Self-Recruitment in the Bumphead Parrotfish Under Different Levels

Self-Recruitment in the Bumphead Parrotfish Under Different Levels
of Fishing Pressure in the Solomon Islands
Thesis by
Diego Fernando Lozano-Cortés
In Partial Fulfillment of the Requirements
For the Degree of
Master of Science in Marine Science
King Abdullah University of Science and Technology, Thuwal,
Kingdom of Saudi Arabia
Approval Date: December 2014
2
The thesis of Diego Fernando Lozano-Cortés is approved by the examination
committee.
Committee Chairperson: Dr. Michael Berumen
Committee Member: Dr. Xabier Irigoien
Committee Member: Dr. Pablo Saenz-Agudelo
3
© 2014
Diego Fernando Lozano-Cortés
All Rights Reserved
4
ABSTRACT
Self-Recruitment in the Bumphead Parrotfish Under Different Levels of Fishing
Pressure in the Solomon Islands
Diego Fernando Lozano-Cortés
Knowledge in the spatial patterns of fish larval dispersal is crucial for the
establishment of a sustainable management of fisheries and species conservation.
Direct quantification of larval dispersal is a challenging task due to the difficulty
associated with larval tracking in the vast ocean. However, genetic approaches can be
used to estimate it. Here, I employed genetic markers (microsatellites) as a proxy to
determine dispersal patterns and self-recruitment levels using parentage analysis in
the bumphead parrotfish (Bolbometapon muricatum) in the Solomon Islands. Tissue
samples of 3924 fish (1692 juveniles, 1121 males and 1111 females) were collected
from a spear-fishery at the Kia District in Santa Isabel Island. The samples come
from three distinct zones with different fishing pressure histories (lightly fished,
recently fished, and heavily fished). The mean dispersal distance estimated for the
bumphead parrotfish was 36.5 Km (range 4 – 78 Km) and the genetic diversity for the
population studied was low in comparison with other reef fishes. The parentage
analysis identified 68 parent–offspring relationships, which represents a selfrecruitment level of almost 50 %. Most of the recruits were produced in the zone that
recently started to be fished and most of these recruits dispersed to the heavily fished
zone. Comparisons of genetic diversity and relatedness among adults and juveniles
suggested the potential occurrence of sweepstakes reproductive success. These
results suggest that management measures must be taken straightaway to assure the
5
sustainability of the spear-fishery. These measures may imply the ban on juveniles
fishing in the heavily fished zone and the larger adults in the recently fished zone.
Overall, the population dynamics of the studied system seem to be strongly shaped by
self-recruitment and sweepstakes reproduction events.
Keywords: Bumphead parrotfish, Self-recruitment, Larval dispersal, Parentage
analysis, Relatedness, Fishery, Demographic connectivity.
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ACKNOWLEDGMENTS
I want to thank to my supervisor Dr. Michael Berumen and the Red Sea Research
Center at KAUST for supporting this thesis project and to Dr. Pablo Saenz-Agudelo
(Universidad Austral de Chile) for teaching me population genetics from zero to hero
(theory, lab work, data analysis, and results interpretation). I also thank Dr Hugo
Harrison (James Cook University) for his help with parentage analysis software and
simulations and Dr. Richard Hamilton (The Nature Conservancy) for providing me
with the samples and information regarding the sampling process and the study site. I
would like to thank the KAUST Bioscience Core Lab for carrying out the fragment
analysis of all the samples. Furthermore, I want to thank to Emily Giles and Vanessa
Robizch for reviewing the draft of the document and for their comments on my
findings. Also thanks to Billy Guevara for his help with the dispersion map, and to
my lab mates Manaelle Al-Salamah, Veronica Chaidez, Remy Gatins, Song He,
Marcela Herrera-Sarrias, Alex Kattan, Dr Joseph DiBattista, May Roberts, Lu Sun
and Eva Salas (University of California Santa Cruz) who helped during the lab work,
data analysis, and provided me moral support. Finally, I would like to thank my
committee members for their constructive criticism and support during my thesis
writing.
7
TABLE OF CONTENTS
•
Examination Committee Approvals Form………………….……………….p.2
•
Copyright Page.…………………….………………….…………………….p.3
•
Abstract……………………………………....…………………...……..…..p.4
•
Acknowledgments…………………………....……………………..…...…..p.6
•
Table of Contents……………………………..…………………….…….....p.7
•
List of Abbreviations………………..…………………………………….....p.8
•
List of Figures…………………..……………………………….……......…p.9
•
List of Table.………………….....……………………………………….…p.12
•
1. Introduction…………..……………...…………………………..……....p.13
•
•
o
1.1 Coral reefs, reef fishes and fisheries…………………………...p.13
o
1.2 Reef fish dispersal: Connectivity and Self-Recruitment…….....p.15
o
1.3 Parentage Analysis……………………………………......……p.17
o
1.4 Parrotfishes: function and fishing on coral reefs……………….p.20
o
1.5 Study site: Solomon Islands……………………………………p.22
o
1.6 Study organism: Bumphead parrotfish (Bolbometapon
muricatum)……………………………………………………..…..p.24
o
1.7 Aim of Study……………………………………………...……p.26
2. Methods……………………………………………………..…..……….p.27
o
2.1 Sample Collection………..…………………………….…........p.27
o
2.2 Genetic Analysis……………………………...………………...p.28
▪
2.2a Population genetics...…………………..……...…....…p.28
▪
2.2b Parentage analysis: Real data and simulation for accuracy
test……………………………………………………..…...p.30
▪
2.2c Sweepstakes Reproductive Success: Testing for
reproduction variability………….……………..………..…p.33
3. Results………………………..……………………………………...…..p.34
o
3.1 Population Genetics……………………………………….........p.34
o
3.2 Parentage analysis: Real data and simulation for accuracy test..p.36
o
3.3 Sweepstakes Reproductive Success: Testing for reproduction
variability …………………………………….……………………p.38
•
4. Discussion……………………………………………………………….p.39
•
5. Conclusion.………….…………………………………………………...p.44
•
References………………………………………………………………….p.46
•
Appendices…………………………………………………………………p.54
8
•
•
•
LIST OF ABBREVIATIONS
1. Introduction
o
N: Adult population size
o
Ne: Effective population size
o
MPAs: Marine Protected Areas
2. Methods
o
NGOs: Non-Governmental Organizations
o
IUCN: International Union for Conservation of Nature
o
PLD: Pelagic Larval Duration
o
DNA: Deoxyribonucleic acid
o
PCR: Polymerase chain reaction
o
K: Cluster genetically homogeneous
o
PIC: Polymorphic Information Content
o
HWE: Hardy-Weinberg Equilibrium
o
LD: Linkage Disequilibrium
o
FDR: False Discovery Rate
o
F IS : Inbreeding coefficient
o
LOD: Log of the odds
o
ANOVA: Analysis of Variance
o
N A : Number of alleles
o
H O : Observed heterozygosity
o
H E : Expected heterozygosity
o
SNPs: Single Nucleotide Polymorphisms
o
R: Relatedness index
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LIST OF FIGURES
1. Figure 1 Left: Map of the study location in the southwestern Pacific Ocean.
Inset showing the Solomon Islands. Lower right: the sample area in the Kia
District, and Upper right; three sampled zones with different fishing pressure
levels for the bumphead parrotfish B. muricatum (Zone 1: Heavily fished,
Zone 2: Lightly fished, Zone 3: Recently fished)…………. …....…p.54
2. Top: Population genetic differentiation using Bayesian clustering testing three
zones from 1 to 10 possible clusters (STRUCTURE 2.3.4; burning = 100,000;
iterations=250,000; 10 repetitions). Bottom: Optimal number of clusters
(K=1) was determined by plotting the mean estimated Ln (K) from the
program STRUCTURE Harvester against potential number of theoretical data
clusters.…………………………………………………..……………...…p.55
3. Figure 3 Effective population size (Ne) estimated with the program
NeESTIMATOR with sub-samples of increasing size for the B. muricatum
population from Santa Isabel Island (Linkage disequilibrium method,
p=0.02).………………………………………………..…….….………...p.55
4. Figure 4 Distribution of simulated LOD score from true and false parentoffspring and the distribution of assigned individuals. Red squares are the
number of false parent-offspring assignments. Black circles are the number of
true parent-offspring assignments. Grey triangles are the total number of
assignments made by the program FAMOZ…………..………………...…p.56
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5. Figure 5 a) Size (mm) distribution of four different life-stages, and three zones
with different historical fishing pressure (b, c, d) of the bumphead parrotfish
B. muricatum collected from a subsistence spear-fishery at Santa Isabel Island,
Solomon Islands. Blue bars: juveniles (mean size ± SD: 293 ± 87.4 mm),
yellow bars: subadults (mean size ± SD: 543 ± 58.5 mm), pink bars: female
adults (mean size ± SD: 842 ± 141.2 mm), green: male adults (mean size ±
SD: 941 ± 156.5 mm)….……….…………………………………………..p.56
6. Figure 6 Distribution of the frequency of distances among reefs for the parentoffspring relationships obtained from parentage analysis as a proxy for
dispersal distance (Km) for the bumphead parrotfish at Kia District in
Solomon Islands………………….………………………………….……..p.58
7. Figure 7 Study site map showing inferred individual dispersal trajectories
(arrows) of the bumphead parrotfish B. muricatum between reefs based on
parentage analysis. Arrow thicknesses are proportional to the number of
juveniles with similar trajectories. Arrows are a schematic representation of
the trajectories and does not represent the real route of dispersion (Original
map: Nate Peterson, TNC Brisbane; Schematic map: Billy Guevara). For more
details about individual trajectories see Table 4 ………………………....p.59
8. Figure 8 Comparison of the number of alleles in the adult and juvenile
population of B. muricatum per each locus (a) and statistically for the whole
population (b) ………………….…………………………………………..p.60
9. Figure 9 Distributions of KINGROUP relatedness index for all pairwise
comparisons of individuals in adult and juvenile samples. The relatedness
index should average zero in randomly mating populations, values above of
11
0.25 and 0.5 are suggestive of the presence of both half and full siblings
respectively…..…………………..................................................................p.61
10. Figure 10 Mean relatedness comparison for 12 different categories sizes (1 12). Each category size differs from the next in 50 mm. The relatedness
pairwise test was carried within each size category and then a comparison
among categories (populations) was done using the Queller and Goodnight
estimator in the software GenAlex. Permutations (999) and maximum
likelihood were used to determine relatedness values. Upper (U) and lower (L)
confidence limits bound the 95% confidence interval about the null hypothesis
of 'No Difference' across the populations as determined by
permutation……………………………………………….…….…………..p.61
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LIST OF TABLE
1. Table 1 Summary of published studies on parentage analysis on coral reef
fishes. PLD (Pelagic Larval Duration). Sample size includes juvenile and
adults. Self-recruitment refers to the proportion of number of juveniles that
returned to the same area where their parents were sampled. Connectivity
represents the proportion of gene flow among sampling sites or the distance
that larvae dispersed from the place where their parents were sampled. ….p.62
2. Table 2 Genetic information and statistics of 29 microsatellites loci used with
Bolbometopon muricatum samples. Genebank number, repeat motif, size (base
pair), Polymorphic Information Content (PIC), number of alleles (Na),
observed (Ho) and unbiased expected (uHe) heterozygosities, Fixation Index
(FIS: Weir and Cockerham) and probability value of Hardy-Weinberg
equilibrium test.. …………………………………………………………...p.63
3. Table 3 Accuracy comparison between the programs COLONY and FAMOZ
using 5 different simulated datasets (with five different LOD and probability
threshold values) and a posterior R script to detect different type of errors (I
and II) associated to the offspring-parent assignment process. Alpha errors are
associates to false assignments (to a single parents or parent pair) while Beta
errors to false exclusions (to a single parents or parent pair)…..........……..p.64
4. Table 4 parent-offspring relationships made by the programs FAMOZ and
COLONY. The sample location for offspring and parents as well the size and
sex are shown. Data for parents with multiple offspring is shown just
once…...…………………………………………………………………....p.67
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INTRODUCTION
1.1.
Coral reefs, reef fishes and fisheries
Healthy coral reefs harbor high biodiversity and are important sources of
economic and cultural resources (Birkeland 1997, Moberg and Folke 1999). Coral
reefs not only provide habitat to a still increasing number of fish species (200–300
new marine fishes are described yearly; Zapata and Robertson 2007) but also they
provide a considerable amount of goods and services to humans. Through tourism,
coastal protection, and as source of food (15 tons-1of fish/seafood km2year-1; Cesar
2003) and medicines, these ecosystems are estimated to be worth billions of dollars
(Burke et al. 2011).
Anthropogenic impacts such as unsustainable fishing practices (e.g. dynamite,
cyanide, trawling, and aquarium trade), overfishing and habitat degradation through
climate change are the main threats to coral reefs' persistence (Wilson et al. 2010). It
is estimated that in recent decades, 20% of the world's coral reefs have been degraded
and more than 60% are at risk of collapse, mainly as a result of human activity
(Wilkinson 2008, Burke et al. 2011). These detrimental impacts in coral ecosystems
not only represent a potential ecological loss by local extinctions but also an
inevitable economic collapse for island reef fisheries (Ferreira et al. 2004, Mumby et
al. 2004, Newton et al. 2007).
Overfishing effects are long-term events and the recovery of fish and coral
populations often take many years or even decades to return to their original state
(McClanahan et al. 2011, 2014, Humphries et al. 2014). For example, herbivorous
fishes play key roles as grazers removing the algae that compete with corals for
recruitment and living space (Burkepile and Hay 2010, Bellwood et al. 2012). Thus,
14
selective removal of herbivores will be detrimental for reef health. On the other hand,
fishermen often target the bigger individuals because of their potential commercial
value (Bejarano et al. 2013). Constant removal of the bigger individuals, especially in
fish spawning aggregations, will directly affect the abundance of the species and the
genetic and size structure of reef fish communities (Clua and Legendre 2008). There
is a positive relationship between fish size and fecundity (Berkely et al. 2004,
Palumbi 2004, Beldade et al. 2012). Thus, selective removal of bigger individuals
will decrease the probability of fish stock replenishment. Also, this reduction in the
adult population size (N) favors the successful reproduction of just a few individuals
(sweepstake reproduction) which could have detrimental effects on the effective
population size (Ne; Turner et al. 2002, Hedrick 2005, Hoarau et al. 2005, Hedgecock
and Pudovkin 2011). Both variables (N e and N) and its relationship are related to the
ability of populations to maintain enough genetic diversity for adaptation to
environmental changes and to cope with extinction (higher values of the N e /N ratio
are associated with a higher expected genetic variance; Hedrick 2005, Palstra and
Fraser 2012).
Global reef degradation and fish population declines have negative outcomes
for human communities that rely on them (Sadovy and Domeier 2005). Thus, public
awareness about the importance for conserving targeted species and coral reef
ecosystems has been increasing. As a result, protection measures such as seasonal
fishing banning, community-based resources management, and no-take marine
protected areas (MPAs) have been established worldwide to reduce reef degradation
and promote fish population recovery (Hughes et al. 2007, Hamilton et al. 2012,
Almany et al. 2013). Despite the growing evidence that protected areas act as a
species refugee and increase the abundance and biomass of fish populations inside
15
and outside of an MPAs (Palumbi 2003, Hawkins and Roberts 2003, Harrison et al.
2012), information regarding early life-stages and demography of targeted
populations remain as gaps in the design of MPAs for proper biodiversity
conservation and fisheries management (Sale et al. 2005).
1.2.
Reef fish dispersal: Connectivity and Self-Recruitment
Dispersal potential is critical in marine populations that are naturally
fragmented and sometimes isolated. The transport of larvae, eggs, and sperm by
ocean currents allows the exchange of individuals (and genes) between populations,
and the colonization of suitable habitats (increasing the biogeographic range). The
ability to disperse is an important determinant in the life-history of an organism and
for species persistence through time. The life cycle of most reef fishes includes a
pelagic larval phase and a benthic adult phase (Leis 1991). The larval phase begins
when the larva hatches from the egg, and ends days, weeks, or even months later
when the larva undergoes metamorphosis and settles on the reef. Because the benthic
adults are relatively sedentary and reefs are patchily distributed, fish dispersal occurs
mainly during the pelagic larval phase (Leis 1991, Shanks 2009). Despite the
ecological and evolutionary importance of dispersal, ecologists only still know little
about how far larvae disperse from their natal sites. The few studies that have tried to
estimate this have found that dispersal declines rapidly with distance and mean
dispersal distances between 25–150 km (Palumbi 2003, Cowen and Sponagule 2009,
Almany et al. 2013, Buston and D´Aloia 2013).
While it is likely that some dispersal is achieved through larval transport by
oceanographic circulation, some larvae also have strong swimming, behavioral and
16
orientation abilities that may influence their dispersal (Leis et al. 1996, Leis and
McCormick 2002, Paris and Cowen 2004, Fisher 2005, Dixson et al. 2014). The
extent to which currents and behavior affect the dispersal of coral reef fishes and how
far larvae disperse from their natal sites are important questions for scientists (Christie
et al. 2010, White et al. 2010). For example, biological responses of the larvae such
as vertical migrations and horizontal swimming coupled with variation in physical
factors such as coastal topography, tides, and eddies may enhance larval retention and
mediate dispersal and connectivity (see review in Cowen and Sponagule, 2009).
However, to track the origin and destination of fish larvae is a challenging task. The
small size of larvae combined with the vast size of the oceanic environment where
they develop makes difficult to quantify larval dispersal and connectivity (Mora and
Sale 2002, Botsford et al. 2009, Selkoe and Toonen 2011).
Roberts (1997) conducted a pioneer study which attempted to map the
dispersal routes of reef fish’s larvae in the Caribbean. Assuming dispersal as a
passive phenomenon dominated by ocean surface currents, his findings encouraged
the belief that marine populations were demographically open relying on larval
connectivity. However, a further study by Cowen et al. (2000) using model
simulations found that larval exchange was not sufficient to sustain fish populations
and thus the assumption of marine populations as open systems started to be
reevaluated. Then, studies involving otolith embryo mass-marking and isotopic
labeling found supporting evidence of up to 60% of larvae returning to their natal
populations, demonstrating the occurrence of self-recruitment (Jones et al. 1999,
Jones et al. 2005, Thorrold et al. 2006, Almany et al. 2007).
The identification of the origin and dispersal patterns of the larvae that
replenish benthic populations now has become a crucial knowledge requirement for
17
conservation efforts and sustainable fisheries management (Mora and Sale 2002,
Christie et al. 2010). Population genetics approaches using hypervariable markers
(e.g., microsatellites) and based on assignment methods have emerged as important
tools to directly estimate the patterns of connectivity and self-recruitment in fishes
(Hedgecock et al. 2007, Saenz-Agudelo et al. 2009, Schunter et al. 2014). Among
these genetic approaches, parentage analysis has become popular due to its sensitivity
to detect demographic connectivity and self-recruitment not only at the small scale
but also with incomplete sampling at the large scale (Planes et al. 2009, D´Aloia et al.
2013, Maduppa et al. 2014).
1.3.
Parentage Analysis
Parentage analysis is a population genetics approach that allows one to
identify offspring-parent relationships through the assignment of juvenile individuals
to their potential parents based on the Mendelian inheritance of the alleles (SaenzAgudelo et al. 2009). These assignments can be carried out with different methods
(e.g. exclusion, categorical or fractional likelihood, and genotype reconstruction;
Jones and Ardren 2003, Jones et al. 2010) and each method can be performed under
certain assumptions. Thus, the choice of which assignment method to use depends on
the aims of the study. Reviews about each method, its significance for biological
populations and limitations are discussed by Jones and Ardren (2003), Jones et al.
(2010), Christie (2009, 2013) and Harrison et al. (2013).
Among the methods, the categorical assignment method is commonly used
because of its potential to generate relationships with a biological meaning (Jones and
Ardren 2003). In this method, the genotype of each juvenile individual (offspring) is
screened against the genotypes of all of the potential parents (Harrison et al. 2013),
18
then each individual in the offspring is assigned to a potential parent depending on the
likelihood/conditional probability of being the true parent of the offspring in
comparison with another parent randomly selected from the population (Jones et al.
2010). When a match of the juvenile genotype to a parent genotype (with at least a
degree of confidence depending on the software) is detected, an assignment is made.
The reliability of this likelihood-based approach had been successfully demonstrated
in reef fish populations (Jones et al. 2005, Saenz-Agudelo et al. 2009, Maduppa et al.
2014).
Incorrect assignments in parentage analysis are associated with the method’s
theoretical and practical constraints. For example, Hardy-Weinberg equilibrium
within the population is one of the main assumptions in parentage analysis, a
condition often violated in wild populations. According to Marshall et al. (1998) and
Harrison et al. (2013), from all of the possible causative factors of incorrect
assignments, a limited number of genetic markers and an incomplete sampling of the
whole pool of potential parents are thought to affect more the accuracy of
assignments. However, some genetic software as CERVUS and COLONY have
incorporated likelihood methods that allow one to deal with incomplete sampling
(Kalinowski 2007, Wang 2004, Jones and Wang 2009).
Thus, a simple increase in the number and quality (highly polymorphic) of
markers (Saenz-Agudelo et al. 2009, Harrison et al. 2013) and in the sample size
might potentially increase the accuracy of assignments. For example, Saenz-Agudelo
et al. (2009) studying a clownfish demonstrated that the simple fact of doubling the
number of markers reduced the statistical errors (type I and II) associated to
likelihood-based assignments to less than 5% error. In published studies using
parentage analysis, the number of polymorphic loci varies between 7 and 18 (Table
19
1), and populations sampled sizes between ~150 to 2800 individuals (Table 1). These
studies have found self-recruitment levels ranging from 0 to 40% (Planes et al. 2009,
Saenz-Agudelo et al. 2009) and larvae dispersing as far as 159 Km (Pusack et al.
2014). In this study, both the number of markers used and population size sampled
exceeds that compared to most published parentage studies.
The majority of parentage studies have been carried out in small-bodied
species with a strong habitat preference (i.e. anemone fishes, territorial damselfishes
or sponge-dwelling gobies). The occurrence of this microhabitat association has
made these organisms easy to sample which is useful considering the constraints of
parentage analysis. Estimates of self-recruitment in anemonefishes (genus
Amphiprion), highly threatened by aquarium trade, have found some variation ranging
from 0 – 22% per site in Papua New Guinea (Saenz-Agudelo et al. 2009, 2011, 2012)
and up to 65% in Indonesia (Madduppa et al. 2014). The occurrence of this variation
in highly traceable species with similar life-history traits are indicative that not only
biological but also physical factors (e.g. local environment) can affect species
dispersal. For this reason, it is necessary to study the patterns of larval dispersal and
its variation in different species and locations (D´Aloia et al. 2013), which probably
are under different level of environmental stressors and anthropogenic. For example,
there is evidence of a relationship between body size and the home range in fishes
(Kramer and Chapman 1999). Identifying the size range of large-bodied (more
mobile) fishes is needed to be able to adequately design MPAs useful to these kind of
organisms. It has been suggested that for a successful replenishment via selfrecruitment a reserve must be at least as wide as the mean larval dispersal distance of
the species in question (Botsford et al. 2003). More recently, Harrison et al. (2012)
and Almany et al. (2013) used parentage analysis in more mobile and exploited
20
species (coral trout, stripey snapper and squaretail grouper). The findings from these
studies elucidate the dynamics of these populations and demonstrate the effectiveness
of marine reserves in the contribution to fisheries replenishment.
1.4.
Parrotfishes: function and fishing on coral reefs
Parrotfishes are key species in coral reefs not only because of their ecological
function as grazers and bioeroders but also for their commercial value in local
fisheries (Bellwood and Choat 1990, Hoey and Bellwood 2008, Taylor and Choat
2014, Taylor et al. 2014a). Ecologically, these fishes provide top-down control of
macroalgae abundance on reefs as a result of their scraping activity (Hughes et al.
2007). Also, as a result of their excavating activity, parrotfishes are constantly
clearing the substratum which allows for recruitment by other organisms thereby
boosting benthic diversity (Bellwood et al. 2012). These two processes (clearing
substrate for recruitment and controlling macroalgae levels) are vital for reef health
and recovery (Taylor and Choat 2014).
Parrotfishes are subject to different fishing pressure in all of the world’s
oceans (Caribbean Sea, Indian and Pacific Ocean; Mumby et al. 2004, Bellwood et al.
2012, Vallés and Oxenford 2014). For example, there are estimates suggesting that
grazers can represent up to 75% of the reef-associated fisheries, while parrotfishes
alone can reach up to 40% of the harvest (Rhodes et al. 2008, Houk et al. 2012,
Taylor and Choat 2014) with single large-bodied species, as bumphead parrotfish,
contributing up to 25.5 % of the biomass catch (Hamilton et al. 2012). Additionally,
for some species of parrotfish, their gregarious behavior while sleeping and during
reproduction, which both often occur in shallow water, make them easy targets for
21
spearfishermen (Hamilton 2004). Life-history research on exploited areas is lacking
(Talyor and Choat 2014) and given the importance and decline of parrotfishes in coral
reefs, information regarding early life-stages is critical to understand their population
dynamics and protect their ecological function.
Large-bodied parrotfishes often have slow growth rates and late maturation,
thus these life history traits decrease their ability to recover from population declines
due to overfishing and habitat degradation (Jennings et al. 1998, Bellwood et al.
2000). For example, McClanahan (2014) found that in overexploited reefs,
parrotfishes were among the slowest taxa to recover. Sabetian (2010) found that in
Solomon Islands after the decline of large parrotfishes, small parrotfishes were
targeted more often, contributing to 30% of the total harvest. The latter study
suggested that the reduction in parrotfish density and changes in parrotfish size due to
fishing could cause subsequent changes to the coral reef ecosystem and this
destruction is further exacerbated now that fishing pressure and human population
density are increasing. Local and governmental concern about reduction of fish
stocks have spurred the creation of some MPAs that apparently have fostered an
increase in the abundance of both small and large body size parrotfishes inside the
MPAs (Sabetian 2010). Given this case, testing whether MPAs enhance the benefits
for overfished species by acting as a source of juveniles to unprotected areas would be
of use to local MPA management and design of subsequent MPAs.
22
1.5.
Study site: Solomon Islands
The Solomon Islands are located southeast side of Papua New Guinea in the
western Pacific (latitudes 5-12°S and longitudes 152-163°E, Figure 1). This
archipelago occupies the eastern portion of the global center of marine diversity,
known as the Coral Triangle, which comprises 76% of the world’s coral species and
37% of the world's coral reef fish species (Veron et al. 2009). It is composed of more
than 990 islands (one third of which are populated by humans) which extend over
1,700 kilometers (Hamilton 2003, Grant and Miller 2004). The total human
population inhabiting the Solomon Islands is approximately 515,000. Santa Isabel
Island is the longest island (composed of 12 districts) in the archipelago with an area
around of 4200 km2 and a human population of around 30,000 habitants. Most
inhabitants of Santa Isabel Island live a subsistence-based lifestyle, conducting
swidden agriculture in the forest and harvesting marine resources from their coral
reefs. This reliance of humans on fisheries is reflected in one of the highest per capita
seafood consumption rates in the world (Johannes and Lam 1999, Bell et al. 2009).
Some fishing practices in the Solomon Islands have been carried out in an
unsustainable manner which represents a threat not only for marine fauna biodiversity
but also to the livelihood of its inhabitants. For example, there is evidence of
macroinvertebrate overexploitation reaching commercially extinct levels (e.g., pearl
oysters, green snails, sea cucumbers) and also declines in large reef fishes (Ramohia
2006, Hamilton 2003). In some cases complete harvest bans on macroinvertebrates
have proven insufficient to result in recovery (Hawes et al. 2011). Rapid population
growth is projected to drive widespread overfishing of coastal resources in Solomon
Islands by 2030 (Bell et al. 2009). In Isabel Province the establishment of fisheries
23
centers at Kia and in Buala District has also increased commercial fishing pressure in
this province. The declining status of marine resources in Isabel Province and
Solomon Islands (Aswani and Sebatian 2014) has resulted in efforts to preserve
biological resources through systems of protected areas in Solomon Islands (Olds et
al. 2014). For example, MPAs have been established in Choiseul, Western Province,
and Isabel Province (Aswani and Hamilton 2004, Peterson et al 2012, Weeks et al.
2014).
In the case of parrotfishes, traditional fisheries were subsistence-based, with
fishers using their traditional knowledge of targeted species behaviors to ensure
capture success (e.g.; Aswani and Hamilton, 2004). However, local parrotfish
fisheries in the Solomon Islands have undergone a transformation since the debut of
night spearfishing in the 1970s following the introduction of fins, masks, snorkels and
underwater flashlights. For example, Hamilton (2003) showed that maximum catches
of bumphead parrotfish changed abruptly from 2-6 fishes per night prior to the 1970s
to 70 fishes per night in the 1990s after the commencement of night spearfishing and
the development of markets for this species. In a different study also in the Western
area of the Solomon Islands, Sabetian (2010) found a transition in landings from large
to small parrotfish species and also a decline in density and mean body size as a result
of an intensive fishery. With diminishing resources management of parrotfish
fisheries has been increasingly encouraged, both by local communities and NGOs.
For example, in the Kia District of Isabel Province there is a currently a nighttime
spearfishery for bumphead parrotfish. Efforts to establish the sustainability of this
spear-fishery have been carried out by NGOs, community members and the
government since 2012. To date, research on abundance and demography of this
species in the Kia District has estimated an adult (> 60 cm total length) population of
24
approximately 25,000 individuals, and this research has suggested that banning
certain areas to protect the juveniles and highly fecund large females of this species
would be beneficial (Hamilton et al. 2013). Understanding patterns of connectivity
for this species in the Kia district would further help design MPA networks to
effectively protect the adult and juvenile bumphead parrotfish populations in the
Solomon Islands.
1.6
Study organism: Bumphead parrotfish (Bolbometapon muricatum)
Bolbometopon muricatum (Valenciennes 1840) is the largest (up to 150 cm
total length and 75 Kg) and the most widespread (Red Sea to French Polynesia)
parrotfish species with a life span up to 40 years (Gladstone 1986, Hamilton and
Choat, 2012). This species has a gregarious behavior and normally swims in groups
of up to 30 individuals. Males are typically larger than females, and males have been
observed displaying aggressive head-butting behavior between them (Muñoz et al.
2012). Juvenile individuals are normally found in shallow inner lagoons (Lieske and
Myers 1994, Hamilton 2004) and have been sighted feeding on the macroalgae
Padina sp (Plass-Johanssen et al. 2014). Adults are often sighted on barrier and
fringing reefs (< 30 m depth), and have an excavator feeding mode over the reef
substratum making important contribution to the bioerosion (each individual can
consume up to 5 ton yr-1) and sedimentation (32 Kg m2 yr-1) processes on coral reefs
(Bellwood et al. 2003, Hoey and Bellwood 2008). The positive and negative effects
of the bumphead parrotfish in coral reefs were recently examined by McCauley et al.
(2014). This species has a significant cultural value in some islands of the Pacific
being harvested for ceremonial events (Dulvy and Polunin 2004). Bolbometapon
25
muricatum has low natural mortality rates but it has been overexploited throughout
almost all its geographical range, leading to local extinctions in some instances (e.g.
Guam, Marshall Islands; Donaldson and Dulvy 2004, Dulvy and Polunin 2004,
Bellwood et al. 2012, Chan et al. 2012). Currently, abundances of the bumphead
parrotfish are low worldwide, with few exceptions such as in Australia, Papua New
Guinea and Solomon Islands, and now it is under the category of vulnerable to
extinction under the IUCN criteria (Chan et al. 2012).
Hamilton et al. (2008) studied the reproduction patterns in the Solomon Islands
and found that the bumphead parrotfish is essentially a gonochoristic species. In this
study, the authors highlighted the occurrence of a bisexual phenomenon in which all
males pass through an immature female phase. They found differences in maturity
size/age with males reaching maturity in younger states (47 – 55 cm) than females (55
– 65 cm total length). Similarly, Bellwood and Choat (2011) found variations in the
size distribution of bumphead parrotfishes in the Great Barrier Reef highlighting the
absence of juveniles (low recruitment). However, the results of this latter study might
be related to the fact they did not sample juvenile habitat (inner lagoons). Overall,
these finding are indicative of poor resilience and suggest long-lasting recovery under
frequent disturbances. The reduction in the mean size of parrotfish not only implies a
loss of biomass but also a loss in the reproductive potential (Mcllwain and Taylor
2009).
Movement patterns in B. muricatum have demonstrated that they consistently
return to specific sites for resting at night while traveling distances up to 6 km during
foraging activity at daytime (Hamilton 2004). Currently there is scarce information
on juvenile movements and early life-stages, for example there is only one a
published estimation of the pelagic larval duration (PLD) for this species (31 days)
26
and it was made from only one individual (Brothers and Thresher 1985). Recent
work on PLD using more individuals (30) in our study area has shown a shorter
dispersal phase (24 – 27 days; Hamilton et al., unpublished data). Genetic
information available for this species is almost absent (but see Priest et al. 2014).
According to Bellwood and Choat (2011) due to the lack of studies on the biology and
ecology of early life stages of the bumphead parrotfish, together with their recent
dramatic decline, information regarding this part of their life cycle will improve the
opportunities for their conservation.
1.7.
Aim of study
The aim of this study was to assess the patterns of self-recruitment within the
local population of the bumphead parrotfish (Bolbometapon muricatum) at the
Solomon Islands among sites with different levels of fishing pressure. This study
considers and evaluates the demographic effects of overfishing, such as reduction of
body length, reproductive output, effective population size (Ne) and genetic structure.
First, the population’s self-recruitment was assessed using parentage analysis based
on the identification of genotypes of parents and offspring using hypervariable
nuclear microsatellite markers. Second, the mean adult size was compared among
three zones with different fishing pressure (lightly fished, recently fished, and
overfished). As overfishing reduces body length and this variable is correlated with
egg production in fishes, a low mean adult length and low juvenile production was
expected from the overfished zone compared with the less-fished zones. Third, as
constant removal of individual (genotypes) decreases the number of individuals
generating progeny, a reduction of the effective population size (and genetic
27
diversity) as well as, the occurrence of sweepstakes reproduction are expected. This
study represents the first parentage study on large-bodied parrotfishes, which have
high mobility and are at risk of extinction. The results of this study will generate
valuable information regarding the dispersal and replenishment capabilities of B.
muricatum and set a reference on population size and genetic diversity, which is
essential for future studies and to establish parameters for a sustainable fishery and
conservation management.
MATERIAL AND METHODS
2.1
Sample Collection
In 2012, Hamilton et al. (2012, 2013) in collaboration with the Solomon
Islands government and Kia communities started a project to assess the sustainability
of the bumphead fishery in the Kia District at Santa Isabel Island. The samples used
in this study came from the aforementioned project. All fish were captured by night
spearfishing between February 2012 to October 2013 and were sold to one of three
fisheries centers in the Kia district. This provided scientists with the ability to collect
tissue samples (1 g of tissue was cut from every fish and stored in 96% ethanol) and
information regarding fish size, sex (by gonad examination) and location of origin for
every specimen that passed through a fisheries center. Additionally, by doing
underwater censuses, the abundance of adults in this area was estimated (<25.000
individuals; Hamilton et al., 2013). In total, tissue samples (n=4033) were taken from
120 reef sites which were divided into three zones (Figure 1). The division in three
zones of this area is based on the historical fishing pressure on B. muricatum in the
Kia district. Zone 1 had experienced high levels of fishing pressure due to its near
28
location to landing-storage facilities. Zone 2 represents the furthest reefs from the
fisheries center and have been lightly exploited historically. Zone 3 used to be lightly
exploited but recently it started to be heavily fished when a fishery center was
established in this area in 2012 (Hamilton et al. personal communication). Size was
recorded (total length) and gonads were examined so that the sex of almost all the
adults were determined (1111 females and 1121 males; representing 8.5% of the total
adult population and between 80-90% of the bumphead landings during the sampled
period). Individuals with a size between 450 and 649 mm (subadults) where the sex
was not determined due to absence of mature reproductive structures were considered
as juveniles in all further genetic analysis. A considerable number of the juveniles
were specifically collected for the purpose of this research (diving and catching at
night with clove oil and also via spear) and do not form part of the normal fishery.
2.2
Genetic Analysis
2.2a Population Genetics
Genomic DNA from Bolbometapon muricatum was extracted from fin clip
tissue using the HotSHOT (hot sodium hydroxide and tris-HCL) method (Truett et al.
2000). This DNA extraction method is quick and reliable for the isolation of PCRquality DNA (Truett et al. 2000). Tissue samples were placed in 96-well plates with
100 µL of 50 mM NaOH (alkaline lysis reagent) per well and then were incubated in
a hot water bath at 95°C for 20 minutes. The plates were then cooled at 4°C for 10
minutes and 10 µL of Tris-HCl 1M (neutralization buffer) were added to each well.
Plates were centrifuged at 6000 rpm for 30 seconds. The supernatant (60 µL) was
transferred to a new plate and was used for the PCRs (polymerase chain reactions).
29
After DNA extraction, four multiplex PCRs (primer premix A, B, C, and D;
see Priest et al. 2014 for a detailed description) containing up to nine fluorescentlabelled primers (for a total of 29 primer pairs), previously optimized for B.
muricatum by Priest et al. (2014), were prepared per individual using the QIAGEN
Microsatellite Type-it kit. PCRs were carried out in a total volume of 9 µL per well.
Each well contained 4.5 µL of QIAGEN Multiplex Master Mix, 3.35 µL of Milli-Q
water, 0.45 µL of primer premix and 0.7 µL of genomic DNA. PCRs were performed
in a GeneAmp PCR 9700 Thermocycler (Applied Biosystems) with the following
protocol: 15 min initial denaturation at 95 °C followed by 30 cycles of 30 sec at 94
°C, 90 sec at 55-63 °C, and 90 sec at 72 °C with a final extension of 10 min at 72 °C.
PCR products were diluted in 130 µL MiliQ water prior to fragment analysis, then
were screened on an ABI 3730XL genetic analyzer (Applied Biosystems), and
individual allele sizes were scored using the software GENEMAPPER v4.0 (Applied
Biosystems). Genotypes with a maximum of 4 loci as missing data were considered
in the further analysis.
A Bayesian analysis implemented in the software STRUCTURE v2.3.4
(Pritchard et al. 2000) was carried out to identify the number of clusters (populations)
genetically homogeneous (K) present in the sample. For each possible number of
populations (K = 1 - 10) was assigned a likelihood value after 1000000 iterations and
a burn-in of 500000. The polymorphic information content (PIC) of each locus was
estimated with the software CERVUS. According to Shete et al. (2000) the PIC value
for codominant markers such as microsatellites range between 0 (poor informative)
and 1 (very informative). Allelic frequencies and unbiased expected heterozygocities
were calculated in GenAlex v6.5 (Peakall and Smouse 2012). Hardy-Weinberg
equilibrium (HWE) exact tests and loci combinations for linkage disequilibrium (LD)
30
with the default Markov chain methods parameters used were done using GENEPOP
on the web (Raymond and Rousset 1995, Rousset 2008). Significance levels were
adjusted for multiple testing using the Bonferroni correction implemented in
CERVUS and also by the false discovery rate (FDR) correction method (Benjamini
and Hochberg 1995). If heterozygote deficiencies were detected, the presence of null
alleles was also evaluated. One of the 29 microsatellite loci presented was difficult to
score and was excluded from the genetic analysis. The effective population size (N e )
was estimated with the software NeESTIMATOR V2 (Do et al. 2014). The reliability
of the N e estimation was tested using subsamples of increasing size (500 individuals)
from the original data set. According to England et al. (2006) and Waples (2006), the
N e is reliable if an asymptotic value is reached when using subsamples of increasing
size.
2.2b Parentage analysis: Real data and simulations for accuracy tests
Parentage studies have been done mostly using the likelihood-based approach
implemented in software as FAMOZ and CERVUS. This method calculates the log
of the odds (LOD) ratio scores for parentage assignments and establish parentoffspring relationships according to the allelic frequencies at each locus and
incompatibilities between their LOD values. However, Harrison et al. (2013)
demonstrated that COLONY is the most robust software for parentage analysis,
followed by FAMOZ. In order to test the accuracy of both programs and decide
which to use with the B. muricatum data set (original data), five data sets containing
genotypes for 250 females, 250 males and 500 offspring with different number of
known parent-offspring relationships were simulated using the software MYKISS
(Kalinowski 2009).
31
In the simulated data sets, the proportion of known parents in the sample was
considered as 10% and genotyping error rate as 1% for each locus. These simulations
were run in both COLONY and FAMOZ, and the results of the assignments were
analyzed in R with scripts developed by Hugo Harrison (James Cook University,
Australia) that allows comparing the accuracy of both software (Harrison et al. 2014).
The script uses the probability values generated by COLONY and the LOD scores
generated by FAMOZ given to the assignments in order to decide if the assignments
were done correctly. The scripts allow us to change the threshold values (probability
and LOD) of the assignments to observe the proportion changes in the errors/accuracy
for each software. According to Harrison et al. (2014) using these scripts makes it
possible to compare for each simulated offspring if the assigned parent/parent pair are
or are not the true parents. When an offspring was assigned to a parent that was not
its true parent or not assigned (excluded), the script can determine whether the true
parent was in the sample and identified it as either a false-positive (type I) or a falsenegative (type II) error (see Fig. S1 in Harrison et al. 2014). The overall accuracy
was measured as the proportion of correct assignments to single parents or parent
pairs and the number of correct exclusions over all possible assignments (Harrison et
al. 2013b).
Based on the results of the aforementioned comparison, I determined that the
best approach to increase the reliability of the results was to run the parentage
analysis combining both programs. First, using the information of the allele
frequency of the parent genotypes, a simulation of 50,000 offspring with a calculation
error of 0.0001 was done in FAMOZ to know the distribution of LOD scores for false
and true parent offspring pairs. Second, a LOD threshold value was set (based on the
aforementioned simulation trying to exclude most of the false positive assignments) to
32
accept putative parent offspring pairs as being true. Parent offspring pairs that had a
LOD score above this threshold were kept for further analysis. Juveniles with no
likely parent inside the sample were removed from posterior parentage analyses.
Finally, the parentage analysis was conducted with the software COLONY.
COLONY uses a full-likelihood method that takes into account parentage and
sibships simultaneously considering the whole data rather than pairs of individuals
(Jones and Wang 2010). The input data is composed of three files containing the
females, males and offspring genotypes. Using the full-likelihood method COLONY
can categorize the offspring as full-sibs (sharing both parents), half-sibs (sharing a
single parent) or unrelated (sharing no parents). To run the parentage analysis in B.
muricatum I assumed a polygamous mating system without inbreeding for diploid
organisms. A complex sibship prior was considered and the analysis method chosen
was the FL-PLS combined method (medium length of run and high likelihood
precision) with updating allelic frequencies option. The probability that the true
parent was present in the sample was considered as (0.10) in the assignment of
parent–offspring pairs in accordance with the proportion of candidate parents sampled
(~8.5%). This approach accounts for genotyping error at each locus for each sample.
The assignments done for both programs FAMOZ and COLONY were compared and
those that matched 100% (a juvenile assigned exactly to the same parent) were
considered as a definitive true assignment.
Finally, to explore potential effects of fishing pressure in the demography
connectivity and structure size of B. muricatum, the origin and destiny of juveniles in
each parent-offspring relationship was quantified by each zone (lightly exploited,
recently exploited and heavily exploited), and then size-frequency distributions were
done per each of the three study zones and using an ANOVA the mean adult size was
33
compared among them. The dispersal distance for B. muricatum was calculated as the
distance between the reefs where the parent and the juvenile where sampled.
2.2c Sweepstakes Reproductive Success: Testing for reproduction variability
Under the occurrence of variation in the adult reproductive success
(sweepstakes reproduction), differences in genetic diversity and relatedness between
juveniles and adults populations are expected (Hedgecock et al. 2007). Studies that
have found evidence of this phenomenon reported lower genetic diversity and higher
relatedness on juvenile populations compared with the adults (Christie et al. 2010,
Hedgecock and Pudovkin 2011, Pusack et al. 2014). To test this in the B. muricatum
data set, a size-frequency distribution was built with the standard length of the fishes
of the whole population (Zones 1, 2, and 3), then the data set was divided by life stage
(juveniles: < 650 mm; adults:> 650 mm), sex (male and females) and size (every 100
mm). For each variable (life stage, sex, and sizes), the allele diversity (Na: number of
alleles) and the standardized multilocus heterozygosity (He) was calculated. The Na
was obtained using the program GenAlex while the He was calculated as the
proportion of heterozygote locus per individual and standardized by the mean
heterozygosity (Jensen et al. 2007). The Na and He were compared between juveniles
and adults, and between males and females using a t-test for dependent samples. The
comparisons among different size categories (every 100 mm from 0 to 650 mm) were
done with non-parametric ANOVAs (Kruskall-Wallis). The software STATISTICA
v8 (Stat-Soft Inc.2007) was used to perform all non-parametric analyses in this study.
The pairwise relatedness among individuals was estimated using the Queller
and Goodnight estimator for both juveniles and adults using the program KINGROUP
(Konovalov et al. 2004). The relatedness index (r) can be defined as the probability
34
of sharing an allele (or several if it is a multilocus genotype) between individuals.
Positive relatedness is suggestive of the presence of kin relationships between
individuals. Relatedness values for unrelated individuals are zero (r = 0, or negative
values), while for half and full-sibs the values are expected to be around 0.25 and 0.5,
respectively (Hedgecock et al. 2007, Buston et al. 2009). A parent-offspring
relationship will have a relatedness value of one. To compare the distribution of
relatedness values between juveniles and adults, the relatedness results obtained from
KINGROUP were tested using a Chi-Square test. Herein, a higher value of the mean
relatedness among juveniles than among adults would be expected under the
occurrence of sweepstakes reproduction. Considering that relatedness can vary as a
function of size (individuals of similar size probably recruited together and are more
closely related) the use of a mean relatedness value for the entire juvenile population
(grouping different cohorts together) can affect the detection of kin relationships. A
comparison of the mean relatedness among different size categories (each 50 mm) for
the juvenile population was done using GenAlEx. For the latter analysis, if the
relatedness varies as a function of juvenile size, differences among the categories
sizes would be expected.
RESULTS
3.1
Population Genetics
A total of 3924 individuals were genotyped at twenty-eight microsatellite loci.
According to the likelihood results from STRUCTURE the number of clusters (K) in
the sample was 1 population (Fig. 2), for this reason metrics of genetic divergence
such as F-statistics were not estimated. The levels of polymorphism were slightly
35
informative with values ranging from 0.015 to 0.746 (mean PIC = 0.39). The mean
allelic diversity was 9.04 with the total number of alleles per locus varying from 3 to
14 (Table 2). According to Priest et al. (2014), the values found in B. muricatum are
low compared with other coral reef fishes but given the number of markers used, the
results obtained from parentage analysis still reliable. The observed heterozygosity
(Ho) ranged from 0.016 to 0.790 (mean = 0.428), and the expected heterozygosity
(He) ranged from 0.015 to 0.780 (mean = 0.432; Table 2).
The Hardy-Weinberg and linkage disequilibrium tests were run for ten
independent subsets of samples (96-well plates), for which no disequilibrium was
found and deviations from HWE were not consistent between subsets. Similarly, the
absence of null alleles was corroborated using the Brookfield's method implemented
in GENEPOP and MICROCHECKER. Therefore, all the loci were retained in the
data set for further analysis as well as these HWE deviations and any potential level
of linkage disequilibrium is unlikely to affect parentage conclusions given the size of
our sample population (Amos et al. 1992).
The estimated effective population size of B. muricatum at Santa Isabel Island
was 18469 (with a 95% confidence interval of 10697-57311). The estimation done by
the software (NeESTIMATOR V2) was confirmed as reliable according to the
method suggested by Waples (2006) as the Ne values tended towards an asymptote
when subsamples of increasing size were used (Fig. 3). The ratio of the effective
population size to adult population size (Ne/N) was (18469/25000=0.738). This Ne/N
ratio is higher than from the median Ne/N value reported for different natural
populations (0.231; Palstra and Frase 2012) indicating a potentially high genetic
variance. However, this result was not supported by genetic diversity estimations
(number of alleles and PIC values; Table 2). This result must be interpreted with
36
caution, as the total amount of adult individuals in this population (N) was assessed
via visual census, a method that does not necessarily represent and rather tends to
underestimate the true population size (Luikart et al. 2010). Therefore the Ne/N
reported herein might be higher than the actual ratio.
3.2
Parentage analysis: Real data and simulations for accuracy tests
The comparison of the five simulated data sets between showed mean
accuracy levels of 83% for COLONY and 74% for FAMOZ (Table 3). The
distribution of LOD scores for false and true parent offspring pairs, simulated in
FAMOZ, overlapped substantially showing that most of the false parent-offspring
relationships had a LOD value of 4.35 while most of the true parent-offspring
relationships had a LOD of 5.47 (Fig. 4). Based on this simulation, a threshold of
LOD ≥ 6.0 was set to accept parent offspring pairs as being true. Based on simulated
data, this choice would exclude most of the false positive assignments. However,
setting this threshold was at the cost of excluding some true parent-offspring pairs.
Given the relatively low accuracy of each method, the parentage analysis with
the original data set was run first in FAMOZ and then in COLONY. Only shared
parent-offspring assignments from both analyses were considered as “true”. This is a
very conservative approach to identify true parent-offspring relationships and thus,
some parent-offspring pairs can be left out. The parent-offspring relationships made
by FAMOZ were sorted out by the LOD scores and only assignments with a LOD ≥
6.0 (817 assignments) were used for the subsequent parentage analysis in COLONY.
The comparison of the results obtained from FAMOZ (817 assignments) and
COLONY (130 assignments) showed that 68 juveniles were assigned to the same
37
single-parents present in the adults sample in both programs (Table 4). These
juveniles were produced by 26 (41) males and 18 (27) females, and recruited onto 27
(22.5%) of the 120 reefs sampled, 14 of these reefs were located in Zone 1 of the
island and 13 in Zone 3. The adults that produce these self-recruiters (male mean size
961 ± 200.3 mm, female mean size 871 ± 79.7 mm) were on average larger than the
mean adults in the population (male mean size 941, female mean size 842 mm; Fig.
5). Some adults produced multiple juveniles that self-recruited (Table 4).
Based on the population size estimated from previous visual censuses
(~25,000 individuals; Hamilton et al. 2013) and considering that in this study only the
8.5% of the population was sampled, the 68 (4.01%) self-recruiters of the 1692
juveniles considered for the parentage analysis represent an overall population selfrecruitment of 47.3%. Finally, most of the juveniles that self-recruited (63 %) were
found in Zone 1 (Zone 2: 0 %; Zone 3: 37 %) and they were generated almost in
similar proportions from Zone 3 (46 %) and Zone 1 (43 %). The absence of juveniles
in Zone 2 is almost certainly due to the lack of available juvenile habitat (inner
lagoon) in this area. Size-frequency distributions by zone show that few large adult
are being collected in the heavily fished zone (Zone 1) in comparison with the area
that began to be exploited recently (Zone 3). Comparison in adult sizes revealed
differences among zones (KS: 24.813, p=0.000) with adults in heavily fished areas
being smaller than in recent fished areas (Unequal Tukey HSD post hoc test; Fig. 5).
Dispersal distances for the bumphead parrotfish at Santa Isabel Island, measured as
the shortest distance in water from the reef where the parent was sampled to the reef
where the juvenile was sampled, ranged between 4.28 and 78.29 Km (50% of larvae
settled in the 36.56 Km of the reef where they were originated; Fig. 6 and 7).
38
3.3
Sweepstakes Reproductive Success: Testing for reproductive variability
The size-frequency distribution of B. muricatum showed that 45.8% of the
spear-fishery landing is composed of individuals that have not reached maturity (size
≤ 650 mm). By sex, the females are slightly more fished (28.8%) than males (26.4%;
Fig. 5). The comparison of number of alleles between life stage (juveniles and
adults), sex (male and females) and among size categories only show statistically
significant differences for the former comparison (t-test for dependent
samples=182779, p=0.025; Fig. 8a and 8b). For the standardized multilocus
heterozygosity, the comparison did not differ between juveniles and adults (MannWhitney=617157, p=0.712), males and females (Mann-Whitney=1.722, p=0.084), or
among different size categories (Kruskall-Wallis=2.048, p=0.915).
The frequency distributions for the pairwise relatedness between juveniles and
adults show that relatedness of most individuals were between zero and 0.2 (ChiSquare=119.3, α=0.05, p=124.3; Fig. 9), even though the results from the parentage
analysis in COLONY showed the presence of siblings in the juvenile population
(Table 4). These relatedness results suggest that most individuals are not related
considering the expected relatedness values in the presence of half or full-sibs are
between 0.25 and 0.50 (Buston et al. 2009). Seeing as how siblings were detected by
the parentage analysis in the data set, it is likely that the relatedness values differ
between juveniles belonging to different cohorts (recruiting at different times and
presenting different sizes). However, the comparison of the mean relatedness among
different size categories did not lead to any differences between them (Fig. 10). The
two smallest categories show higher relatedness values in comparison with the other
size categories but this result was not statistically significant.
39
DISCUSSION
The results of this study suggest that almost half of the bumphead parrotfish
population in the Kia District at Solomon Islands (47.3%) originated from selfrecruitment. Given that only 8.5% of the adult population was sampled, it is likely
that some of the juveniles unassigned as offspring in the parentage analysis were
generated by unsampled adults which potentially increases the self-recruitment level
recorded. Moreover, due to the fishing pressure in the study area is likely that some
mature adults (potential parents) were removed before our sampling period. Fished
parents may explain part of the unassigned juveniles. For instance, most of the
collected juveniles have a mean size of 300 mm which represents an age of three
years (Couture and Chauvet 1994), time enough for its parents to be fished. However,
caution must be taken when interpreting this parentage analysis results. First, because
the arbitrary establishment threshold value of LOD in the assignments made by
FAMOZ (Fig. 4) can increase the number of false positives (assigning an offspring to
a wrong parent) and leads to an overestimation of parentage assignment and hence,
self-recruitment. Second, because the parentage analysis is conditioned by the
resolution of the genetic markers used (quality and quantity of markers) and for the
level of genetic diversity (loci polymorphism is low within the present data set) of the
individuals in question. The bumphead population studied shows low levels of allele
diversity compared with other reef fishes (Hoarau et al. 2005, Priest et al. 2014) and it
has been demonstrated that error rates in parentage analysis increase when using low
polymorphic loci (Saenz-Agudelo et al. 2009). Nonetheless, as the accuracy of the
assignments was similar (73 – 84 %) to another study comparing parentage methods
(65 – 98 %; Harrison et al. 2013) and all the assignments done by FAMOZ were
confirmed by COLONY, it is most likely that the parent-offspring relationships are
40
reliable. All in all, these results are of a very conservative nature since only
assignments by both programs were taken into account (excluding parent-offspring
pairs that could be true).
Self-recruitment has been documented for different marine taxa (Swearer et al.
2002) and quantified with different techniques in reef fishes (Jones et al. 1999, 2005,
Almany et al. 2007). Estimations based on parentage analysis have found recruitment
levels varying from 0 to 65 % (Table 1), and similar values to those reported here
(47.3 %) have been recorded only for anemonefishes (Planes et al. 2009, Maduppa et
al. 2014), which have a strong habitat association (i.e. anemones). The fact that the
origin of half of the juvenile population is unknown suggest that the population
studied is connected via larval dispersal with other populations. However, how far
these bumphead parrotfish larvae are dispersing still remains an unanswered question.
Based on the species PLD (up to a month) it is possible to assume that the larvae are
traveling long distances but there is not ultimate evidence of correlation between PLD
and self-recruitment. Indeed, there is evidence of high self-recruitment (66-87%)
even among species with PLD as long as 3-6 months (Miller and Shanks 2004). As
the presence of genetically different populations was not detected in the Bayesian
analysis, it is very likely that the 50% of the juveniles that did not originate from the
Kia population are coming from nearby populations, which maintains the gene flow
constant over many generations and results in a genetically homogeneous
metapopulation.
The reduced genetic diversity (measured as number of alleles and
heterozygocity per locus) detected in the bumphead parrotfish was very remarkable.
The causes of this low genetic diversity are unknown but some scenarios can be
proposed. The occurrence of low allelic diversity in natural populations can be
41
associated with genetic-evolutionary phenomena like the founder effect, the Wahlund
effect, bottlenecks, or genetic drift, as well as ecological processes related to
differential success in reproduction such as sweepstakes reproduction, the Allee
effect, and differences in adult-sex ratio, or habitat fragmentation/degradation. More
recently, a study by Romiguier et al. (2014) demonstrated that genetic diversity is
determinate/predictable by some species' life-history traits (e.g., propagule size).
Changes in genetic diversity of natural populations are not easy to demonstrate due to
the absence of suitable populations for comparisons (e.g. exploited vs unexploited;
Hauser et al. 2002) but speculation on the events associated to genetic-evolutionary
phenomena can be made. In this specific study, the low genetic diversity in the
bumphead population can be a result of sweepstakes reproductive events, potentially
as a consequence of the high fishing pressure.
The sweepstakes reproduction hypothesis states that under the occurrence of
differential reproductive success due to the effect of unpredictability of the
environment on spawning reproduction, only a small subset of adults genotypes are
going to be passed to the next generation (Hedgecock and Pudovkin 2011).
Sweepstakes reproduction in marine organism has been demonstrated for
echinoderms (Flowers et al. 2002), molluscs (Hedgecock and Pudovkin 2011) and
fishes (Planes and Lenfant 2002). Recently, the existence of this phenomenon was
documented for two studies in a damselfish species in the Caribbean Sea (Stegastes
partitus). The first study concluded that the occurrence of self-recruitment and
sweepstakes reproduction were acting together causing the patterns of larval dispersal
observed in the study area (Christie et al. 2010), while the second stated that the
existence of sweepstakes reproduction may be caused by the local oceanographic
patterns (Pusack et al. 2004).
42
As a result of sweepstakes reproduction, a low N e /N ratio (< 0.01) is expected
(Hedgecock et al. 2007), as well as low values of genetic diversity and higher levels
of relatedness when comparing cohorts of juveniles against adults (Hausser et al.
2002, Levitan 2005). However, for the bumphead population the evidence found was
contradictory, showing a high N e /N ratio and a reduction of genetic diversity from
adults to juveniles (no patterns were detected in relatedness; Fig.8-10). As high N e /N
ratio is indicative of a greater genetic variance, the low genetic diversity in B.
muricatum is an unexpected result. This high N e /N ratio potentially suggests the
existence of a high connectivity with near populations. While the low diversity can
indicate that this connectivity has been reduced drastically in a short period of time,
on the other hand, it may indicate that the present connectivity is among populations
all with low genetic diversity. In both cases this is highly likely to be a consequence
of overfishing. Thus, for the first scenario, an ecologically very recent event that
reduced the actual population size (N) potentially has not yet produced a genetic
signature (low N e /N ratio) but can later on lead to a detectible change in the future
genetic composition of the population. For example, there is already evidence for a
sharp decline of the closest B. muricatum population in Western Provinces in
Solomon Islands due to recent overfishing (Hamilton 2004). Overfishing affects an
organism’s life cycle, altering processes such as dispersal and recruitment, potentially
reducing genetic diversity and inhibiting the potential replenishment of populations
(Hauser et al. 2002, Miller et al. 2009). For instance, the removal of adult individuals
will directly impact the local reproductive output reducing the numbers of recruits,
and small populations are more susceptible to suffer genetic drift (because of
inbreeding) followed by the loss of genetic diversity (Hoarau et al. 2005, Allendorf
and Luikart 2007, Miller et al. 2009). Indeed, fishing pressure has been suggested as
43
a factor affecting self-recruitment levels for anemonefishes in the Coral Triangle
(Maduppa et al. 2014). Alternatively, it has been pointed out that a similar pattern to
the one produce under sweepstakes reproduction can be caused by differences in
fitness (e.g. the largest individuals producing more offspring with higher survival
rates) and bottlenecks (Christie et al 2010, Hoban et al. 2013).
Even more severely, the demographic data shown that the juveniles and the
larger females are overall the most fished in this area (Fig. 5a-d). This is a direct
threat for the sustainability of the spear-fishery associated for two main reasons.
First, as mentioned above, the removal of the larger females will impact
disproportionally the production of progeny, potentially greatly reducing the amount
of local recruitment and thus the genetic diversity of the next generation. The results
found here support the aforementioned scenario as the adults that produced all the
recruits were larger than the average adult size. Second, if the few recruits are fished
before they reach maturity, the population size and health will be under even greater
threat. Furthermore, the results suggests that lightly fished areas that recently began
to be fished (Zone 3) are acting as a source of recruits while the highly fished areas
rather represent a recruitment sink (Zone 1: Fig. 7). Harrison et al. (2012) and
Almany et al. (2013) reported similar results among commercial fish species, in
which small areas with less or without fishing pressure are contributing significant
numbers of recruits to fished areas alleviating the effects of overfishing. As Zone 1
and 3 seem to have similar suitable juvenile habitat (slightly more availability in Zone
3), a lower level of recruitment in Zone 3 compared to Zone 1 is most likely an effect
of logging activities carried out in this area (Hamilton et al., unpublished) and not a
naturally induced event (e.g. habitat preferences). The logging enhances
sedimentation on the reef and consequentially affects the search of fish larvae for a
44
suitable settlement site (Rogers 1990, Wenger et al. 2011). If this is the case, Zone 1
represents the only pristine environment for larvae settlement and it must be
protected.
As recovery of declining populations largely depends on successful
recruitment, the knowledge about the origin and destiny of new recruits is critical for
the establishment of sustainable fisheries and conservation (Miller et al. 2009).
Linked with the protection of recruits, it is important to maintain a healthy adult
reproductive stock. For example, it has been demonstrated that larger and older fish,
provide survivor benefits to larvae (Palumbi 2004, Taylor et al. 2014b). Thus, from a
management and conservation point of view, restrictions on the extraction of
individuals below the reproductive size (Zone 1) and regulations on the size and
numbers of landings in the most recent fished zone (Zone 3) must be implemented as
soon as possible to sustain the production of substantial larvae.
CONCLUSION
As quantifying larval dispersal remains a challenging task because the size of
the larvae and the size of their environment, genetics tools can provide insights to and
elucidate spatial patterns of larval movements. By investigating the population
genetics and demography connectivity of an overfished reef fish species, this study
provides essential information to ensure the sustainable management of the bumphead
spear-fishery. This thesis represents the first parentage study on large-bodied
parrotfish, which has high mobility and is at risk of extinction. Furthermore, the use
of both genetic and demographic data in this study set a reference on population size,
body length and genetic diversity, which is essential for future studies and to establish
45
the sustainable fishery in the Solomon Islands (one of the few places with a
significant abundance of this endangered species).
With regard to population dynamics, the findings in this study provide
evidence of the occurrence of self-recruitment as a strong component of the
population replenishment and also highlight the existence of connectivity with near
populations. The results supported what has been found in previous studies (Harrison
et al. 2012, Almany et al. 2013) where less fished areas are exporting larvae to
overfished areas alleviating the effects of the constant removal of individuals.
Besides providing information concerning the mechanism of population maintenance
and recovery (exportation of larvae from Zone 3 to Zone 1), the occurrence of
variability in the adult reproduction success was also evaluated. Even though the
results did not totally confirm this phenomenon, there was clear evidence of reduction
in genetic diversity when comparing adults against juveniles, and also the
demography data show that self-recruits were produced by individuals larger than the
mean adult in the population. The reduction in genetic diversity between adults and
juveniles is a genetic signature of the occurrence of sweepstakes reproduction. The
findings provided in this thesis are valuable information regarding the dispersal and
replenishment capabilities of B. muricatum under differential fishing pressure and can
be used to implement sustainability and conservation measures.
46
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54
APPENDICES
Figure 1.
55
Figure 2.
Figure 3.
30000
25000
Ne
20000
15000
10000
5000
0
0
500
1000
1500
2000
2500
Sample size
3000
3500
4000
4500
56
Figure 4.
300
250
0.2
200
0.15
150
0.1
100
0.05
50
0
0
0
5
10
15
20
25
LOD score
Figure 5.
30
35
40
45
Number of assignments
Proportion of simulated assignmnets
0.25
57
58
Figure 6.
Figure 7.
Figure 8.
61
Figure 9.
Pairwise comparisons
600000
500000
400000
300000
200000
100000
0
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
Relatedness
Adults
Figure 10.
juveniles
0.4
0.6
0.8
1
62
Table 1.
PLD
(days)
No.
Markers
Sample size
Self-recruitment (%)
9 – 12
11
158
16 - 32
11
16
906
40
9 – 12
11
458
11
7
Zebrasoma flavescens
9 – 12
Amphiprion polymnus
Amphiprion chrysopterus
Fish Species
Amphiprion polymnus
Amphiprion percula
Amphiprion polymnus
Stegastes partitus
Connectivity (%) -
Parentage
Dispersal distance (Km)
method
Famoz
Jones et al. 2005
Famoz
Planes et al. 2009
0
Famoz
Saenz-Agudelo et al. 2009
751
2* - 42%
Bayesian
Christie et al. 2010
15
1073
4*
15 - 184 km
Bayesian
Christie et al. 2010b
9 – 12
18
942
18
82%
Famoz
Saenz-Agudelo et al. 2011
27*
Famoz
Beldade et al. 2012
Famoz
Berumen et al. 2012
Famoz
Berumen et al. 2012
Famoz
Buston et al. 2012
15 - 35
9 – 12
11
378
Amphiprion percula
9 – 12
16
978
Chaetodon vagabundus
29 – 48
15
533
64
40
Amphiprion percula
9 – 12
16
975
106*
0–1
Plectropomus maculatus
Reference
26
11
959
7
83 - 30km
Famoz
Harrison et al. 2012
Lutjanus carponotatus
25
13
1628
19
55 -30km
Famoz
Harrison et al. 2012
Amphiprion polymnus
9 – 12
18
2806
18-22.5
75
Famoz
Saenz-Agudelo et al. 2012
Plectropomus aerolatus
19 – 31
21
23
1198
Famoz
Almany et al. 2013
14
619
17 - 25
4.6
Cervus
D´Aloia et al. 2013
30
7
3278
8*
Bayesian
Pusack et al. 2014
Elacatinus lori
Stegastes partitus
Amphiprion ocellaris
0 - 159 km
8 - 12
7
364
44 - 65.2
Famoz
Madduppa et al. 2014
Amphiprion perideraion
18
5
105
46.9
Famoz
Madduppa et al. 2014
Tripterygion delaisi
18
192
1573
6.5
11.5 km
Cervus
Schunter et al. 2014
28
3924
68* (47.3%)
4 – 78 km
Famoz - Colony
This study
Bolbometapon muricatum
24 - 31
*parent-offspring pairs detected **SNPs
Table 2.
Locus
Genbank
number
Repeat
motif
Bm20
KJ489028
(AC)9
Size
range
(bp)
94-102
Bm42
KJ489029
(AC)17
Bm29
KJ489030
Bm43
KJ489031
Bm37
KJ489032
(AC)9
Bm72
KJ489033
(AC)11
Bm98
KJ489034
(AC)8
Bm75
KJ489035
FIS
(W&C)
P
Bonferroni/FRD
corrections
0.662 0.654
-0.013
0.3888
NS
13
0.411 0.412
0.001
0.0000
*
0.36
10
0.422 0.421
-0.003
0.0008
*
A
0.293
8
0.348 0.353
0.015
0.4442
NS
176-180
A
0.364
6
0.440 0.443
0.005
0.7871
NS
201-209
A
0.113
9
0.117 0.116
-0.007
0.5810
NS
206-208
A
0.015
3
0.016 0.015
-0.006
0.9710
NS
(AC)14
200-214
A
0.472
12
0.563 0.566
0.006
0.6292
NS
Bm105 KJ489036
(AC)8
346-354
A
0.323
4
0.408 0.404
-0.010
0.5811
NS
Bm88
(AC)12
127-135
B
0.441
8
0.478 0.486
0.017
0.5071
NS
104-128
B
KJ489037
Mulitplex
PIC
Na
A
0.583
9
113-121
A
0.367
(AC)15
116-130
A
(AC)10
130-144
Bm118 KJ489038 (AAT)17
Ho
uHe
0.746
14
0.790 0.780
-0.013
0.5970
NS
KJ489039
(AC)12
205-213
B
0.65
8
0.712 0.706
-0.009
0.0067
NS
Bm108 KJ489040
(AT)8
242-244
B
0.319
6
0.388 0.395
0.017
0.3571
NS
Bm14
KJ489041
(AC)13
219-229
B
0.65
13
0.678 0.690
0.017
0.2338
NS
Bm76
KJ489042
(AC)9
271-277
C
0.207
10
0.217 0.221
0.017
0.0566
NS
Bm25
KJ489043
(AC)10
150-166
C
0.128
5
0.136 0.137
0.010
0.6434
NS
Bm32
KJ489044
(AC)9
167-169
C
0.605
10
0.663 0.669
0.009
0.0712
NS
Bm87
KJ489045
(AC)12
168-178
C
0.166
10
0.169 0.177
0.046
0.0022
NS
Bm109 KJ489046 (AAG)11
143-158
C
0.482
10
0.523 0.525
0.005
0.9944
NS
Bm45
KJ489047
(AC)10
234-248
C
0.188
9
0.208 0.203
-0.024
0.1452
NS
Bm54
KJ489048
(AG)12
250-260
C
0.379
7
0.489 0.498
0.019
0.2390
NS
Bm17
KJ489049
(AC)11
260-264
C
0.122
6
0.122 0.128
0.049
0.0041
NS
Bm79
KJ489050
(AC)9
275-291
D
0.571
8
0.632 0.637
0.008
0.8939
NS
Bm81
KJ489051
(AC)11
278-294
D
0.097
7
0.068 0.099
0.314
0.0000
*
Bm112 KJ489052 (AAT)12
256-289
D
0.586
14
0.586 0.627
0.065
0.0000
*
Bm110 KJ489053 (AAT)11
177-192
D
0.284
9
0.312 0.308
-0.013
0.7563
NS
Bm119 KJ489055 (AAT)17
181-211
D
Bm8
0.657 14 0.706 0.704 -0.004 0.3205
Bm120 KJ489056 (AAT)19 180-204
D
0.671 11 0.712 0.709 -0.005 0.7431
*indicate significant departures from expected Hardy-Weinberg equilibrium after Bonferroni and False
Rate Discovery corrections. NS: no significant.
NS
NS
Table 3.
Simulation
Threshold
Known
singles
True
Single
Known
pairs
True
Pair
True
Single
Known
exclusions
True
Exclusion
Alpha
1s1
Alpha
2s
Original
98
34
2
2
34
400
376
0.9
98
21
2
2
21
400
386
0.7
98
27
2
2
27
400
381
0.5
98
34
2
2
34
400
0.3
98
34
2
2
34
Original
74
29
4
0.9
74
14
0.7
74
0.5
Alpha
2p
True
Beta Beta 1 Beta 2 Accuracy
4
24
60
0.824
14
77
0.818
4
19
67
0.82
379
4
21
60
0.83
400
376
4
24
60
0.824
29
422
397
3
25
4
14
422
413
15
4
15
422
412
1
10
58
4
0.854
74
16
4
16
422
410
1
12
57
4
0.852
0.3
74
16
4
16
422
410
1
12
57
4
0.852
Original
90
33
4
33
406
370
4
36
2
53
2
0.806
0.9
90
22
4
22
406
392
2
14
1
66
3
0.828
0.7
90
23
4
23
406
387
2
19
1
65
3
0.82
0.5
90
23
4
23
406
387
2
19
1
65
3
0.82
0.3
90
23
4
23
406
387
2
19
1
65
3
0.82
Original
95
35
4
2
35
401
376
3
25
1
56
1
0.826
0.9
95
22
4
2
22
401
391
1
10
1
71
2
0.83
0.7
95
22
4
2
22
401
388
1
13
1
71
2
0.824
0.5
95
33
4
2
33
401
382
2
19
1
1
59
1
0.834
0.3
95
33
4
2
33
401
382
2
19
1
1
59
1
0.834
Original
83
32
8
5
32
409
374
3
34
1
1
47
2
0.822
0.9
83
24
8
3
24
409
388
2
21
1
1
56
4
0.83
0.7
83
31
8
4
31
409
383
2
26
1
1
49
3
0.836
COLONY
1
2
3
4
5
1
9
42
3
0.852
60
4
0.854
65
0.5
83
31
8
4
31
409
377
2
32
1
1
49
3
0.824
0.3
83
31
8
4
31
409
377
2
32
1
1
49
3
0.824
12
98
13
2
13
400
399
1
1
85
1
0.824
9
98
35
2
35
400
394
6
1
63
1
0.858
7
98
51
2
51
400
376
6
24
2
41
0.854
5
98
70
2
70
400
299
13
101
2
15
0.738
3
98
76
2
76
400
130
19
270
2
3
0.412
12
74
3
4
3
422
422
1
71
3
0.85
9
74
20
4
20
422
421
1
1
54
3
0.882
7
74
35
4
35
422
400
3
22
2
36
2
0.87
5
74
57
4
57
422
308
7
114
3
10
1
0.73
3
74
60
4
60
422
136
12
286
4
2
12
90
7
4
7
406
406
1
83
3
0.826
9
90
29
4
29
406
400
6
3
61
1
0.858
7
90
55
4
55
406
385
3
21
4
32
0.88
5
90
68
4
68
406
293
8
113
4
14
0.722
3
90
78
4
78
406
123
11
283
4
1
0.402
12
95
9
4
9
401
401
2
86
2
0.82
9
95
28
4
28
401
397
4
3
67
1
0.85
7
95
52
4
52
401
388
2
13
4
41
0.88
5
95
71
4
71
401
313
13
88
4
11
0.768
19
266
4
2
2
77
6
0.83
FAMOZ
1
2
3
4
5
0.392
3
95
74
4
74
401
135
12
83
6
8
6
409
409
0.418
9
83
24
8
24
409
407
1
2
4
58
4
0.862
7
83
45
8
45
409
389
3
20
6
35
2
0.868
5
83
57
8
57
409
303
13
106
8
13
0.72
66
3
83
50
8
50
409
133
21
276
8
2
0.386
Offspring ID
Table 4.
Destination Sample
Reef
Zone
Size
(mm)
Parent ID
Origin
Reef
Sample
Zone
Size
(mm)
Sex
Distance
Traveled (Km)
Pop8017
Popu
1
478
Fag1457
Fagho
3
940
F
41.49
Vag7222
Vaga
1
209
Fag1471
Fagho
3
1060
M
5.59
Pop8014
Popu
1
530
Fag1673
Fagho
3
1100
M
64.7
Ved7645
Vedekeo
3
460
Gal6926
Galido
1
830
M
21.93
Pop8033
Popu
1
360
Gha1646
Ghariha Hobu
3
670
M
50.56
Sog7516
Sogobuo
1
330
Kuk6794
Kukumusu
3
371
Gha2148
Ghariha Hobu
3
990
M
4.28
Gha4490
Ghariha Hobu
3
910
M
24.16
11.4
Noh281
Nohatilo
1
625
Sig8189
Sigholehe
3
235
Hil2122
Hilihavo
3
590
Gha65
Ghariha Hobu
3
890
F
11.13
Kor7262
Korapodolo
3
250
Gha71
Ghariha Hobu
3
751
F
35.39
Kuk5674
Kukumusu
3
260
Sog7310
Sogobuo
1
265
Gol4225
Golora
1
780
M
45.33
Pop8030
Popu
1
436
Had4270
Hadana
3
900
F
34.46
Sar8139
Saraporo
1
410
Kob5652
Kobete
3
470
Vag7269
Vaga
1
415
Kor7570
Korapodolo
3
400
Kal2767
Kale
1
714
F
25.4
Mae279
Maefa
3
555
Kal2802
Kale
1
810
F
35.21
Put6175
Putuo
1
160
29.35
Tal8005
Talignani
3
230
41.18
Gob2731
Gobikobaka
1
630
Tau5852
Tauloho
1
110
25.9
78.29
18.28
Jol4174
Joli
1
900
M
29.27
25.69
Kel6361
Kelekele
2
920
M
41.7
48.79
Pop8212
Popu
1
245
Kel6363
Kelekele
2
830
F
40.93
Gho7705
Ghoti
3
510
Ket257
Keto naruo
3
1025
M
29.35
Put7476
Putuo
1
205
Kuk5681
Kukumusu
3
400
Koe4714
Koela
3
930
M
50.5
Gho7703
Ghoti
3
510
Koh6237
Kohirio
2
910
M
70.01
Kuk6685
Kukumusu
3
210
24.4
Put7449
Putuo
1
310
32.76
Kab6620
Kabalane
1
140
Kol188
Kab7359
Kabalane
1
300
Bas8259
Basana
1
470
Pop8015
Popu
1
518
Kav7423
Kavo
1
300
Vag7226
Vaga
1
380
Kei1725
Keipali
3
Kav6102
Kavo
Kuk6779
Kukumusu
30.62
Kologose
1
1065
M
26.52
Lav4912
Lave
Leg4125
Legoai
1
680
M
38.72
1
1000
F
63.5
44.71
Lel2996
Lelego
3
1140
M
10.29
580
Mae2981
Maefa
3
900
F
39.99
1
230
Mal6216
Maladaro Is.
2
910
M
48.95
3
273
Nar234
Naruo
1
1040
M
29.79
44.36
68
Pop8116
Popu
1
413
Lav2807
Lave
1
570
Nar4580
Naruo
3
1040
F
14.86
32.75
Noh40
Nohatilo
1
560
Nar7679
Naruo
3
1100
M
35.21
Put1799
Putuo
1
89
Kuk5621
Kukumusu
3
360
Nar7768
Naruo
3
1090
M
13.21
Tal8059
Talignani
3
440
Noh290
Nohatilo
1
1015
M
59.83
Kab274
Kabalane
1
510
Pop6940
Popu
1
850
F
21.28
Alo4075
Alohaki
3
470
Pop6942
Popu
1
860
M
43.24
Rap2737
Rapita
1
870
F
59.64
Sog7506
Sogobuo
1
260
Kab7316
Kabalane
1
280
13.49
Put318
Putuo
1
500
Put6751
Putuo
1
251
Rap370
Rapita
1
1155
M
61.3
Kuk8277
Kukumusu
3
613
Ret5920
Retu
1
880
F
58.65
Sar8128
Saraporo
1
172
Kak8306
Kakadeke
3
265
Mam6308
Mamarava
1
620
41.74
24.52
22.96
Ret5925
Retu
1
1100
M
14.97
52.08
Noh39
Nohatilo
1
520
Pop8022
Popu
1
450
Ret5936
Retu
1
1140
M
27.97
23.55
Had4655
Hadana
3
620
Rur4444
Ruruma
3
1070
M
32.32
Put5638
Putuo
1
310
Noh4620
Nohatilo
1
410
Put1873
Putuo
1
113
Vik8312
Viketogana
1
559
Suk2841
Suki
2
840
F
37.79
Kuk6686
Kukumusu
3
415
Tap2914
Tapuruna
3
890
F
38.67
Ved7642
Vedekeo
3
470
Sog5582
Sogobuo
1
278
Tap2960
Tapuruna
3
850
F
29.88
Put6746
Putuo
1
278
Vol335
Volauruna
3
752
F
16.3
Sog5122
Sogobuo
1
380
Zao2795
Zao Ponoe
1
788
M
73.69
Tal8056
Talignani
3
500
71.55
Rur4474
Ruruma
3
950
F
67.86
53.8
31.27
36.61