, Pará. Estes dados foram utilizados na elaboração da Matriz E.
Alt (m)
Dia (cm)
Cob (%)
Ab (m2.ha-1)
Den (ind.ha-1)
Riq
Der (ind.ha-1)
Rir
5 anos
1,20
1,79
6,33
2,62
3060
33
16567
25
10 anos
2,41
3,88
42,47
13,14
2807
34
21149
32
15 anos
3,09
5,39
33,80
9,68
1473
22
24583
26
Igapó Natural
4,22
10,73
95,98
99,26
2193
54
28049
39
Legenda: Alt – altura média do estande (m), Dia – diâmetro médio do estande (cm), Cob – cobertura média do estande (%), Ab – área basal média do estande (m2.ha-1); Den
– densidade média de indivíduos (ind.ha-1), Riq - riqueza máxima do estande, Der – densidade média de regenerantes (ind.ha-1), Rir - riqueza máxima de regenerantes do
estande.
118
5.4.2. Segundo passo: previsão dos estados futuros
No segundo passo da modelagem, os estados futuros das florestas artificiais
foram previstos através da cadeia Markoviana. Esta análise foi realizada no software
MULTIV v.2.95β com base na abundância dos tipos funcionais de plantas das florestas
artificiais resultante da análise funcional descrita no passo anterior. Como dado de
entrada, foi considerada a abundância relativa dos tipos funcionais de plantas nas
florestas artificiais, organizados na Matriz X. A abundância dos tipos funcionais de
plantas das florestas naturais de igapó não foi inclusa nesta análise. Foram considerados
três estados (floresta artificial com cinco anos, floresta artificial com dez anos e floresta
artificial com quinze anos) e dois passos. O intuito desta etapa foi verificar o momento
em que a comunidade permanece no mesmo estado estacionário (Orloci et al. 1993).
Após determinar a composição dos tipos funcionais de plantas do estado estacionário
(estabilização), a mesma foi comparada com a composição das florestas naturais de
igapó (referência) através do Teste do Qui-quadrado. Esta análise foi conduzida no
pacote estatístico Statistica 6.0.
119
RESULTADOS
1.
Composição florística e estrutura fitossociológica
O levantamento fitossociológico realizado nas florestas artificiais e florestas
naturais de igapó localizadas nas margens do lago Batata registrou, em 1,2 hectares, o
total de 2,562 indivíduos adultos, distribuídos em 21 famílias, 47 gêneros e 51 espécies.
As famílias que apresentaram maior representatividade foram Fabaceae (19 espécies),
Chrysobalanaceae (4 espécies), Euphorbiaceae (3 espécies), Malpighiaceae (3 espécies)
e Myrtaceae (3 espécies) (Tabela 5).
Nas florestas artificiais (cinco, dez e quinze anos), em 0,9 hectares foram
registrados 2,063 indivíduos adultos, distribuídos em 18 famílias, 41 gêneros e 43
espécies. As famílias de maior representatividade foram Fabaceae (16 espécies),
Chrysobalanaceae e Euphorbiaceae (3 espécies), Clusiaceae, Malpighiaceae, Myrtaceae,
Rubiaceae e Sapotaceae (2 espécies respectivamente). Já nas florestas naturais de igapó
foram registrados, em 0,3 hectares, 499 indivíduos adultos, distribuídos em 16 famílias,
51 gêneros e 54 espécies. As famílias de maior representatividade foram Fabaceae (16
espécies), Myrtaceae (3 espécies), e Clusiaceae, Chrysobalanaceae Euphorbiaceae,
Malpighiaceae (2 espécies respectivamente). Dezesseis taxons não foram identificados.
Dentre as espécies inventariadas, doze foram encontradas tanto nas florestas
artificiais (cinco, dez e quinze anos) quanto nas florestas naturais de igapó (Acosmium
nitens, Buchenavia oxycarpa, Couepia paraensis, Dalbergia inundata, Eschweilera
blanchetiana, Glandonia spp., Ormosia excelsa, Panopsis rubescens, Parkia pendula,
Swartzia polyphylla, Tabebuia barbata e Zygia cauliflora), treze espécies ocorreram
apenas nas florestas artificiais (Burdachia prismatocarpa, Cassia ssp., Catostemma
albuquerquei, Chrysophyllum oppositum, Couepia paraensis subsp. glaucescens,
Genipa spruceana, Hevea brasiliensis, Martiodendron parviflorum, Miconia ssp.,
120
Micropholis spp., Poraqueiba sericea, Simarouba spp. e Vatairea guianensis), e nove
espécies estavam presentes apenas nas florestas naturais de igapó (Andira retusa,
Calliandra spp., Dicypellium manausense, Leopoldinia pulchra, Licania bracteata,
Ouratea, Psidium spp.1, Psidium spp.2 e Pterocarpus amazonicus).
Com relação aos indivíduos regenerantes, o levantamento fitossociológico nas
florestas artificiais e naturais registrou 1,351 indivíduos, distribuídos em 20 famílias, 34
gêneros e 39 espécies. As famílias de maior riqueza florística foram Fabaceae (14
espécies), Myrtaceae (5 espécies), Chrysobalanaceae (3 espécies), e Clusiaceae,
Euphorbiaceae, Malphigiaceae e Rubiaceae (2 espécies respectivamente) (Tabela 6).
Nas áreas de florestas artificiais foram registrados 891 indivíduos regenerantes,
distribuídos em 18 famílias, 37 gêneros e 38 espécies. As famílias de maior riqueza
florística foram Fabaceae (14 espécies), Chrysobalanaceae (3 espécies), Clusiaceae,
Euphorbiaceae, Malpighiaceae, Myrtaceae e Rubiaceae (2 espécies respectivamente). Já
nas áreas de igapó natural foram registrados 460 indivíduos regenerantes, distribuídos
em 13 famílias, 24 gêneros e 28 espécies. As famílias de maior riqueza florística foram
Fabaceae (11 espécies), Myrtaceae (5 espécies), e Malpighiaceae (2 espécies).
Dentre as espécies regenerantes inventariadas, nove estavam presentes nas
florestas artificiais (5, 10 e 15 anos) e nas florestas naturais de igapó (Acosmium nitens,
Buchenavia oxycarpa, Byrsonima spp., Dalbergia inundata, Eschweilera blanchetiana,
Leopoldina pulchra, Panopsis rubescens, Parkia pendula e Tabebuia barbata), quatorze
espécies regenerantes foram encontradas somente às florestas artificiais (Chrysophyllum
oppositum, Couepia paraensis subsp. glaucescens, Genipa spruceana, Licania apetala,
Mabea nitida, Macrolobium multijugum, Martiodendron parviflorum, Miconia spp.
Naucleopsis caloneura, Ouratea spp., Pterocarpus amazonicus, Rheedia macrophylla,
Ruprechtia spp. e Simaba guianensis), e seis espécies regenerantes foram comuns
121
apenas às florestas naturais de igapó (Psidium spp.1, Psidium spp.2, Psidium spp.3,
Psidium spp.4, Systemonodaphne spp., Tapirira guianensis).
As florestas artificiais, sobretudo aquelas com quinze anos de idade,
apresentaram menores valores de riqueza total de espécies e diversidade florística
quando comparadas com as florestas naturais de igapó (Tabela 7). As florestas artificiais
mais jovens (cinco e dez anos) apresentaram valores riqueza de espécies total e de
diversidade florística de regenerantes inferiores aos valores das florestas artificiais com
quinze anos e florestas naturais de igapó (Tabela 8).
122
Tabela 5. Relação das espécies inventariadas em florestas artificias com diferentes idades e florestas de igapó naturais localizadas em áreas
marginais do lago Batata - Porto Trombetas, Pará.
Nome científico
Família
5 anos
10 anos
15 anos
Igapó natural
Acosmium nitens (Vogel) Yakovlev
Fabaceae
X
X
X
X
Alchornea schomburgkii Klotzsch
Euphorbiaceae
X
X
Andira retusa (Poir.) DC.
Fabaceae
X
Buchenavia oxycarpa (Mart.) Eichler
Combretaceae
X
X
X
X
Burdachia prismatocarpa A. Juss.
Malpighiaceae
X
Byrsonima Rich. ex Juss.
Malpighiaceae
X
X
Calliandra Benth.
Fabaceae
X
Calophyllum brasiliense Cambess.
Clusiaceae
X
X
X
Campsiandra comosa Benth.
Fabaceae
X
X
X
Cassia L.
Fabaceae
X
Catostemma albuquerquei Paula
Malvaceae
X
Chrysophyllum oppositum (Ducke) Ducke
Sapotaceae
X
X
X
Couepia paraensis (Mart. & Zucc.) Benth. ex Hook. f.
Chrysobalanaceae
X
X
X
X
Couepia paraensis subsp. glaucescens (Spruce ex Hook. f.) Prance Chrysobalanaceae
X
X
X
Crataeva benthamii Eichler
Capparaceae
X
X
Cynometra spruceana Benth.
Fabaceae
X
X
X
Dalbergia inundata Spruce ex Benth.
Fabaceae
X
X
X
X
Dicypellium manausense W.A. Rodrigues
Lauraceae
X
Eschweilera blanchetiana (O. Berg) Miers
Lecythidaceae
X
X
X
X
Genipa spruceana Steyerm.
Rubiaceae
X
X
X
Glandonia Griseb.
Malpighiaceae
X
X
X
X
Hevea brasiliensis (Willd. ex A. Juss.) Müll. Arg.
Euphorbiaceae
X
X
Leopoldinia pulchra Mart.
Arecaceae
X
Licania apetala (E. Mey.) Fritsch
Chrysobalanaceae
X
X
X
Licania bracteata Prance
Chrysobalanaceae
X
123
Continuação Tabela 5...
Nome científico
Mabea nitida Spruce ex Benth.
Macrolobium acaciifolium (Benth.) Benth.
Macrolobium multijugum (DC.) Benth.
Martiodendron parviflorum (Amshoff) R. Koeppen
Miconia Ruiz & Pav.
Micropholis (Griseb.) Pierre
Myrciaria dubia (Kunth) McVaugh
Naucleopsis caloneura (Huber) Ducke
Ormosia excelsa Benth.
Ouratea Aubl.
Panopsis rubescens (Pohl) Rusby
Parkia pendula (Willd.) Benth. ex Walp.
Peltogyne venosa (Vahl) Benth.
Pithecellobium Mart.
Poraqueiba sericea Tul.
Psidium spp.1
Psidium spp.2
Pterocarpus amazonicus Huber
Rheedia macrophylla (Mart.) Planch. & Triana
Simarouba Aubl.
Stachyarrhena Hook. f.
Swartzia polyphylla DC.
Tabebuia barbata (E. Mey.) Sandwith
Tachigali paniculata Aubl.
Vatairea guianensis Aubl.
Zygia cauliflora (Willd.) Killip
Família
Euphorbiaceae
Fabaceae
Fabaceae
Fabaceae
Melastomataceae
Sapotaceae
Myrtaceae
Moraceae
Fabaceae
Ochnaceae
Proteaceae
Fabaceae
Fabaceae
Fabaceae
Icacinaceae
Myrtaceae
Myrtaceae
Fabaceae
Clusiaceae
Simaroubaceae
Rubiaceae
Fabaceae
Bignoniaceae
Fabaceae
Fabaceae
Fabaceae
5 anos
X
X
10 anos
X
X
X
X
15 anos
X
Igapó natural
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
124
Nome científico
Indet. 1
Indet. 2
Indet. 3
Indet. 5
Indet. 6
Indet. 7
Indet. 8
Indet. 10
Indet. 11
Indet. 12
Indet. 13
Indet. 14
Indet. 15
Indet. 16
Indet. 17
Indet. 19
Família
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
5 anos
10 anos
15 anos
Igapó natural
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
125
Tabela 6. Relação das espécies regenerantes inventariadas em florestas artificias com diferentes idades e florestas de igapó naturais localizadas em áreas
Nome científico
Família
5 anos
10 anos
15 anos
Igapó natural
Fabaceae
X
X
X
X
Euphorbiaceae
X
X
X
Combretaceae
X
X
X
X
Malpighiaceae
X
X
X
X
Clusiaceae
X
X
Chrysobalaneceae
X
X
X
Chrysobalaneceae
Couepia paraensis subsp. glaucescens (Spruce ex Hook. f.) Prance
X
Fabaceae
X
X
Fabaceae
X
X
X
X
Lecythidaceae
X
X
X
X
Rubiaceae
X
X
X
Malpighiaceae
Glandonia Griseb.
X
X
Arecaceae
X
X
X
X
Chrysobalanaceae
X
X
Euphorbiaceae
X
X
X
Fabaceae
X
X
X
Fabaceae
X
X
Fabaceae
X
Melastomataceae
Miconia Ruiz & Pav.
X
X
X
Myrtaceae
X
X
Moraceae
X
Fabaceae
X
X
X
Ochnaceae
Ouratea Aubl.
X
Proteaceae
X
X
X
X
126
Nome científico
Psidium spp.1
Psidium spp.2
Psidium spp.3
Psidium spp.4
Ruprechtia C.A. Mey.
Simaba guianensis Aubl.
Systemonodaphne Mez
Tapirira guianensis Aubl.
Indet. 1
Indet. 2
Indet. 4
Indet. 6
Indet. 8
Indet. 10
Indet. 12
Indet. 13
Família
Fabaceae
Fabaceae
Fabaceae
Myrtaceae
Myrtaceae
Myrtaceae
Myrtaceae
Fabaceae
Clusiaceae
Polygonaceae
Simaroubaceae
Rubiaceae
Fabaceae
Lauraceae
Bignoniaceae
Fabaceae
Anacardiaceae
Fabaceae
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
5 anos
10 anos
15 anos
Igapó natural
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
127
Nome científico
Indet. 14
Indet. 15
Indet. 18
Indet. 20
Indet. 21
Indet. 22
Indet. 23
Família
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
Indeterminada
5 anos
10 anos
15 anos
Igapó natural
X
X
X
X
X
X
X
X
128
Tabela 7. Índices de diversidade das florestas artificiais e florestas naturais de igapó
localizadas em áreas marginais do lago Batata - Porto Trombetas, Pará.
N
S
H'
J
Floresta artificial com 5 anos
918
33
2,96
0,84
821
34
2,88
0,82
324
22
2,44
0,77
Floresta natural de igapó
499
54
3,16
0,79
N: número de indivíduos, S: riqueza de espécies, H': índice de Shannon-Weaver, C: índice
de Simpson.
129
Tabela 8. Índices de diversidade dos indivíduos regenerantes amostrados nas
florestas artificiais e florestas naturais de igapó localizadas em áreas marginais do
lago Batata - Porto Trombetas, Pará.
N
S
H'
J
222
24
2,27
0,83
315
32
2,46
0,84
354
26
2,64
0,90
Floresta natural de igapó
460
39
2,82
0,89
N: número de indivíduos, S: riqueza de espécies, H': índice de Shannon-Weaver, C: índice
de Simpson.
130
1.1. Florestas artificiais com cinco anos
Nas florestas artificiais com cinco anos foram amostrados 918 indivíduos
oriundos de plantios, distribuídos em 33 espécies. As espécies com os maiores valores
de importância (VI) foram C. paraensis, D. inundata, G. spruceana, A. nitens e E.
blanchetiana. Juntas, elas totalizaram 450 indivíduos, que corresponderam a 46% do VI
(Tabela 9).
Foram amostrados 222 indivíduos oriundos da regeneração natural nas florestas
artificiais com cinco anos, distribuídos em 25 espécies, sendo que destas, quatro não
foram identificadas. Dentre as espécies regenerantes, G. spruceana, D. inundata, M.
nitida, Byrsonima spp. e E. blanchetiana foram as dominantes. Elas totalizaram 162
indivíduos, que correponderam a 64% do VI (Tabela 10). A. schomburgkii, Byrsonima
spp., L. pulchra, Miconia spp. e as quatro espécies não identificadas são espécies
regenerantes oriundas da dispesão natural, uma vez que não estavam inclusas dentre as
espécies utilizadas nos plantios.
131
Tabela 9. Parâmetros fitossociológicos das espécies adultas inventariadas em florestas artificiais com 5 anos localizadas nas áreas
Nome Científico
N
DR
DoR
VC (%) VI (%)
Burdachia prismatocarpa A. Juss.
Glandonia Griseb.
Simarouba Aubl.
82
109
115
77
67
65
54
35
37
44
18
28
19
20
20
15
13
15
17
9
8
12
9
7
3
8.9100
11.8500
12.5000
8.3700
7.2800
7.0700
5.8700
3.8000
4.0200
4.7800
1.9600
3.0400
2.0700
2.1700
2.1700
1.6300
1.4100
1.6300
1.8500
0.9800
0.8700
1.3000
0.9800
0.7600
0.3300
15.4400
10.6300
8.1000
10.4900
8.9600
5.4300
5.9100
7.4000
3.0400
1.6300
4.3100
1.5700
2.4700
1.6700
1.7500
1.7700
1.5500
0.9500
0.6400
1.5800
1.4700
0.4300
0.2500
0.4600
0.8400
12.1800
11.2400
10.3000
9.4300
8.1200
6.2500
5.8900
5.6000
3.5300
3.2100
3.1300
2.3100
2.2700
1.9200
1.9600
1.7000
1.4800
1.2900
1.2400
1.2800
1.1700
0.8700
0.6100
0.6100
0.5800
10.4600
9.9000
9.2800
8.5600
7.8200
6.1800
5.9500
5.1000
4.1100
3.9600
2.9400
2.7700
2.6200
2.2600
2.2200
1.9100
1.7700
1.7700
1.7400
1.3100
1.3000
1.2300
0.9300
0.8000
0.5800
132
Nome Científico
N
DR
DoR
VC (%)
VI (%)
5
0.5400
0.3400
0.4400
0.5500
4
0.4300
0.1300
0.2800
0.4500
4
0.4300
0.2000
0.3200
0.4100
Catostemma albuquerquei Paula
3
0.3300
0.0800
0.2000
0.3300
1
0.1100
0.2400
0.1800
0.1800
Poraqueiba sericea Tul.
1
0.1100
0.0600
0.0900
0.1200
1
0.1100
0.0100
0.0600
0.1100
1
0.1100
0.0200
0.0700
0.1100
Total
918 100.0000 100.0000 100.0000 100.0000
N: número de indivíduos, DR: densidade relativa (%), DoR: dominância relativa, VC (%): valor de cobertura relativo, VI (%):
valor de importância relativo.
133
Tabela 10. Parâmetros fitossociológicos das espécies regenerantes inventariadas em florestas artificiais com 5 anos localizadas
nas áreas marginais do lago Batata - Porto Trombetas, Pará.
Nome Científico
N
DR
DoR
VC (%)
VI (%)
Indet. 2
Miconia Ruiz & Pav.
Indet. 10
Indet. 20
Indet. 12
25
77
19
36
5
10
1
5
7
8
5
2
1
4
5
2
2
1
1
1
1
1
1
1
11.6700
33.9000
8.3900
15.3200
2.2000
4.9900
0.4000
2.3900
3.3200
4.0800
2.1700
0.7900
0.5200
2.0400
2.4600
0.9100
0.7900
0.5200
0.4900
0.4900
0.4900
0.4900
0.4000
0.4000
44.5500
1.4200
6.0300
0.1700
12.5400
1.0700
10.4100
5.1000
0.2300
3.3000
3.3100
5.2800
4.9200
0.2500
0.0600
0.0400
0.1100
0.6000
0.2300
0.1600
0.1300
0.0100
0.0600
0.0000
28.1100
17.6600
7.2100
7.7500
7.3700
3.0300
5.4000
3.7400
1.7800
3.6900
2.7400
3.0400
2.7200
1.1500
1.2600
0.4800
0.4500
0.5600
0.3600
0.3300
0.3100
0.2500
0.2300
0.2000
22.5500
18.7600
8.3000
8.0200
6.1800
4.2400
3.9200
3.4500
3.4100
3.4100
3.1000
2.6600
2.1300
2.0300
1.7900
0.9500
0.9300
0.6900
0.5600
0.5400
0.5300
0.4800
0.4700
0.4500
134
Nome Científico
N
DR
DoR
VC (%)
VI (%)
Ouratea Aubl.
1
0.4000
0.0000
0.2000
0.4500
Total
222 100.0000
100.0000
100.0000
100.0000
135
1.2. Florestas artificiais com dez anos
Foram amostrados 821 indivíduos distribuídos em 34 espécies nos plantios com
dez anos de idade. As cinco espécies com maiores VI foram C. paraensis, A. nitens, E.
blanchetiana, Gladonia sp. e G. spruceana. Juntas, totalizaram 463 indivíduos, que
corresponderam a 52% do VI (Tabela 11).
Nestes plantios foram amostrados 315 indivíduos oriundos da regeneração
natural, distribuídos em 32 espécies. Quatro táxons não foram identificados. As espécies
mais abundantes foram D. inundata, P. pendula, A. nitens, Miconia spp. e
Stachyarrhena spp. Elas totizaram 182 indivíduos, que corresponderam a 61% do VI
(Tabela 12). Dentre as espécies que regeneraram naturalmente, L. pulchra, Miconia
spp., Pithecellobium spp., Pterocarpus amazonicus e Ruprechtia spp. não estavam
inclusas dentre as espécies plantadas.
136
Tabela 11. Parâmetros fitossociológicos das espécies adultas inventariadas em florestas artificiais com 10 anos localizadas nas
áreas marginais do lago Batata - Porto Trombetas, Pará.
Nome Científico
N
DR
DoR
VC (%) VI (%)
Glandonia Griseb.
Ormosia excelsa 2Benth.
Vatairea guianensis Aubl.
114
128
84
75
62
48
32
27
30
20
26
20
20
17
13
10
13
5
12
11
8
6
6
5
5
13.8500
15.6500
10.2000
9.0700
7.5000
5.8600
3.9100
3.3000
3.5900
2.4500
3.1500
2.4900
2.3900
2.1100
1.6300
1.2300
1.5700
0.6100
1.4700
1.3800
0.9800
0.7300
0.7500
0.6000
0.6000
20.7300
15.4500
12.5100
5.6000
4.9800
8.1800
5.5800
5.5700
3.2500
3.6100
0.8500
1.5800
1.3900
0.6600
0.2800
2.0100
0.3700
3.2300
0.3500
0.2000
0.9000
0.5200
0.1200
0.1700
0.4300
17.2900
15.5500
11.3500
7.3300
6.2400
7.0200
4.7500
4.4400
3.4200
3.0300
2.0000
2.0400
1.8900
1.3900
0.9600
1.6200
0.9700
1.9200
0.9100
0.7900
0.9400
0.6300
0.4300
0.3800
0.5200
14.6100
13.3700
10.1700
7.5800
6.7700
6.2600
4.3500
4.1400
4.0200
3.3600
2.8300
2.5400
2.5300
2.0300
1.5900
1.5600
1.4400
1.4400
1.4000
1.2400
0.9400
0.8100
0.7600
0.6500
0.5800
137
Nome Científico
N
DR
DoR
VC (%)
VI (%)
5
0.6000
0.4300
0.5200
0.5800
Psidium spp.1
4
0.4800
0.1600
0.3200
0.4500
3
0.3700
0.2500
0.3100
0.4400
4
0.4800
0.0900
0.2800
0.4300
2
0.2500
0.1700
0.2100
0.3000
2
0.2500
0.0900
0.1700
0.2700
2
0.2500
0.0600
0.1600
0.2600
Cassia L.
1
0.1200
0.1200
0.1200
0.1600
1
0.1200
0.0800
0.1000
0.1500
Total
821 100.0000 100.0000 100.0000 100.0000
138
Nome Científico
N
DR
DoR
VC (%)
VI (%)
Miconia Ruiz & Pav.
Indet. 12
Ruprechtia C.A. Mey.
Indet. 1
Indet. 2
111
10
15
38
8
31
10
11
14
16
4
7
9
4
3
3
2
2
2
2
2
1
1
1
36.8100
2.5500
4.1400
9.4100
1.9800
8.1800
4.4000
5.7100
4.6300
5.1800
0.9900
3.3800
2.9100
1.3000
1.1000
0.8200
0.6500
0.8500
0.6500
0.5000
0.5000
0.2500
0.6000
0.3200
9.6300
33.0100
23.8500
1.6100
15.2500
2.9400
4.7000
2.7700
1.0200
0.0700
2.8100
0.2600
0.0200
0.0100
0.1800
0.0300
0.5500
0.0500
0.1000
0.1000
0.0000
0.8100
0.0200
0.1000
23.2200
17.7800
14.0000
5.5100
8.6200
5.5600
4.5500
4.2400
2.8200
2.6300
1.9000
1.8200
1.4700
0.6500
0.6400
0.4200
0.6000
0.4500
0.3700
0.3000
0.2500
0.5300
0.3100
0.2100
21.3100
13.2500
11.6600
7.4000
7.1400
5.8000
5.1300
4.4600
4.2100
2.9200
2.2000
2.1400
1.4500
1.1300
1.1200
0.9800
0.8700
0.7700
0.7100
0.6700
0.6300
0.5900
0.4400
0.3700
139
Nome Científico
N
DR
DoR
VC (%)
VI (%)
1
0.3200
0.0000
0.1600
0.3400
Indet. 10
1
0.3200
0.0100
0.1700
0.3400
Psidium spp.1
1
0.3200
0.0100
0.1700
0.3400
1
0.2500
0.0500
0.1500
0.3300
1
0.2500
0.0000
0.1200
0.3200
1
0.2500
0.0200
0.1300
0.3200
1
0.2500
0.0000
0.1200
0.3200
1
0.2500
0.0000
0.1200
0.3200
Total
315 100.0000
100.0000
100.0000
100.0000
140
1.3. Florestas artificiais com quinze anos
Foram amostrados 324 indivíduos distribuídos em 22 espécies. As cinco
espécies com maiores VI amostradas nos plantios com quinze anos de idade foram M.
acaciifolium, Gladonia spp., A. nitens, C. paraensis subsp. glaucescens e P. pendula.
Juntas, totalizaram 203 indivíduos, que corresponderam a 58% do VI (Tabela 13).
Com relação aos indivíduos oriundos da regeneração natural, foram amostrados
nestes plantios 354 indivíduos distribuídos em 26 espécies. As espécies dominates
foram D. inundata, C. paraensis, C. paraenses subsp. glaucescens, A. nitens e
Byrsonima spp. Elas somaram 169 indivíduos, que corresponderam a 49% do VI
(Tabela 14). Três táxons não foram identificados. Dentre as espécies que regeneraram
naturalmente nos plantios de quinze anos, L. pulchra, M. nitida, S. guianensis e T.
paniculata não estavam inclusas dentre as espécies plantadas.
141
Tabela 13. Parâmetros fitossociológicos das espécies adultas inventariadas em florestas artificiais com 15 anos localizadas nas
Nome Científico
N
DR
DoR
VC (%) VI (%)
67 19.0800 14.8500 16.9700 16.9300
Glandonia Griseb.
68 19.1100 11.5500 15.3300 15.2400
36 10.3100 9.3800
9.8500
9.9800
19 5.3600
16.7000 11.0300 8.5600
13 7.1200
9.9900
8.5500
7.7100
25 7.1400
8.2900
7.7200
7.1500
26 7.6200
2.8500
5.2400
6.9000
14 4.5900
4.5200
4.5500
5.6500
18 5.1500
4.8500
5.0000
5.3400
9
3.8100
8.4100
6.1100
5.2800
6
1.7300
1.2100
1.4700
1.9800
5
1.4400
2.9700
2.2100
1.6700
3
1.4800
1.0100
1.2400
1.4300
5
1.4000
0.9700
1.1900
1.3900
1
0.5900
1.1000
0.8500
0.7700
2
0.8800
0.2100
0.5500
0.7700
2
0.5600
0.0400
0.3000
0.6000
1
0.2900
0.5800
0.4300
0.4900
1
0.2900
0.2200
0.2600
0.3700
1
0.2900
0.1200
0.2100
0.3400
1
0.2900
0.0000
0.1500
0.3000
Miconia Ruiz & Pav.
1
0.2900
0.0100
0.1500
0.3000
Total
324 100.0000 100.0000 100.0000 100.0000
142
Nome Científico
N
DR
DoR
VC (%) VI (%)
Indet. 1
Miconia Ruiz & Pav.
Indet. 12
Glandonia Griseb.
Simaba guianensis Aubl.
Indet. 21
81
9
1
24
54
33
19
5
12
22
13
14
17
11
5
4
7
4
6
3
2
3
2
1
23.2900
3.5400
0.5600
6.8900
12.4300
8.6300
3.5700
1.7700
3.5300
4.2100
4.0200
4.0100
6.7500
3.8300
1.1100
1.1100
2.4200
2.2500
1.9700
0.8400
1.1300
1.0300
0.4600
0.2800
6.8400
22.4700
26.0100
10.5200
1.0000
2.3800
2.2000
8.6800
4.4100
0.8100
3.8600
1.7900
0.1000
0.0700
3.8200
3.2400
0.0000
0.0700
0.2900
0.5300
0.7600
0.0800
0.0100
0.0100
15.0600
13.0100
13.2800
8.7100
6.7100
5.5000
2.8900
5.2300
3.9700
2.5100
3.9400
2.9000
3.4300
1.9500
2.4700
2.1800
1.2100
1.1600
1.1300
0.6900
0.9400
0.5500
0.2400
0.1400
14.1400
9.9700
9.0400
8.0400
7.8300
5.9000
4.5300
4.4100
4.3200
4.2800
4.1200
3.9800
3.5900
2.9800
2.2000
2.0100
1.7400
1.3300
1.3100
1.0200
1.0000
0.9300
0.5300
0.2800
143
Nome Científico
N
DR
DoR
VC (%)
VI (%)
1
0.1800
0.0400
0.1100
0.2600
1
0.1800
0.0000
0.0900
0.2500
Total
354 100.0000 100.0000 100.0000 100.0000
144
1.4. Florestas naturais de igapó
Foram amostrados 499 indivíduos distribuídos em 54 espécies. Dezesseis táxons
não foram identificados. As espécies mais abundantes foram S. polyphyla C. paraensis,
Ouratea spp., L. pulchra e A. nitens. Juntas, elas totalizaram 237 indivíduos, que
corresponderam a 44% do VI (Tabela 15).
Nas florestas naturais de igapó foram registrados 460 indivíduos oriundos da
regeneração natural, distribuídos em 39 espécies. Onze táxons não foram identificados.
As cinco espécies dominantes foram C. paraenses, Stachyarrhena spp., A. nitens e A.
schomburgkii. Junta, elas totalizaram 249 indivíduos, que corresponderam a 53% do VI
(Tabela 16).
145
Tabela 15. Parâmetros fitossociológicos das espécies adultas inventariadas em florestas naturais de igapó localizadas nas áreas
Nome Científico
N
DR
DoR
VC (%)
VI (%)
Ouratea Aubl.
Indet. 8
Psidium spp.2
Glandonia Griseb.
Andira retusa (Poir.) DC.
Indet. 14
Indet. 6
Indet. 1
Licania bracteata Prance
Indet. 16
50
84
46
36
37
21
29
25
15
7
8
10
4
10
6
6
5
10
7
5
3
6
3
4
5
10.3000
15.0900
11.6000
7.4100
9.3300
3.8600
5.2200
4.3900
2.6300
1.3300
1.3900
1.7600
0.6600
1.6700
1.0500
1.2100
1.0100
1.7100
1.2100
1.0200
0.7600
1.5100
0.6000
1.0100
0.8800
22.2200
12.6500
8.2200
2.9400
3.4600
2.0900
3.2600
2.3000
2.3700
2.6100
2.2200
1.3800
4.1100
0.7900
2.2800
1.6000
2.3100
0.7400
1.6500
1.8400
2.6900
0.8500
2.9500
1.0600
0.5500
16.2600
13.8700
9.9100
5.1800
6.4000
2.9700
4.2400
3.3500
2.5000
1.9700
1.8000
1.5700
2.3900
1.2300
1.6700
1.4000
1.6600
1.2300
1.4300
1.4300
1.7200
1.1800
1.7800
1.0400
0.7100
13.1500
13.0000
7.1800
5.4700
4.9900
4.2900
4.2700
4.1100
2.6800
2.1800
2.0700
1.9100
1.8800
1.8300
1.8300
1.8000
1.6900
1.5400
1.5300
1.5300
1.4400
1.3700
1.3300
1.2700
1.2000
146
Nome Científico
N
DR
DoR
VC (%)
VI (%)
Indet. 13
Calliandra Benth.
Indet. 19
Psidium spp.1
Indet. 17
Indet. 5
Dicypellium manausense W.A. Rodrigues
Indet. 3
Indet. 2
Indet. 10
Indet. 7
5
4
3
3
4
4
3
2
2
2
1
2
2
2
1
2
1
3
1
1
1
1
1
1
1
1
0.8800
0.6800
0.6700
0.6700
0.8400
0.6600
0.5000
0.3400
0.5000
0.5000
0.1800
0.5000
0.3300
0.5000
0.1800
0.3300
0.1700
0.5000
0.1700
0.1700
0.2500
0.2500
0.2500
0.2500
0.1800
0.2500
1.7300
0.5900
0.8200
0.5900
0.2000
0.7400
0.2800
0.7700
0.5100
0.4500
1.1000
0.1900
0.2300
0.0300
0.7000
0.0700
0.5600
0.1600
0.3300
0.3300
0.1400
0.0900
0.0800
0.0700
0.0800
0.0000
1.3100
0.6400
0.7500
0.6300
0.5200
0.7000
0.3900
0.5500
0.5100
0.4800
0.6400
0.3400
0.2800
0.2700
0.4400
0.2000
0.3600
0.3300
0.2500
0.2500
0.2000
0.1700
0.1700
0.1600
0.1300
0.1300
1.1600
1.0000
0.9300
0.8500
0.7800
0.7600
0.6900
0.6600
0.6300
0.6100
0.5700
0.5200
0.4700
0.4700
0.4400
0.4200
0.3800
0.3600
0.3100
0.3100
0.2800
0.2600
0.2600
0.2500
0.2300
0.2300
147
Nome Científico
N
DR
DoR
VC (%)
VI (%)
Indet. 11
1
0.2500
0.0100
0.1300
0.2300
Indet. 12
1
0.2500
0.0000
0.1300
0.2300
Indet. 15
1
0.1800
0.0000
0.0900
0.2000
Total
499 100.0000 100.0000 100.0000 100.0000
148
Tabela 16. Parâmetros fitossociológicos das espécies regenerantes inventariadas em florestas naturais de igapó localizadas nas
Nome Científico
N
DR
DoR
VC (%)
VI (%)
Indet. 22
Indet. 12
Psidium spp.2
Psidium spp.1
Glandonia Griseb.
Indet. 14
Indet. 8
89
15
108
21
26
5
28
12
11
13
11
8
15
10
7
9
6
10
3
6
5
4
4
5
19.1800
2.7600
19.9500
4.7100
7.1900
1.4000
5.4000
3.4200
2.6300
2.8100
2.3300
3.4100
2.8200
2.0800
2.0700
1.6600
2.0700
1.8400
0.6700
1.4000
0.9200
0.7400
0.9800
0.9200
33.5000
28.7000
0.1700
18.6300
3.8400
9.5300
0.0600
0.0800
0.8800
0.6300
0.1600
0.3800
0.0000
0.2700
0.1100
0.0400
0.3800
0.1600
1.4500
0.6000
0.0000
0.0000
0.0200
0.0200
26.3400
15.7300
10.0600
11.6700
5.5100
5.4700
2.7300
1.7500
1.7500
1.7200
1.2400
1.8900
1.4100
1.1800
1.0900
0.8500
1.2300
1.0000
1.0600
1.0000
0.4600
0.3700
0.5000
0.4700
22.1600
11.2800
10.6700
9.5300
6.2100
4.2800
3.4100
2.5900
2.4400
2.4200
1.9400
1.9000
1.8900
1.7400
1.5200
1.5200
1.4500
1.3000
1.1800
1.1400
0.9400
0.8800
0.8100
0.7900
149
Nome Científico
N
DR
DoR
VC (%)
VI (%)
3
0.7300
0.0700
0.4000
0.7400
3
1.0400
0.0500
0.5400
0.6800
Tapirira guianensis Aubl.
3
0.7300
0.0100
0.3700
0.5600
Indet. 13
3
0.5500
0.0000
0.2800
0.5000
Indet. 15
3
0.5500
0.0000
0.2800
0.5000
2
0.3700
0.0200
0.1900
0.4500
2
0.3700
0.0000
0.1900
0.4400
Indet. 4
2
0.4900
0.0900
0.2900
0.3500
Indet. 20
1
0.4300
0.1000
0.2600
0.3300
Systemonodaphne Mez
2
0.4900
0.0100
0.2500
0.3200
Psidium spp.4
1
0.1800
0.0200
0.1000
0.2300
Indet. 18
1
0.1800
0.0000
0.0900
0.2200
Indet. 23
1
0.1800
0.0000
0.0900
0.2200
Indet. 6
1
0.1800
0.0000
0.0900
0.2200
Psidium spp.3
1
0.1800
0.0000
0.0900
0.2200
Total
460 100.0000 100.0000 100.0000 100.0000
150
2.
Similaridade florística entre as florestas artificiais e florestas naturais de igapó
A primeira a análise de agrupamento indicou a formação de dois grupos: um
grupo composto pelas florestas naturais de igapó (adultos e regenerantes) e florestas
aritificiais, considerando apenas os indivíduos plantados, e outro grupo formado pelas
florestas artificiais considerando apenas a regeneração natural. Cerca de 25% das
espécies que regeneram naturalmente nas áreas de plantios estão presentes nas florestas
naturais de igapó e nas florestas artificiais (Figura 4).
As maiores similaridades florísticas observadas dentro do primeiro grupo
ocorreram entre os indivíduos adultos e os regenerantes nas florestas naturais de igapó,
e entre os plantios com cinco e dez anos. Os plantios com quinze anos apresentaram a
maior distinção florística dentre as florestas artificiais (Figura 4).
No segundo grupo, a maior similaridade florística ocorreu entre os regenerantes
das florestas artificiais com dez e quinze anos. A composição florística dos regenerantes
das florestas artificiais com cinco anos foi distinta das demais florestas artificiais
(Figura 4).
A segunda análise de agrupamento, que considerou separadamente as áreas
amostradas, demonstrou que plantio realizado em 1995 e mensurado em 2010 (floresta
artificial com quinze anos) diferiu das demais áreas em termos de composição florística
(similaridade florística de cerca de 10%). A floresta natural de igapó mensurada em
2006 apresentou um padrão de composição florística distinto das demais áreas de
referência. As espécies regenerantes desta área se distinguiram das espécies adultas. A
composição florística de regenerantes desta área apresentou maior similaridade com a
composição florística das florestas artificiais (Figura 5).
151
5r
15r
10r
15p
5p
10p
Rr
Ra
1.0
0.9
Similaridade (Bray-Curtis)
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Figura 4: Dendrograma de similaridade florística entre as florestas artificiais com distintas
idades (5, 10 e 15 anos) e florestas naturais de igapó (referência) localizadas em áreas marginais
do lago Batata - Porto Trombetas, Pará. Nesta análise a abundância das espécies amostradas nas
áreas de plantios com a mesma idade foi agrupada, assim como nas florestas naturais de igapó.
Legenda: 5p – florestas artificiais com cinco anos considerando somente os indivíduos
plantados; 5r - florestas artificiais com cinco anos considerando somente os indivíduos oriundos
da regeneração natural; 10p – florestas artificiais com dez anos considerando somente os
indivíduos plantados; 10r - florestas artificiais com dez anos considerando somente os
indivíduos oriundos da regeneração natural; 15p – florestas artificiais com quinze anos
considerando somente os indivíduos plantados; 15r - florestas artificiais com quinze anos
considerando somente os indivíduos oriundos da regeneração natural; Ra – florestas naturais de
igapó considerando somente os indivíduos adultos; Rr - florestas naturais de igapó considerando
somente os indivíduos oriundos da regeneração natural.
152
RIa
RIr
RIIIa
RIIIr
DIp
QIp
QIIIp
CIIp
CIIIp
DIIp
DIIIp
CIp
RIIa
DIr
QIr
QIIr
CIr
CIIr
DIIr
CIIIr
DIIIr
QIIIr
RIIr
QIIp
1.0
0.9
Similaridade (Bray-Curtis)
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Figura 5: Dendrograma de similaridade florística entre as nove áreas com florestas artificiais
(cinco, dez e quinze anos) e três áreas de florestas naturais de igapó localizadas em áreas
marginais do lago Batata - Porto Trombetas, Pará. Nesta análise foram consideradas as
abundâncias das espécies amostradas em cada área de plantio e em cada floresta natural de
igapó. As legendas das áreas podem ser consultadas na Tabela 1. A letra “p” indica que apenas
os indivíduos plantados foram considerados; a letra “r” indica que apenas os indivíduos
regenerantes foram considerados; e a letra “a” indica que apenas os adultos nas áreas de
referência foram considerados.
153
3.
Avaliação do processo de reabilitação do lago Batata através de indicadores
ecológicos
Os resultados das ANOVAs hierarquizadas revelaram que os valores médios dos
indicadores biológicos variaram entre as distintas idades de plantios (Apêndice 3 deste
capítulo). Os indivíduos amostrados nas florestas artificiais com cinco anos
apresentaram valores médios de altura menores do que aqueles amostrados nas florestas
artificiais com quinze anos, que por sua vez, apresentaram valores médios similares aos
encontrados nas florestas naturais de igapó. As florestas artificiais com dez anos
apresentaram indivíduos cujos valores médios de altura foram intermediários. O
diâmetro médio e a área basal média dos indivíduos aumentaram com o passar do tempo
nas florestas artificiais. No entanto, as florestas naturais de igapó apresentaram os
maiores diâmetros e áreas basais médios quando comparados com as outras florestas. A
cobertura de copa média dos indivíduos amostrados nas florestas artificiais com cinco
anos foi inferior àquela registrada nas florestas artificiais com quinze anos, que não
alcançaram o valor médio das florestas naturais de igapó. A densidade média de
indivíduos plantados nas florestas artificiais diminuiu com o passar do tempo. As
florestas artificiais com quinze anos apresentam atualmente densidades médias similares
àquelas registradas nas florestas naturais de igapó. A riqueza média de espécies diminui
com o passar do tempo nas florestas artificiais, e atualmente, as florestas artificias com
quinze anos apresentam riqueza média menor do que a das florestas naturais. Com
relação à regeneração, as maiores densidades médias de regenerantes foram registradas
nas florestas naturais de igapó. As florestas artificiais com quinze anos de idade e as
florestas naturais de igapó apresentaram os maiores riquezas médias de espécies (Tabela
17).
154
As ANOVAs hierarquizadas demonstraram ainda diferenças significativas nos
variáveis biológicas mensuradas em áreas com florestas da mesma idade, com exceção
da área basal média, que foi similar entre as áreas com a mesma idade (Apêndice 4
deste capítulo). As variáveis de estrutura vegetacional, de diversidade e de processos
ecológicos não variaram entre as três florestas artificiais com cinco anos de idade (CI,
CII e CIII). O plantio de 1998 mensurado em 2008 (DIII) apresentou valores médios de
altura, cobertura de copa e densidade de indivíduos inferiores das outras duas florestas
artificiais com dez anos. Já as florestas artificiais com quinze anos (QI, QII e QIII)
apresentaram variações entre si quanto à densidade média de indivíduos e riqueza média
de indivíduos. Na floresta plantada em 1997 e mensurada em 2010 (QIII) foram
registrados os maiores valores de densidade e riqueza quando comparada com a floresta
plantada em 1995 e mensurada em 2010 (QII). As florestas naturais de igapó
apresentaram diferenças na altura, diâmetro, densidade e riqueza de regenerantes
(Figura 6).
155
Tabela 17: Valor médio ± desvio padrão das variáveis biológicas mensuradas em florestas artificiais e florestas naturais de igapó localizadas em áreas
marginais do lago Batata - Porto Trombetas, Pará. Médias seguidas da mesma letra não diferem estatisticamente a 5% de probabilidade.
Densidade de
Riqueza de
Área basal
Cobertura de
Densidade
Riqueza de
Altura (m)
Diâmetro (cm)
regenerantes
espécies
2
-1
-1
(m ha )
copa (%)
(ind. ha )
espécies
(ind. ha-1)
regenerantes
5 anos
1,20 ± 0,03 c
1,79 ± 0,05 d
0,02 ± 0,00 c
6,33 ± 0,49 c
3060 ± 101 a
6 ± 0,17 a
16567 ± 2655 b 2 ± 0,19 b
10 anos
2,41 ± 0,08 b
3,88 ± 0,11 c
0,11 ± 0,01 b
42,47 ± 4,54 ab 2737 ± 125 a
5 ± 0,13 b
21148 ± 2830 b 2 ± 0,18 ab
15 anos
3,09 ± 0,12 a
5,39 ± 0,28 b
0,11 ± 0,02 b
33,80 ± 5,33 b
1080 ± 111 b
2 ± 0,13 d
24583 ± 3169 b 3 ± 0,24 a
Referência 4,22 ± 0,31 a
10,73 ± 1,02 a
1,09 ± 0,20 a
95,98 ± 16,60 a 1663 ± 234 b
3 ± 0,24 c
28048 ± 3935 a 3 ± 0,25 a
156
(b)
6.0
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
a
a
a
a
a
a
b
b
a
Diâmetro médio (cm)
Altura média (cm)
(a)
a
b
a
CI
CIII
CII
DII
DI
QI
DIII
QIII
QII
18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0
-2.0
a
a
b
a
a
a
RII
RI
a
a
CIII
DII
CII
DI
QIII
QII
RII
RI
RIII
Áreas
(d)
200
a
180
160
140
120
100
a
a
80
a
a
60
a a
b
40 a a a
20
0
-20
-40
CI CII CIII DI DII DIII QI QII QIII RI RII
2.0
Área basal média (m2 ha -1)
(c)
Cobertura de copa média (%)
QI
DIII
Áreas
a
a
1.5
a
1.0
0.5 a
a
a
a
a
a
a
a
a
0.0
-0.5
-1.0
CI
CIII
CII
DII
DI
QI
DIII
QIII
QII
RII
RI
RIII
Áreas
Áreas
(f)
Densidade média (ind. ha -1)
4500
a
a
4000 a a
a
3500
3000
b
a
a
a
a
2500
b
2000
1500
c
1000
500
0
-500
-1000
CI
CIII
DII
QI
QIII
RII
CII
DI
DIII
QII
RI
RIII
8
Riqueza média de espécies
(e)
7
a
a
a
6
a
a
a
5
a
4
a
ab
3
a
a
b
2
1
0
-1
CI
CIII
CII
DII
DI
QI
DIII
Áreas
QIII
QII
RII
RI
RIII
Áreas
(h)
60000
a
50000
40000
-1
ha )
a
a
a
30000
a
a
a
a
a
a
b
b
20000
10000
0
-10000
CI
CIII
CII
DI
DII
QI
QIII
RII
DIII
QII
RI
RIII
Áreas
Riqueza média de regenerantes
(g)
Densidade de regenerantes (ind.
a
a
a
CI
RIII
a
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
a
a
a
a
a
a
a
a
a
b
b
a
CI
CIII
CII
DII
DI
QI
DIII
QIII
QII
RII
RI
RIII
Áreas
Figura 6: Comparação das variáveis biológicas mensuradas entre florestas artificiais com a
mesma idade e entre as três florestas naturais de igapó localizadas áreas marginais do lago
Batata - Porto Trombetas, Pará. (a) Altura média (m); (b) Diâmetro médio (cm); (c) Cobertura
da copa média (%); (d) Área basal média (m2 ha-1); (e) Densidade média (ind.ha-1); (f) Riqueza
média de espécies; Densidade média de regenerantes (ind.ha -1); h) Riqueza média de espécies
regenerantes. As barras verticais indicam intervalos de confianças (p<0,05). Médias seguidas da
157
mesma letra não diferem estatisticamente a 5% de probabilidade ao examinar áreas com a
mesma idade. As legendas das áreas podem ser consultadas na Tabela 1.
158
A relativização das variáveis biológicas possibilitou a visualização das distintas
trajetórias de recuperação. A altura média, o diâmetro médio e a densidade média de
indivíduos plantados e a densidade média de regenerantes apresentaram trajetórias de
recuperação que convergiram para as áreas de florestas naturais de igapó. A cobertura
de copa média e o somatório da área basal apresentaram trajetórias de recuperação
estáveis e abaixo dos valores obtidos nas florestas naturais (Figura 7).
159
1.60
1.40
Valores relativizados
1.20
1.00
0.80
0.60
0.40
0.20
0.00
5 anos
10 anos
15 anos
Altura média (m)
Diâmetro médio (m)
Cobertura de copa média (%)
Somatório da área basal (m2 ha-1)
Densidade média (ind. ha-1)
Riqueza máxima de espécies
Densidade média de regenerantes (ind. ha-1)
Riqueza máxima de regenerantes
Figura 7. Relativização (florestas artificiais/florestas naturais) dos valores das variáveis
biológicas mensuradas ao longo de áreas marginais do lago Batata - Porto Trombetas, Pará. A
linha contínua em vermelho representa o valor relativizado das florestas naturais de igapó.
160
A análise de componentes principais revelou que a variância total acumulada
nos dois primeiros eixos foi de 69,45%. O primeiro eixo separou as áreas de referência
entre si (RI e RII nos quadrantes superiores e RIII nos quadrantes superiores) e duas
florestas artificiais (CIII e DIII) das demais florestas artificiais. O segundo eixo separou
as florestas artificiais das mais jovens (CI, CII, CIII e DIII) das mais antigas (QI, QII e
QIII) e áreas de referência (RI, RII e RIII) (Figura 8).
161
RII
Eixo 2 (27,37%)
riq
RI
ab
den
cob
das
DIII
CIII
CI
CII
DII
DI
QIII
h
QI
riqr
RIII
denr
QII
Eixo 1 (42,08%)
Figura 8. Diagrama de ordenação produzido pela análise dos componentes principais (PCA)
das variáveis biológicas mensuradas nas florestas artificiais e naturais de igapó localizadas ao
longo de áreas marginais do lago Batata - Porto Trombetas, Pará. Os resultados do PCA
demonstram que 69,45% da variação total foram atribuídos aos dois primeiros eixos. Legenda:
Áreas: CI, CII, CIII (áreas com plantios de cinco anos de idade), DI, DII, DIII (áreas com
plantios de 10 anos de idade), QI, QII, QIII (áreas com plantios de 15 anos de idade) e RI, RII,
RIII (áreas com florestas naturais de igapó); Variáveis de estrutura vegetacional: h (altura), d
(diâmetro), cob (cobertura de copa), ab (área basal), den (densidade); Variável de diversidade:
riq (riqueza de espécies); Variáveis de processos ecológicos: denr (densidade de regenerantes),
riqr (riqueza de espécies de regenerantes).
162
4.
4.1. Seleção dos atributos ótimos e definição dos tipos funcionais de plantas das
florestas artificiais
Os resultados do processo de modelagem dos dados mostraram que o maior
valor do critério de classificação encontrado na análise da seleção os atributos ótimos
foi de 0,99. Além disso, foram determinados três subconjuntos de tipos funcionais de
plantas, definidos pelos seguintes atributos: largura foliar, área foliar, zoocoria,
tolerância a inundação e capacidade de fixar nitrogênio (Tabela 18). Os atributos ótimos
foram considerados no cálculo da semelhança entre os três tipos funcionais de plantas,
cuja identidade e caracterização pode ser visualizada na Tabela 19.
163
Tabela 18: Resultados da análise para seleção dos atributos ótimos e definição dos tipos funcionais de plantas nas florestas artificiais e
naturais de igapó localizadas ao longo de áreas marginais do lago Batata - Porto Trombetas, Pará. As análises foram realizadas no software
SYNCSA for Windows - Version 2.6.9.
Valor do critério de classificação Número de grupos funcionais ótimos Conjunto de atributos ótimos
0.880926
3
Ti
0.994508
6
Af, Ti
0.990086
4
Ts, Ti, Fn
0.988959
5
Lf, Zo, Ts, Ti
0.998247
3
Lf, Af, Zo, Ti, Fn
0.995352
6
Lf, Ae, Me, Zo, Ts, Ti
0.987738
3
Lf, Af, Ae, Me, An, Zo, Ti
0.967783
3
Al, Lf, Af, Ae, An, Zo, Ti, Fn,
0.954691
3
Al, Di, Af, Ae, Zo, Hi, Ts, Ti, Fn
0.943317
3
Di, Af, Ae, Me, An, Zo, Hi, Ts, Ti, Fn
0.938234
3
Al, Di, Af, Ae, Me, An, Zo, Hi, Ts, Ti, Fn
0.919306
3
Al, Di, Lf, Af, Ae, Me, An, Zo, Hi, Ts,Ti, Fn
Legenda: Al – Altura máxima, Di – Diâmetro máximo, Lf – Largura foliar, Af – Altura foliar, Ae – Área foliar específica, Me – Massa foliar específica, An - Anemocoria, Zo
- Zoocoria, Hi - Hidrocoria, Ts – Tamanho da semente, Ti - Tolerância a inundação e Fn - Capacidade de fixar nitrogênio. An, Zo, Hi, Ti e Fn são dados dicotômicos (0 =
ausência do atributo; 1 = presença do atributo).
164
Tipos funcionais ótimos
Tabela 19: Valores médios dos atributos dos tipos funcionais de plantas (TFP) identificados através da análise funcional nas florestas artificiais e
naturais de igapó localizadas ao longo de áreas marginais do lago Batata - Porto Trombetas, Pará. As análises foram realizadas no software SYNCSA
for Windows - Version 2.6.9.
Al
Di
Lf
Af
Ae
Me
Ts
An Zo
Hi
Ti
Fn
(m)
(cm) (cm) (cm) (cm2g-1) (g-1cm2)
(cm2)
Acosmium nitens
TFP 1
18,5
25
65,46 27,86 2219,66 0,0005 0,5
0
0,5
0,5
1
1
Dalbergia inundata
Couepia paraensis
TFP 2 Eschweilera blanchetiana
28,4
47,6
24,4
10,7
263,8
0,009
0
1
0
8
0,8
0,2
Genipa spruceana
TFP 3 Swartzia polyphylla
30
60
73,9
31,7
681,6
0,001
0
0
1
25
0
1
Legenda: Al – Altura máxima, Di – Diâmetro máximo, Lf – Largura foliar, Af – Altura foliar, Ae – Área foliar específica, Me – Massa foliar específica, An - Anemocoria, Zo Zoocoria, Hi - Hidrocoria, Ts – Tamanho da semente, Ti - Tolerância a inundação e Fn - Capacidade de fixar nitrogênio.
165
4.2 Previsão dos estados futuros
Após a seleção dos atributos ótimos e delimitação dos tipos funcionais de plantas
para as florestas artificiais com diferentes idades e para a floresta artificial de igapó, a
abundância dos tipos funcionais de plantas foi estimada para as florestas artificiais
(Tabela 20). Estas informações foram utilizadas como dados de entrada (Matrix X) para
a modelagem com base na Cadeia de Markov da abundância relativa dos tipos
funcionais de plantas das florestas artificiais no futuro.
A projeção futura da abundância dos tipos funcionais de plantas nas florestas
artificiais demonstrou que os níveis de instabilidade entre os estados markovianos foi
inferior a 1% no estado 35, ou seja, as abundâncias futuras dos tipos funcionais
atingiram a estabilidade após 175 anos, considerando a última medida empírica, isto é,
os plantios de 15 anos (Figura 9).
A projeção futura da abundância dos tipos funcionais de plantas sugere um
incremento do número de indivíduos de Acosmium nitens e Dalbergia inundata (TFP1)
em detrimento das outras espécies plantadas. É importante considerar que tais projeções
consideraram apenas os indivíduos plantados (Figura 9).
Posteriormente, a abundância futura dos tipos funcionais de plantas nas florestas
artificiais foi comparada com a abundância de tipos funcionais das florestas naturais de
igapó. Após 175 anos, os valores de abundância encontrados para as florestas naturais
de igapó e florestas artificiais diferiram significativamente (χ2=4.33; g.l.=2; p < 0.00)
(Figura 9).
166
Tabela 20: Abundância estimada dos tipos funcionais de plantas
definidos através da análise funcional utilizada na modelagem dos
estados futuros dos reflorestamentos localizados em áreas marginais do
Lago Batata - Porto Trombetas, Pará. Estes dados foram utilizados na
elaboração da Matriz X. Esta matriz contem os dados de entrada da
modelagem da abundância futura dos TFP nas florestas artificiais.
TFP 1
TFP 2
TFP 3
Florestas artificiais com 5 anos
0,40611
0,58734
0,00655
Florestas artificiais com 10 anos 0,31855
0,62702
0,05444
Florestas artificiais com 15 anos 0,20388
0,75243
0,04369
167
1.00
0.90
Abundância relativa
0.80
0.70
PFT3
0.60
PFT2
0.50
PFT1
0.40
0.30
0.20
0.00
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
115
120
125
130
135
140
145
150
155
160
165
170
175
180
185
Ref.
0.10
Anos
Figura 9: Análise dos cenários futuros com base na abundância relativa de tipos funcionais das
florestas artificiais localizadas em áreas marginais do lago Batata - Porto Trombetas, Pará.
Legenda: PFT: tipo funcional de plantas; Ref: referência (Floresta de igapó natural). O teste do
chi-quadrado mostrou que a composição de PFT do estado vegetacional com 185 anos diferiu
significativamente da composição do estado vegetacional de referência (χ2=4.33; g.l.=2; p <
0.00).
168
DISCUSSÃO
1.
Similaridade florística entre as florestas artificiais e a floresta natural de igapó
De acordo com Müller-Dombois & Ellenberg (1974) formações vegetais podem
ser consideradas semelhantes quando apresentam ao menos 25% de concordâncias
florísticas. Os resultados aqui encontrados mostram que as florestas artificiais e a
floresta natural de igapó compartilham cerca de 30% das espécies. As florestas artificiais
de cinco e dez anos compartilharam cerca de 40% das espécies vegetais entre si e entre a
floresta natural de igapó, enquanto que as florestas artificiais com quinze anos
compartilharam cerca de 27% das espécies com as outras florestas artificiais e com a
floresta natural de igapó (vide Figura 4). Estes distintos graus de similaridade florística
observados entre as florestas artificiais entre si e entre as florestas naturais de igapó
podem ter sido determinados pela variação entre as condições gerais de execução dos
plantios, proporções de espécies utilizadas e qualidade das mudas (Bozelli et al. 2000).
As florestas artificiais apresentaram 25% de concordância em relação às espécies
que regeneram naturalmente nas margens do lago Batata. É provável que as diferenças
na composição e riqueza de espécies verificadas nas florestas artificiais e florestas
naturais estejam associadas às variações topográficas. Nas planícies de inundação
amazônicas as comunidades vegetais respondem ao tipo de solo, à topografia e à
hidrologia (Lockaby et al. 2008). Contudo, o tempo de inundação é a condição
ambiental local mais importante para a determinação da composição de espécies e
riqueza de espécies vegetais (Junk et al. 1989, Ferreira & Almeida 2005, Lockaby et al.
2008). Segundo Jones et al. (1994), a elevação topográfica foi um importante fator de
predição do fluxo e da densidade de regenerantes. Os autores demonstraram que o pico
de densidade populacional de regenerantes ocorreu nas áreas mais elevadas quando
comparadas com áreas mais baixas.
169
O processo de regeneração natural nas áreas de estudo, além de ser determinado
pelo período de inundação, também é influenciado pela proximidade com a vegetação
natural, pelo padrão de circulação das correntes internas do lago Batata (Bozelli et al.
2000), e atributos espécie-específicos. Nas florestas de igapó, as plantas apresentam
algumas características que lhes conferem a capacidade de tolerar a submersão
prolongada ou escapar dela (Parolin 2002), como por exemplo, germinação hipógea,
cotilédones carnosos, grandes e persistentes, sementes grandes, folhas longevas e
esclerófilas, crescimento lento. Segundo Parolin (2001, 2002) as adaptações
morfológicas são limitadas em virtude da baixa disponibilidade de nutrientes no igapó.
De uma maneira geral, pode-se dizer que em termos de composição florística os
plantios abtiveram sucesso em comparação com as florestas naturais de igapó. No
entanto, é possível que no futuro as florestas artificiais formem um mosaico de
formações vegetais com graus distintos de similaridade florística. Neste contexto, é
importante considerar múltiplos destinos alternativos (Choi 2007) para a avaliação do
sucesso do processo de restauração no lago Batata.
2.
Análise da efetividade dos plantios e trajetórias ecológicas
A análise dos atributos estruturais e de diversidade revelou uma tendência à
convergência de valores em relação à floresta natural de igapó, com exceção da área
basal e cobertura de copa (vide Tabela 17 e Figura 7). As florestas de igapó são sistemas
sujeitos à inundação sazonal, fator que, além de influenciar diretamente a distribuição e
o estabelecimento das plântulas (Klinge et al. 1995), reduz a duração da estação de
crescimento das plantas (Schongart et al. 2005). As condições desfavoráveis e a baixa
disponibilidade de nutrientes na água contribuem para o lento crescimento das espécies
plantadas. Neste caso, é importante a promoção de práticas capazes de acelerar o
170
processo de restauração no lago Batata, como por exemplo, a adição de liteira
concomitantemente à adição de sementes (Dias et al. 2012).
Os resultados aqui encontrados demonstram que as atividades de restauração
conduzidas nas margens do lago Batata apresentaram sucesso em termos estruturais e de
diversidade. De acordo com Zedler & Callaway (1999) o sucesso da restauração deve ser
avaliado com base na estrutura e no funcionamento do ecossistema, no entanto, os
autores consideram mais importante restaurar o componente estrutural inicialmente.
Portanto, torna-senecessário que o processo de monitoramento seja continuado para
acompanhamento do crescimento e mortalidade das espécies plantadas. Assim, será
possível avaliar a necessidade de replantio de mudas em áreas com alta mortalidade de
plantas, como por exemplo, as áreas mais baixas dos plantios, sujeitas a períodos
maiores de inundação. Além disso, é essencial que atributos de funcionamento
ecossistêmico, como por exemplo, acúmulo de matéria orgânica no solo, presença de
microorganismos no solo, presença de agentes polinizadores, sejam monitorados e
comparados com os valores encontrados nas florestas de igapó natural.
3.
A modelagem dos dados demonstrou que a atividade de reabilitação no lago
Batata não necessariamente conduzirá as florestas artificiais a um estado similar aquele
encontrado nas florestas naturais de igapó. Portanto, é essencial neste caso, considerar
distintos alvos ao avaliar o sucesso das práticas adotadas. É importante ressaltar que a
projeção futura é uma probabilidade e não uma certeza. Ela serve como base a avaliação
de metas, e para o desenvolvimento de um modelo de referência, que engloba as
realidades contemporâneas e antecipa direções futuras no desenvolvimento da trajetória
do ecossistema histórico (Clewell & Aronson 2007).
171
Apêndice 3: Resultados das ANOVAs hierarquizadas para a comparação dos atributos
estruturais e de diversidade mensurados em florestas artificiais e florestas naturais de
igapó localizadas em áreas marginais do lago Batata - Porto Trombetas, Pará.
Variável dependente
Altura (m)
Diâmetro (cm)
Cobertura (%)
2
Área basal (m /ha)
Densidade (indivíduos.ha-1)
Sobrevivência dos
indivíduos plantados (%)
Riqueza de espécies
plantadas (sp/25m2)
Fonte da variação
Idade
Áreas (Idade)
Erro
Idade
Áreas (Idade)
Erro
Idade
Áreas (Idade)
Erro
Idade
Áreas (Idade)
Erro
Idade
Áreas (Idade)
Erro
Idade
Áreas (Idade)
Erro
g.l.
3
8
468
3
8
468
3
8
468
3
8
468
3
8
468
2
6
351
QM
0,69
0,46
0,05
1,55
0,85
0,10
1,56
0,95
0,06
0,00
0,00
0,00
6,76
0,94
0,07
21,07
3,17
0,14
F
13,07
8,67
p
0,00*
0,00*
15,69
8,59
0,00*
0,00*
24,04
14,71
0,00*
0,00*
27,55
0,62
0,00*
0,76
92,01
12,83
0,00*
0,00*
152,27
22,91
0,00*
0,00*
Idade
Áreas (Idade)
Erro
3
8
468
6,50
0,44
0,04
159,29
10,75
0,00*
0.00*
Valores seguidos por asterístico são siginificativos (α=0,05).
172
Apêndice 4: Resultados das ANOVAs hierarquizadas para a comparação dos atributos
estruturais e de diversidade mensurados em florestas artificiais e florestas naturais de
igapó (considerando apenas a regeneração natural) localizadas áreas marginais do lago
Batata - Porto Trombetas, Pará.
Variável dependente
Altura (m)
Diâmetro (cm)
2
Cobertura da copa (m )
2
Área basal (m /ha)
-1
Densidade (indivíduos.ha )
Densidade de espécies
(espécies/25m2)
Fonte da variação
g.l.
QM
F
p
Idade
Áreas (Idade)
Erro
Idade
Áreas (Idade)
Erro
Idade
Áreas (Idade)
Erro
Idade
Áreas (Idade)
Erro
Idade
Áreas (Idade)
Erro
Idade
Áreas (Idade)
Erro
3
8
468
3
8
468
3
8
468
3
8
468
3
8
468
3
8
468
0,14
0,09
0,02
0,13
0,15
0,03
0,03
0,04
0,00
0,00
0,00
0,00
0,61
1,86
0,12
0,36
1,08
0,06
8,64
5,43
0,00*
0,00*
4,72
5,40
0,00*
0,00*
5,51
7,65
0,00*
0,00*
1,59
1,79
0,19
0,08
5,15
15,71
0,00*
0,00*
5,57
16,86
0,00*
0,00*
Valores seguidos por asterístico são siginificativos (α=0,05).
173
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FINAL REMARKS
179
FINAL REMARKS
Practice and theory when considered interdependent are mutually benefited
through a constant improvement that comes from a dynamic relationship (Serafim 2001).
Within this perspective, this thesis aimed to integrate the knowledge derivated from a
literature review embedded on the theoretical framework of “Ecology” and a restoration
initiative conducted in an Amazonian lake.
The literature review identified a bias in the studies of vegetation dynamics
modelling towards native vegetation from temperate zones of the Northern Hemisphere
over tropical artificial forests. Moreover, studies conducted in the perspective of
“Restoration ecology” frequently used statistical analyses rather than ecological
modelling, and few restoration efforts projected successional trajectories and future
scenarios. This gap was filled through the analysis, from the perspective of ecological
modelling, of the success of an artificial forest planted to rehabilitate an impacted
Amazonian lake. This approach enabled (a) a better understanding about the artificial
tropical forest assemblage rules, (b) the identification of multiple targets for restoration
of lago Batata, (c) the possibility of development of a novel ecosystem in 75 years, (d)
and that restoration has not yet reached the goal of establishing a self-sustainable
artificial forest in lago Batata.
Regarding the complex nature of ecological systems and spatial-temporal
variability of ecological phenomena, many environmentalists say it would be impossible
to construct ecological generalizations in the form of laws, as in physics and chemistry.
On the other hand, there are those who believe in the existence of such generalizations,
although there is no consensus about their identities (El-Hani 2006). According to
Kingsland (1995) apud El-Hani (2006), this dichotomy highlights the tension between
two tendencies of ecological literature: bottom-up approach that valorizes case studies
180
and top-down approach that relies on theories to explain the casualties of particular
situations.
The discussion about the adequacy of these trends permeates another point of
controversy: can Ecology, due to epistemological limitations and its low degree of
predictability, be applied to solve practical problems? Studies show that both
approaches, bottom-up and top-down, when used to treat problems related to biological
conservation, appear effective and complementary (Shrader-Frechette & McCoy 1993,
Scarano 2006). However, the literature shows inclination to use the bottom-up approach
to address issues related to environmental management and conservation, given the
greater predictability and thus better basis for decision making (Shrader-Frechette &
McCoy 1993, Giacomini 2007), fact also indicated by the results found in this thesis.
181
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182
ANEXO 1
TROPICAL ARTIFICIAL FORESTS
D.J. Capossoli, J.B.B. Sansevero, M.L. Garbin, F.R. Scarano, (2009), TROPICAL ARTIFICIAL
FORESTS, in International Commission on Tropical Biology and Natural Resources, [Eds. Kleber Del
Claro,Paulo S. Oliveira,Victor Rico-Gray,Ana Angelica Almeida Barbosa,Arturo Bonet,Fabio Rubio
Scarano,Francisco Jose Morales Garzon,Gloria Carrion Villarnovo,Lisias Coelho,Marcus Vinicius
Sampaio,Mauricio Quesada,Molly R.Morris,Nelson Ramirez,Oswaldo Marcal Junior,Regina Helena
Ferraz Macedo,Robert J.Marquis,Rogerio Parentoni Martins,Silvio Carlos Rodrigues,Ulrich Luttge],in
Encyclopedia of Life Support Systems(EOLSS), Developed under the Auspices of the UNESCO, Eolss
Publishers, Oxford ,UK, [http://www.eolss.net] [Retrieved July 23, 2011]
183
TROPICAL ARTIFICIAL FORESTS
D.J. Capossoli, J.B.B. Sansevero, M.L. Garbin, F.R. Scarano
Universidade Federal do Rio de Janeiro, CCS, IB, Depto. Ecologia, Brasil
Instituto de Pesquisa Jardim Botânico do Rio de Janeiro, Diretoria de Pesquisa
Científica, Brasil
Keywords: Exotic Species, Forestry, Monoculture, Productive Forests, Restoration
Ecology
Contents
1. Introduction
2. Concepts, Definitions and Purposes
3. Historical Aspects
4. Quantitative Data
5. Criticisms and Ways to Increase Sustainability of Planted Forests
6. Case Studies in the Tropics
7. Conclusions
Related Chapters
Glossary
Bibliography
Biographical Sketches
184
Summary
Artificial forests comprise non-native and/or native tree species and differ from natural forests in
structure, composition, intensity of management, orderliness and uniformity. Natural habitat loss
represents the main threat to the maintenance of biodiversity in the tropical region. Artificial
forests may help alleviate the damaging consequences of the loss of natural forests on ecological,
social and economic basis providing effective ways to reduce the pressure over remaining
natural forests. Here, we provide a brief overview about artificial forests implementation efforts
in the tropics. We address some important concepts on the topic as well as historical and
quantitative aspects. Illustrative case studies are presented and commented. We conclude that, if
properly projected and managed, artificial forests in the tropics can contribute to ecological
restoration efforts. For this, special attention to key aspects is needed such as the spatial scales
over which plantations are implemented and the integration of protective and production efforts.
The higher productivity and biodiversity of tropical forests provide a challenging and mostly
unexplored potential to tackle these objectives.
185
1. Introduction
Natural habitat loss represents the main threat to the maintenance of biodiversity in the tropical
region. It is caused by deforestation, land conversion and degradation which are motivated by
the increasing demand for agricultural, urban and industrial areas. Every year ca. thirteen million
hectare of native forests are lost in the world, which is a problem mostly concentrated in Latin
America, Caribbean and Africa (FAO 2007). Only tropical humid forests decreased about 2.36
% in its area between 2000 and 2005 (Hansen et al. 2008). Those anthropogenic impacts create
novel ecosystems which have different structural and functional characteristics and alter the
ecosystem services provided by tropical forests, such as climate control, water cycling, erosion
and sediment retention, nutrient cycling and soil formation.
Current deforestation rates and the increasing accumulation of degraded areas in tropical regions
reveal the urgency of human interventions to restore biodiversity, functions and provisions of
ecological goods and services, mainly in poor agricultural zones. In addition, there is an
increasing demand for wood and non-wood products that may not be fulfilled by the remaining
natural forests. Within this context, distinct types of artificial forests may help filling the gap led
by the loss of natural forests on ecological, social and economic basis (Lamb et al. 2005), and
may provide effective ways to reduce the pressure over remaining natural forests.
Artificial forests cover globally about 2 % of land area, which represents 7 % of global forest
area (about 300 million hectares). In spite of its low quantitative representation in relation to
global forested area, they provide more than half of the industrial wood produced in the world.
Those forests are found from boreal to tropical zones, and can use native or introduced tree
species, although exotic species are more common in tropical plantations designed for timber
production or rural development. In the tropics, artificial forests cover about 88 million hectare
(Evans & Turnbull 2004).
Here, we aimed to give an overview about artificial forests implementation efforts in the tropics
and their role to improve the production of goods and services, and to restore and recover
degraded land. In order to tackle these objectives, we first briefly review the main concepts and
terms related to planted forests theoretical framework. Brief historical and quantitative aspects
are addressed along with the potential of artificial forests to contribute to restoration efforts and
the criticism associated with their implementation in tropical areas. Finally, some case studies
are presented.
2. Concepts, Definitions and Purposes
Artificial forests differ from native forests in that they comprise both non-native and native tree
species and differ in structure, composition and intensity of management and because of the
orderliness and uniformity that they show. Agricultural areas, gardens, agroforestry systems,
186
enrichment planting and linear planting are not included in this definition. The terms ‘artificial’,
‘planted’ and ‘human-made forests’ are all synonyms of forest plantations and will be used
interchangeably hereafter.
Planted forests have multiple purposes, though their targets may be polarized in production or
protection forests. The types of interfaces of these planted forests with natural forests perform a
continuum and, in some cases, they may be very similar to natural adjacent forests. On one hand,
there are productive plantations which are defined by the rotation period. Fast-wood plantations
may be smaller in extent than longer-rotation plantations and demand huge financial and
technological investments. They are usually composed by a single species and are intensively
managed reaching maturity faster and producing 1 - 2 times more wood/hectare/year than
longer-rotation plantations. Longer-rotation softwood plantations are less productive and take 20
to 35 years to reach maturity. They occupy 2 to 3 times more lands than the fast-wood
plantations and require longer investment periods. Logs yielded have higher timber quality and
income value, so those kinds of plantations have a much higher financial return. Furthermore, as
their biodiversity values depend on local management practices and the landscape context, they
may contribute effectively to improve local economies and also to provide biodiversity benefits.
Production forests, both from long and fast-wood plantations, may use native and/or exotic tree
species.
On the other hand, there are protective forests established for provision of environmental
services (soil and water protection) and sustain habitats for biodiversity maintenance. They may
be composed by native or non native tree species. This category includes efforts for recovering
degraded areas, aiming biodiversity restoration and/or conservation. Thus, in the tropics, they
constitute a major challenge to the scientific community, environmental agencies and private
initiative due to the high diversity of these forests.
These approaches, protection and production, should ideally provide both, goods and ecosystem
services, but in different proportions (Figure 1). However, the optimization of productive,
environmental and social benefits is a challenging enterprise. There is a handful of reasons to
implement artificial forests: (i) to compensate ecological and economic losses as well as social
impoverishment caused by deforestation; (ii) to supply raw materials for industry such as pulp,
paper and high-quality products for both, domestic uses and exportation; (iii) to restore, recover
and rehabilitate degraded sites in order to increase biological diversity and/or ecosystem services
as well as genetic diversity; (iv) the higher wood productivity of planted forest when compared
to native forests; and (v) other purposes such as rural development, to provide firewood,
windbreaks, protection of water sources for irrigation, and may also be used to carbon
sequestration and storage.
187
Tree cover has significant positive impacts in environmental protection. It can reduce soil
erosion, slow wind speed, trap airborne sand and dust particles, moderate the forces of rain and
slows water runoff after heavy rain. Farther, in a context of degraded tropical areas, the
establishment of tree cover means the first step in soil rehabilitation and land restoration. Tree
cover, whether artificial or natural, protects soil and reduces erosion through (i) high filtration
rates reducing surface runoff and soil transport, (ii) binding action of roots increasing soil
stability in slopes and reducing erosion, (iii) forest canopy, understory and ground layers acting
in rainfall interception dissipating rain force, (iv) reducing wind speed force and consequent
wind erosion, (v) presence of litter and humus layer reducing erosion and increasing moisture
retention. Vegetation cover has also effects on the hydrology of a watershed as it reduces soil
erosion preventing loss of fertile topsoil and increase retention of sediments. Tree cover also act
in shelter provision through shelterbelts, thus reducing wind velocity, filtering airborne particles
of sand and dust, protecting animals, agricultural crops and habitations (Evans & Turnbull
2004).
Figure 1. Planted forest for production or protection and their relationship with economical and
biodiversity benefits. Traditional monoculture plantations mostly generate just financial benefits
(production planted forests). Plantations aiming protection maximize diversity and/or ecosystem
services having few direct financial benefits at least in the short term. Optimal benefits are
attained by: (i) initially aiming financial benefits by using few plant species, generally
monocultures, and, after some cutting cycles, diversity in the site is enhanced with native tree
species increasing biodiversity and/or ecosystem services; and (ii) high diversity plantations may
be managed by harvesting only tree species that maximize economic profit. Modified from
Lamb et al. (2005).
3. Historical Aspects
Trees always had important roles for human societies and for global ecosystem functioning. To
many people they are sacred and have been used for ceremonial and religious purposes. For
others the relation is material as consequence of their dependence on wood and non-wood
products, such as raw material, food and drink, medicinal compounds, ornaments, source of
perfume, and many other utilities. Thus, the act of planting trees is an old practice. In fact, the
first evidence of a woody species planted may have been the olive tree (Olea europaea) early in
4000 BC in Greece (Evans & Turnbull 2004).
188
Until late 1800s industrial plantations were not needed due to the low population density and the
great availability of natural forests which were sufficient for human demanding for forest
products in tropical regions. The introduction and test of exotic species were the main activities
associated to plantation forestry by that time. Extensive plantations became a more usual
practice by 1900, mainly in countries with little availability of natural forests and where
European settlers have been established (Cossalter & Pye-Smith 2003, Evans & Turnbull 2004).
Wood production plantations were mainly composed by Pinus patula, P. elliottii, P. taeda and
Eucalyptus spp. in South Africa, P. elliottii and Araucaria cunninghamii in Australia, and
Eucalyptus spp. in India and Brazil (Table 1). Plantations designed for non-timber products were
mainly composed by rubber tree (Hevea brasiliensis) in Malaysia, and Australian black wattle
(Acacia mearnsii) in Australia. These plantations were also present in South Africa (115 000 ha)
and Kenya (25 000 ha), and others countries, such as, Zimbabwe, Tanzania, India, and Brazil
(Evans & Turnbull 2004).
After 1945, artificial forests became an important subject, and planting programs started to be
developed for industrial purposes, pulpwood, sawtimber, and also for supplying of human needs
and also for environmental protection. This trend was observed in the eastern and western
Europe, United States, New Zealand, South Africa, India, Chile, Indonesia and Brazil. Japan,
Korea and China promoted massive restorations programs later, in the 1950s. In the tropical and
subtropical regions, plantations were established based on large-plantations programs from the
sixties. In fact, land covered by planted forests in the tropical region tripled between 1965 and
1980 (Cossalter & Pye-Smith 2003). Large wood and pulp production transnationals companies
had important roles on the establishment of artificial forests worldwide aiming to produce
homogeneous, abundant and cheaper raw materials. Many organizations, by that time, were
responsible for the establishment of protective plantings.
In the world, 30 % (ca. 1.4 million hectare) of the 4.5 million hectare of new artificial forests
established annually have failed. The problem associated to these plantations has its roots in
socio-economic and environmental factors, and in some cases contributed for the failure of
industrial plantations. Socio-environmental conflicts appear as a result of inadequate practices.
Responsible management of artificial forests is hindered by the lack of post-planting
maintenance, fires and pests, low priority given technical knowledge and adequate public
policies, including laws and regulations, appropriate link between plantations and industrial
consumers, poor marketing, and the end of external financial support (Evans & Turnbull 2004,
FAO 2006). Certification schemes and specific instruments that ensure the application of best
practices for sustainable forest management can be viewed as important developments as they
ensure high standards of silviculture and management (Evans & Turnbull 2004).
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Protective plantations implemented specifically for reforestation of exposed lands date from one
of the most ancient Chinese Empire, the Chou Empire (ca. 1100-256 BC). The following
empires continued to encourage reforestation and stimulated the planting of trees for wood and
timber production (Evans & Turnbull 2004). Well documented initiatives of protective forests
implementation date from the first half of the 19th century. In South America, probably the first
project of environmental restoration was made in the city of Rio de Janeiro (Brazil) in 1882.
During the construction of the city of Rio de Janeiro, a great amount of wood originated from the
Atlantic Forest was used. Additionally, sugarcane, coffee plantations, and pastureland gradually
substituted the original vegetation. The deforestation caused problems in the city water supply,
and reduced soil fertility. This situation urged human intervention, and from 1862 to 1874,
Major Manuel Gomes Archer was nominated by Emperor D. Pedro II to start the reforestation
project. With a few slaves he planted about 72.000 seedlings of native and exotic tree species.
Initially targeted to restore ecosystem services, the project is now one of the most successful
restoration enterprises in the world.
4. Quantitative Data
In the last decades, rates of increase of planted forests reflected the growing demand for forest
products. Annually, 4.5 million ha of artificial forests are planted in the world. From this total,
48% are designed and managed to produce material for the wood processing industries and 26%
are established for non-industrial uses, such as fuelwood and environmental protection. The
remaining has unspecified purposes. Five countries account for 65% of the world’s plantations:
China, United States, the Russian Federation, India and Japan (Cossalter & Pye-Smith 2003).
The total area occupied by production forests around the world is estimated in 10 million ha with
an average rate of increasing about 0.8 to 1.2 ha (Cossalter & Pye-Smith 2003). The five
countries with the largest area of planted forests in the world are: China, India, United States of
America, Russian Federation and Japan (FAO 2007). In the last 35 years there was an increment
of about 13 times in the planted area in tropical and subtropical regions in the world (Figure 2).
This increase is mainly due to the increment in the Asiatic continent where the greater
contribution is due to China and India achievements in the area. Both, these countries have the
largest planted area in tropical and subtropical regions, followed by Indonesia, Brazil and
Thailand (Evans & Turnbull 2004) (Figure 3). The great increment in area occupied by those
forests indicates that a larger portion of wood removals may come from forest plantations in the
future. In general, tropical and subtropical regions have higher productivity than temperate zones
plantations and natural forests in same region. Although the planted total area in temperate zones
are greater than tropical and subtropical regions (about 88 million ha for tropical and subtropical
planted forests [Evans & Turnbull 2004] vs. ca 99 million ha for temperate planted forests [FAO
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2001]), these account for the greatest productivity. For instance, the mean annual increment for
the genera Pinus ranges from about 5 to 14 m3/ha/year in Europe (FAO 2006 [global planted
forests]) whereas for tropical and subtropical regions this rate may range from 16 to 30
m3/ha/year (SBS 2007) (Table 1). However, this increment may significantly vary according to
site quality, species behavior, genetic material, plantation age and forest management. For the
fast growing species (e.g. Eucalyptus and Pinus), growth rates are highest in favorable
temperatures, higher soil fertility and soil water-storage capacity.
Table 1 shows the main tree species used in the four countries with the highest planted area in
tropical and subtropical regions. The most used trees are from genera Eucalyptus, Pinus and
Acacia, and the species Tectona grandis (FAO 2006). Together, Eucalyptus and Pinus comprise
43 % of all planted area in the tropics (FAO 2001). For Eucalyptus, this expansion is due to the
great plasticity of the genus to acclimate to different environmental conditions, to the great
variety of commercial products that they give origin as charcoal, pulpwood, plywood, wood
panel, and secondary products such as essential oils and honey. Therefore, final products
determine the forest management. Brazil has the greatest mean annual increment in volume
(m3/ha/year) in Eucalyptus plantations when compared to India and Indonesia. Even using
similar rotation lengths, Brazil reaches 525 m3 per ha.
In Brazil, the main tree species planted are: Eucalyptus grandis, E. urophylla, E. robusta, E.
saligna and their hybrids. The Pinus species planted in tropical and subtropical regions originate
mainly from the American and Asian tropics and, in the same way as Eucalyptus, their
productivity in different countries varies drastically (Table 1). The great popularity of Pinus is
due to the great number of species used in the plantings which allow for a greater flexibility to
choose the best species for a given environmental condition. Also, this allows maintenance or
increase in volume production even under unfavorable site conditions, choice of species suited
for reforestation and for simple silviculture, and to give uniform coniferous wood valued for
production of lumber, pulpwood, paper and particleboard. The main planted species in South and
Central America are Pinus caribaea, P. elliottii, P. taeda and P. oocarpa. In Asian the
predominant species are Pinus roxburghii, P. massoniana, P. tabulaeformis, P. merkusii (Table
1).
Tectona grandis is another tree species which has its planted area enormously increased in the
last decades. Though the largest fraction of planting sites in the world is concentrated in the
Asian continent (94%), growth rate results indicate that the tree species has great potential for
countries like Brazil for example (Table 1). The species fast expansion in production initiatives
may be explained by establishment limitations of other valuable tree species that have more
specific ecological requirements and higher susceptibility to disease and insect attack. Thus, due
to wood specific characteristics such as durability, stability, and fungal resistance, the species
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has a high market value and is used for civil and naval construction and also for furniture. For
these kinds of uses, large piece sizes are needed. Thus, the cutting cycle of the tree is longer than
other planted tree species (Table 1). The hardwood tree species Dalbergia sissoo and Swietenia
macrophylla are cultivated under these same conditions.
Protective forests in the world are increasing in planted area. In 1990 they covered 296 million
ha, in 2000 they increased to 335 million ha, reaching, in 2005, 541 million ha (FAO 2007).
However, this increment is not homogeneously distributed. Whereas some regions increased in
planted area: Asia and the Pacific regions (4.5 million ha), Europe (13 million ha) and Latin
America and Caribbean (3 million ha); the same was not true for Africa which decreased from
21.4 million ha in 1990 to 20.6 million ha in 2005.
Figure 2. Increase of the planted area in tropical and subtropical areas in the world and in the
different continents. Adapted from Evans & Turnbull 2004.
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Figure 3. Countries with the highest planted area (thousands hectares) in tropical and subtropical
regions. Oceania (480 000 ha) and Central America (1 311 000 ha) had the lowest total planted
area and were not shown in the figure. Adapted from Evans & Turnbull (2004).
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Table 1. Countries with highest planted area in tropical and subtropical regions, planted tree
species and growth parameters. Mean annual increment (MAI) - (m3/ha/years); Rotation length
(Years); Harvested volume (m3/ha). (Source: FAO 2006, SBS 2007).
Rotation
Harvested
MAI
length
vol.
(m3/ha/year
(Years)
(m3/ha)
)
Country /Species
Family A
min max
min max
min max
INDIA
Dalbergia sissoo Roxb.
Eucalyptus spp.
Gmelina arborea Roxb.
Pinus roxburghii Sarg.
Shorea robusta A.DC.
Tectona grandis L.f.
CHINA
Cunninghamia lanceolata Lamb.
Pinus massoniana Lamb.
Castanea mollissima Blume
Populus spp.
Pinus tabulaeformis Hort. ex
K.Koch
Larix spp.
INDONESIA
Acacia mangium Willd.
Eucalyptus spp.
Paraserianthes falcataria (L.)
I.C.Nielsen
Pinus merkusii Jungh. & de
Vriese
Swietenia macrophylla King
BRAZIL
Acacia mearnsii De Wild.
Araucaria angustifolia (Bertol.)
Kuntze
Eucalyptus spp.
Mimosa scabrella Benth.
Pinus spp.
Fabaceae
Myrtaceae
Lamiaceae
Pinaceae
Dipterocarpac
eae
Lamiaceae
4
8
15
3
6
21
23
5
30
7
18
80
40
15
30
100
4
6
80
100
5
11
34
Taxodiaceae
Pinaceae
Fagaceae
Salicaceae
3
3
1
9
14
16
6
18
Pinaceae
3
Pinaceae
100
119
295
201
58
127
268
18
15
30
20
30
30
40
35
44
42
30
67
405
489
240
199
7
35
45
107
325
4
9
43
49
143
335
Fabaceae
Myrtaceae
20
8
32
21
6
7
12
15
110
100
200
295
Fabaceae
22
44
7
13
139
166
Pinaceae
2
14
10
50
100
197
Meliaceae
Lamiaceae
5
5
10
11
29
34
50
58
111
127
154
268
Fabaceae
16
25
10
20
43
138
Araucariaceae 17
25
10
18
150
525
Myrtaceae
Fabaceae
Pinaceae
Lamiaceae
40
25
30
15
7
8
15
20
21
14
25
25
127
80
130
250
268
350
304
350
30
10
16
10
A - The Angiosperm Phylogeny Group II was adopted for taxonomic classification (APG II
2003).
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5. Criticisms and Ways to Increase Sustainability of Planted Forests
Common arguments against planted forests include: (i) their implementation is made on sites
formerly occupied by native forests; (ii) loss of biodiversity, proportional to the plantation size;
(iii) the roads built to transport planted wood may serve to exploration of native adjacent forest
areas; (iv) there may be alterations in water cycle; (v) monocultures may be more vulnerable to
disturbances, diseases, and biological invasions; (vi) acidification of soil and water; (vii) harmful
changes in the physical, chemical and biological conditions of the soil; and (viii) displacement of
the local flora and fauna. These negative impacts depend on the planted species, history of the
site and forest management practices, making generalization a risky task that may lead to
inappropriate conclusions and recommendations. In face of these problems, can commercial
plantations of exotic tree species offer an opportunity to increase biodiversity, improve
ecosystem services and also provide social benefits?
This may be seen as a controversial topic since plantations are typically viewed as sterile
monocultures with little biodiversity and, therefore, harmful to the environment. Recently new
approaches for traditional industrial plantations have been developed since they provide a way
by which extensive degraded tropical areas can be reforested. Aware that production plantations
support less biodiversity and comprise different communities than natural forest, they can still be
projected and managed to compensate for forest loss in the tropics. Though the use of
monocultures may seem controversial, they offer an effective tool to vegetation recover in
tropical areas. Commercial plantations have also great potential for the recuperation of degraded
native tropical forests where anthropogenic impacts are extensive through soil amelioration,
creation of habitats for seed-dispersing wildlife, and microclimate alteration that favors
establishment of wood vegetation. They may also help increasing its species diversity by
providing nurse effect for the regenerating native forest (Lugo 1997). Since native forest
rebuilding may be a slow process and there is a need for sustainable actions to improve the
standard of living, large-scale reforestation programs may use fast-growing trees to accomplish
two objectives: (i) restore the biological control of watershed hydrology, and (ii) to produce high
value forest products. Clearly, the increase in biodiversity and the improvement on ecosystem
services and social achievements of commercial plantations passes through a series of steps. In
other words, plantations may produce a “catalytic effect”: they might facilitate natural
regeneration representing an important management tool for restoration of degraded lands.
Therefore, the role of artificial and native forests is complementary and compatible in different
landscape and regional contexts.
In the tropics, there is a sequence of events where the understory of monocultures allow for
successful tree species reestablishment on degraded sites (Lugo 1997): (i) proper site preparation
and species selection; (ii) changes in the abiotic environment provided by the trees such as
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shade, humidity, temperature and soil chemistry; (iii) protection of the understory trees from fire,
harvesting, weeding and grazing; (iv) wildlife attracted into the site in searching for food, and
provided forest structure; (v) wind, water and wildlife disperse propagules from the surrounding
areas; and (vi) planted species may fail to regenerate under their own canopy allowing other
species to grow in. This events offer a basis for management of exotic tree species plantations.
However, implementing such strategy has advantages and shortcomings and plantations must be
managed adequately. Different approaches to improve the biodiversity of tree plantations may be
hindered by slow growth of native species with an incomplete silvicultural knowledge, more
complicated management of higher diversity systems and more complex marketing. Approaches
that would keep the economic gain in timber and increase biodiversity of plantations include:
using indigenous instead of exotic species, creation of species mosaics, embed the monoculture
in a matrix of intact or restored vegetation, use mixed species plantations rather than
monocultures or encouraging high diverse understory monocultures. Key points to consider
when restoring tropical forests include the species to be chosen (Table 2), seedling establishment
related issues such as getting appropriate seeds and allowing adequate conditions for plant
growth, making the restoration effort economically attractive to land managers, and finally
allowing recolonization of reforested areas by native flora and animals (Lamb 2005). The
introduction of exotic tree species may be a necessary step in some degraded sites in order to
help the restoration process. It is important to realize that there is a myth around the use of exotic
tree species in restoration and conservation efforts. This is probably due to harmful forestry
practices. For instance, the impact of eucalypt plantations on water resources is no different than
that of other forest monocultures.
In a wider perspective, FAO (2006) suggests some principles for good practices on planted
forests, divided into four categories: institutional, economical, social and cultural and
environmental. They will be summarized as follow:
Institutional principles

Good governance: governments should facilitate an environment of stable
economic, legal and institutional conditions;

Integrated decision-making and multi stakeholder approaches: policymakers should encourage integrated decision-making by stakeholders in
planning, managing and utilizing planted forests;

Effective organizational capacity: deliver knowledge, technology and other
support services for sound management.
Economical principles

Recognition of the value of goods and services: planted forests, whether
productive or protective, should be recognized for their provision of both
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market and non-market benefits, including wood and non-wood forest
products and social, cultural and environmental services;

Enabling environment for investment: governments should create the
enabling conditions to encourage investors to make long-term investments;

Recognition of the role of the market: Investors should design their
planning and management to respond to signals from international and
national markets: establishment and management of planted forests should
be market- rather than production-driven, unless established for
environmental, protective or civic reasons.
Social and cultural principles

Recognition of social and cultural values: Social and cultural values should
be taken into consideration in planning, managing and using planted
forests;

Maintenance of social and cultural services: adopting planning,
management, utilization and monitoring mechanisms to avoid adverse
impacts.
Environmental principles

Maintenance and conservation of environmental services: planted forest
management will impact the provision of ecosystem services. Thus
planning, management, utilization and monitoring mechanisms should be
adopted in planted forests in order to minimize negative impacts and
promote positive ones, as well as to maintain or enhance the conservation
of environmental services;

Conservation of biological diversity: incorporating the conservation of
biological diversity at stand, forest and landscape levels;

Maintenance of forest health and productivity: ensure that planted forests
are managed so as to maintain and improve forest health and productivity
and reduce the impact of abiotic and biotic damaging agents;

Landscape approach: management of landscapes for social, economic and
environmental benefits: as planted forests interact with and impact local
land uses, livelihoods and the environment, integrated planning and
management approaches should be adopted within a landscape or watershed
to ensure that upstream and downstream impacts are planned, managed and
monitored within acceptable social, economic and environmental standards.
Production oriented plantations may be viewed as simplified ecosystems and often constitute
monocultures where the successional process is intentionally interrupted. However, these planted
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forests may be planned in order to maximize biodiversity in tropical regions and have the
potential to complement the efforts made in the ambit of restoration ecology and conservation
biology. In areas where disturbance limits or modifies natural regeneration mechanisms,
ecological restoration techniques must be applied to restore community structure and ecological
processes.
Planting trees is one of the most widespread ecological restoration strategies in the world. The
plantations may act breaking barriers that hinder natural regeneration and producing a catalytic
effect on secondary succession facilitating natural regeneration of native vegetation, improving
soil quality and reestablishing ecological interactions. Factors, such as the particular tree species
planted, soil conditions and the location of the plantation in the landscape may affect the
effectiveness of plantation efforts to restore diversity and functions. The understanding of the
interaction of those factors with natural regeneration is fundamental for the development of more
effective restoration strategies.
However, a common problem is how we can evaluate success in a restoration project. The
success of a restoration program must take into account structural components of the plant
community, species diversity and ecological processes. Preferentially, at least two variables of
each component should be evaluated (Ruiz-Jaen & Aide 2005). This success may also be
interpreted as a continuum from the beginning of the project implantation until the establishment
of the attributes that will ensure the sustainability and ecosystem functioning. Another
fundamental aspect is that these results must be compared in some way to a reference system.
The area, or areas, to be compared to the restored site should be preferentially located near to
this site and subjected to the same large scale environmental conditions, such as climate and soil.
Table 2. Priority species for use in planting programs for forest restoration.
Species that
Important in
 Improve soil fertility (nitrogen-fixing Reducing the need for fertilization
species for example)
Creating appropriate microclimate conditions
 Grow rapidly
 Are attractive to frugivores by and excluding weeds
mutualistic interactions, sustain Improving seed dispersal, maintaining
wildlife in unfavorable periods are wildlife populations, and colonizing sites that
would not be occupied otherwise
poor dispersers
 Are rare or threatened
Modified from Lamb et al. (1997)
Increasing local population sizes
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6. Case Studies in the Tropics
6.1. Productive Plantations
Brazil is broadly recognized for its production forests, even though the experience is relatively
recent. Brazilian level of productivity in wood production is related to its climate and soil
conditions, research and technological development and specialized workmanship. One of the
most successful Brazilian initiatives comes from Aracruz SA (Figure 4). The company is the
world leader in production of bleached eucalyptus pulp, and has 24% of the global supply of the
product (Aracruz Celulose 2007). Bleached eucalyptus pulp is currently used to manufacture
printing and writing, tissue, and high value added specialty papers. Aracruz established its first
industrial plantation in 1978 at southeast Brazil, and nowadays its forestry operations occur
286.099 hectares of plantation distributed in four Brazilian states (Espírito Santo, Bahia, Minas
Gerais and Rio Grande do Sul). Annually, ca. 3.000.000 tons of cellulose are produced, and
mainly exported to North America (34%), Europe (41%) and Asia (23%). The averaged age of
wood varies from site to site, and ranges from 6.4 to 9.7 years. Harvest cycle varies from 6 to 8
years. Forestry practices are certified by the Cerflor (Brazilian System of Forest Certification)
recognized internationally by the Programme for Endorsement of Forest Certification Schemes
(PEFC). Besides plantations, the company possesses 170.191 hectares of native reserves
intended to protect plantations, combat pests, and maintain environmental balance and
biodiversity. It also foments eucalyptus plantation for more than 3.000 agricultural producers
that are benefit as they complement the income gotten through the farming activity. Aracruz also
invests on production of more than 1.000.000 native seedlings that are donated to those
agricultural partners and used in the recovery of degraded areas.
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Figure 4. Eucalyptus sp. plantantions established by Aracruz S.A. at Minas Gerais State, Brazil.
Source: Fabio Mareto
6.2. The Use of Multiple Native Tree Plantations for Restoring a Conservation Unit in
Brazil
In 1993, the Botanical Garden of Rio de Janeiro started a project to facilitate and increase the
forest cover in the Poço das Antas Biological Reserve (PABR), a federal conservation unit. The
aim was to develop and test different approaches of restoration ecology by planting native tree
species (Figure 5). With a total area of 5,160 ha, PABR forests are the last refuge of an
endangered and endemic monkey species, Leontopithecus rosalia L., known as golden lion
tamarins.
The tree species chosen for the restoration effort have three characteristics: rapid growth, being
attractive to frugivores thereby improving seed dispersal, and have great seed availability for
seedling production. Twelve planting sites with a varying size from 0.8 to 1.5 ha were
established and a total of 40 native tree species were tested. After 4 years, the mortality was
about only 10 % and a total of 28 not planted native tree species were recorded into the sites.
The biomass fixed in these planting sites, proxy measured by basal area (25 m2/ha), were similar
to that of old-growth forests (24 m2/ha). The rapid biomass increase inhibited the growth of the
exotic grasses Panicum maximum and Brachiaria mutica, which otherwise would outcompete
regenerating tree seedling from the sites and hence facilitate the establishment of other tree
species in the planted forest understory (Moraes et al. 2002).
200
Figure 5. Multiple native tree plantations established at Poco das Antas Biological Reserve, Rio
de Janeiro State, Brazil. Source: Tania S. Pereira
6.3. Pure and Mixed Plantation at La Selva Biological Station, Costa Rica
Plantations were established between 1987 and 1990 to test the development of native and exotic
tree species and also the performance of different planting treatments for both production and
protection. They were established in areas of abandoned pastures, a common scenario in the
region. A total of 80 tree species were tested, 51 of native, 15 from other regions of Costa Rica
and 14 of exotic trees. The treatments were assigned to mixed plantings (8-12 species) and
monocultures and the results showed that mixed planted forests have productivity equal or
higher than monocultures. Mixed plantations showed beneficial effects on soil by increasing
organic matter and cation retention (Montagnini & Porras 1998). The presence of late
successional tree species was greater in the mixed plantations. Also, natural regeneration, as
measured by density and richness, was greater in all mixed plantations when compared to an
abandoned pasture and to monocultures (Carnevale & Montagnini 2002). These results
highlights the facilitative role of plantations in accelerate secondary succession.
6.4. Forest Restoration of Bauxite-Mined Sites in Central Amazon, Brazil
In 1979, the Brazilian mining company Mineração Rio do Norte S.A. established a restoration
program in areas formerly utilized for bauxite mining at Porto Trombetas, western Pará State,
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Brazil (Parrotta & Knowles 1999). Mining activities causes an annual loss estimated in 20003000 ha of tropical forests and although the area directly affected may be smaller than that used
for agriculture for example, their impacts can be magnified due to erosion and runoff resulting in
siltation and deterioration of water quality in nearby water bodies. This large scale initiative,
aiming the rehabilitation of 100 ha of mined land per year, had critical steps for its
implementation including: seed viability, phenological and germination studies in order to
determine the more effective propagation method, and careful site preparation before planting.
Plantations were assigned to a 2 x 2 m spacing (2,500 plants per ha) using seedlings, cuttings or
seeding. Reforestation treatments were mixed planting with native tree species (about 70 species
from different successional groups), mixed planting using commercial tree species (both native
an exotic), direct seeding (48 early successional tree species) and natural regeneration
(secondary succession after topsoil replacement). Vegetation structure and floristic composition
in each treatment were analyzed between 1995 and 1997. Both, planted component development
and natural regeneration were observed. There was an increase in the number of species in all
treatments when compared to the number of planted species. Between 70-83 % of the species
richness and about 88-98 % of seedling densities and larger individuals (more than 2 m high) in
the sites were individuals originated from the seed bank and from outside sources (Parrotta et al.
1997). However, landscape structure had a substantial effect on the regeneration in the sense that
richness and density were positively correlated in the natural regeneration treatment as the
distance to natural forests diminished. Total species richness had strong variation among
reforestation treatments. Mixed plantation with native species presented the higher number of
species (141) and the mixed plantation with commercial tree species the lowest number of
species (40) (Parrotta et al. 1997). Overall, the results showed that mixed plantations using
native species and the use of alternative techniques, such as seeding, facilitate the establishment
and natural regeneration of local diversity.
6.5. The Application of Different Plantation Styles to Deforested Areas in Queensland,
Australia
Between 1900 and 1950 mainly vast areas of tropical forests were deforested in eastern
Australia. In the last decades there was an increase of reforestation efforts integrating ecological,
social and economical aspects. In one of these efforts, different reforestation methods were
implemented and were compared, based on various community attributes (such as canopy cover,
basal area and abundance of wood stems, life forms, understory and ground cover), to naturally
regenerated sites (old-field regrowth) and to reference sites (old-growth forests) in order to
measure the degree of development of these plantations (Kanowski et al. 2003). Plantations
varied with objective, production or restoration, species richness and plantation age. The
202
treatments comprised (i) young and old monoculture timber plantations, (ii) Mixed-species
cabinet timber plantations and (iii) restoration plantings. Monoculture timber plantations used
different tree species, namely Araucaria cunninghamii, Agathis robusta, Flindersia brayleyana
and Toona ciliata. The mixed-species cabinet timber plantations were established by the
Community Rainforest Reforestation Program (CRRP). Plantations comprised indigenous
rainforest tree species, Eucalyptus trees and some exotic cabinet timber trees. The planting sites
have between 6-22 years old and comprised a mixture of trees and shrubs (20 to 100 native
species) in high density plantations (6.000 stems/ha) (Lamb et al. 1997). Old monoculture timber
plantations were strongly similar in structural characteristics such as canopy cover, basal area
and density of woody stems, to reference sites. Understory occupancy by herbaceous plants and
grasses was inversely correlated to canopy cover. Epiphytes, hemi- epiphytes and lianas had low
density in all plantations, except in old monoculture timber plantations. These old monocultures
and restoration plantations were more similar to reference sites than the others treatments. These
results have some implications to the management and design of artificial forests. Perhaps, the
most important, is that long rotation period monocultures may have a structural complexity very
similar to preserved forests.
6.6. Regeneration of Native Tree Species under Eucalyptus Plantations in Southeastern
Brazil
The study of Silva Junior et al. (1995) well exemplifies how productivity and biodiversity
conservation might be reconciled. They have found underneath an Eucalyptus grandis stand the
regeneration of 123 tree Atlantic rainforest species in 1.37 hectare, which was comparable to the
124 species found in 1 hectare of a neighboring natural forest. Such regeneration was possible
from the moment human activities inside the plantation were halted (6 years prior to the survey)
and also to the proximity of a large natural forest nearby where seeds possibly came from. The
dominant species of the study site, Apuleia leiocarpa, was not frequently seen among adults and
regenerants of the control site, suggesting that species composition and vegetation structure in
the study site was more typical of an intermediate stage of succession, contrasting with adults
and regenerants of the control site which are typical of climax or near-climax vegetation.
However, this is indicative of a very high success of initial recuperation of biodiversity and
demonstrates how productive monocultures may also serve the purpose to promote species
enrichment in such areas.
7. Conclusions
Most of the attempts to restore forests with protective plantations are made over small spatial
scales. This is in contrast with the scale where production forests are implemented. There is a
203
need for integrative approaches linking productive and protective plantations. Planting trees
should move beyond the mere action implicit on it. Both, productive and protective efforts will
gain more if each moves towards one another. This means bringing more economic return to
protective forests and also forcing production forests to provide significant ecosystem services
and increased biodiversity. Tropical ecosystems provide a great, and mostly unexplored
potential, to increase both, economic and ecological returns using artificial forests due to their
higher productivity and biodiversity.
204
Glossary
: Is the storage of carbon dioxide (usually captured from the atmosphere) in a
Carbon
sequestration solid material through biological or physical processes
and storage
Cutting cycle : The period of time between major harvests in a stand.
Ecosystem
services
: Refer to habitat biological or system properties or processes of ecosystems.
Example: climate and water regulation, erosion control, soil formation, nutrient
cycling, biological control, etc.
Exotic species : A common definition of the term is when a species is outside its native
distributional range and arrived by human agency. Synonyms as nonindigenous, introduced, invasive or alien species are also common.
Fast-wood
plantation
Forest
management
: Intensively managed commercial plantations producing wood at high growth
rates (no less than 15 m3/ha of MAI) being harvested in less than 20 years.
Hardwood
Lumber
: Trees with broad, flat leaves as opposed to coniferous or needled trees.
: Wood or wood products used for construction.
Old-growth
forests
: In tropical region an old-growth forest often has large individual trees, a multilayered crown canopy, a significant accumulation of coarse woody debris and
high species richness.
: A system of practices for stewardship and use of forest land aimed at fulfilling
relevant ecological (including biological diversity), economic and social
functions of the forest in a sustainable manner.
Particleboard : Panels manufactured by bonding wood particles with synthetic resins under
heat and pressure.
: A building panel made by gluing together thin layers of wood. Alternating
Plywood
grain directions from one layer to the next adds strength.
: Wood suitable for use in paper manufacturing.
Pulpwood
Rehabilitation : To re-establish the productivity and some, but not necessarily all of the
original species diversity of the forest previously present before the site was
degraded. Rehabilitation will mean that many, though not necessarily all, of the
original functions and processes will have been re-established.
: To re-establish the structure, productivity and species diversity of the forest
Restoration
previously present before the site was degraded. Ecological restoration means
the original functions and processes will also have been re-established. This is
difficult to achieve and to verify.
Rotation
length
: The number of years required to grow a stand to a desired size for login.
Sawtimber
: Wood of large enough size to be used to produce lumber for construction and
furniture.
Seedling
: A young plant growing from its seed.
Silviculture
: The art, science and practice of establishing, tending and reproducing forest
stands of desired characteristics. It is based on knowledge of species
characteristics and environmental requirements.
Softwood
plantations
: Plantations using any tree of the gymnosperm group, including pines,
hemlocks, larches, spruces, firs, and junipers. Softwoods often are called
conifers although some, such as junipers and yews do not produce cones.
205
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different styles of reforestation. Forest Ecology and Management 183: 265-280. [Case study
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example of an integrative approach from the humid tropics. Environmental Management 22:
459-470 [Important study on the use and impacts of mixed forest plantations to increase
biomass accumulation]
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Atlantic Rainforest Restoration by the Rio de Janeiro Botanic Garden Research Institute. In
”Plant Conservation in the Tropics” (Eds Maunder, M., Clubbe, C., Hankamer, C. & Grove,
M.) p. 151-170. Kew Publishing, London. [Case study on a forest restoration effort to
produce new habitats for a threatened species of monkey]
Parrotta J.A., Knowles O.H. & Wunderle J.M. (1997). Development of floristic diversity in 10year-old restoration forests on a bauxite mined site in Amazonia. Forest Ecology and
Management 99: 21-42 [This work provides an analysis of understory and overstorey
floristic composition, light conditions, soil properties, and also dispersal syndromes in
Amazon reforestation sites]
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lands in the Brazilian Amazon. Restoration Ecology 7: 103-116.
207
Ruiz-Jaen M.C. & Aide T.M. (2005). Restoration success: How is it being measured?
Restoration Ecology 13: 569-577 [The work provides important recommendations for
effective evaluation of restoration efforts]
Silva Junior M.C., Scarano F.R. & Cardel F.D.S. (1995). Regeneration of an Atlantic Forest
formation in the understorey of a Eucalyptus-grandis plantation in South-eastern Brazil.
Journal of Tropical Ecology 11: 147-152. [Case study on natural regeneration of rainforest
underneath Eucalyptus plantation]
Sociedade Brasileira de Silvicultura (SBS). (2007). Fatos e números do Brasil florestal. 109 pp.
Brasil. pp 107. SBS: Sao Paulo, Brazil. [The text presents facts and numbers about planted
and native forests in Brazil as well as important informations about the forest sector activity
in the country]
208
Biographical Sketches
Danielle Justino Capossoli is a doctoral student at the Graduate Programme in Botany at the
Botanical Gardens of Rio de Janeiro, Brazil. She is currently interested in ecological models to
describe and predict restoration and successional processes.
Jerônimo Boelsums Barreto Sansevero is a doctoral student at the Graduate Programme in
Botany at the Botanical Gardens of Rio de Janeiro, Brazil. He is currently interested in
ecological models to describe and predict restoration and successional processes.
Mário Luís Garbin is a doctoral student at the Graduate Programme in Ecology at the
Universidade Federal do Rio de Janeiro, Brazil. He is currently interested in plant-soil
interaction, spatial ecology and ecological modeling.
Fabio Rubio Scarano is a forester and Professor of Plant Ecology at the Ecology Department of
the Universidade Federal do Rio de Janeiro, Brazil. He is currently the Research Director of the
Botanical Gardens of Rio de Janeiro. His present research interests are Community Ecology (in
which of the lines focus on restoration ecology) and Plant Ecophysiology, with particular focus
on the vegetation types that are marginal to the Atlantic rain forest and on the floodplain forests
of the Amazon.
To cite this chapter
D.J. Capossoli, J.B.B. Sansevero, M.L. Garbin, F.R. Scarano , (2009), TROPICAL
ARTIFICIAL FORESTS, in International Commission on Tropical Biology and Natural
Resources, [Eds. Kleber Del Claro,Paulo S. Oliveira,Victor Rico-Gray,Ana Angelica Almeida
Barbosa,Arturo Bonet,Fabio Rubio Scarano,Francisco Jose Morales Garzon,Gloria Carrion
Villarnovo,Lisias Coelho,Marcus Vinicius Sampaio,Mauricio Quesada,Molly R.Morris,Nelson
Ramirez,Oswaldo Marcal Junior,Regina Helena Ferraz Macedo,Robert J.Marquis,Rogerio
Parentoni Martins,Silvio Carlos Rodrigues,Ulrich Luttge],in Encyclopedia of Life Support
Systems(EOLSS), Developed under the Auspices of the UNESCO, Eolss Publishers, Oxford ,UK,
[http://www.eolss.net] [Retrieved July 23, 2011]
©UNESCO-EOLSS
Encyclopedia of Life Support Systems
209
ANEXO 2
MODELING THE SUCCESS OF
RESTORATION IN TROPICAL ECOSYSTEMS
Artigo em preparação para subimissão ao periódico New Forests: Garbin, M.L, Sansevero,
J.B.B, Capossoli, D.J, Durigan, G, Engel,V.L, Faria, S.M, Ganade, G, Madureira, C, Marques,
M.C.M, Melo, A.C.G, Pereira, T.S, Rodrigues, P.F.J.P, Scarano, F.R, Sosinski, E.E, Zamith, L.R
and Pillar, V.P. Modeling the success of restoration in tropical ecosystems.
210
Abstract
Optimization of ecological restoration in the Brazilian Atlantic forest demands a model allowing
predicting how initial conditions might determine the final stages of restoration projects. In
highly diverse ecosystems such attempts should be especially difficult due to the greater
complexity of such systems. Here, we propose a general model to predict sucessional trajectories
based on the variation of plant functional traits to be applicable to tropical forests. The model is
composed of six steps, all of which are fully independent and consolidated in the ecological
literature. Plant functional types are defined and changes in functional composition are tracked
by Markov chain modeling. Future states of environmental conditions and the effects of changes
in functional diversity in altering such conditions can be analyzed. A matrix of attractors is used
in combination to linear models to provide an assessment for comparison with a reference
system. The model aims to provide a way to predict the effects of different restoration strategies
in altering ecosystem properties and the way that different environmental conditions (community
attributes or ecosystem effects) alter the functional composition of successional ending points. It
is expected that the application of the model under different environmental conditions within a
wide variety of restoration approaches will provide ways to anticipate the effectiveness of
restoration success efforts in the Brazilian Atlantic Forests.
Key words: restoration ecology, tropical forests, plant functional types, successional trajectory.
211
A general empirical model to restore the Atlantic Forest
Current estimates of remaining Brazilian Atlantic Forest cover ranges from 11.4% to 16%
mostly distributed in small and spaced fragments. Nature reserves are responsible for only 9% of
the remaining forest and 1% of the original forest (Ribeiro et al. 2009). These forests comprise a
priority area for conservation (Myers 2000), as their high diversity systems with high levels of
endemism and remnants under severe anthropogenic pressure (Morellato & Haddad 2000). The
loss of great extensions of forests is problematic not only because many species may become
extinct (Silva & Tabarelli 2000) but also because of the loss of ecosystem services (e.g. climate
control, carbon sequestration and storage, control of soil erosion), which negatively affect many
aspects of human well being (Diaz et al. 2007). To increase current forest cover, there are strictly
two options: stimulate spontaneous recovery or forest restoration (Brown & Lugo 1994). Since
spontaneous recovery is a very slow process that may span more than a century (Liebsch et al.
2008), forest restoration is a necessity rather than an option.
Rodrigues et al. (2009) reviewed experiences on the restoration of Brazilian Atlantic Forests and
concluded that neither all past efforts did not attained self-perpetuating forests. The authors
pointed out that forest reconstruction is feasible and it is dependent on the strategies applied and
on the surrounding landscape conditions. Some of the greatest challenges include the reduction
of costs and the selection of species for planting. The latter should be based not only on their
light requirements, but also on community-level traits such as phenology, dispersal syndromes,
ability of vegetative reproduction and to fix nitrogen, deciduousness and litter production. Thus,
the identification of complementary functional roles of the species and their effects on ecosystem
and community characteristics are fundamental aspects to be incorporated into restoration
projects in the Atlantic Forest. Up to now these aspects are waiting effective application.
We argue that restoration efforts of tropical forest ecosystems are hindered by the lack of
predictive power of the different restoration initiatives. Most of the restoration efforts in the
Brazilian Atlantic rainforest consists on isolated attempts focused on the fast recover of the
ecosystem structure and function (Ferretti & Debritez 2006), comparisons of methodologies
(Souza & Batista 2004) and the trade-offs between efficiency and costs (Engel & Parrota 2001;
Bruel et al. 2010). The evaluation of the success of those restoration models are based on a
posteriori analysis of ecosystem structure and processes (such as biomass accumulation,
diversity, etc.) which are obviously slow and weakly predictable. Predictive models that work on
a wide range of environmental conditions are a fundamental tool to increase our efficiency in
restoring tropical forest ecosystems.
Restoration is an interventionist activity in essence (Hobbs & Cramer 2008). The objective is to
create conditions prompting the ecosystem to follow what is believed to be its natural pathway
(SER 2004). Moreover, the final condition of the restored system should be similar to the
212
believed ecosystem state before disturbance (SER 2004). The successional trajectory in a
restoration effort may take several different pathways having a complex nature, i.e., nonlinear,
unpredictable, and leading to multiple attractors, turning modeling into an inevitable demand
(Anand & Desroches 2004; Suding & Hobbs 2008). Also, most studies are based on a small set
of variables not covering diversity, vegetation structure, and ecological processes all together
(Ruiz-Jaen & Aide 2005). The use of community level approaches to understand restoration
processes would render better results as they provide a more holistic view of the successional
trajectories (Anand & Desroches 2004). In this context, success has been rarely assessed in
restoration projects (Ruiz-Jaen & Aide 2005). The problem is how to determine the ending point
of a restoration and how it may be affected by initial conditions (Moore et al. 1999). This
knowledge would enable us to change management practices in order to redirect the trajectory to
what is believed to be its natural pathway, or redirect it to any other target.
We propose here an empirical model that allows description and prediction of successional
trajectories in restoration projects of vegetational communities based on plant functional types
(PFTs). Initially, our focus is in the Atlantic forests. The model is composed of six steps aiming
to describe and predict the response of PFTs to a wide range of environmental conditions. Our
interest on PFTs in restoration modeling is pragmatic. They are used as a simplification tool
allowing the study of different ecosystems dynamics. The optimization of restoration efforts
depends on knowing how predictable the successional trajectory is once the initial environmental
conditions are well known or established. An effective model should allow the treatment of
temporal changes in PFTs and the adjustment to different environmental conditions.
Plant functional types and their relevance in restoration efforts
Restoration projects in the Brazilian Atlantic Forest it seems to be much more concerned with
species diversity than functional diversity (Rodrigues et al. 2009; Aronson et al. 2011).
However, planting systems with high species richness but with a low functional diversity (high
functional redundancy), in this case, a higher proportion of early successional tree species had
their development negatively affected due to premature death of the early tree species (Souza &
Batista 2004; Parrota & Knowles 1999). But there are other reasons to use functional aspects
when evaluating the success of a restoration project. First, the recovery processes in tropical
ecosystems may take very different pathways and a common trend is that diversity and
ecosystem properties, such as biomass and nutrient retention, may be restored despite attaining
earlier species composition (Finegan 1996; Guariguata & Ostertag 2001; Denslow & Guzman
2000). Overall, ecosystem properties tend to be less responsive to environmental changes than
species composition (Ernest & Brown 2001).
213
In this way, the same recovery process in terms of ecosystem properties may be attained by
different species compositions. Thus, the importance of restoring original species composition to
determine forest function is still an insufficiently tested subject in the tropics (Guariguata &
Ostertag 2001). Secondly, tropical forests may show a high functional redundancy so that up to
75% of the species may be lost before losing the first functional group (Fonseca & Ganade
2001). Therefore, when comparing restored stands to a reference system, we may have different
species composition but similar functional diversity. This would be possible wherever
component species display high levels of functional redundancy.
The use of functional traits in restoration practices is just beginning (Funk et al. 2008; Aubin et
al. 2009). Most studies are based on a priori definition of functional groups such as early/late
successional differentiation or by different dispersal syndromes (Rodrigues et al. 2009). In
regard to the tropics, high species diversity imposes a severe limitation to modeling such an
enormous complexity. However, the use of plant functional types provides a simplification tool
and enhances our understanding about structure and function of restoration and natural
regeneration processes (Gitay et al. 1999; Chazdon et al. 2009). When included in predictive
models they also provide a way to produce robust generalizations and effective ways to simulate
the vegetation dynamics (Noble & Gitay 1996; Pausas 2003). However, a central problem is to
determine functional groups in the context of forest restoration.
The concept of PFT is that species may be grouped according to similar responses to
environmental conditions and/or similar effects on ecosystem processes (Lavorel & Garnier
2002; Diaz & Cabido 1997; Cornelissen et al. 2003). Specifically, a PFT may be defined as “a
group of plants that, irrespective of phylogeny, are similar to a given set of traits and similar in
their association to certain environmental variables” (Pillar & Sosinsky 2003). These variables
are factors to which plants are responding, e.g. soil conditions, or their effects in ecosystem
processes such as biomass production and litter accumulation. Identification of PFTs is primarily
made in three different ways (Gitay & Noble 1997): a) subjective, defined in an inductive way;
b) deductive, when a functional classification is derived from a priori model about the
importance of specific processes or properties of ecosystem functioning; and c) by using
multivariate data analysis. In the latter, the search for PFTs generally involves a sequence of
steps from deciding which type of functional group is needed, selecting the criteria for inclusion
of species and which functions should be considered, choosing the traits to be measured, and
applying multivariate methods to the species–trait matrix (Fonseca & Ganade 2001).
Within this perspective, there are three fundamental approaches (Pillar & Sosinski 2003): those
using only one matrix, e.g. species by traits, matrix (Grime et al. 1997); those using a two matrix
approach, species by traits and a species by communities matrix (Diaz & Cabido 1997); and a
three matrix approach, species by traits, species by communities, and environmental variables by
214
communities (Pillar 1999; Pillar & Sosinski 2003). The main limitation of using one or two
matrices approaches is that plant types are not defined at all and there is no guarantee that the
PFTs so defined may reflect a direct relationship with ecosystem processes (functional effect
groups) or environmental factors (functional response groups). In the three matrix approach,
optimization algorithms are used to refine subsets of traits and based on these subsets, find types
with maximum association with environmental factors or effects. The model we present here
formalizes a general way to define PFTs and modeling successional trajectories in restoration
practices and secondary forests in Brazilian Atlantic Forest.
The model
Optimization of restoration efforts will be possible after we know the initial conditions
of the system, define the functional redundancy of the system’s components and how changes in
composition affect ecosystem properties and, finally, how changes in environmental conditions
affect the community properties. In this way, the model is composed by six steps (Fig. 1); all of
them are independent and fully consolidated in the ecological literature (see Orloci et al. 1993;
Legendre & Legendre 1998; Pillar 1999; Pillar & Sosinski 2003). In the first step, numerical
analyses are used to search relevant traits and define PFTs. In the second step, a transition matrix
based on markovian models is applied to a matrix of PFTs by sites and an attractor matrix is
generated. The third step aims to give linear equations and/or ordinations scores describing the
relationship between the attractors and environmental factors or experimental treatments. The
fourth step establish the relationship among PFTs and community-level attributes, and in the
fifth step, linear equations produced in step 4 are applied to the attractors matrix to give a matrix
of futures stables states (EF). This matrix is compared to a reference system’s matrix in the last
step.
215
W
X
P
A
Product: matrix X
*
PFTs
variables
PFTs
E1
sites
sites
variables
B
Step 3: predicting the response of
PFTs to environmental conditions
Each vector of X
is used to build P
PFTs
sites
traits
OTUs
Step 2: setting the successional endpoint
PFTs
Step 1: identifying PFTs
E2
P
Products:
Linear equations
Product: matrix A
Ordination scores
X
sites
variables
sites
variables
PFTs
sites
PFTs
sites
A
EF
E1
Applied to
Product: Linear equations
Step 4: setting the relationship among
ecosystem effects and the PFTs
Product: matrix
EF
Step 5: predicting ecosystem effects
using the attractors
Compared to a
Reference system
Step 6: comparing to a reference
system
Figure 1. General overview of the model to guide restoration efforts for Brazilian Atlantic Forests. OTUs are operational taxonomic unities (individuals,
local populations, species, or any other taxonomic unities); B, describes populations by traits; W, describes the sites by the densities of OTUs; E1,
describes sites by community attributes and/or ecosystem effects; X is the optimized composition of PFTs; P, a transition matrix for a given restoration
effort; A, the attractor’s matrix; E2, a matrix containing environmental factors or experimental treatments; EF, a matrix of future states to be compared to a
reference system.
216
Step 1: Indentifying PFTs
The first step is the search and definition of PFTs. It is based on the method described in Pillar
(1999), Pillar & Sosinski (2003) and Pillar & Orloci (2004). The data are organized in three
matrices (Figs. 1 and 2): matrix B describes operational taxonomic unities (OTUs) by traits; matrix
W describes the sites by the quantities (densities) of these OTUs; matrix (E1) describes the sites by
variables (qualitative or quantitative) such as environmental and disturbance factors or ecosystem
effects. OTUs are individuals, local populations, species, or any other taxonomic unities to which
the trait description refers (Pillar et al. 2009).
The matrix B is based on the selection of a larger trait set based on past experience and known
practicality, which is used for community description (Pillar et al. 2003). A problem, however, is
which traits to choose in order to build this larger trait set. Despite specific goals of the study, plants
face common challenges in order to successfully occupy a given site. These may be grouped into
three main categories: dispersal, establishment and persistence (Weiher et al. 1999). We suggest a
list of relevant traits to define PFTs specific for restoration of Brazilian Atlantic Forests and
grouped them according to these three categories (Table 1). Matrix W describes the sites or, in an
operational perspective, communities {see Palmer & White 1994) by the presence/absence or
quantities of these populations, such as the density of species in the sites. We suggest a useful
criterion of inclusion is of woody species equal to or higher than 50 cm tall. The third matrix (E1)
describes the sites by community attributes (Table 2).
The definition of PFTs process is recursive and the objective is to search traits and find optimal
PFTs (Fig. 2). The analytical procedure is to find a subset of traits and based on it to define plant
functional types so that a maximum association is revealed with environmental factors or effects
(Pillar & Sosinski 2003). Through this recursive algorithm, at any given iteration a subset of traits is
extracted from the initial set in matrix B and, based on this matrix, population types more similar
are identified by cluster analysis or by degrees of belonging of an OTU to a type (Pillar & Sosinski
2003). Based on the functional types, the quantities of corresponding populations on matrix W are
pooled and a reduced matrix of types by sites is obtained (Matrix X). The dissimilarities among
sites, or communities, are calculated (matrix D). The congruence is obtained by matrix correlation
between D and the dissimilarity matrix of community variables (see Table 2) using E1 matrix
(matrix Δ). At each recursive step, a new subset of traits is selected in matrix B and a new cluster
analysis is used to indentify functional types of populations. The partition level that maximize the
objective function, that is, for each partition level defining PFTs, a new matrix X is generated by
217
pooling, within communities, the performance values of populations belonging to the same PFTs
(Pillar & Sosinski 2003).
218
Table 1. Plant traits relevant to be used in matrix B in the restoration model.
Categories*
Functional traits
Dispersal
Dispersal syndrome (biotic or abiotic) 1, diaspore size
seed longevity3.
Establishment
Growth rate (slow, medium, rapid)1,3; nitrogen fixation ability1,2, leaf length
2,3
, leaf area 2,3, specific leaf area3, leaf dry matter content3, tolerance to stress
(flooding, drought)1,3, shade tolerance 1,2.
1,2,3
, seed mass
2,3
, and
Potential height1,3, maximum longevity1, maximum DBH1,2,3, wood
density1,3, phenology (evergreen, deciduous, semideciduous)1,2,3, leaf
length2,3, leaf area2,3, specific leaf area3, leaf dry matter content 3, pollination
syndrome (wind, birds, mammals, large and small invertebrates) 1,3,
resprouting ability (present, absent)3, life form (tree, shrub, liana) 1,2,3, shade
tolerance1,2, sexual system (hermaphrodite dioecious, monoecious) 1,2,3.
1
Data from literature; 2 Data from herbarium; 3 Field and experimental measures; * see Weiher
et al. (1999)
Persistence
In addition, trait convergence and trait divergence assembly patterns may be decoupled in the
analytical process allowing revealing traits and types maximally associated to environmental filters
and biotic interactions (e.g. competition). In this case, fuzzy types are used and X indicates the
performances of the types fuzzy weighted by traits (Pillar et al. 2009).
The product of interest in this step is matrix X (Pillar 1999; Pillar & Sosinski 2003). This matrix
contains n sites by PFTs and represents the optimal composition of PFTs at a given site. PFTs so
defined are expected to be more functional than PFTs defined using non-optimal traits (Pillar 1999).
The matrix X is the basis for the next step.
219
populations
Traits
Sites
B
W
W
Pooling population quantities
according to types
populations
Traits
C
Cluster
analysis
Sites
F
X
variables
PFTs
Different partition levels
Subset of traits
Traits
E1
D
Δ
ρ(D; Δ)
Figure 2. General overview of step 1. An algorithm to find optimal trait subset in PFT based data of
matrices (B, W and E1). ῤ(D,∆) – Mantel correlation. For details see Pillar & Sosinski (2003).
Table 2. Community variables to be used in matrix E1 in the restoration model
Categories
Variables
Structure
Total basal area, total density, DBH size variation (Gini
coefficient), plant area index (PAI), invasive grasses cover, total
sapling density (50 - 100 cm tall)
Diversity
Life form richness (pteridophytes, mosses, lichens, epiphytes,
hemi-epiphytes, herbs, shrubs, trees, lianas and parasites), richness,
equitability
220
Ecological processes
Proportion of newly recruited plants (two categories: less than 50
cm tall and higher than 50 cm), presence of nodulation by nitrogen
fixers, litter deposition (biomass/ha/year), litter moisture, soil
organic matter and pH
221
Step 2: Predicting the successional trajectory
Ecology has seen in the last two decades a shift in emphasis from an equilibrium perspective
centered in deterministic processes, closed and internally homogeneous systems with directional
changes to a nonequilibrium perspective, where indeterminism, frequent disturbances,
heterogeneity, scale, multiple steady states and system openness are the main attributes of
ecological systems (Allen & Hoekstra 1992; Shrader-Frechette & Mccoy 1993; Wallington et al.
2005; Holling et al. 1998; Pickett 2007). The nature of forest succession has both historical and
endogenous factors. Modeling is complicated by the high diversity of these systems, such as
tropical forests. A powerful model for restoration need to capture at least three elements: functional
redundancy (step 1), predictive power and the contingent nature of ecological systems. The second
step of the present model is based on the application of complex systems theory to restoration
ecology (see Anand & Desrochers 2004), and aims to give predictability to the model.
Models based on matrices offer a way to envisage forest succession. Succession, in this way, is
viewed as a plant-by-plant replacement process (Horn 1974). The basic idea is that successional
trajectories may be modeled by the probability of a community component be replaced by another
component or by itself in a given site. Markovian models of transition of species bring up the notion
that the recovering process of an ecosystem develops progressively until a steady state clementsian
climax condiditions (Anand & Desrochers 2004). The nature and direction of a trajectory is
governed by the system’s attractor which can be very diverse (Anand & Desrochers 2004).
There is a handful of simplifying assumptions in the simplest markovian models. Two deserves
special attention: the transition probabilities are (i) density independent and (ii) remain constant
over time (Morin 1999). The first is a controversial topic as there is no clear definition for what
constitute the factors determining this independence even though density is related to population
growth (Murray 1994). The second is more complicated since communities change continuously
over time accordingly to variations in the abiotic and biotic environment and the behavior of the
organisms reflects those changes (Lippe et al. 1985). Furthermore, the last state record of the
system corresponds to a stable state found in the Markov process (Lanzer & Pillar 2002). Thus, is
desirable that simpler markovian models as here proposed could be modified in order to increase its
predictive power, even though these limitations do not make Markov models unimportant or not
useful.
Markovian models assume that the successional trajectory is clementsian. This is an equilibrium
perspective in that does not consider the possibility of multiples stable states each dependent on
specific site characteristics and different situations. However, the presented model can handle
222
multiple stable states in that the attractor matrix is composed by multiple localities and the
researcher may set these different initial conditions into the experimental design or natural local
conditions. In doing so we do not say that succession is always a strictly directional and predictable
process. Instead, we consider the transition matrix as a useful starting point to help us define
possible stable endpoints of the restoration initiative.
In this step, Markov chain modeling is used to produce a matrix describing future stable states, a
matrix of attractors (Fig. 3). The objective is to create a matrix containing the future composition of
the PFTs. Transition matrices are applied to each site or community vectors of the X matrix (PFTs
matrix). The transition matrix describes the probability of a given PFT be replaced by another type
or by itself in a given site. These probabilities are obtained by identifying which PFTs had their
abundances or cover changed at each measured interval or temporal series (Orloci et al. 1993). A
markovian series describes the system’s trajectory in that the transition from a state to a future state
is a probabilistic process. When a trajectory is pure Markov, the description of its Ut + 1 state can
be derived from the site composition of its Ut state and a known transition matrix P,
Xt + 1 = Xt P
A typical element of P expresses the rate at which population X loses ground to population Y when
the series moves from one of its states to a future state (adapted from Orlóci et al. 1993).
It is worth to note that each site in matrix W describes different planting strategies at different time
periods after the restoration is implemented. If there is only one restoration project with two or
more temporal sites, then A will be a vector describing the final stable state modeled. However, and
desirably, multiple restoration projects may be included in this matrix. If it is the case, then A will
be a matrix describing the final stable states of each restoration project. Moreover, it must be
emphasized that the present step provides also a way to infer the time span demanded to reach the
final stable state.
223
Vegetation surveys
PFTs t1 ... tn
S1 ... Sn
Abundance
Cover
X matrix
Sites
x (t+1)
PFTs t1 ... tn
xi
*
PFTs t1 ... tn
PFTs t1 ... tn
x1... xn
Probability of a
type (ti) loose
cover to
another type or
to itself
=
Composition of types
after one round of types
replacement
Figure 3. A Markovian model of PFTs transitons. The product of the first round (x (t+1)) is
multiplied again by the P matrix and the process is repeated successively times so that x * P N
express the composition of types after N replacement rounds.
Step 3: Predicting the response of PFTs to changes in environmental conditions
We aim here to establish the relationship among environmental factors and the attractors earlier
defined and predict, or forecast, the PFTs response to the factors. This is done by multiple
regression and/or canonical analysis (Legendre & Legendre 1998) using the matrix of attractors and
a matrix of environmental factors, here called E2 (Fig. 1). The results are equations describing the
relationship among attractors and the factors and/or ordinations scores describing this relationship.
Predictors, in the restoration context, may be: species introduction, management strategies, soil
types, rainfall, fertilization strategies, distance from propagules sources, presence of exotic species,
and intensity and frequency of disturbances.
Both, regression and canonical analyses are widely used methods in ecology and will not be
detailed here. Briefly, in regression analyses, each vector in the attractor’s matrix is compared to
each vector on the explanatory matrix of environmental variables. The aim is to describe the
relationship among dependent random variables (Y) and a set of explanatory, independent, variables
(X) in order to predict the behavior of Y as a function of X (Legendre & Legendre 1998). Extend
the model to two or more X variables implies that correlations among predictors, multicollinearity,
224
are checked in order to clearly separate the contributions of each factor to the response variable
(Gotelli & Ellison 2004). Canonical analysis, by its turn, is the simultaneous analysis of two or
more matrices to evaluate the relationship among attractors and environmental factors. It allows the
direct comparison between two data matrices and the matrix of descriptors interfere in the
ordination calculus forcing the ordination vectors to be maximally related to the combination of
variables of the explanatory matrix (Legendre & Legendre 1998).
Steps 4 and 5: Setting the relationship among ecosystem effects and the PFTs and predicting
ecosystem effects from the attractor’s matrix
A key aspect making the use of PFTs a powerful tool to predict ecosystem changes as a function of
environmental changes is that ecosystem functioning may be predictable from the composition of
functional types (Gitay & Noble 1997; Diaz & Cabido 1997; Diaz et al. 2004). Besides deciding the
best method to define PFTs, we need to adequately understand the way the groups in fact reflect the
environmental changes faced by plants. This knowledge allows us to know how organisms would
respond to future changes in the environmental conditions.
Here, the statistical procedure is similar from that of step 3. The objective is to first establish the
relationship among explanatory variables, in this case, the ecosystem effects (E1) and the PFTs
composition (X) (Fig. 1). The objective is to predict ecosystem effects based on the functional
composition. The resulting linear equations are then applied to the attractor’s matrix (step 5). The
result is a matrix of future states (EF), the attractors predicted by equations describing types and
ecosystem effects. E1 and EF are the same matrices in essence but correspond to different periods
of time in modeling process.
Step 6: Comparison with a reference system
The idea is to compare the modeled restoration strategy to a well know data set (reference system)
in order to validate the whole restoration strategy. This data is not used in the transition matrix of
the step 2 to avoid redundancy in the analytical process. The comparison is among the projected
ecosystem effects based on PFTs using the same variables of EF. Departures of the modeled EF
from the reference system mean, at least, three things: 1) the planting management needs
adjustments; 2) the modeling process is inadequate and need to be improved; or that 3) the reference
system is insufficient to validate the model.
225
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Restauração ecológica: ligando prática e teoria

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