patterns and persistence of larval retention and...

Patterns and persistence of larval retention andconnectivity in a marine fish metapopulation

PABLO SAENZ-AGUDELO,*† 1 GEOFFREY P. JONES,† SIMON R. THORROLD‡

and SERGE PLANES*

*USR 3278 Laboratoire d’excellence CORAIL, CNRS-EPHE, CRIOBE – Centre de Biologie et d’Ecologie Tropicale et

Mediterranneenne, Universite de Perpignan, 66860, Perpignan Cedex, France, †School of Marine and Tropical Biology, and ARC

Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, 4811, Qld, Australia, ‡Biology Department, MS

50, Woods Hole Oceanographic Institution, Woods Hole, MA, 02543, USA

Abstract

Connectivity, the demographic linking of local populations through the dispersal of

individuals, is one of the most poorly understood processes in population dynamics,

yet has profound implications for conservation and harvest strategies. For marine

species with pelagic larvae, direct estimation of connectivity remains logistically

challenging and has mostly been limited to single snapshots in time. Here, we

document seasonal and interannual patterns of larval dispersal in a metapopulation of

the coral reef fish Amphiprion polymnus. A 3-year record of larval trajectories within

and among nine discrete local populations from an area of approximately 35 km was

established by determining the natal origin of settled juveniles through DNA parent-

age analysis. We found that spatial patterns of both self-recruitment and connectivity

were remarkably consistent over time, with a low level of self-recruitment at the scale

of individual sites. Connectivity among sites was common and multidirectional in all

years and was not significantly influenced by seasonal variability of predominant

surface current directions. However, approximately 75% of the sampled juveniles could

not be assigned to parents within the study area, indicating high levels of

immigrations from sources outside the study area. The data support predictions that

the magnitude and temporal stability of larval connectivity decreases significantly with

increasing distance between subpopulations, but increases with the size of subpopula-

tions. Given the considerable effort needed to directly measure larval exchange, the

consistent patterns suggest snapshot parentage analyses can provide useful dispersal

estimates to inform spatial management decisions.

Keywords: Amphiprion polymnus, connectivity, larval dispersal, microsatellites, parentage

analysis, temporal series

Received 21 March 2012; revision received 16 June 2012; accepted 20 June 2012

Introduction

Many species have a patchy distribution that consists of

spatially discrete local populations (Hanski 1998). The

persistence of these species has been viewed as a

dynamic process of local extinction and recolonization

events, historically encompassed in a metapopulation

framework that was first formalized by Levins (1969).

The dynamics and persistence of such metapopulations

depend to a large degree on the demographic links

among local populations through dispersal. Metapopu-

lation models have proven to be useful for describing

and predicting the dynamics of species in a wide range

of systems (Hanski & Gaggiotti 2004; Sale et al. 2006).

The behaviour of these complex systems is critically

Correspondence: Pablo Saenz-Agudelo, Fax: +966 (0) 2

8020085;

E-mail: [email protected] Present address: Red Sea Research Center, King Abdullah

University of Science and Technology, Thuwal, Kingdom of

Saudi Arabia.

© 2012 Blackwell Publishing Ltd

Molecular Ecology (2012) doi: 10.1111/j.1365-294X.2012.05726.x

dependant on spatial and temporal variation in the

exchange of individuals among constituent populations

(Hanski & Gaggiotti 2004). In current metapopulation

theory, particular attention has been given to specific

factors such as the area and proximity of neighbouring

populations. These metrics are widely used as predic-

tors of dispersal rates and colonization-extinction

dynamics in natural terrestrial populations but have

been rather neglected in marine systems (Sale et al.

2006).

Many marine species have complex life cycles with a

benthic adult phase that occurs in discrete habitat

patches and a pelagic larval phase that assures popula-

tion connectivity (Sale et al. 2006; Jones et al. 2009). Dur-

ing the larval phase, marine larvae are subject to

multiple sources of mortality and advective-diffusive

processes that transport them varying distances away

from the populations of origin. Recent evidence from a

diverse range of approaches including biophysical mod-

elling (James et al. 2002; Cowen et al. 2006; Paris et al.

2007; Treml et al. 2008), otolith or shell chemistry

(Swearer et al. 1999; Carson et al. 2010) otolith tagging

(Jones et al. 1999; Almany et al. 2007), population genet-

ics (Hogan et al. 2012) and parentage analysis (Jones

et al. 2005; Planes et al. 2009; Christie et al. 2010; Saenz-

Agudelo et al. 2011; Berumen et al. 2012) have shown

that at least some larvae return to their population of

origin, while others may travel long distances. However,

little is known about the levels of variability of larval

connectivity, both in space and in time, or the underly-

ing factors that dictate metapopulation dynamics.

There is evidence that temporal and spatial variation

in larval connectivity can be responsible to some extent

for spatial and temporal patchiness in the genetic struc-

ture of coastal marine populations (Selkoe et al. 2006;

Hogan et al. 2010). However, due to significant logistical

challenges of tracking dispersing larvae in the ocean,

long-term patterns of demographically relevant connec-

tivity have rarely been measured in marine metapopu-

lations. Certainly, no empirical studies have tested how

well habitat metrics including patch size and distance

may predict dispersal patterns. Assessing the relation-

ship between dispersal and habitat metrics, as well as

the magnitude of temporal variation of connectivity, is

essential for determining the broader population conse-

quences of habitat loss, fragmentation and exploitation

in spatially complex systems (Hanski 1998; Clobert et al.

2001; Bowler & Benton 2005; Levin 2006; Sale et al. 2006;

Ronce 2007). An understanding of connectivity is also

seen as vital for long-term spatial planning for conser-

vation and resource management (Sale et al. 2005, 2006).

In theory, predictable patterns of connectivity could

be explained by many factors, including consistent

oceanographic processes (Galindo et al. 2006; Treml

et al. 2008), local population size (Caley et al. 1996) and

distance among neighbouring populations (Jones et al.

2007). There is also potential for significant fluctuations

in dispersal dynamics as a result of large-scale oceano-

graphic processes (e.g. seasonal wind patterns, upwell-

ing) and small-scale interactions among turbulent flow,

larval behaviour and settlement cues (Hamilton et al.

2006; Navarrete et al. 2008). To our knowledge, only

one study has documented the seasonal and interannual

variability of larval connectivity for a marine metapopu-

lation (Carson et al. 2010). Using elemental composition

of shells as natural tags of natal origins in two species

of mussels, Carlson and co-workers found consistent

seasonal larval exchange coinciding with the direction

of near-surface currents during each season.

Studies of larval connectivity have received consider-

able attention in coral reef systems, which are character-

ized by extreme habitat patchiness (Sale et al. 2005;

Jones et al. 2009). Despite a growing literature on the

dynamics of coral reef ecosystems, the influence of fac-

tors, such as patch size, distance and oceanography, on

the dispersal of coral reef organisms remains poorly

understood (Botsford et al. 2009). Only a few studies

have linked regional scale genetic structure with dis-

persal patterns predicted from hydrodynamic models of

the study areas (Galindo et al. 2006; Salas et al. 2010;

Foster et al. 2012). Another study showed that for a

coral reef fish with strong homing behaviour, genetic

patterns were not consistent with oceanographic model

predictions (Gerlach et al. 2007). Yet to date, there have

been no studies in which direct estimates of individual

dispersal trajectories in a metapopulation have been

monitored over time to evaluate consistent patterns and

their underlying causes.

In a previous study we used a likelihood-based par-

entage assignment method to measure larval retention

within and exchange among nine discrete anemone

aggregations hosting the anemonefish Amphiprion polym-

nus encompassing an area of approximately 35 km of

coastline near Port Moresby, Papua New Guinea. We

found this metapopulation to be characterized by high

levels of connectivity and low self-recruitment rates

within sites with an average self-recruitment rate of

~10% (Saenz-Agudelo et al. 2011). However, the tempo-

ral stability of these patterns remains unknown. Here,

we set out to provide the first estimates of interannual

variability in patterns of larval dispersal in a coastal

marine metapopulation. We report a three consecutive

year record (2008–2010) of measurements of larval

exchange and retention in our focal A. polymnus meta-

population. The study region is characterized by two

contrasting seasons (wet summer and dry winter) with

contrasting wind patterns and associated surface cur-

rents (Wyrtki 1960; Dennis et al. 2001). We tested for the


2 P. SAENZ-AGUDELO ET AL.

evidence of directionality in larval transport associated

with dominant surface currents by comparing larval

exchange patterns between both seasons over the

3 years. Finally, we evaluated the role of subpopulation

size and distance among subpopulations in explaining

both the magnitude and the temporal variation of dis-

persal patterns observed in this system.

Materials and methods

Study species, site and data collection

The panda clownfish (Amphiprion polymnus) is a south-

east Asian endemic that lives in close association with

discrete aggregations of two species of anemones

(Stichodactyla hadonni and Heteractis crispa) occurring in

sandy habitats associated with coral reefs (Fautin &

Allen 1992). Each anemone is usually occupied by one

breeding pair and up to eight smaller nonbreeders and

juveniles. The female (the largest individual) lays

demersal eggs on the upper surface of shells or dead

coral next to the anemone. The embryos develop over a

period of 6–7 days before hatching (Fautin & Allen

1992) and postlarvae settle into anemones after a pela-

gic larval phase lasting 9–12 days (Thresher et al. 1989).

We sampled a total of 1394 potential A. polymnus par-

ents and 1412 juveniles over 3 years (2008–2010)

(Table 1) from nine discrete anemone aggregations

(hereafter referred to as ‘sites’) distributed across

~35 km of coastline around Bootless Bay (Papua New

Guinea) (Fig. 1). With the exception of Fisherman Island

(FI) anemones within each site were confined to a ~1 ha

patch of shallow sand and seagrass. For a 2-week per-

iod, each year, an exhaustive search for all anemones

colonized by A. polymnusat all sites was performed and

tissue samples were collected from all fish present at all

sites with one exception [Fishermen Island (FI)]. The

anemone aggregation at Fishermen Island (FI) was

spread over a much larger area and only a small pro-

portion of the protected side of the island were ran-

domly explored in 2008 and 2009 (44 and 41 anemones

in 2008 and 2009, were found, respectively). In 2010,

sampling effort in FI was doubled and 91 anemones

were found. However, we estimated this last figure rep-

resents around 80% of the anemones present at this site.

A small site (SE) with eight anemones was found in a

search for additional sites in 2009, and therefore, no

data are available for this site in 2008.

At each site, all fish were captured on SCUBA using

hand nets, measured (total length TL), fin clipped

underwater in situ and then released back onto the

same anemone they were captured from. Fish that were

too small to be fin clipped (<30 mm) were collected. For

all analyses, fish were divided in to three categories

according to their size. The first category ‘breeders’ con-

sisted of the female and male (the two biggest individu-

als) of each anemone. The remaining fish were then

divided in to ‘nonbreeders’ (>50 mm) and ‘juveniles’

(<50 mm).

Sampling was conducted once each year, and there-

fore, we had to estimate settlement times for all sam-

pled juveniles. We used a combined approach of otolith

reading and multilocus genotype–based individual

identification to approximately determine the size range

of individuals that settled during the dry or the wet

season each year. Lapilli otoliths were dissected from a

subsample of 245 fish collected in the field (up to

30 mm). The age of each fish was estimated by counting

the number of daily increments from the nucleus along

the longest axis of the otolith. Clear readings were

obtained for 145 of these fish (ranging from 7 to 26 mm

in total length), but age estimation was not always

Table 1 Description of the nine subpopulations of Amphiprion polymnus sampled between 2008 and 2010 considered in this study.

For each year, the number of anemones colonized by at least one A. polymnus (NA), number of adult and subadult A. polymnus

(A + SA) and juveniles (J) of the two size categories considered are shown.


PATTERNS AND PERSISTENCE OF LARVAL CONNECTIVITY 3

possible, particularly for fish larger than 26 mm. The

maximum age at size 25 mm TL observed from these

otoliths estimates was ~102 days (Fig. S1, Supporting

information). Fish of ~100 days old sampled in Febru-

ary recruited around the last week of October in the

previous year, which coincided with the end of winter

season; therefore, 25 mm TL was used as a proxy to

delineate between winter and summer. Fish of 25 mm

TL or smaller were assumed to have settled in the sum-

mer season and fish larger than 26 mm TL were

assumed to have settled before this point in time.

Genetic analyses

We screened 18 polymorphic microsatellite DNA mark-

ers previously reported (Quenouille et al. 2004; Beldade

et al. 2009) (Table S1, Supporting information). In our

previous study (Saenz-Agudelo et al. 2011), we found

that all these loci satisfied Hardy–Weinberg and linkage

disequilibrium assumptions for this particular species

and study site. In addition, we also found no evidence

of significant genetic structure among sites, so we trea-

ted all sites as one single genetic pool for this study.

We used the FAMOZ platform (Gerber et al. 2003) to

assign juveniles (TL < 50 mm) back to sampled adults

in the metapopulation. This procedure combines exclu-

sion probabilities and maximum likelihood ratios to

select the most likely parent for each offspring based on

population allele frequencies, genotype matching

among parent/offspring pairs and the distribution of

LOD scores of true parent offspring pairs and false

pairs (share one allele per locus by chance), allowing

for the inclusion of genotype scoring errors. Details of

parentage analysis procedure can be found in the sup-

porting information. We also compared individual

microsatellite multilocus genotypes from all juveniles

fin clipped over the 3 years to identify juveniles that

were clipped on more than one occasion and had not

yet reached the 50 mm TL to avoid repeated assign-

ments among years. We used the Genalex 6 package

(Peakall & Smouse 2006) to identify pairs of juvenile

samples among years that had the same multilocus

genotype. Given the combined probability of identity

and identity between sibs (Waits et al. 2001) for the 18

loci (given the sample size and allele frequencies) were

small (3.26 9 10�18 and 6.19 9 10�7, respectively), we

assumed that perfect matches between two samples

from different years corresponded to the same indi-

vidual.

The possibility of genotyping errors was accounted

for by allowing up to 3 (of 36) allele mismatches per

pair in a first run of pairwise comparisons. Genotypes

Fig. 1 Map showing sites of the nine anemone aggregations hosting Amphiprion polymnus in Bootless Bay area (black filled circles)

and prevailing surface currents during the summer monsoon (November–March) and during winter (April–October). Crosses indi-

cate sites outside Bootless Bay with potential suitable habitat for A. polymnus host anemones that were explored but no anemones

were found. Dashed lines indicate shallow reef limits. Inset: Location of Bootless Bay in Papua New Guinea. Site abbreviations are as

follows: Fishermen Island (FI), Manubada Island (BE), Lion Island (LI), Taurama (TA), Motupore north patch reef (MN), Motupore

Island (MO), Loloata Island (LO), Loloata South Bank (BA), SouthEast patch reef (SE). Photo: A. polymnus colony in a Stichodactyla

hadonii anemone (credit Serge Planes).



that matched at all but 1, 2 or 3 alleles were rescored. If

observed differences were maintained after re-examination,

then samples were assumed to belong to different indi-

viduals. Juveniles that were only genotyped on one

occasion were considered to have settled between sam-

pling events. Juveniles genotyped over two consecutive

years were only included in parentage analysis in the

first year they were captured.

In situ current measurement

Current velocity and direction was measured at five

sites (LI, MO, BE, TA and BA) from February 4th to

April 18th in 2008 and from January 26th to April 6th

in 2009 using an Aanderaa DCM12 acoustic Doppler

current profiler (ADCP). The instrument was deployed

at a depth of 8–10 m at each of the sites for a period of

10 days in 2008 and 15 days in 2009 by moving it from

one site to the next after 10 or 15 days, respectively.

The ADCP sampled in 30-minute intervals over 2.5 m

depth bins. For the purpose of this study, only surface

current measures were used (first 2.5 m). Deeper bins

where excluded to eliminate complex current patterns

at depth caused by the complex bathymetry near reefs

where the instrument was deployed.

Analysis of temporal patterns

Prevailing winds and associated surface currents in this

region flow from northwest to southeast in the wet

summer season (November to March) and from south-

east to northwest during the dry winter season (April–

October) (Wyrtki 1960; Dennis et al. 2001) (Fig. 1). We

investigated if there was a distinct seasonal pattern in

the direction of A. polymnus larval transport along the

coast that was correlated with the seasonal variability

in surface currents. We tested for significant differences

in the proportion of juveniles travelling southeast or

northwest at each season. If the influence of larval

behaviour and swimming speed was less important

than predominant currents, then one should be able to

detect directionality in the seasonal larval connectivity

patterns reflecting dominant current flows. Under this

assumption, it would be expected that during summer,

the proportion of larvae transported from the southeast

to northwest should be greater than transport in the

opposite direction, and the reversed patterns should be

observed during winter.

Role of size and distance between sites

We investigated the role of subpopulation size and dis-

tance among subpopulations in the stability and magni-

tude of connections in the focal metapopulation. Two

sites were not included in this analysis due to incom-

plete observations (site SE: only 2 years of data) and

incomplete sampling of the subpopulation (site FI). We

used the number of anemones colonized by A. polymnus

as a proxy for site size and the shortest over-water dis-

tance between pairs of sites. The stability of any given

connection between two sites (e.g. between the ith and

jth sites) was quantified as the proportion of seasons

where at least one juvenile went from site i (source) to

site j (settlement). The magnitude of each connection

was quantified as the cumulated number of juveniles

that went from source-site i to settlement-site j over the

3-year period. Analysis of deviance via general linear

models (GLMs) in R (R Development Core Team 2007)

was performed to estimate the proportion of variance in

stability and magnitude of connections that was

explained by the size of source sites (measured as the

number of anemones hosting A. polymnus) and the dis-

tance between source and settlement sites. As both sta-

bility and magnitude of connections were measured as

count data (number of juveniles and number of sea-

sons), GLMs were fitted using a log link (to ensure that

fitted values are bounded below) and quasi-Poisson

errors (to account for non-normality and over-disper-

sion) (Crawley 2007).

Results

Interannual and seasonal variability of larval dispersal

Our estimates of per cent self-recruitment within the

metapopulation (including all sites) in 2008, 2009 and

2010 were surprisingly similar. Of the 490 juveniles col-

lected in 2008, 88 (18%) were identified as being prog-

eny of adults from this metapopulation, compared with

128 of 507 (21.5%) in 2009 and 94 of 417 (22.5%) in 2010.

The number of self-recruits within sites varied from 0 to

19 individuals. One site (TA) consistently had higher

self-recruitment than all the others (16, 19 and 13 self-

recruits in 2008, 2009 and 2010, respectively) (Fig. 2).

The average per cent self-recruitment at the level of

sites was low in all seasons, ranging from 4.1% to 9.7%

(Table 2). All sites, with one exception (site TA),

showed low and steady levels of self-recruitment

(0–15%) regardless of the year or the season considered.

Self-recruitment at site TA was consistently higher

(22–42%) in five of six seasons but was zero in one

(summer 2009) (Fig. 3A). Despite this drastic difference

in self-recruitment for one season at site TA, differences

in self-recruitment among seasons within sites were

much smaller than differences in self-recruitment

among sites within each season. An analysis of variance

demonstrated that while differences among sites within

seasons explained 64% of total variation, differences



among seasons explained only 4.5% and did not differ

from random expectations (Table S2A, Supporting

information). The same pattern was observed when site

TA (highest contribution to spatial variation) was

dropped from the ANOVA. (Table S2B, Supporting infor-

mation).

A major proportion of juveniles (~75%) could not be

assigned to any of the sampled parents, indicating high

levels of migration from sites outside the study area.

The dispersal distances within the study area inferred

from parental analyses (~25% of juveniles) ranged

between 1.2 km (TA-MN) and 35.5 km (FI-SE). Connec-

tivity was observed between most sites in all three con-

secutive years analysed (Fig. 2). Mean local connectivity

among sites was higher than self-recruitment in all sea-

sons (7.9–17.3%) (Table 2).

In terms of the contribution to recruitment of each

site to the metapopulation (all nine sites together), none

of the individual sites contributed more than 10% of

juveniles to the metapopulation, regardless of the sea-

son or year analysed (Fig. 3B). Patterns of contribution

to the recruitment of each site were consistent among

different seasons. Analysis of variance indicated that

most of the variation in the local contribution of each

site to total recruitment was explained by differences

among sites within seasons (~48%), while differences

among seasons within sites explained <10% regardless

of whether the analysis was performed with or without

site TA (Table S2A, B, Supporting information).

Larval transport and dominant surface currents

Acoustic Doppler current profiler measurements con-

firmed that for both periods measured (wet summer

season in 2008 and 2009) dominant surface current

directions were mostly to the southeast (see Fig. S2,

Supporting information), but with frequent reversals of

their direction (See Fig. S3, Supporting information).

We found no evidence of directionality in larval trans-

port for any of the 3 years or six seasons, with long-

distance dispersal trajectories in both directions (Fig. 2).

None of the 2-sample binomial tests for the equality of

proportions were significant at a = 0.05. That is, larvae

from both size categories dispersed northwest and

southeast in similar proportions regardless of the sea-

son or the year considered (Table 1). (A detailed con-

nectivity matrix for each season in each year is

available in Table S3, Supporting information).

Connectivity, site size and distance between sites

The size of the source site and the distance between

source and settlement sites had significant but opposite

effects on the magnitude of connectivity (‘Source size’

deviance = 29.76, F1,47 = 5.74, p = 0.021; ‘source-settlement

distance’ deviance = 91.94, F1,46 = 17.72, p < 0.001. Dis-

persion parameter for quasi-Poisson errors = 5.18)

(Figs. 4A, B). The size of the source site explained 9.5%

of the variation in the magnitude of connections, while

the distance between source and settlement sites

explained 29.3%. Similarly, the stability of connections,

that is, the proportion of times (seasons) a particular

connection was observed, was also associated with the

size of the source and the distance between source and

settlement sites (‘Source size’ deviance = 4.72, F1,47 =5.49, p = 0.023; ‘source-settlement distance’ deviance =7.20, F1,46 = 5.8.39, p = 0.005. Dispersion parameter for

quasi-Poisson errors = 0.858) (Fig. 4C, D). The size of

Fig. 2 Maps of Bootless Bay Area showing inferred individual

trajectories (arrows) of A. polymnus juveniles among sampled

sites over three consecutive years (2008, 2009 and 2010) based

on parentage analysis. Black circles represent self-recruitment.

Thickness of arrows and diameter of circles are proportional to

the number of juveniles with similar trajectories. A zoomed

map of bootless bay area is shown as an inlet at the top-right

corner of each map. A detailed matrix with the data used to

generate this figure is available as supplementary material.



Table 2 Number of Amphiprion polymnus juveniles that dispersed southwest and northeast, mean self-recruitment and local connec-

tivity per site for Bootless Bay area for each juvenile size category as a proxy for recruitment in different seasons. Last column corre-

sponds to the proportion of juveniles of each size category assigned by parentage analysis, all sites confounded (metapopulation),

corresponding to the overall self-recruitment for each season.

Year

Size category

(Season)

Total

juveniles

Assigned by

parentage

Juveniles

dispersing

SW – NE

% Self-

recruitment

mean ± SD

% Local

connectivity

mean ± SD

Overall

self-

recruitment (%)

2008 >25 mm <50 mm

(winter)

264 48 12–12 9.4 ± 9.6 11.3 ± 7.7 18.1

<25 mm

(summer)

226 40 15–14 4.1 ± 7.7 10.9 ± 8.8 17.7

2009 >25 mm <50 mm

(winter)

369 106 39–28 9.7 ± 12.7 16.9 ± 8.3 28.7

<25 mm

(summer)

138 22 7–7 5.1 ± 6.9 7.9 ± 9.7 15.9

2010 >25 mm <50 mm

(winter)

329 77 27–22 6.0 ± 12.4 17.3 ± 13.7 23.4

<25 mm

(summer)

88 17 8–3 6.4 ± 9.7 10.7 ± 12.4 19.3

(A) (B) Fig. 3 Distribution of relative frequencies

of (A) self-recruitment and (B) contribu-

tion of the site to metapopulation among

the nine anemone aggregations hosting

A. polymnus in Bootless Bay for each year

(2008 circles, 2009 triangles and 2010 dia-

monds) and seasons: <25 mm (summer)

grey filled symbols and >26 mm (winter)

empty symbols.

(A) (B)

(C) (D)

Fig. 4 Effects of subpopulation source

size (measured as the number of anemo-

nes per site) and distance between source

and sink subpopulations on the magni-

tude: cumulated number of juveniles

produced over 3 years (A and B) and the

stability of connections: number of sea-

sons that a particular connection was

observed in the Bootless Bay Amphiprion

polymnus metapopulation system (C and

D). GLMs fitted lines are: (A)

y = e0.777 + 0.058x (B) y = e2.038�0.109x (C)

y = e0.442 + 0.030x (D) y = e1.022�0.0301x



source sites explained 7.7% of the variation in the stabil-

ity of connections, while the distance between source

and settlement sites explained 11.9%. There was no evi-

dence of interaction between source size and distance

between source and settlement subpopulations. In both

cases, model simplification allowed for the removal of

the interaction term without any significant conse-

quences.

Discussion

This study provides the first empirical, multiyear

description of the magnitude and direction of larval

connectivity in a marine fish metapopulation. We con-

firmed that local replenishment in this system is

dominated by connectivity, with low levels of self-

recruitment at the scale of small subpopulations. Suffi-

cient juveniles could be assigned to adults in the nine

subpopulations to test three distinct hypotheses. First,

our 3-year survey provided strong evidence that pat-

terns of local replenishment and connectivity can be

surprisingly stable even at small geographic scales of

individual reefs. Second, we found connectivity to be

multidirectional and among all subpopulations, with no

evidence for directional larval dispersal related to domi-

nant surface current patterns. Finally, we showed that

both the size of the source sites and distance between

source and settlement sites are reasonably good predic-

tors of both the magnitude and stability of larval con-

nectivity.

Temporal stability was observed in terms of the per

cent of recruitment explained by self-retention within

and local connectivity among individual sites. It seems

that even at the scales of individual reefs larval reten-

tion patterns can be highly conserved over several

years. Three other empirical larval connectivity studies

have incorporated temporal data. Berumen et al.(2012)

showed that two coral reef fish species in a small iso-

lated Island in Papua New Guinea displayed consis-

tently high self-recruitment rates over 3 years, while

dispersal to adjacent areas was more variable. Similarly,

Hogan et al.(2012) reported fluctuating annual connec-

tivity patterns for a another coral reef fish in the Carib-

bean. Temporal stability has been also described for

two temperate mussel species, with connectivity follow-

ing regular seasonal patterns and rather constant self-

recruitment rates (Carson et al. 2010). Together with this

study, these empirical time series suggest that temporal

stability is more general at demographic timescales than

previously thought (but see Hogan et al. 2012), and that

geographic settings may play a significant role in the

shape and magnitude of these patterns (Jones et al.

2009; Pinsky et al. 2012). However, only the addition of

more and longer time series will help to validate this

idea and to develop a mechanistic understanding of

larval connectivity in marine fishes.

We found no evidence of seasonal associations

between directionality in larval dispersal and average

surface currents. Direct evidence for a correlation

between currents and the directionality of larval con-

nectivity exists only for the two previously mentioned

species of mussels (Carson et al. 2010). While average

surface currents in the study area had opposite domi-

nant directions between seasons, we also documented

several current reversal events in summer months. We

therefore cannot exclude the possibility that a signifi-

cant proportion of larvae were released in the water col-

umn during these current reversals as Amphiprion

polymnus reproduces all year long in this region. Over-

all, our observations appear consistent with Carson

et al.’s conclusion that connectivity may be more diffu-

sive than advective at small spatial scales. However, the

origin of approximately 75% of larvae remains

unknown in our study and whether directionality dri-

ven by major oceanographic settings is more evident at

larger scales remains unresolved.

Despite the lack of predictable directionality of larval

dispersal, the magnitude and the stability of connec-

tions among sites were significantly correlated with

both the size of the source and the distance between

source and settlement sites. While increasing distance

between sites was associated with decreasing magni-

tude and stability of connections, increasing source size

was associated with increasing magnitude and stability.

Both variables had similar effects on the magnitude and

stability of connectivity. However, the distance between

sites explained near twice the variance compared with

the size of source sites for both magnitude and stability

of connectivity. These results are encouraging for the

implementation of metapopulation models that use sim-

ple connectivity formulations based on this kind of

information when empirical estimates are not available

(Moilanen & Nieminen 2002). There are, however, two

major limitations that need to be addressed. First, more

empirical studies for a wide range of taxa and locations

are warranted before this pattern can be generalized.

Second, recent metapopulation modelling studies have

highlighted that local demography might be more

important than connectivity for the persistence of the

metapopulation (Figueira 2009). This has been sup-

ported recently by one empirical study (Carson et al.

2011). Time series, like the one presented here, com-

bined with life history-based population projections will

definitely help to assess to what extent the concept of

marine metapopulations can be generalized and will

provide managers with a strong solid baseline to target

the processes that may have a significant effect on

metapopulation persistence.



Both the high temporal consistency in self-recruit-

ment and local connectivity and the lack of associa-

tion between larval transport and dominant current

directionality seem to indicate that processes other

than passive dispersal may be dominant at this scale.

One possible explanation comes from the suggestion

that the magnitude of stochasticity in recruitment pat-

terns should diminish for species that spawn through-

out the year and have larvae with short pelagic

durations and high swimming capacities (Siegel et al.

2008), which are all characteristic of A. polymnus.

Another possibility is that, for organisms like coral

reef fish whose larval stage has excellent swimming

capacities and sensory abilities (Kingsford et al. 2002),

larval behaviour might play an important role in buf-

fering stochastic patterns of turbulent circulation at

small spatial scales (Paris et al. 2007). For example,

active homing behaviour will probably change expec-

tations based on passive dispersal (Gerlach et al.

2007). Such behaviour has been suggested for

A. polymnus in a different location (Jones et al. 2005).

However, the rather consistent low self-recruitment

rates in Bootless Bay suggest that homing was not

predominant in this location. It has been shown that

clownfish can detect and discriminate several different

environmental cues (Munday et al. 2009). Unfortu-

nately, the full extent of environmental cues and the

extent to which they can be used to predict consistent

larval behaviour remain unresolved.

Our results are encouraging from the point of view of

conservation and models of marine population dynam-

ics. First, they support the applicability of models of

marine protected areas which assume that dispersal

kernels and recruitment patterns remain fairly constant

over relevant timescales (e.g., Lockwood et al. 2002;

Kaplan et al. 2009), at least at small spatial scales. Sec-

ond, they suggest that factors such as subpopulation

size and distance between subpopulations do play a

more important role in larval connectivity than does

local hydrodynamic settings, at least at small spatial

scales. Also, compared with previously reported self-

recruitment for the same species in a different location

(Jones et al. 2005), the low self-recruitment rates found

here reinforces the idea that distance between subpopu-

lations (or geographic settings) can be an important fac-

tor determining the degree of population openness

(Pinsky et al. 2012). This information may be used to

inform management decisions when no other informa-

tion on larval dispersal is available. Finally, our results

show that single-year estimates using this kind of

approach provide reliable information of the dynamic

processes that occur over longer timescales in marine

populations and therefore might be considered as

appropriate guidelines for marine conservation strate-

gies and MPA network design (Botsford et al. 2009;

Kaplan et al. 2009).

It is important to highlight that the full extent of this

metapopulation remains to be determined. At the

spatial scale of this study, the parental origin of ~75%of the juveniles samples remains unknown. Therefore,

conclusions about temporal stability in connectivity

refer only to local connectivity within the 35 km study

area. We can only speculate about the location and size

of subpopulations outside our study that produced the

juveniles that were not assigned to parents. Reefs and

potential suitable habitat for the anemones that host

A. polymnus extend over 50 km northwest and ~100 km

southeast from the study area based on satellite images,

after which the reefs are interrupted by large river del-

tas. Given the rather short pelagic larval duration for

this species (~12 days), we hypothesize that most of the

incoming larvae replenishment our study system are

from populations within this range, but this remains to

be tested.

Finally, our results suggest that simple metrics such

as subpopulation size and distance among them may

be good predictors of connectivity when no other

information is available. Long-term empirical estimates

of larval connectivity on organisms with different life

history traits at similar spatial scales are required to

confirm the generality of this pattern. However, empir-

ical estimation of dispersal kernels using available

methods such as parentage analysis remains logisti-

cally overwhelming over larger spatial and temporal

scales. Therefore, it will probably be necessary to com-

bine direct methods of larval dispersal estimation, bio-

physical modelling and larval behaviour to provide

estimates of connectivity at scales relevant to spatial

management approaches in reef ecosystems (James

et al. 2002; Cowen et al. 2006). Combining these

approaches will also help to estimate the relative

importance of larval behaviour and oceanographic pro-

cesses in dispersal patterns.

Acknowledgements

We thank Chris McKelliget, Vanessa Messmer, Juan David

Arango, Jennifer Smith, Agnes Rouchon and the Motupore

Island Research Centre staff for assistance in the field. Nuria

Raventos assisted with otolith analyses. The ARC Centre of

Excellence for Coral Reef Studies, the National Science Founda-

tion (OCE 0424688), the Coral Reef Initiatives for the Pacific

(CRISP), the TOTAL Foundation, Populations Fractionees et In-

sulares (PPF EPHE) and the Connectivity Working Group of

the global University of Queensland—World Bank—Global

Environmental Facility project, Coral Reef Target Research and

Capacity Building for Management provided financial support.

Special thanks to Motupore Island Research Centre, Dik Knight

and Loloata Island Resort for logistic support.



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All authors participated in the fieldwork, P.S.A. performed

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Data accessibility

Microsatellite individual genotype per locus data:

DRYAD entry doi:10.5061/dryad.cq700.

Supporting information

Additional Supporting Information may be found in the online ver-

sion of this article.

Appendix S1 Details of DNA extraction and parentage analy-

sis.

Table S1 Microsatellite loci used in this study.

Table S2 Summary of analysis of variance.

Table S3 Representation of the seasonal connectivity matrices

for both inferred seasons.

Fig. S1 Relationship between total length and age at the time

of collection for Amphiprion polymnus in Bootless Bay area esti-

mated from Otoliths.

Fig. S2 Frequency of surface current direction measured at

each of five sites.

Fig. S3 Time series of v (A) and u (B) current components in

Bootless Bay in 2008 and 2009.

Please note: Wiley-Blackwell are not responsible for the content

or functionality of any supporting materials supplied by the

authors. Any queries (other than missing material) should be

directed to the corresponding author for the article.



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