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SYMPOSIUM
Reproductive Output and Duration of the Pelagic Larval StageDetermine Seascape-Wide Connectivity of Marine PopulationsEric A. Treml,1,* Jason J. Roberts,† Yi Chao,‡ Patrick N. Halpin,† Hugh P. Possingham* andCynthia Riginos*
*School of Biological Sciences, The University of Queensland, St. Lucia, Qld, 4072, Australia; †Marine Geospatial Ecology
Laboratory, Nicholas School of the Environment, Duke University, Durham, NC 27708, USA; ‡Remote Sensing Solutions,
Inc., Pasadena, CA 91107, USA
From the symposium ‘‘Dispersal of Marine Organisms’’ presented at the annual meeting of the Society for Integrative and
Comparative Biology, January 3–7, 2012 at Charleston, South Carolina.
1E-mail: [email protected]
Synopsis Connectivity among marine populations is critical for persistence of metapopulations, coping with climate
change, and determining the geographic distribution of species. The influence of pelagic larval duration (PLD) on con-
nectivity has been studied extensively, but relatively little is known about the influence of other biological parameters,
such as the survival and behavior of larvae, and the fecundity of adults, on population connectivity. Furthermore, the
interaction between the seascape (habitat structure and currents) and these biological parameters is unclear. We explore
these interactions using a biophysical model of larval dispersal across the Indo-Pacific. We describe an approach that
quantifies geographic patterns of connectivity from demographically relevant to evolutionarily significant levels across a
range of species. We predict that at least 95% of larval settlement occurs within 155 km of the source population and
within 13 days irrespective of the species’ life history, yet long-distant connections remain likely. Self-recruitment is
primarily driven by the local oceanography, larval mortality, and the larval precompetency period, whereas broad-scale
connectivity is strongly influenced by reproductive output (abundance and fecundity of adults) and the length of PLD.
The networks we have created are geographically explicit models of marine connectivity that define dispersal corridors,
barriers, and the emergent structure of marine populations. These models provide hypotheses for empirical testing.
Introduction
Quantifying the connectivity of populations is fun-
damental to understanding the dynamics of popula-
tions and metapopulations (Possingham and
Roughgarden 1990; Hanski 1998; Botsford et al.
2009b), the way in which species expand their
range (Lester et al. 2007), and how species might
cope with a changing climate (Hughes et al. 2003).
Connectivity also provides valuable insights into the
geographic distribution of species (Lester et al. 2007),
patterns in genetic divergence (Palumbi 1994; Jones
et al. 2009), and endemism (Meyer et al. 2005;
Paulay and Meyer 2006). As a result, understanding
connectivity and its geographic structure is critical
for effective management and conservation of
marine communities (Jones et al. 2007; Almany
et al. 2009; Planes et al. 2009).
Population connectivity explicitly refers to the
movement of individuals between and within sub-
populations (Cowen et al. 2007). The spatiotemporal
scale of individual (or larval) movement can range
from much less than a meter and minutes to thou-
sands of kilometers and many months depending on
the species’ life-history characteristics and the envi-
ronment (Kinlan and Gaines 2003). Characterizing
population connectivity for a given species and
region, therefore, must span much of this spatiotem-
poral continuum (Levin 2006). At one end of the
spectrum, ecological connectivity is concerned only
with those strong connections that consistently
Integrative and Comparative BiologyIntegrative and Comparative Biology, volume 52, number 4, pp. 525–537
doi:10.1093/icb/ics101 Society for Integrative and Comparative Biology
Advanced Access publication July 19, 2012
� The Author 2012. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved.
For permissions please email: [email protected].
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impact local demographics over short time-scales.
This ecological connectivity may be at levels exceed-
ing 10% of total recruitment to a single site (Cowen
and Sponaugle 2009), translating into hundreds to
many thousands of successful settlers recruiting per
year into a population (Cowen et al. 2006). On the
opposite end of this spectrum, evolutionary connec-
tivity (i.e., gene flow) can occur through rare or
weak connections over time-scales of many genera-
tions. Genetic differentiation is expected to arise at
connectivity levels far below a few individual immi-
grants per generation (Slatkin 1993). Therefore, iden-
tifying the magnitude of connectivity that is
meaningful or relevant is essential to understanding
the scaling and geographic structure of population
connectivity (Cowen et al. 2006), as groups of pop-
ulations may be genetically interconnected yet demo-
graphically isolated (Leis 2002; Swearer et al. 2002).
Here, we focus on tropical coral reef systems and
specifically define marine population connectivity as
the spatially explicit recruitment potential of larvae,
i.e., the likelihood that larvae released at a natal site
will survive and settle in a downstream habitat patch.
This is in contrast to realized connectivity, which
results from a complex combination of four pro-
cesses: reproduction at source patches; transport,
survival, and settlement of larvae; postsettlement sur-
vival; and the eventual reproduction of new migrants
at recipient patches (Hedgecock et al. 2007; Cowen
and Sponaugle 2009). Our analysis of potential pop-
ulation connectivity excluded the postsettlement
processes.
Quantifying the importance of biological parame-
ters and the role of the seascape in determining con-
nectivity has been challenging. Much of the recent
literature has focused on the role of the pelagic larval
duration (PLD) in determining realized dispersal dis-
tances and has resulted in mixed conclusions (Shanks
et al. 2003; Kinlan et al. 2005; Lester and Ruttenberg
2005; Bowen et al. 2006; Gaines et al. 2007; Shanks
2009; Weersing and Toonen 2009; Riginos et al.
2011). For example, PLD has been shown to be a
strong predictor of dispersal distances (Shanks
et al. 2003), yet a poor predictor of genetic similarity
(Weersing and Toonen 2009) and size of species’
ranges (Mora et al. 2012). The length of the larval
precompetency period influences local retention
within the source population (Black et al. 1991;
Paris and Cowen 2004) but has been unexplored
with respect to broad-scale connectivity. The
impact of larval mortality on connectivity can be
profound (Cowen et al. 2000), yet the empirical
data are lacking for most species and environmental
conditions (Graham et al. 2008; Connolly and Baird
2010), and the implications for realized connectivity
are unknown. Type of eggs and larvae are significant
predictors of connectivity in fishes but encapsulate
many correlated biological traits (Bradbury et al.
2008; Riginos et al. 2011). Swimming behavior
(Leis 2007) and larval sensory systems are also be-
lieved to play key roles in larval retention, yet the
influence of larval behavior has only been shown for
a few species and in a few locations (Paris and
Cowen 2004; Gerlach et al. 2007), with other species
showing no pattern (Gerlach et al. 2007). Finally, the
role of the seascape and currents in facilitating or
restricting connectivity is largely unknown, except
for several theoretical studies (Gaines et al. 2003;
Largier 2003) and unique seascapes (James et al.
2002; Baums et al. 2006; Banks et al. 2007; White
et al. 2010). Contributing to these mixed results are
several issues in the design of studies, including (1)
the use of simple Euclidean distance as a proxy for
population connectivity, instead of methods incorpo-
rating geography and oceanic currents (Mitarai et al.
2009; White et al. 2010); (2) bias toward certain spe-
cies (Bradbury et al. 2008) that results from a paucity
of biological data available on life-history parameters,
behavioral characteristics, and mortality; (3) a gen-
eral lack of spatially explicit data and reporting; and
(4) ambiguous definitions of connectivity often used
with respect to the ecological or evolutionary context
of the data.
Unfortunately, measuring and predicting connec-
tivity of marine populations is extremely difficult due
to the unknown and variable biology of larvae, the
complex physical environment, and the inherent dif-
ficulties in considering the range in spatial and tem-
poral scales covered by larval dispersal (Werner et al.
2007; Botsford et al. 2009a; Jones et al. 2009).
Although a diversity of approaches and techniques
have been used to provide information on connec-
tivity, ranging from following larvae in situ (Leis
et al. 2006), marking individual larvae (Jones et al.
2005), and otolith microchemistry (Swearer et al.
2003), to biophysical modeling (Cowen et al. 2000;
Mitarai et al. 2009; Kool et al. 2011), population
genetic approaches (Hedgecock et al. 2007), analyti-
cal approaches (Largier 2003; Botsford et al. 2009a),
and data reanalysis (Bradbury et al. 2008; Riginos
et al. 2011), no single methodology has been able
to provide a full quantitative and geographic picture
of realized population connectivity (Levin 2006;
Cowen and Sponaugle 2009; Jones et al. 2009). The
dynamics and spatial structure of this connectivity,
across scales, remains poorly understood (Siegel et al.
2008).
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Currently, biophysical modeling provides one of
the few methods that can accommodate the temporal
and spatial scales of marine population connectivity.
These approaches have been used at a variety of
scales as a way to understand the spatial patterns
in connectivity by integrating seascape data with a
species’ life-history characteristics (Sammarco and
Andrews 1988; Black and Moran 1991; Cowen
et al. 2000, 2006; Treml et al. 2008; Mitarai et al.
2009; White et al. 2010; Kool et al. 2011). Although
biophysical modeling can be costly, highly technical,
and difficult to verify, the approach has shed new
light on the processes and patterns of connectivity
for some species and geographies. This approach has
the capacity to help disentangle the mixed results
regarding the extent to which life-history parameters
and seascape characteristics determine realized pop-
ulation connectivity.
Here, we have developed a publically available bio-
physical model of marine dispersal that is capable of
creating spatially explicit predictions of population
connectivity across real seascapes in the Indo-
Pacific Ocean. [See Condie et al. (2005) for a similar
system specific to Australia]. This framework allows
one to model and evaluate the influence of life his-
tory and oceanography on marine population con-
nectivity across a region. Specifically, we: (1) produce
multispecies, geographically explicit models of
marine population connectivity for coral reefs
across the entire Indo-Pacific Ocean, (2) determine
the spatial and temporal structure of multispecies
marine population connectivity, and (3) quantify
the role and importance of key physical and biolog-
ical parameters in determining connectivity.
Materials and methods
A biophysical modeling approach was used to simu-
late larval dispersal among all coral reefs (1002 hab-
itat patches) across the Indo-Pacific Ocean revealing
the strength and structure of population connectiv-
ity. This dispersal model has three components: (1) a
gridded map of the seascape, including coral reefs
and coastline features; (2) biological parameters de-
scribing species’ characteristics of adults and larvae;
and (3) data on oceanic currents’ velocity derived
from an independent regional model. Our modeled
seascape included the marine biodiversity hotspot of
the Coral Triangle and the surrounding reefs of
Australia and Micronesia (Fig. 1), from 1008E to
1708E and 308N to 308S. The seascape features
were derived from high-resolution data on shorelines
(Wessel and Smith 1996) and coral reefs (Spalding
et al. 2001). The biological parameters used were as
follows: larval release time and periodicity, reproduc-
tive output per area of habitat (adult density and
fecundity), maximum PLD, precompetency period,
larval swimming and homing behavior during settle-
ment, and larval mortality (Table 1). These parame-
ters allow a wide range of species and of dispersal
strategies to be represented and their influence on
connectivity quantified. For this study, oceanic cur-
rents were obtained from the US Jet Propulsion
Laboratory’s Regional Ocean Modeling System
(ROMS). This circulation model solves the hydro-
static, primitive equations on a coordinate system
following the terrain, with approximately 12.5 km
horizontal resolution and 30 vertical layers for the
entire Pacific Ocean Basin (658N to 458S, 998E to
708W) using realistic coasts and bathymetry (Wang
and Chao 2004; Wang et al. 2005). ROMS was forced
with the NCEP/NCAR reanalysis air–sea fluxes
(Kalnay et al. 1996), including winds, temperature,
and solar radiation. Although validation is ongoing,
the ROMS data have been shown to reproduce sea-
sonal and interannual variability measured across the
equatorial Pacific (Wang and Chao 2004). As a
result, we believe these data adequately represent me-
soscale to broad-scale dynamics of the ocean across
the study region. A 3-year subset was chosen to rep-
resent the decadal-scale and seasonal variability:
1997, a strong El Nino year; 1999, a strong La
Nina year; and 2001, a neutral year. Data on surface
currents were averaged over 3 days and saved and
interpolated to the modeled seascape grid for use in
this analysis. A diffusivity term (50 m2 s�1) was in-
cluded in the dispersal model to represent oceanic
turbulence below the resolution of the ROMS circu-
lation data.
A quantitative assessment of the biological and
physical attributes affecting dispersal of marine
larvae was carried out across species. The simulations
modeled the 2D larval dispersal kernel directly, as a
‘‘cloud’’ of larvae, not from individually based tracks
of particles or larvae. This larval density, or proba-
bility, surface was advected, dispersed, and/or con-
centrated, dependent on the biophysical parameters.
The model moved the dispersal kernel through
the current velocity fields using an advection trans-
port algorithm (Smolarkiewicz 1983; Smolarkiewicz
and Margolin 1998; Smolarkiewicz 2006). This trans-
port scheme minimizes numerical diffusion, mini-
mizes computational requirements, and quantifies
the full dispersal kernel (including the evolutionarily
significant tails) that is often difficult to resolve using
particle-tracking methods.
A simulation of dispersal consisted of releasing a
cloud of larvae over a habitat patch and tracking the
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Fig. 1 The model domain extending from 1008E to 1708E, 308N to 308S. The inset highlights the model’s grid structure and resolution
around a representative coastal area (Cenderawasih Bay, Indonesia). Reef habitat cells are shown in black and land cells in gray.
Table 1 Biological parameters used to represent a species
Parameter Description Acceptable values
Initial larval release time Date and time dispersal starts Month, day, year, and time
Larval release site Initial location of larval release or habitat patch Spatial location represented in gridded habitat
map
Larval output per habitat area Adult density * fecundity Integer value, or relative amount (e.g., 1 unit)
Maximum pelagic larval duration Maximum length of the larval dispersal period Integer value (5, 15, 20, 25, 30, 40, 50, 60 days)
Larval precompetency period Period of time in which larvae cannot settle Gamma probability density function with shape
and scale parameters (e.g., scale¼ 10,
shape¼ .5 results in �5 day precompetency
period)
Daily settlement likelihood Probability that larvae will settle on suitable
habitat with time
Probability value (0.50, 0.70, 0.80, 0.90, 0.94)
Homing behavior Homing behavior used within sensory zone.
If TRUE, all larvae within a habitat cell will
settle, if FALSE, settlement will occur in
proportion to the amount of available habitat
TRUE/FALSE
Daily larval mortality Larval mortality probability over time while
planktonic
A probability function or daily probability
(e.g., 0.5, 0.4, 0.3, 0.2, 0.1, 0.0)
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cloud as it moved through the seascape. As this
cloud came in contact with suitable habitat, the
quantity of larvae settled was recorded. This dispersal
process was repeated for every habitat patch in the
seascape until all larval connections were quantified.
The total quantity of larvae that settled on every
habitat patch was recorded through time and saved
as the dispersal matrix. A number of connectivity
matrices were derived from this dispersal matrix.
The settlement matrix, S, represents the cumulative
number of larvae exchanged between all patches after
considering larval mortality. The connectivity proba-
bility matrix rescales S to the probability of larval
exchange between patches, and its diagonal repre-
sents local retention. The migration matrix quantifies
the proportion of settlers to each patch that came
from each source patch, and the diagonal of this
matrix representing self-recruitment. These matrices,
along with the location of the habitat, were used to
build connectivity networks showing the geographic
structure of the populations’ connectivity (Cowen
et al. 2006; Treml et al. 2008).
The migration rate threshold (MRT) represents a
critical recruitment or connectivity level used in de-
termining what connections are relevant (Cowen
et al. 2006). This limit may be in terms of the pro-
portion of successful settlers or a required number of
larval recruits and provides a method for separating
evolutionarily relevant connectivity from demo-
graphically significant levels (Cowen and Sponaugle
2009). For example, the MRT can be set relative to
the reproductive output of the species of interest (e.g.,
1 recruit out of 100,000 larvae released), or calcu-
lated to match adult mortality or to maintain popu-
lation persistence (Cowen et al. 2006, Supplementary
Table S4). The advection transport algorithm has a
high level of precision (e.g., 1 out of 109 larvae per
model cell) and allows connectivity to be estimated
along this entire gradient from ecologically significant
to evolutionarily significant levels.
A series of 1140 larval dispersal simulations were
completed for the entire Indo-Pacific Ocean to quan-
tify multispecies connectivity across a full range of
parameter values (Table 1) representing unique life
histories. The resultant connectivity matrices and
networks were used to quantify the geographic struc-
ture and scaling in population connectivity, and to
determine the importance of biological and physical
parameters to connectivity.
Marine population connectivity networks were il-
lustrated for three model species: (1) a coral with
high fecundity and abundance, no homing, and
weak swimming capabilities, a 60-day PLD with
a 7-day precompetency period, and an annual
spawning periodicity; (2) a damselfish with moderate
fecundity and abundance, strong swimming, seasonal
spawning periodicity, and a 20-day PLD with a 9-day
precompetency period; (3) an anemonefish with low
fecundity and abundance, strong swimming, homing
behavior, a 10-day pelagic stage with a 2-day pre-
competency period, and seasonal spawning. The con-
nectivity probability matrix was calculated and the
networks mapped to illustrate the geographic struc-
ture emerging from the process of larval dispersal.
These maps and matrices represent geographically
explicit hypotheses of the marine population connec-
tivity for these model species. See Supplementary
Material for technical details of the model.
Scaling in connectivity
The estimates of connectivity for the three model
species were used to illustrate the spatial and tempo-
ral context of marine population connectivity across
the Indo-Pacific by examining graphs of cumulative
larval settlement. Larval settlement was calculated by
summing the connections (for the entire Indo-Pacific
seascape) in the settlement matrix for each species
and across geographic distance and larval durations.
The cumulative larval settlement was plotted against
the downstream distance from the source population
(kilometers) and against time spent in the pelagic
larval stage (days).
Geographic distance is often used as a proxy for
population connectivity under the assumption that
populations that are geographically closer would
have stronger connectivity than would those farther
apart. To explore the validity of this assumption for
marine systems, we explored the relationship be-
tween geographic distance and the strength of con-
nection (probability) across the seascape for the three
model species. A tight inverse relationship (e.g., neg-
ative exponential) could justify the use of geographic
distance as a proxy for population connectivity.
Importance of physical and biological parameters
in local and seascape-wide connectivity
To explore the relative importance of the biological
and physical parameters in determining local to re-
gional population connectivity, we used a model sen-
sitivity approach based on variance decomposition
for multiple linear regression models (Groemping
2007). Using the relaimpo package in R (R
Development Core Team 2011), we calculated the
relative importance of biophysical parameters using
two complementary methodologies: the LMG
(Lindeman, Merenda, and Gold) approach based
on the sequential sums of squares and the
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Proportional Marginal Variance Decomposition
(PMVD) approach. Data were derived from the
series of 1140 matrices of population connectivity,
each representing the regional connectivity resulting
from a unique suite of life-history traits (e.g., PLD,
mortality, behavior) and time period. We first used
this approach to ask what biological and physical
parameters determine local connectivity on a
reef-patch level, in terms of percent self-recruitment
and percent local retention. Percent self-recruitment
was defined as the proportion of total larval settlers
to a habitat patch that originated from that patch.
Local retention was defined as the proportion of
larvae released from a source patch that settled
back to that patch. The biological parameters
include PLD, larval mortality, homing behavior,
precompetency period, likelihood of settlement, and
the MRT (which scales with reproductive output).
The physical parameters include the following: hab-
itat area, patch size, strength of local currents, sur-
rounding suitable habitat, and the number and area
of upstream sources. The patch-level analysis was
restricted to those ecologically significant connec-
tions that were made within 20 days and that con-
tributed more than 0.1% to the total settlement of
individual patches (961,920 observations of 1002 reef
patches over 960 dispersal simulations). We then
asked what biological parameters are most important
in determining seascape-wide connectivity. We used
seven metrics to quantify the seascape connectivity
for each simulation (response variables), all related
to the broad-scale connectedness: total number of
dispersal connections, median distance, upper quar-
tile distance, maximum distance, total settlement,
number of connected clusters, and the size of the
largest connected cluster. The seascape-wide analysis
was based on the full suite of biological parameters,
across all years and seasons. The model’s response
variables, regressors, and values are detailed in
Supplementary Table S1.
Results
Geographic structure of marine population
connectivity
After completing a series of dispersal simulations, the
resultant population connectivity networks were
mapped using the locations of the source and desti-
nation and the estimates of connectivity strength.
The geographic patterns of population connectivity
for the three model species are shown in Fig. 2. Each
dispersal network represents the connectivity poten-
tial for that species, in terms of the probability that
larvae released at a source patch will settle in a
Fig. 2 Population connectivity networks for the three model
species: (a) coral, (b) damselfish, and (c) anemonefish. Red nodes
represent coral reef habitat with the size scaled to the area of
habitat. Dispersal connections between reefs are represented by
gray and white links for strengths above and below the MRT,
respectively. The MRT for each species is as follows: 10�7 (coral),
10�5 (damselfish), and 10�2 (anemonefish).
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particular habitat patch based on the unique charac-
teristics of the seascape and of larval life histories.
We mapped the species’ connectivity networks with
MRTs scaled to the species-specific reproductive
output. Visualizing the connections made above
and below this MRT illustrates the geographic impli-
cations of focusing on evolutionary levels (below
MRT) that include all rare, long-distant, or weak
connections; to stronger ecologically significant
levels (above MRT) where only strong and/or persis-
tent connections are concerned.
Scaling in marine population connectivity
The spatial and temporal scaling of population con-
nectivity for the three model species was quantified
by plotting the cumulative proportional settlement of
all larvae with respect to distance from the source
population and number of days into the pelagic
larval phase. Proportional cumulative settlement
with respect to the time spent in the planktonic
larval phase is shown in Fig. 3a, revealing a temporal
aspect of population connectivity. Proportional set-
tlement of larvae was greatest for these three model
species during the period immediately following the
onset of larval competency. The 95% threshold for
cumulative larval settlement occurred within 13 days
for the coral, 9 days for the damselfish, and 4 days
for the anemonefish. As the duration of the larval
stage increased, larvae continued to settle in suitable
habitat, yet the proportion of successful settlement
later in the pelagic larval period was much lower.
The spatial scaling of marine connectivity across
the same species is shown in Fig. 3b. Most settlement
occurred within 155 km of the source patch for
all species (95% of the total cumulative settlement
occurred within 147, 155, and 28 km for the coral,
damselfish, and anemonefish, respectively). Although
dispersal connections were made at greater distances
(Fig. 2), the proportion of larvae settling at these
greater distances was relatively small.
To explore the relationship between strength of
connectivity and geographic distance, we combined
data across the three model species for all connec-
tions made between habitat patches. We fit the data
to a negative exponential curve to quantify the rela-
tionship between connectivity and distance. Across
these species, over 22% of the variance was explained
(Fig. 4). The relationship and fit were similar for the
two species of fish [damselfish: y¼ 0.947x(�1.091),
R2¼ 0.32; anemonefish: y¼ 1.023x(�1.003), R2
¼
0.32], but somewhat different for the coral species
[y¼ 0.051x(�0.985), R2¼ 0.10]. The fit weakens when
restricted to distances less than 200 km (e.g.,
R2¼ 0.16 for full dataset) with great variability in
connectivity strength and direction across these
shorter distances.
Importance of physical and biological parameters in
local and seascape-wide connectivity
A variance-decomposition approach (Groemping
2007) was used to explore the relative importance
of the biological and physical parameters in deter-
mining population connectivity across all simulations
and time periods. The importance analysis for
Fig. 3 Spatial and temporal scaling of larval settlement. For each of the three model species mapped in Fig. 2, the cumulative
settlement of larvae was calculated with respect to days into the larval period (a) and distance from the source population
(b). For each species, the connectivity probability was used to calculate the proportional settlement of larvae across the entire
seascape. Across species, the vast majority of larval settlement occurs within 155 km and 13 days from larval release, irrespective of
larval life-history characteristics. The 90% and 95% cumulative settlement thresholds per species are as follows: coral larvae settled
within 88 km in 11 days (90%) and 147 km in 13 days (95%), Damselfish larvae settled within 82 km in 8 days (90%) and 155 km in 9
days (95%), and anemonefish larvae settled within 21 km in 3 days (90%) and 28 km in 4 days (95%).
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patch-level self-recruitment and local retention is
shown in Table 2 and analysis of seascape-wide con-
nectivity is shown in Table 3. The results from the
importance analysis were consistent between the two
approaches, so only the LMG values are shown here;
the PMVD results are presented in Supplementary
Tables S2 and S3. Across the seascape and all dis-
persal realizations, self-recruitment was generally
high (mean of 53%). The relationship between
patch-level self-recruitment and the biological and
physical variables was relatively strong (R2¼ 0.48)
and determined primarily by local oceanography,
characteristics of upstream habitats (number and
size of larval sources), the size of the focal habitat
patch, and several larval characteristics (mortality,
length of the precompetency period, and PLD).
Local retention, or the proportion of larvae released
that settle in the natal patch, was consistently low
across all patches and simulations (mean of 5%).
The most important variable determining local reten-
tion (R2¼ 0.39) was the length of the precompetency
period, followed by the presence of homing behavior,
larval mortality, and the strength of local currents.
Across all seven seascape connectivity metrics, the
proportion of variance explained by the biological
parameters was high, ranging between 53 and 75%
(Table 3). Across all seascape-wide connectivity met-
rics, the most important biological parameters deter-
mining connectivity were as follows: (1) duration of
the pelagic larval stage, (2) the MRT, and (3) larval
mortality. Behavior and larval competency consis-
tently played a minor role in regional connectivity.
Discussion
This model provides an approach for generating geo-
graphically explicit larval-connectivity predictions in-
corporating empirical data on the reproductive
output, larval life histories, and the seascape (habitat
configuration and oceanic currents). We discovered
that broad-scale connectivity of marine populations
is determined by the reproductive output of the sub-
populations (MRT), larval mortality, and PLD,
whereas the magnitude of self-recruitment and local
retention is determined primarily by larval behavior,
the length of the precompetency period, local cur-
rents, and the number of populations upstream. This
approach enables the estimation of connectivity from
local and demographically relevant scales to evolu-
tionarily significant levels when rare or weak connec-
tions and persistent barriers may dominate the
spatial patterns of interest. This work moves
beyond identifying a general scale of population con-
nectivity by quantifying the geographic structure of
the full dispersal kernel (Palumbi 2004; Bradbury
et al. 2008), including the strength and direction of
potential connectivity.
Fig. 4 Relationship between geographic distance and the strength
of population connectivity across all model species. There is one
datum point per successful dispersal connection per species.
Values of connectivity strength are in terms of the proportion of
larvae released that survive and settle in downstream habitat.
Distance is in kilometers from the source patch and local re-
tention of larvae was excluded. Data fit to a negative exponential
curve, with best fit of y¼ 0.664x(�1.032), R2¼ 0.22.
Table 2 Relative importance analysis of local-scale population connectivity
Importance analysis for local-scale connectivity
Variable importance (normalized to sum to 100%)
Response R2(%) PLD Mortality Homing Precompetency Settle Habitat
area
Patch
area
Currents Area
in 50km
Sources Upstream
area
Self-recruitment 48.3 2.7 5.4 0.3 3.0 0.2 2.3 5.6 45.3 1.3 25.1 8.8
Local retention 39.2 0.4 14.4 20.8 32.6 8.8 2.9 6.2 13.7 – – –
Note. Results for self-recruitment at an ecologically relevant MRT (MRT40.001), and local retention, showing the proportion of variance
explained by the model (R2), and the normalized importance values for each parameter, based on the LMG approach. The biological parameters
are defined in text and in Table 1.
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Essential criteria for evaluating all estimates of
connectivity include understanding the assumptions
of the model (e.g., parameters, process, resolution)
and aligning the inherent spatial and temporal scale
of the data with the intended uses. For example, al-
though dispersal of a few larvae may be widespread,
it is often undesirable to consider such rare events,
especially when one is concerned with only ecologi-
cally significant processes or management decisions.
As a result, it is critical to scale these connectivity
estimates appropriately to match the species’ charac-
teristics when determining realistic or demographi-
cally significant settlement rates (Cowen et al. 2006).
Although this scaling may vary greatly among spe-
cies, critical annual settlement rates relevant to de-
mographic processes may be on the order of one to
five successful settlers per reproductive adult per year
into the receiving population (Cowen et al. 2006).
These settlement rates translate to MRTs (critical
proportion of larvae released) between 0.01 and
0.00001, depending on the species’ life histories (ex-
amples in Supplementary Table S4). Identifying these
thresholds provides a means of interpreting estimates
of connectivity in the context of the species of inter-
est and of focus of the study (ecological versus
evolutionary).
Importance of physical and biological parameters
in local and seascape-wide connectivity
The predictions presented here are generally consis-
tent with many theoretical studies (Botsford et al.
2001; Siegel et al. 2003; Hastings and Botsford
2006), other broad-scale biophysical models of con-
nectivity (Cowen et al. 2006; Kool et al. 2011), and a
number of empirical studies exploring the impor-
tance of life-history parameters in relation to
potential for dispersal (Bradbury et al. 2008;
Riginos et al. 2011). Our estimates of
self-recruitment, with a mean of 53% across all sim-
ulations and locations, are supported by field studies
across a number of species and locations (e.g., Jones
et al. 1999; Almany et al. 2009). Although highly
variable, the magnitude of self-recruitment is depen-
dent on the characteristics of the local seascape (cur-
rents, habitat topology), and on larval mortality, and
the larval precompetency period. The mean local
larval retention is 5% and predominantly determined
by larval biology (precompetency period, behavior,
mortality) and local oceanography. It is important
to realize that these low values of larval retention
translate to many hundreds of larvae when the pro-
portions are scaled to the species’ reproductive
output (Supplementary Table S4).
Although the length of the pelagic dispersal stage
appears to play a minor role in local-scale connec-
tivity (Table 2), it is much more important in deter-
mining broad-scale connectivity across the seascape
(PLD is of primary importance in 6/7 metrics). In
general, the longer larvae can remain in the plank-
ton, the more opportunities there are for establishing
long-distant connections, leading to a more con-
nected seascape. This is not to imply that PLD de-
termines the connectivity distance per se, but that it
contributes to the development of broad-scale pat-
terns in connectivity. Although the correlation be-
tween PLD and distance has been a recent focus in
the literature (Shanks et al. 2003; Shanks 2009;
Weersing and Toonen 2009), we demonstrate that
it may be a better predictor of evolutionary connec-
tions (Supplementary Fig. S1a) and a rather weak
predictor of ecological connectivity (Supplementary
Fig. S1b). These results, combined with the strong
Table 3 Relative importance analysis of seascape-wide population connectivity
Importance analysis for seascape-wide connectivity
Variable importance (normalized to sum to 100%)
Response R2(%) MRT PLD Mortality Homing Precompetency Settle
Total connections 66.1 37.8 42. 3 13.8 ns ns ns
Median distance (km) 75.0 40.5 39.2 14.8 3.6 ns ns
Upper quartile (km) 73.7 35.5 44.4 14.3 ns ns ns
Maximum distance (km) 66.9 24.1 57.1 11.0 ns ns ns
Total settlement 58.2 ns 13.8 63.3 15.3 2.4 ns
Number of clusters 53.5 66.2 8.5 12.5 8.2 0.9 3.7
Size of largest cluster 70.8 50.9 30.9 11.0 4.8 0.3 ns
Note. Results are shown for all connectivity metrics, showing the proportion of variance explained by the model (R2), and the normalized
importance values for each regressor variable. Nonsignificant (ns) parameters from the regression model were removed from this table.
Determinants of marine population connectivity 533
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geographic structuring, may help explain the mixed
patterns found between PLD and dispersal distances
in the literature (Weersing and Toonen 2009).
Scaling in connectivity
Here, we reveal the full geographic structure of po-
tential connectivity of marine populations across the
Indo-Pacific Ocean (Fig. 2) and find the spatial scale
to be on the order of 150 km. These patterns support
previous estimates and also add a temporal context
to connectivity (Fig. 3a), revealing that most larval
settlement occurs with the first 13 days of the pelagic
larval stage, dependent on the characteristics of larval
competency, irrespective of PLD, larval mortality,
larval behavior, and other life-history characteristics.
These spatial and temporal predictions warrant ad-
ditional work and validation to determine whether
(or where) these patterns hold true across species.
Interestingly, (1) the lack of a consistently strong
correlation between connectivity strength and geo-
graphic distance across species (Fig. 4) and (2) the
strong asymmetries in connectivity, highlight the
need to reconsider using Euclidean distance as a
proxy for population connectivity in related research.
For example, genetic models should be able to incor-
porate isolation by ‘‘dispersal distance’’ as an alter-
native to geographic distance, and marine managers
could consider using these, or similar, ecologically
meaningful measures of distance (and direction) to
replace spatial proximity or adjacency in decision
software (Beger et al. 2010). These data are now
being made available through our efforts, and other
biophysical models.
Geographic structure of connectivity
The geographic structure revealed in the dispersal
networks (Fig. 2) emerged from integrating repro-
ductive output estimates, larval life-history data, hab-
itat characteristics, and oceanic current data. The
connectivity matrices and networks enable unique
insights into the potential connectivity at levels rele-
vant to ecologists, marine managers, biogeographers,
and evolutionary ecologists. Clearly, the geographic
setting and physical oceanography are important in
shaping local to broad-scale patterns in connectivity.
This broad geographic variability reinforces the need
to be cautious when extrapolating connectivity esti-
mates from specific study sites or species to other
geographies as these patterns are highly dependent
on local hydrodynamics and species’ characteristics.
At broad scales, the predicted geographic patterns
in multispecies connectivity (dispersal corridors and
barriers) support those reported in other studies,
ranging from Indo-Pacific biophysical models (Kool
et al. 2011), population genetic studies (Barber et al.
2002), and comparative phylogeographic studies of
the area (Carpenter et al. 2011). Our model repro-
duces dominant dispersal corridors along major oce-
anic currents such as through the Solomon Islands,
along the Great Barrier Reef, through Micronesia,
and along the Indonesian throughflow (Nuryanto
and Kochzius 2009). At the same time, our model
predicts several additional potential dispersal corri-
dors, for example, one connecting Indonesia (and
Timor Leste) with the Kimberley Coast of Australia
and a strong corridor across the South China Sea
between Vietnam and the Spratly Islands. The loca-
tion of dispersal barriers (often semi-permeable) is
also readily apparent in the estimates of connectivity.
Although several of these multispecies barriers are
strongly supported by genetics data such as the
break between Papua New Guinea and reefs of
Papua, Indonesia (Barber et al. 2002; Nuryanto and
Kochzius 2009) and the isolation of reefs in Western
Australia (Underwood et al. 2009), quantitative eval-
uation of the location and strength of these, and
other, barriers requires a more detailed and
species-specific analysis.
Model caveats
There are several shortcomings of this model which
should be considered in future biophysical modeling.
First, increasing the spatial and temporal resolution
(and accuracy) of the modeled seascape and oceanic
currents would be important for improving estimates
of fine-scale population connectivity. The availability
of higher resolution data for oceans and coral reefs is
improving quickly. Second, including more dynamic
and spatially explicit functions of larval mortality
and postsettlement mortality into the model may
lead to more appropriate estimates of connectivity.
Third, vertical swimming needs to be implemented
for a more robust evaluation of its importance to
seascape-wide population connectivity. Fourth, im-
proving our understanding of species-specific larval
life-history characteristics through increased field-
based and laboratory research is critical. Currently,
our empirical data of PLD, larval behavior, and larval
mortality, for example, are from very few species,
individuals, and locations, and virtually unknown
for the vast majority of cases. Finally, for a robust
and quantitative test of predictions of connectivity, a
more detailed analysis is required. This would in-
volve aligning the assumptions of the empirical
data (e.g., genetic, chemical tags, otolith metrics)
with the assumptions and limitations of the
534 E. A. Treml et al.
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population connectivity model, enabling the incon-
sistencies between the data and the model to be ex-
posed, thereby highlighting regions where the
connectivity model is wrong, where the empirical
data may be inconsistent, or where other processes
not presently considered may be influencing the ob-
served patterns.
Conclusions
Although perfect information does not exist for any
species or parameter, we are getting closer to identi-
fying the important parameters leading to local and
broad-scale marine population connectivity. These
location-specific predictions can be used to develop
sampling strategies to investigate species-specific dis-
persal patterns and quantify multi-species corridors
and barriers. Once validated, network analysis may
help reveal critical island stepping-stones, quantify
barriers, and highlight the emergent geographic
structure of multispecies connectivity (Treml et al.
2008; Kininmonth et al. 2010).
If scaled properly to the species of interest, these
estimates of connectivity may help inform conserva-
tion priorities from local to regional scales (Beger
et al. 2010). At a minimum, they highlight the role
of local protection in maintaining local to regional
connectivity through decreasing adult mortality and
increasing fecundity, thereby enhancing a local pop-
ulation’s reproductive output. Simply accommodat-
ing the strong geographic structure of marine
connectivity will assist in moving beyond simple
‘‘scales of connectivity’’ to more geographically ex-
plicit ‘‘ocean neighborhoods’’ (Palumbi 2004), pro-
ducing a framework that is more ecologically
meaningful, quantitative and testable, and ultimately
more informative (Treml and Halpin, in press).
The population connectivity code used in this
analysis is freely available through the Marine
Geospatial Ecology Tools software (Roberts et al.
2010). The model may be parameterized for other
regions or spatial scales to help illuminate the com-
plex roles of geography, oceanography, and life his-
tories in conferring population connectivity upon
marine species.
Acknowledgments
We thank Scott Burgess, Libby Liggins, and Jude
Keyse for helpful comments on earlier drafts.
Yarema Reshitnyk and Shaun Coutts provided gen-
erous analysis assistance. Comments from Harold
Heatwole and two anonymous reviewers greatly im-
proved this article.
Funding
Funding for this work was provided by the
Australian Research Council (DP0878306 to CR
and HPP; Federation Fellowship to HPP), the
World Wildlife Fund (Kathryn Fuller Postdoctoral
Research Fellowship to EAT), and the National
Science Foundation (NSF OISE�0730256 to PNH).
Participation in the Dispersal of Marine Organisms
symposium at the Society for Integrative and
Comparative Biology meeting was supported by the
American Microscopical Society, SICB Divisions of
Evolutionary Developmental Biology, Ecology &
Evolution, and Invertebrate Zoology, and by NSF
Grant IOS-1148884 to Sara M. Lindsay.
Supplementary Data
Supplementary Data are available at ICB online.
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