# Identifying barriers to gene flow and hierarchical conservation units from seascape genomics: a modelling framework applied to a marine predator

Germain Boussarie, Paolo Momigliano, William D. Robbins, Lucas Bonnin, Jean-Francois Cornu, Cécile Fauvelot, Jeremy J. Kiszka, Stéphanie Manel, David Mouillot, Laurent Vigliola

<figure><img src="image_1.png" /></figure>

## To cite this version:

Germain Boussarie, Paolo Momigliano, William D. Robbins, Lucas Bonnin, Jean-Francois Cornu, et al.. Identifying barriers to gene flow and hierarchical conservation units from seascape genomics: a modelling framework applied to a marine predator. Ecography, 2022, pp.e06158. 10.1111/ecog.06158. hal-03669632

HAL Id: hal-03669632

https://hal.umontpellier.fr/hal-03669632

Submitted on 17 May 2022

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Research

# Identifying barriers to gene flow and hierarchical conservation units from seascape genomics: a modelling framework applied to a marine predator

Germain Boussarie, Paolo Momigliano, William D. Robbins, Lucas Bonnin, Jean-François Cornu, Cécile Fauvelot, Jeremy J. Kiszka, Stéphanie Manel, David Mouillot* and Laurent Vigliola*

G. Boussarie (https://orcid.org/0000-0002-0809-1555) ✉ (germain.boussarie@gmail.com), L. Bonnin (https://orcid.org/0000-0002-5674-1911) and L. Vigliola (https://orcid.org/0000-0003-4715-7470), UMR ENTROPIE (IRD, UR, UNC, IFREMER, CNRS), Centre IRD de Nouméa, Nouméa, Nouvelle-Calédonie, France. – GB, LB, J.-F. Cornu and D. Mouillot (https://orcid.org/0000-0003-0402-2605), UMR MARBEC (CNRS, IRD, IFREMER, UM), Univ. de Montpellier, Montpellier, France. – P. Momigliano (https://orcid.org/0000-0001-6390-6094), Organismal and Evolutionary Biology Research Programme, Univ. of Helsinki, Helsinki, Finland and Dept of Biochemistry, Genetics, and Immunology, Univ. de Vigo, Vigo, Spain. – W. D. Robbins (https://orcid.org/0000-0003-1106-1733), Wildlife Marine, Perth, WA, Australia and Dept of Biodiversity, Conservation and Attractions, Marine Science Program, Biodiversity and Conservation Science, Kensington, WA, Australia. – C. Fauvelot (https://orcid.org/0000-0003-0806-1222), UMR ENTROPIE (IRD, UR, UNC, IFREMER, CNRS), Laboratoire d'Océanographie de Villefranche, Villefranche-sur-Mer, France and UMR LOV (Sorbonne Université, CNRS), Villefranche-sur-Mer, France. – J. J. Kiszka (https://orcid.org/0000-0003-1095-8979), Inst. of Environment, Dept of Biological Sciences, Florida International Univ, Miami, FL, USA. – S. Manel (https://orcid.org/0000-0001-8902-6052), CEFE (Univ. Montpellier, CNRS, EPHE-PSL Univ., IRD), Montpellier, France.

Ecography

2022: e06158

doi: 10.1111/ecog.06158

Subject Editor: Malin Pinsky

Editor-in-Chief: Miguel Araújo

Accepted 2 March 2022

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

www.ecography.org

The ongoing decline of large marine vertebrates must be urgently mitigated, partic-
ularly under increasing levels of climate change and other anthropogenic pressures.
However, characterizing the connectivity among populations remains one of the great-
est challenges for the effective conservation of an increasing number of endangered
species. Achieving conservation targets requires an understanding of which seascape
features influence dispersal and subsequent genetic structure. This is particularly chal-
lenging for adult-disperser species, and when distribution-wide sampling is difficult.
Here, we developed a two-step modelling framework to investigate how seascape fea-
tures drive the genetic connectivity of marine species without larval dispersal, to better
guide the design of marine protected area networks and corridors. We applied this
framework to the endangered grey reef shark, *Carcharhinus amblyrhynchos*, a reef-asso-
ciated shark distributed across the tropical Indo-Pacific. In the first step, we developed
a seascape genomic approach based on isolation-by-resistance models involving circuit
theory applied to 515 shark samples, genotyped for 4991 nuclear single-nucleotide
polymorphisms. We show that deep oceanic areas act as strong barriers to dispersal,
while proximity to habitat facilitates dispersal. In the second step, we predicted the
resulting genetic differentiation across the entire distribution range of the species, pro-
viding both local and global-scale conservation units for future management guidance.
We found that grey reef shark populations are more fragmented than expected for such
a mobile species, raising concerns about the resilience of isolated populations under
high anthropogenic pressures. We recommend the use of this framework to identify

© 2022 The Authors. Ecography published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos

*Contributed equally.

This is an open access article under the terms of the Creative Commons
Attribution License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited.
barriers to gene flow and to help in the delineation of conservation units at different scales, together with its integration across
multiple species when considering marine spatial planning.

Keywords: circuit theory, conservation, gene flow, isolation-by-resistance, reef sharks, seascape genomics

**Introduction**

Marine ecosystems across the globe are under increasing pressure due to habitat fragmentation, overexploitation and climate change (McCauley et al. 2015, Young et al. 2016, Albouy et al. 2020). Due to their conservative life-history traits of low reproductive rates, high longevity and slow growth, large marine vertebrates such as marine mammals, seabirds and elasmobranchs are particularly vulnerable to human-induced mortalities: their rate of extinction is indeed higher due to fisheries (including bycatch), habitat disturbance and pollution (Estes et al. 2016, McClenachan et al. 2016, MacNeil et al. 2020, Yan et al. 2021). Their effective protection is an unprecedented challenge that must be addressed in the coming decades (Duarte et al. 2020, Sala et al. 2021).

The implementation of effective conservation measures for large marine vertebrates requires that space use by these potentially highly mobile species is taken into account (Harrison et al. 2018, Jacoby et al. 2020), and to better understand the factors driving connectivity among populations in increasingly fragmented seascapes (McRae and Beier 2007, Balbar and Metaxas 2019). Indeed, through the exchange of genes, connectivity plays a vital role in maintaining thriving natural populations (Cowen and Sponaugle 2009, Jangjoo et al. 2016, Dunn et al. 2019), ensuring biodiversity conservation and fisheries sustainability (Gaines et al. 2010, Edgar et al. 2014, Krueck et al. 2017, Álvarez-Noriega et al. 2020).

Population connectivity of most marine animals depends on a dispersive planktonic larval phase. This life-history stage can last from days to months, and larval dispersal can be modelled using biophysical or genetic frameworks (Bryan-Brown et al. 2017, Manel et al. 2019, Harrison et al. 2020). Genetic connectivity, a measure of the degree to which gene flow affects evolutionary processes among populations, has been widely studied among larval dispersers (Benestan et al. 2021), since gene flow plays a key role in maintaining genetic diversity and healthy populations able to adapt to a changing environment (Slatkin 1987, Lowe and Allendorf 2010, Song et al. 2013, Goetze et al. 2021).

In contrast, investigating the population or genetic con-
nectivity of species whose dispersal is realized by adults is
more challenging as adult connectivity cannot be modeled
using the same oceanographic models (Pazmiño et al. 2017,
Boissin et al. 2019, Pirog et al. 2019). Yet, the question of
which factors drive nektonic adult connectivity has received
less attention, while its knowledge is just as important for
those species relying on dispersal of larger individuals to
maintain connectivity (Momigliano et al. 2015). Indeed,
little is known about which habitat, environmental and

biogeographic features drive the connectivity among popu-
lations for adult dispersers across generations. Investigating
these factors can provide key information to properly design
corridors and networks of marine protected areas (MPAs)
(Almany et al. 2009, Magris et al. 2014, 2018, Balbar and
Metaxas 2019, Jacoby et al. 2020).

One approach to investigate the factors shaping genetic connectivity among populations and identify subsequent barriers to gene flow is the use of isolation-by-resistance (IBR) models (McRae 2006). These models, popular in terrestrial ecology (Dickson et al. 2019), remain largely overlooked in the marine realm (Selkoe et al. 2016). Unlike isolation-by-distance (IBD) models, IBR models incorporate the effects of heterogeneous habitats on gene flow, thus they can account for the effect of seascape features on the genetic differentiation among populations and also make predictions for sites that have not been sampled (McRae and Beier 2007). A combination of large empirical genetic datasets and modelled genetic differentiation could therefore be used to delineate conservation units (groupings of a species which contain sufficient biodiversity for persistence through subsequent generations) throughout the entire range of a species.

Separating a species' range into conservation units can
indeed identify key areas for dispersal along with populations
under potential threats (Allendorf et al. 2010, Funk et al.
2012, Barbosa et al. 2018). Although the definition of con-
servation units has been debated (Waples and Gaggiotti
2006, Palsbøll et al. 2007, Lowe and Allendorf 2010), the
hierarchical delineation of population subdivisions, based on
genetic connectivity, can provide significant clues for both
local and global management strategies (Dilts et al. 2016,
Barbosa et al. 2018). Surprisingly, the degree of habitat
fragmentation and the subsequent delineation of conserva-
tion units are poorly investigated in threatened and mobile
marine species. Advances in genetic tools and computational
power (Schadt et al. 2010, Funk et al. 2012, Balkenhol et al.
2017, DiBattista et al. 2017, Barbosa et al. 2018) now permit
the development of models predicting how seascape features
shape connectivity over a large scale at high spatial resolution
(Leonard et al. 2017).

With no larval stage, the adult dispersion of sharks is of
high importance (Hirschfeld et al. 2021), and shark conser-
vation remains challenging with many species showing exten-
sive geographic ranges spanning several countries (Dulvy et al.
2017, 2021, Pacoureau et al. 2021). Additionally, most shark
species are highly vulnerable to fishing pressure given their
life history traits, e.g. slow growth, late sexual maturity and
low fecundity (Dulvy et al. 2014). Over one-third of chon-
drichthyans are threatened with extinction (International
Union for Conservation of Nature Red List; Dulvy et al.
2021), and some have undergone declines greater than 70% in abundance in the last few decades (Roff et al. 2018, Pacoureau et al. 2021). The size of MPAs is known to be a major driver of their protection effectiveness (Juhel et al. 2017, 2019, Dwyer et al. 2020, Bonnin et al. 2021), however these areas often only encompass a small proportion of each population's distribution.

Here, we focused on the grey reef shark *Carcharhinus amblyrhynchos* as a model species to explore the potential offered by seascape genetics for the characterization and prediction of genetic connectivity of adult dispersers, the identification of barriers and resistance to dispersal, and possible implications for the spatial delineation of conservation units for management purposes. This species has strongly declined in non-protected reefs close to human habitation (Robbins et al. 2006, Juhel et al. 2017, 2019, Ruppert et al. 2017) and is now listed as *Endangered* on the IUCN Red List. It shows a high level of residency and small home range but adults can perform long-range movements (> 700 km) along reefs and across oceanic waters (Espinoza et al. 2015a, White et al. 2017, Bonnin et al. 2019, 2021).

We followed a two-step approach to investigate the genetic connectivity of this near-threatening coral reef-associated predator and delineate hierarchical conservation units based on estimates of genetic connectivity. Firstly, we employed IBR modelling and electrical circuit theory (CT) (McRae 2006, McRae et al. 2008) to determine how seascape features shape the genetic differentiation of this species, using an extensive genetic dataset of over 500 sharks collected in 17 locations across the Indian and Pacific oceans. We then used this modelling framework to delineate hierarchical conservation units across the whole species distribution range (Indo-Pacific), to better inform conservation strategies and identify the most vulnerable populations.

# Material and methods

## Shark sampling and locations

We collected fin clips from 515 individual grey reef sharks across 17 locations in the Indian and Pacific Oceans (Fig. 1). Samples from the Indian Ocean (n=99), Indonesia (n=24) and the Great Barrier Reef (n=48) were already described and genotyped in a published study (Momigliano et al. 2017). The remaining samples (n=344) were collected in the New Caledonian Archipelago, between June 2015 and November 2016, using barbless circle hooks for mouthhooking and easy release after sampling. Sharks were caught on single lines to reduce bycatch and minimize handling and processing times.

## Population genomics

We extracted DNA from fin clips using DNEasy Blood and Tissue kit (QIAGEN) for the 344 samples from New Caledonia. Each DNA solution was adjusted, after quality

control, to 12–15 µl at 50 ng µl⁻¹ prior to DNA sequencing at Diversity Arrays Technology Pty. Ltd (Canberra, Australia), using DArTseq protocol (Sansaloni et al. 2011). Post-extraction laboratory protocols and SNP calling and filtering procedures used were the same as described in Momigliano et al. (2017), except for filtration to remove loci with minor allele frequencies, where we filtered SNPs for MAF > 0.02 instead of 0.05, as sampling was extended to numerous additional locations.

After combining these SNPs with those of a previous study (Momigliano et al. 2017), outlier tests were used to filter loci for which genetic differentiation (FST) is higher than expected under neutral processes only. We applied a combination of two methods to identify and exclude from further analyses these loci potentially under selection; OutFLANK (Whitlock and Lotterhos 2015) and FLK, i.e. extensions of the Lewontin–Krakauer test that accounts for population coancestry (Bonhomme et al. 2010). To improve the detection of outlier loci, we removed the Cocos and Chagos samples to perform the OutFLANK and FLK tests. Those two sampling populations are very remote and show great genetic differentiation compared to the other populations.

## Population structure

We applied a Bayesian unsupervised clustering method (fastSTRUCTURE) to investigate genetic structure at neutral loci (Raj et al. 2014). fastSTRUCTURE implements an efficient algorithm for approximate inference of the admixture model from STRUCTURE (Pritchard et al. 2000). We ran fastSTRUCTURE with simple and logistic priors, at multiple numbers of clusters, K ranging from 1 to 10.

We also carried out discriminant analysis of principal components (DAPC) using the R package adegenet, with sampling location of each individual used as prior information (Jombart et al. 2010) to investigate patterns of genetic structure at neutral loci. The number of principal components (PCs) to retain for DAPC analyses was determined by cross-validation using a training set of 80% of the data and we therefore retained the number of PCs for which the obtained mean square error was the lowest.

## Isolation-by-distance and isolation-by-resistance models

The relationship between genetic distance at neutral SNP loci (FST) and geographic distance, i.e. isolation-by-distance (IBD), as well as isolation-by-resistance (IBR) patterns, were investigated using multiple regression on distance matrices (MRM; Lichstein 2007; Fig. 2 – step 1). First, pairwise genetic distances between all locations (Weir and Cockerham FST, Weir and Cockerham 1984) were calculated using the R package diveRsity (Keenan et al. 2013). Pairwise shortest geographic distances by sea between all locations were measured with the R package marmap (Pante and Simon-Bouhet 2013). IBR was then investigated, to check for further effects of some seascape features (bathymetry, distance-to-habitat)
<figure><img src="image_5.png" /><figcaption>Figure 1. Maps of the 17 sampling locations where 515 grey reef shark samples were collected. (A) Global sampling locations (B) Detailed sampling at the scale of the New Caledonian archipelago (EEZ outlined in grey). The number of individuals sampled for SNP analysis from each location is in brackets.</figcaption></figure>

on the dispersal of grey reef shark populations. IBR models assume a linear relationship between pairwise genetic distance and pairwise resistance distance, a metric both taking into account geographic distance and landscape features between locations (McRae and Beier 2007).

Furthermore, we used IBR models implementing electrical circuit theory (CT) (McRae and Beier 2007, McRae et al. 2008), and tested their performance in explaining genetic differentiation across our sampling locations of grey reef sharks across the Indo-Pacific. This allowed us to explore different biological hypotheses about gene dispersal for this adult-disperser species. Methods based on CT allow the calculation of a resistance distance between each pair of sampled locations by simultaneously considering all possible pathways connecting these locations, and ascribing resistance values to each pathway.

## Resistance maps

Resistance maps were generated in Python (gdal) with 10 km cells, based on different hypotheses. The spatial resolution of 10 km was arbitrarily chosen to allow a reasonable computation time, and because grey reef sharks have shown a high residency and relatively small home range of the same order of magnitude (Espinoza et al. 2015a, White et al. 2017, Bonnin et al. 2021). First, we produced a map with homogeneous resistance values across every cell to be used in a 'CT null model', corresponding to CT in a homogeneous seascape. Then, bathymetry (GEBCO, gebco.net) and distance-to-habitat resistance maps were drawn separately and in combination as seascape features potentially driving gene flow. We included coral reefs (<www.data.unep-wcmc.org>; Spalding et al. 2001) and island nearshores as suitable habitats
STEP 1 : OPTIMIZATION OF ISOLATION-BY-RESISTANCE MODELS

<figure><img src="image_6.png" /></figure>

STEP 2 : SIMULATIONS AND CLUSTERING

<figure><img src="image_7.png" /></figure>

Figure 2. Conceptual framework of the two-step procedure of isolation-by-resistance models to produce conservation units based on simulated locations. In step 1, numerous resistance maps are produced via the parametrization step described in the Supporting information, then used as input in GFLOW, resulting in one matrix of resistance distance per resistance map. For each map corresponding to one parameter set, resistance values are then linked by dyad to genetic distance values (linearized FST), and model robustness evaluated with the regression coefficient (R²) obtained from a multiple regression on distance matrices (MRM). In step 2, GFLOW is run on simulated locations for grey reef sharks (Supporting information) with the resistance map corresponding to the best IBR model selected in step 1. The output, a matrix of resistance distance between all simulated locations, is then converted to genetic distances based on the best IBR linear model. The subsequent matrix of simulated genetic distance is then submitted to a density peaks clustering method described in Rodriguez and Laio (2014).
for grey reef sharks (<www.earthworks.stanford.edu/catalog/harvard-glb-volc>). Distance-to-habitat maps were calculated with and without the inclusion of shallow seamounts as suitable habitats (Yesson et al. 2011), selected at a threshold of 280 m corresponding to the reported preferential depth for this species (Last and Stevens 2009). Different types of relationships between seascape features and resistance were then explored (Supporting information). Indeed, the grey reef shark being a shallow reef-associated species, we hypothesized that resistance to gene flow was likely to increase with distance-to-habitat and depth, and thus tested multiple values of parameters for linear, logarithmic and exponential relationships with minimum and maximum thresholds. Different maximum resistance values were also used to calculate the set of resistance maps to run through CT. Full details on resistance maps parametrization are reported in the Supporting information.

## Pairwise distances using circuit theory

As shown in Fig. 2 – step 1, the set of obtained resistance maps was then used as input for IBR models using GFLOW (Leonard et al. 2017), an optimized version of the Circuitscape software, estimating pairwise resistance distances between sampling locations using circuit theory (McRae 2006, McRae and Beier 2007). This batch of obtained pairwise resistance distances between all locations and for every resistance map was then correlated to pairwise genetic distances (pairwise F<sub>ST</sub> estimates linearized using the formula F<sub>ST</sub>/(1 - F<sub>ST</sub>); Fig. 2). As a result, in addition to the IBD and CT null models, one IBR model was obtained for each resistance map previously obtained.

Models were compared and the best model was chosen by looking at a combination of R² values from multiple regression matrices (MRMs), Mantel tests (Mantel 1967) and AICc (R package AICcmodavg). Because of the high correlation between bathymetry and distance-to-habitat, tests like partial Mantel (Smouse et al. 1986) and MRMs ran independently on the two variables are not optimal (Legendre and Fortin 2010, Peterman and Pope 2021). Univariate models were optimized on one hand, representing the best correlations between seascape features independently and genetic distances. On the other hand, multivariate models accounting for both bathymetry and distance-to-habitat cumulated in single resistance maps were also tested with multiple combinations of maximum resistance, relationship shapes and associated parameters. More details on the general framework, parametrization and model optimization are available in Fig. 2 and the Supporting information.

## Simulations

Locations with possible presence of grey reef sharks were identified following the same criteria described before as suitable habitat (excluding seamounts) and subsampled at different scales (Froese et al. 2010). The first scale was at the

extent of our sampling locations, from the Cocos Keeling Islands to the Eastern New Caledonian volcanic islands. Locations with at least 50 km separation were randomly chosen based on a Matérn process maximizing the number of chosen points (Kiderlen and Hörig 2013). This process randomly selects points of suitable habitat, while incrementally preventing adjacent habitat from being selected by the algorithm at a given distance threshold. As a result, the number of selected points varies each time such process is applied. We applied it 1000 times and selected the result with the highest number of points selected. Next, at the scale of the entire distribution range of the species (tropical Indo-Pacific), including hypothetical presences only based on habitat suitability (ranging between 32°E–130°W longitude and 30°S–30°N latitude), further locations separated by a distance of at least 100 km were randomly chosen using the same method. Such distances between locations were chosen to keep computation time reasonable. The extent to which locations were randomly chosen was narrower than the extent of the resistance maps used as input from GFLOW. Indeed, the artificial boundaries created by the edges of a map can have a non-negligible impact on the calculations of landscape resistance to gene flow (Koen et al. 2010).

The best CT model obtained with our optimization framework, and thus the resistance map best explaining genetic differentiation between the sampling locations, was further used to run GFLOW on the randomly chosen locations (Fig. 2 – step 2). It allowed computation of pairwise resistance distances between all possible locations for grey reef shark presence previously selected with the Matérn process. Such pairwise resistance distances were then converted to pairwise genetic distances (predicted Weir and Cockerham F<sub>ST</sub>), using the best relationship between distance matrices obtained in the optimization step (i.e. from empirical data).

## Clustering of subpopulations

The obtained dissimilarity matrices of genetic distances were then subjected to the clustering procedure developed by Rodriguez and Laio (2014) to delineate conservation units (CUs) at both scales. Based on the automatic identification of local density peaks, this method allows the detection of clusters and outliers based on the distance between data points. It is similar to density-based algorithms such as DBSCAN (Ester et al. 1996), however it delineates clusters without introducing a noise-signal cutoff, thus decreasing the probability of low-density clusters being classified as noise (Rodriguez and Laio 2014). The only variable parameter *d*<sub>c</sub> (cutoff distance) was fixed so that the average number of neighbors represents 5% of the total number of points in the dataset (Rodriguez and Laio 2014, Du et al. 2016). Results produced by this clustering method are very robust across variation of this parameter, particularly on large-scale datasets (Xu et al. 2020).
## Results

OutFLANK and FLK tests identified 8 shared outlier loci, that we excluded before further analyses, leaving a total of 4983 SNPs considered as neutral.

The discriminant analysis (DAPC) indicated that grey reef shark populations could be split into four distinct clusters (Fig. 3A–C), also identified by fastSTRUCTURE ($K=4$ genetic clusters, Fig. 3D). This clustering revealed greater differentiation between areas separated by large distances or deep waters (Fig. 3A–C). Sharks sampled at the far remote Chagos showed greater genetic differentiation compared to other sampling locations (Fig. 3A). This effect was also observed in populations from the oceanic island of Matthew on the New Hebrides Plate, and the Cocos Keeling Islands, which are both isolated coral reef islands separated by deep oceanic waters (Fig. 3B). The DAPC also suggested that sharks from the remote reefs of Chesterfield were more related to sharks from the Great Barrier Reef (GBR) than to sharks from the rest of the New Caledonian archipelago (Fig. 3C). Pairwise genetic distances (Weir and Cockerham $F_{ST}$ values) confirmed the patterns of

differentiation observed by DAPC and fastSTRUCTURE (Supporting information).

During model optimization, 618 resistance maps were obtained when testing single parameter hypotheses (bathymetry or distance-to-habitat; with or without including seamounts as suitable habitat). Likewise, a total of 93 632 resistance maps combining both seascape features in every possible combination were obtained and used as input for GFLOW.

Isolation-by-distance (IBD) calculated with linear geographic shortest distances between sampling locations explained an important part of genetic differentiation ($R^2=0.493$), as well as IBD calculated with Circuit Theory (GFLOW null model, pixels of value 1, $R^2=0.284$), but uncertainty and many outliers remained (Fig. 4). The distance-to-habitat- and bathymetry-based univariate models, respectively a model with high resistance value when at more than 200 km from any suitable habitat and a model with low resistance at depths shallower than 2000 m, attaining very high resistance values at depths below 4000 m, were highly predictive ($R^2=0.952$; Mantel=0.976; $AIC_c=-904$ and $R^2=0.985$; Mantel=0.992; $AIC_c=-1041$, respectively).

<figure><img src="image_8.png" /><figcaption>Figure 3. Results from discriminant analysis of principal components (DAPC) performed on the set of 4983 filtered neutral SNP data (excluding outliers) from all locations (A), all locations except Chagos (B) and locations in the Pacific Ocean including Eastern Australia and New Caledonia except Matthew (C). Colors and inertia ellipses correspond to sampling locations. (D) Results from fastSTRUCTURE using simple prior and 4 clusters. Samples from Chagos were included in this analysis.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure 4. Results of the optimization framework showing (A) the best resistance maps associated with different hypotheses: 'GFLOW Null Model', distance-to-habitat alone, bathymetry alone and the best combined model (distance-to-habitat and bathymetry, without including seamounts in the distance-to habitat layer); (B) the associated GFLOW maps representing current flowing between every pair of locations for the given resistance map. Brighter colors indicate higher current flow; (C) the subsequent linear relationships between pairwise resistance distances obtained with GFLOW and linearized FST for every pair of locations. R², Mantel statistic and AICc are indicated for each model.</figcaption></figure>

The best model combining both bathymetry and distance-to-habitat was even more predictive (R² = 0.988; Mantel = 0.994; AICc = -1070). This best model did not include seamounts as suitable habitat and suggests that deep oceanic waters represent a strong barrier to dispersal. It also suggests that habitat proximity of less than 50 km promotes gene flow (Supporting information). This best model was selected despite the moderate increase in R² compared to univariate models, as ΔAICc >> 10 in both cases (ΔAICc = 160 and 29 for distance-to habitat and bathymetry respectively; Burnham et al. 2011), suggesting support for the more complex model.

At the scale of the entire distribution range, the GFLOW run using the best resistance map produced a matrix of 480

690 pairwise resistance distances between the 981 simulated locations that were randomly selected across the Indo-Pacific, separated by 100 km (Supporting information). Density peaks clustering revealed a total of 38 conservation units comprising ≥ 2 locations, along with 202 isolated locations (Fig. 5A). The widest unit was comprised of reefs and oceanic islands in the eastern part of the Indo-Australian plate, along with the southeastern part of the Eurasian plate (Sunda plate), while the western frontier of the unit delineated by the Java Trench. Another wide unit connected reefs from the Solomon and Bismarck plates, while remote islands in the southern part of the Solomon Islands were connected to Vanuatu. Interestingly, Tonga, Fiji, Wallis and Futuna, as
<figure><img src="image_10.png" /><figcaption>Figure 5. Resistance maps showing the delineation of conservation units (same color), and buffers of radius equal to (A) 100 km at the scale of the whole distribution range of the grey reef shark and (B) 50 km at the scale of our sampling extent. Isolated units comprised of a single location are shown in black with a smaller radius. The grey rectangle on (B) corresponds to the extent on which locations were simulated. Color scale in the background corresponds to resistance values from the Supporting information, red corresponding to high values, blue to low values.</figcaption></figure>

well as the southern islands of Tuvalu formed a major unit in the Western Pacific Ocean. Five distinct units encompassed reefs from the Red Sea and the Oman Sea/Persian Gulf, while the western coast of Madagascar and the Comoros, including Mayotte, in the Mozambique Channel were part of a single unit together with a wide section of the eastern coast of Africa. The Seychelles formed a single unit, which was also the case of Chagos. Another unit in the Indian plate was composed of surrounding reefs in India and Sri Lanka, the Laccadive Islands and the Maldives. Lastly, except from some wide units comprised of archipelagos like for instance the western part of Micronesia, or the Tuamotu archipelago in French Polynesia which formed single units, reefs and oce- anic islands from the Pacific plate were much fragmented,

with the largest proportion of small units and completely isolated patches of habitat (e.g. Cocos Keeling Islands).

At the smaller scale of our sampling extent, GFLOW similarly produced a matrix of 402 753 pairwise resistance distances between the 898 simulated locations separated by 50 km, randomly selected during the Matérn process (Supporting information). Density peaks clustering revealed a total of 21 units (≥ 2 locations) at the scale of our sampling extent, along with 81 isolated locations (Fig. 5B). At this scale, the single unit comprising mostly reefs from the Indo-Australian plate was fragmented into several conservation units. Noticeable ones in terms of conservation comprised a western Australian unit, a separate unit constituting the Rowley Shoals, while western Indonesia and northwestern Australia were grouped
into a single unit, joined together by Scott Reef and Ashmore and Cartier Islands. Eastern Indonesia and northern Australia off the Northern Territory constituted another very close unit with the east Timor Sea and Arafura Sea acting as corridors. The GBR, along with reefs from the Coral Sea, was connected to Papua New Guinea via the Torres Strait. Reefs from the Bismarck Sea (northern Papua New Guinea) formed a single unit, as well as most Solomon Islands reefs. Interestingly, the Chesterfield Reefs, belonging to New Caledonia, formed a unit by themselves. The rest of the New Caledonian archipelago also formed a single unit, except Matthew and Hunter Islands that were isolated on the far east side of the archipelago, and the Petrie atoll, isolated in the north-east of the main island. Further east, Vanuatu was separated from New Caledonia by the New Hebrides Trench. Among the 81 isolated locations identified by the clustering algorithm, Cocos Keeling and Christmas Islands in the Indian ocean, reefs in the Banda Sea (southeast Asia), Nauru, Tuvalu, Kiribati and other remote islands of the Pacific, as well as remote reefs in the Coral Sea were identified.

## Discussion

Common approaches in landscape or seascape genetics usually focus on genetic connectivity per se and propose ad hoc explanations based on coincident landscape features (McRae and Beier 2007, Hirschfeld et al. 2021), therefore hindering the potential of genetic studies to inform conservation planning. Conversely, this study is the first to date linking fine-scale seascape and genetic connectivity of a species with a priori testing of hypotheses, followed by predictions at the entire range of a species. Here, with the development of an analytical framework using a custom pipeline that could be applied to a variety of different species and ecosystems, we show its potential for the delineation of hierarchical conservation units at various scales for more targeted protection measures. Applying this methodology for multiple species could provide key information for high resolution management scenarios, particularly for the implementation of MPAs and MPA networks of improved effectiveness (Momigliano et al. 2015). It provides a complementary approach to other modelling frameworks based on movement data from individuals (Martín et al. 2020), and represents an efficient means to predict large-scale conservation units.

Our results reveal that geographic distance is a poor predictor of the genetic structure of grey reef sharks. While genetic and geographic distance are correlated ($R^2=0.493$), the explanatory power of this null model is low compared to IBR models accounting for seascape features (best model $R^2=0.988$). This is not surprising as two underlying assumptions of IBD are clearly unrealistic. The first and most important assumption of IBD is that dispersal occurs through a homogeneous seascape. Grey reef sharks are habitat specialists, being associated almost exclusively with coral reefs (particularly exposed outer slopes) and rocky shoals (Chin et al. 2010, Espinoza et al. 2014, White et al. 2017). Therefore,

their dispersal is likely constrained by the availability of suitable habitats (Espinoza et al. 2015a, b). This is further supported by the distribution of clusters along the first two axes of the DAPC (Fig. 3), displaying a hierarchical islands structure, typical of a stepping stone model of dispersal (Jombart et al. 2010). Another assumption of IBD and more specifically of least cost path (LCP) is that sharks use direct pathways between locations. This assumption has been shown to bias inference in many organisms (McRae and Beier 2007), and in the case of grey reef sharks, there is scarce evidence of direct long-distance migration pathways (Bonnin et al. 2019, 2021).

We show that the best model explaining the genetic differentiation of grey reef sharks is one supported by CT and with a very low resistance associated to waters at less than 50 km from optimal habitats, but with very high resistance associated with deep oceanic waters acting as barriers to dispersal. As shown in the Supporting information, this model combining both bathymetry and distance-to-habitat provides improvements, particularly for deep areas close to suitable habitat (e.g. volcanic island with a steep slope) and shallow areas away from suitable habitat (e.g. shallow continental slope). Although the majority of grey reef sharks have been found to be highly resident, some individuals are known to travel large distances across the open ocean (Espinoza et al. 2015a, White et al. 2017). Altogether, our results are congruent with previous studies highlighting that large MPAs (> 50 km) could be effective for a substantive proportion of individuals (Edgar et al. 2014, Dwyer et al. 2020, MacNeil et al. 2020, Bonnin et al. 2021), even though a small number of individuals may disperse further using contiguous habitat patches as travel routes to avoid high resistance barriers such as deep oceanic waters (Bonnin et al. 2019, 2021). We recognize that expanding our modelling wider than the sampling extent (i.e. the central Pacific) can be problematic, and we call for extensive genetic sampling at a wider scale to confirm these findings. Nevertheless, studies have yet to demonstrate that sharks from unsampled regions have different dispersal behaviors than the Indo-West-Pacific sharks sampled here.

Based on empirical evidence provided by genetic data, our results prove that at a time scale of several generations, a small number of sharks dispersing genes via migration (Bonnin et al. 2021) may have a significant impact on the global genetic structure of the species, and consequently in maintaining standing genetic variation and inbreeding connectivity (sufficient gene flow to avoid harmful effects of local inbreeding, Lowe and Allendorf 2010) among and between conservation units (CUs). There is, however, an important consideration to be made: $F_{ST}$ is a proxy of migration only when populations are at migration-drift equilibrium. Given the long generation time of grey reef sharks (16.4 years, Robbins 2006), the young age of some of the sampled habitats (like the GBR), and the evidence of recent population expansions in other coral reef associated requiem sharks (Maisano Delser et al. 2016, 2018), this assumption could be considered as invalid. Assuming all populations have a recent history, as in the closely related *C. melanopterus* (Maisano
Delser et al. 2016, 2018), F<sub>ST</sub> is still expected to be corre-
lated to N<sub>e</sub>m (effective migration rate), but defining CUs
using a cut-off based on F<sub>ST</sub> may be misleading: if popula-
tions are not at migration-drift equilibrium, F<sub>ST</sub> may be much
lower than expected for a given N<sub>e</sub>m. A possible solution to
such limitation would be to estimate migration rates with-
out assuming equilibrium using the coalescent, or approxi-
mations of the coalescent, within an approximate Bayesian
computation or composite likelihood approach for parameter
estimation (Beaumont et al. 2002, Gutenkunst et al. 2009,
Excoffier et al. 2013, 2021, Jouganous et al. 2017). A frame-
work that incorporates IBR models and direct estimates of
migration rates based on coalescent simulations would be
a significant step forward in seascape genetics, potentially
enabling the estimation of much higher migration rates than
F<sub>ST</sub>-based methods, while taking into account the demo-
graphic history of all populations. There are however poten-
tial issues to consider. As Momigliano et al. (2021) recently
demonstrated, unaccounted demographic events may cause
strong biases in parameter estimation, although migration
rates are among the least affected demographic parameters.

During our model optimization, we used F<sub>ST</sub> as the sole
metric of genetic differentiation. We could have used other
measures such as Jost's D estimator (Jost et al. 2018), but
we found that F<sub>ST</sub> and Jost's D were highly correlated in our
dataset, especially as we did not include samples from Chagos
in the IBR framework (R<sup>2</sup>=0.999; Supporting information).

Protecting threatened mobile species requires a better knowledge of habitat fragmentation and physical barriers in the seascape (Hirschfeld et al. 2021). While the concept of ‘populations’ is used to guide management policy, it covers multiple definitions (Waples and Gaggiotti 2006) but is often approached by the identification of CUs (Funk et al. 2012). There are also various definitions of CUs in the scientific literature, with a major distinction between evolutionary significant units (ESUs) and management units (MUs), but there is a consensus on the fact that identifying CUs is a crucial first step for the conservation of wild populations (Funk et al. 2012, Barbosa et al. 2018). CUs are also recognized as being hierarchical, with units at wider scale comprising multiple smaller units (Funk et al. 2012, Barbosa et al. 2018, Weckworth et al. 2018). The investigation of local genetic and demographic additional clues (i.e. genetic diversity, N<sub>e</sub>, relative abundance) might help to better delineate units and take more appropriate management measures (Domingues et al. 2017, Barbosa et al. 2018).

One of the important aspects of our results is that the
defined CUs, even at local scale, generally encompass
the exclusive economic zones (EEZs) of multiple coun-
tries. As such, conservative spatial planning would require
coordinated international efforts (Harrison et al. 2018,
Mackelworth et al. 2019). Moreover, slowing the ongoing
decline of natural populations of mobile species like sharks
requires not only scientific collaborations, but also support
from managers and policy-makers across borders (Dunn et al.
2019, Sequeira et al. 2019). A further issue impacting mobile
predators such as the grey reef shark is the fragmentation of

populations observed through a high proportion of putative
conservation units represented only by a single location of
suitable habitat. This highly fragmented pattern holds true for
the two hierarchical scales (81 of 103 units at the scale of our
sampling extent, 202 of 240 at the entire distribution range
of the species), knowing that the clustering algorithm used is
conservative in the number of detected outliers (Rodriguez
and Laio 2014). Special attention should thus be given by
managers to such isolated locations that deserve high conserv-
vation priority, hosting populations potentially vulnerable to
anthropogenic pressures such as harvesting, with a low capac-
ity of rebuilding populations via migration and subject to
inbreeding depression for depleted populations (Kardos et al.
2018, Ralls et al. 2018).

Conclusions

We developed and used a predictive modelling framework to
infer barriers to gene flow and map the connectivity of grey
reef sharks across the Indo-Pacific. We provide novel insight
on the conservation of this marine predator by estimating
connectivity beyond sampled locations and by delineating
hierarchical conservation units. We conclude that the dis-
tribution and movement of grey reef sharks are reliant on
more than just geographical availability and demonstrate
the importance of using this framework for the integration
of genetic connectivity in the field of marine spatial plan-
ning. Our findings are not limited to grey reef sharks, and
this framework can be applied to any adult disperser species.
Hence, we call for the use of this approach to better under-
stand dispersal patterns of other marine species at different
scales. We recommend including such information along-
side ecological data, habitat use and governance of areas used
when considering management strategies, and even applying
this framework on multiple species as part of a systematic and
integrated conservation planning approach (Sala et al. 2021).

Acknowledgements – We thank the crew of the Research Vessel
Amborella (Government of New Caledonia), the many volunteers
that contributed to field trips in New Caledonia, and Laurent
Millet of the genetic lab at IRD Nouméa for help in the processing
of samples. We appreciate the support of Pierre-Edouard
Guerin (CEFE/CNRS) for SNPs filtering and of Jérôme Lefèvre
(ENTROPIE) and Eric Mermet (EHESS) on map computation.

Funding – Sampling in New Caledonia was funded by research grants of Laurent Vigliola (Programme APEX, Government of New Caledonia, Pew Charitable Trust, Total Foundation, IRD core funding), and David Mouillot (Programme APEX, Total Foundation). Data collection in other countries was supported by the Great Barrier Reef Marine Park Authority, Cooperative Research Centre (CRC) Reef, Australian Coral Reef Society and PADI Aware. PM was funded by the Sea World Research and Rescue Foundation, the Academy of Finland (grant no. 316294), the Spanish Agencia Estatal de Investigación (JC2020-042611-I/ MCIN/AEI /10.13039/501100011033) and the European Union (NextGenerationEU/PRTR).
*Conflict of interest* – The authors declare no conflict of interest.

*Ethics statement* – Genetic samples in New Caledonia were collected under permits to Laurent Vigliola by the Government of New Caledonia (permit no. 2015-1351/GNC), the Northern Province of New Caledonia (permit no. 60912-1508-2015/JJC) and the Southern Province of New Caledonia (permits no. 479-2016/ARR/DENV and no. 2093-2016/ARR/DENV). Great Barrier Reef Marine Park permits include G01/356, G02/021 and G03/8048.1.

## Author contributions

**Germain Boussarie:** Conceptualization (lead); Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Validation (lead); Visualization (lead); Writing – original draft (lead). **Paolo Momigliano:** Conceptualization (supporting); Data curation (supporting); Investigation (supporting); Methodology (supporting); Validation (supporting); Writing – review and editing (equal). **William D. Robbins:** Data curation (supporting); Writing – review and editing (supporting). **Lucas Bonnin:** Data curation (supporting); Writing – review and editing (supporting). **Jean-François Cornu:** Data curation (supporting); Writing – review and editing (equal). **Jeremy J. Kiszka:** Data curation (supporting); Writing – review and editing (equal). **Stéphanie Manel:** Writing – review and editing (equal). **David Mouillot:** Conceptualization (supporting); Funding acquisition (equal); Project administration (equal); Supervision (equal); Validation (supporting); Writing – review and editing (equal). **Laurent Vigliola:** Conceptualization (supporting); Data curation (supporting); Funding acquisition (equal); Project administration (equal); Supervision (equal); Validation (supporting); Writing – review and editing (equal).

## Transparent peer review

The peer review history for this article is available at <https://publons.com/publon/10.1111/ecog.06158>.

## Data availability statement

All raw sequence data from New Caledonia are available from the Zenodo Digital Repository (<https://doi.org/10.5281/zenodo.5935133>) and GenBank Sequence Read Archive (Bioproject PRJNA795958). All SNP data are also available in public repositories (Zenodo for data from New Caledonia, <https://doi.org/10.5281/zenodo.5943789>; Dryad Digital Repository for other locations, <https://doi.org/10.5061/dryad.mt2m3>).

## Supporting information

The supporting information associated with this article is available from the online version.

## References

Albouy, C. et al. 2020. Global vulnerability of marine mammals to global warming. – Sci. Rep. 10: 548.

Allendorf, F. W. et al. 2010. Genomics and the future of conservation genetics. – Nat. Rev. Genet. 11: 697–709.

Almany, G. R. et al. 2009. Connectivity, biodiversity conservation and the design of marine reserve networks for coral reefs. – Coral Reefs 28: 339–351.

Álvarez-Noriega, M. et al. 2020. Global biogeography of marine dispersal potential. – Nat. Ecol. Evol. 4: 1196–1203.

Balbar, A. C. and Metaxas, A. 2019. The current application of ecological connectivity in the design of marine protected areas. – Global Ecol. Conserv. 17: e00569.

Balkenhol, N. et al. 2017. Landscape genomics: understanding relationships between environmental heterogeneity and genomic characteristics of populations. – In: Rajora, O. (ed.), Population genomics. Springer, pp. 261–322.

Barbosa, S. et al. 2018. Integrative approaches to guide conservation decisions: using genomics to define conservation units and functional corridors. – Mol. Ecol. 27: 3452–3465.

Beaumont, M. A. et al. 2002. Approximate Bayesian computation in population genetics. – Genetics 162: 2025–2035.

Benestan, L. et al. 2021. Restricted dispersal in a sea of gene flow. – Proc. R. Soc. B 288: 20210458.

Boissin, E. et al. 2019. Contrasting global, regional and local patterns of genetic structure in gray reef shark populations from the Indo-Pacific region. – Sci. Rep. 9: 15816.

Bonhomme, M. et al. 2010. Detecting selection in population trees: the Lewontin and Krakauer test extended. – Genetics 186: 241–262.

Bonnin, L. et al. 2019. Repeated long-range migrations of adult males in a common Indo-Pacific reef shark. – Coral Reefs 38: 1121–1132.

Bonnin, L. et al. 2021. Recent expansion of marine protected areas matches with home range of grey reef sharks. – Sci. Rep. 11: 14221.

Bryan-Brown, D. N. et al. 2017. Patterns and trends in marine population connectivity research. – Mar. Ecol. Prog. Ser. 585: 243–256.

Burnham, K. P. et al. 2011. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. – Behav. Ecol. Sociobiol. 65: 23–35.

Chin, A. et al. 2010. An integrated risk assessment for climate change: analysing the vulnerability of sharks and rays on Australia's Great Barrier Reef. – Global Change Biol. 16: 1936–1953.

Cowen, R. K. and Sponaugle, S. 2009. Larval dispersal and marine population connectivity. – Annu. Rev. Mar. Sci 1: 443–466.

DiBattista, J. D. et al. 2017. Seascape genomics reveals fine-scale patterns of dispersal for a reef fish along the ecologically divergent coast of Northwestern Australia. – Mol. Ecol. 26: 6206–6223.

Dickson, B. G. et al. 2019. Circuit-theory applications to connectivity science and conservation. – Conserv. Biol. 33: 239–249.

Dilts, T. E. et al. 2016. Multiscale connectivity and graph theory highlight critical areas for conservation under climate change. – Ecol. Appl. 26: 1223–1237.

Domingues, R. R. et al. 2017. The importance of considering genetic diversity in shark and ray conservation policies. – Conserv. Genet. 19: 501–525.
Du, M. et al. 2016. Study on density peaks clustering based on k-nearest neighbors and principal component analysis. – Knowl. Based Syst. 99: 135–145.

Duarte, C. M. et al. 2020. Rebuilding marine life. – Nature 580: 39–51.

Dulvy, N. K. et al. 2014. Extinction risk and conservation of the world's sharks and rays. – eLife 3: e00590.

Dulvy, N. K. et al. 2017. Challenges and priorities in shark and ray conservation. – Curr. Biol. 27: R565–R572.

Dulvy, N. K. et al. 2021. Overfishing drives over one-third of all sharks and rays toward a global extinction crisis. – Curr. Biol. 31: 4773–4787.

Dunn, D. C. et al. 2019. The importance of migratory connectivity for global ocean policy. – Proc. R. Soc. B 286: 20191472.

Dwyer, R. G. et al. 2020. Individual and population benefits of marine reserves for reef sharks. – Curr. Biol. 30: 480–489.

Edgar, G. J. et al. 2014. Global conservation outcomes depend on marine protected areas with five key features. – Nature 506: 216–220.

Espinoza, M. et al. 2014. Quantifying shark distribution patterns and species-habitat associations: implications of marine park zoning. – PLoS One 9: e106885.

Espinoza, M. et al. 2015a. Residency patterns and movements of grey reef sharks *Carcharhinus amblyrhynchos* in semi-isolated coral reef habitats. – Mar. Biol. 162: 343–358.

Espinoza, M. et al. 2015b. Contrasting movements and connectivity of reef-associated sharks using acoustic telemetry: implications for management. – Ecol. Appl. 25: 2101–2118.

Ester, M. et al. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. – In: Proc. 2nd Int. Conf. Knowl. Discov. Data Min., pp. 226–231.

Estes, J. A. et al. 2016. Megafaunal impacts on structure and function of ocean ecosystems. – Annu. Rev. Environ. Resour. 41: 83–116.

Excoffier, L. et al. 2013. Robust demographic inference from genomic and SNP data. – PLoS Genet. 9: e1003905.

Excoffier, L. et al. 2021. fastsimcoal2: demographic inference under complex evolutionary scenarios. – Bioinformatics 37: 4882–4885.

Froese, R. et al. 2010. FishBase – <www.fishbase.de>.

Funk, W. C. et al. 2012. Harnessing genomics for delineating conservation units. – Trends Ecol. Evol. 27: 489–496.

Gaines, S. D. et al. 2010. Designing marine reserve networks for both conservation and fisheries management. – Proc. Natl Acad. Sci. USA 107: 18286–18293.

Goetze, J. S. et al. 2021. Increased connectivity and depth improve the effectiveness of marine reserves. – Global Change Biol. 27: 3432–3447.

Gutenkunst, R. N. et al. 2009. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. – PLoS Genet. 5: e1000695.

Harrison, A. et al. 2018. The political biogeography of migratory marine predators. – Nat. Ecol. Evol. 2: 1571–1578.

Harrison, H. B. et al. 2020. A connectivity portfolio effect stabilizes marine reserve performance. – Proc. Natl Acad. Sci. USA 117: 25595–25600.

Hirschfeld, M. et al. 2021. Barriers in a sea of elasmobranchs: from fishing for populations to testing hypotheses in population genetics. – Global Ecol. Biogeogr. 30: 2147–2163.

Jacoby, D. M. P. et al. 2020. Shark movement strategies influence poaching risk and can guide enforcement decisions in a large, remote marine protected area. – J. Appl. Ecol. 57: 1782–1792.

Jangjoo, M. et al. 2016. Connectivity rescues genetic diversity after a demographic bottleneck in a butterfly population network. – Proc. Natl Acad. Sci. USA 113: 10914–10919.

Jombart, T. et al. 2010. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. – BMC Genet. 11: 94.

Jost, L. et al. 2018. Differentiation measures for conservation genetics. – Evol. Appl. 11: 1139–1148.

Jouganous, J. et al. 2017. Inferring the joint demographic history of multiple populations: beyond the diffusion approximation. – Genetics 206: 1549–1567.

Juhel, J. B. et al. 2017. Reef accessibility impairs the protection of sharks. – J. Appl. Ecol. 55: 673–683.

Juhel, J. B. et al. 2019. Isolation and no-entry marine reserves mitigate anthropogenic impacts on grey reef shark behavior. – Sci. Rep. 9: 2897.

Kardos, M. et al. 2018. Genomic consequences of intensive inbreeding in an isolated Wolf population /631/208/730/631/181/457/649 article. – Nat. Ecol. Evol. 2: 124–131.

Keenan, K. et al. 2013. DiveRsity: an R package for the estimation and exploration of population genetics parameters and their associated errors. – Methods Ecol. Evol. 4: 782–788.

Kiderlen, M. and Hörig, M. 2013. Matérn's hard core models of types I and II with arbitrary compact grains. – CSGB Res Rep. 5.

Koen, E. L. et al. 2010. The effect of map boundary on estimates of landscape resistance to animal movement. – PLoS One 5: e11785.

Krueck, N. C. et al. 2017. Marine reserve targets to sustain and rebuild unregulated fisheries. – PLoS Biol. 15: e2000537.

Last, P. R. and Stevens, J. D. 2009. Sharks and rays of Australia. – CSIRO Publishing.

Legendre, P. and Fortin, M. J. 2010. Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data. – Mol. Ecol. Resour. 10: 831–844.

Leonard, P. B. et al. 2017. Gflow: software for modelling circuit theory-based connectivity at any scale. – Methods Ecol. Evol. 8: 519–526.

Lichstein, J. W. 2007. Multiple regression on distance matrices: a multivariate spatial analysis tool. – Plant Ecol. 188: 117–131.

Lowe, W. H. and Allendorf, F. W. 2010. What can genetics tell us about population connectivity? – Mol. Ecol. 19: 3038–3051.

Mackelworth, P. C. et al. 2019. Geopolitics and marine conservation: synergies and conflicts. – Front. Mar. Sci. 6: 759.

MacNeil, M. A. et al. 2020. Global status and conservation potential of reef sharks. – Nature 583: 801–806.

Magris, R. A. et al. 2014. Integrating connectivity and climate change into marine conservation planning. – Biol. Conserv. 170: 207–221.

Magris, R. A. et al. 2018. Biologically representative and well-connected marine reserves enhance biodiversity persistence in conservation planning. – Conserv. Lett. 11: e12439.

Maisano Delser, P. et al. 2016. Population genomics of *C. melanopterus* using target gene capture data: demographic inferences and conservation perspectives. – Sci. Rep. 6: 33753.

Maisano Delser, P. et al. 2018. Demographic inferences after a range expansion can be biased: the test case of the blacktip reef shark *Carcharhinus melanopterus*. – Heredity 122: 759–769.
Manel, S. et al. 2019. Long-distance benefits of marine reserves: myth or reality? – Trends Ecol. Evol. 34: 342–354.

Mantel, N. 1967. The detection of disease clustering and a generalized regression approach. – Cancer Res. 27: 209–220.

Martín, G. et al. 2020. Estimating marine protected area network benefits for reef sharks. – J. Appl. Ecol. 57: 1969–1980.

McCauley, D. J. et al. 2015. Marine defaunation: animal loss in the global ocean. – Science 347: 247–254.

McClenachan, L. et al. 2016. Rethinking trade-driven extinction risk in marine and terrestrial megafauna. – Curr. Biol. 26: 1640–1646.

McRae, B. H. 2006. Isolation by resistance. – Evolution 60: 1551.

McRae, B. H. and Beier, P. 2007. Circuit theory predicts gene flow in plant and animal populations. – Proc. Natl Acad. Sci. USA 104: 19885–19890.

McRae, B. H. et al. 2008. Using circuit theory to model connectivity in ecology, evolution and conservation. – Ecology 89: 2712–2724.

Momigliano, P. et al. 2015. Conserving coral reef organisms that lack larval dispersal: are networks of marine protected areas good enough? – Front. Mar. Sci. 2: 16.

Momigliano, P. et al. 2017. Genetic structure and signatures of selection in grey reef sharks *Carcharhinus amblyrhynchos*. – Heredity 119: 142–153.

Momigliano, P. et al. 2021. Biases in demographic modelling affect our understanding of recent divergence. – Mol. Biol. Evol. 38: 2967–2985.

Pacoureau, N. et al. 2021. Half a century of global decline in oce-
anic sharks and rays. – Nature 589: 567–571.

Palsbøll, P. J. et al. 2007. Identification of management units using population genetic data. – Trends Ecol. Evol. 22: 11–16.

Pante, E. and Simon-Bouhet, B. 2013. marmap: a package for importing, plotting and analyzing bathymetric and topographic data in R. – PLoS One 8: e73051.

Pazmiño, D. A. et al. 2017. Genome-wide SNPs reveal low effective population size within confined management units of the highly vagile Galapagos shark *Carcharhinus galapagensis*. – Conserv. Genet. 18: 1151–1163.

Peterman, W. E. and Pope, N. S. 2021. The use and misuse of regression models in landscape genetic analyses. – Mol. Ecol. 30: 37–47.

Pirog, A. et al. 2019. Population structure, connectivity and demographic history of an apex marine predator, the bull shark *Carcharhinus leucas*. – Ecol. Evol. 9: 12980–13000.

Pritchard, J. K. et al. 2000. Inference of population structure using multilocus genotype data. – Genetics 155: 945–959.

Raj, A. et al. 2014. FastSTRUCTURE: variational inference of popu-
lation structure in large SNP data sets. – Genetics 197: 573–589.

Ralls, K. et al. 2018. Call for a paradigm shift in the genetic manage-
ment of fragmented populations. – Conserv. Lett. 11: e12412.

Robbins, W. D. 2006. Abundance, demography and population structure of the grey reef shark *Carcharhinus amblyrhynchos* and the whitetip reef shark *Triaenodon obesus* (Fam. Carcharhinidae). – PhD thesis, James Cook Univ.

Robbins, W. D. et al. 2006. Ongoing collapse of coral-reef shark populations. – Curr. Biol. 16: 2314–2319.

Rodriguez, A. and Laio, A. 2014. Clustering by fast search and find of density peaks. – Science 344: 1492–1496.

Roff, A. G. et al. 2018. Decline of coastal apex shark populations over the past half century. – Commun. Biol. 223: 1–11.

Ruppert, J. L. W. et al. 2017. Human activities as a driver of spatial variation in the trophic structure of fish communities on Pacific coral reefs. – Global Change Biol. 24: e67–e79.

Sala, E. et al. 2021. Protecting the global ocean for biodiversity, food and climate. – Nature 592: 397–402.

Sansaloni, C. et al. 2011. Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome-wide, high throughput, highly informative genotyping for molecular breeding of *Eucalyptus*. – BMC Proc. 5: P54.

Schadt, E. E. et al. 2010. Computational solutions to large-scale data management and analysis. – Nat. Rev. Genet. 11: 647–657.

Selkoe, K. A. et al. 2016. A decade of seascape genetics: contribu-
tions to basic and applied marine connectivity. – Mar. Ecol.
Prog. Ser. 554: 1–19.

Sequeira, A. M. M. et al. 2019. Overhauling ocean spatial planning to improve marine megafauna conservation. – Front. Mar. Sci. 6: 1–12.

Slatkin, M. 1987. Gene flow and the geographic structure of natu-
ral populations. – Science 236: 787–797.

Smouse, P. E. et al. 1986. Multiple regression and correlation exten-
sions of the mantel test of matrix correspondence. – Syst. Zool.
35: 627–632.

Song, G. et al. 2013. Gene flow maintains genetic diversity and colonization potential in recently range-expanded populations of an oriental bird, the light-vented bulbul (pycnonotus sinensis, aves: Pycnonotidae). – Divers. Distrib. 19: 1248–1262.

Spalding, M. et al. 2001. World atlas of coral reefs. – Univ. of California Press.

Waples, R. S. and Gaggiotti, O. 2006. What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity. – Mol. Ecol. 15: 1419–1439.

Weckworth, B. V. et al. 2018. Lines on a map: conservation units, meta-population dynamics and recovery of woodland caribou in Canada. – Ecosphere 9: e02323.

Weir, B. S. and Cockerham, C. C. 1984. Estimating F-statistics for the analysis of population structure. – Evolution 38: 1358.

White, T. D. et al. 2017. Assessing the effectiveness of a large marine protected area for reef shark conservation. – Biol. Conserv. 207: 64–71.

Whitlock, M. C. and Lotterhos, K. E. 2015. Reliable detection of loci responsible for local adaptation: inference of a null model through trimming the distribution of F<sub>ST</sub>. – Am. Nat. 186: S24–S36.

Xu, X. et al. 2020. A robust density peaks clustering algorithm with density-sensitive similarity. – Knowl. Based Syst. 200: 106028.

Yan, H. F. et al. 2021. Overfishing and habitat loss drives range contraction of iconic marine fishes to near extinction. – Sci. Adv. 7: eabb6026.

Yesson, C. et al. 2011. The global distribution of seamounts based on 30 arc seconds bathymetry data. – Deep Res. Part I Oceanogr. Res. Pap. 58: 442–453.

Young, H. S. et al. 2016. Patterns, causes and consequences of Anthropocene defaunation. – Annu. Rev. Ecol. Evol. Syst. 47: 333–358.