Population models reveal unexpected patterns of local persistence ...

Received: 19 December 2017

Revised: 23 April 2018

Accepted: 8 May 2018

DOI: 10.1111/conl.12567

LETTER

Population models reveal unexpected patterns of local persistence despite widespread larval dispersal in a highly exploited species Lysel Garavelli1,2

J. Wilson White3,4

1 Harbor Branch Oceanographic Institute,

Florida Atlantic University, Fort Pierce, Florida 2 Pacific Northwest National Laboratory,

Richland, Washington 3 Department of Biology and Marine Biology,

University of North Carolina Wilmington, Wilmington, North Carolina 4 Department of Fisheries and Wildlife,

Coastal Oregon Marine Experiment Station, Oregon State University, Newport, Oregon 5 Smithsonian Marine Station, Smithsonian

Institution, Fort Pierce, Florida Correspondence Lysel Garavelli, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99352. Email: [email protected]

Iliana Chollett5

Laurent Marcel Chérubin1

Abstract Nearshore marine populations are structured in metapopulations that are connected through larval dispersal across national boundaries. One of the main challenges for effective management of these metapopulations is the need for partnerships between nations that share the same resource. By coupling large-scale connectivity information to a dynamic population model, we analyzed the patterns of connectivity and population persistence for the Caribbean spiny lobster (Panulirus argus) metapopulation both within and across national boundaries. Although spiny lobster subpopulations are highly connected at the basin scale, several nations located in the northern Caribbean and ecoregional networks could persist independently of the larger basinwide metapopulation. Based on these results, we propose transnational neighborhoods for spiny lobster management. Our analysis suggests that the dynamics and management of those subpopulations neighborhoods are not intrinsically dependent on “upstream” connectivity even though current rates of upstream larval supply are very high. KEYWORDS fisheries, larval connectivity, Panulirus argus, persistence, population models, transboundary management

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I N T RO D U C T I O N

Nearshore marine populations commonly exist as metapopulations, in which discrete subpopulations of adult fishes or invertebrates are demographically linked by larval connectivity (Kritzer & Sale, 2004). Quantifying patterns of larval connectivity is essential to understanding metapopulation dynamics and thus the persistence and spatial management and conservation of marine species (Burgess et al., 2014; Fogarty & Botsford, 2007). Despite calls for greater incorporation of connectivity information into management plans, such integration remains rare due to the high data requirements (Hidalgo et al., 2017). Historically, larval connectivity in nearshore marine metapopulations has been examined within the “source-sink”

paradigm (Costello et al., 2010; Crowder, Lyman, Figueira, & Priddy, 2000): sources produce more larvae than they receive, while sinks receive more than they produce. The implication for spatial management of such populations is that sources should be protected from harvest, while sinks could be harvested more freely (Costello et al., 2010; Crowder et al., 2000). However, connectivity is not necessarily a single-step process (Hastings & Botsford, 2006). To persist, each adult in a population must (on average) eventually replace itself with one successful offspring. Therefore, a network of nominally “sink” populations could yet persist if they exchange sufficient larvae among themselves over multiple generations, so that adults in each population are eventually replaced by a descendant (Hastings & Botsford, 2006).

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. Copyright and Photocopying: © 2018 The Authors. Conservation Letters published by Wiley Periodicals, Inc. Conservation Letters. 2018;e12567. https://doi.org/10.1111/conl.12567

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F I G U R E 1 Map of the Caribbean region showing the exclusive economic zones (EEZ; gray lines) and the location of the 2,930 habitat areas (80 km2 polygons) defined for spiny lobster (green) Notes. The Guatemala EEZ is not mentioned in the map because no habitat was described there.

Metapopulation persistence can be described in terms of two processes: self-persistence and network persistence (Hastings & Bostford, 2006). A subpopulation will be selfpersistent if enough larvae return to the population to achieve replacement in one generation. A self-persistent subpopulation could be managed without regard for the level of harvest elsewhere in the metapopulation (White, Botsford, Hastings, & Largier, 2010). A metapopulation will by definition persist if it contains at least one self-persistent subpopulation. However, even if there are no self-persistent subpopulations, the metapopulation will persist if there is sufficient connectivity among subpopulations to achieve replacement over multiple generations. This is termed network persistence (Burgess et al., 2014). For exploited marine resources, evaluation of the pattern of population persistence is necessary for effective management (Fogarty & Botsford, 2007). In general, spatial resource management aims to create a balance between fisheries benefits and ecosystem protection (Gaines, Lester, Grorud-Colvert, Costello, & Pollnac, 2010), but both goals fundamentally depend on maintaining persistent populations (Fogarty & Botsford, 2007). Therefore, a key first step in successful conservation and management is to identify patterns of self and network persistence in the metapopulation. In the Caribbean region, coral reef fisheries are a major component of the economy (Schuhmann & Mahon, 2015) and each nation manages its own fishery stocks. However, most fisheries species exist as metapopulations that span national boundaries. A species that poses a particular challenge to management is the Caribbean spiny lobster, Panulirus argus. It supports a valuable fishery throughout the region (Ehrhardt et al., 2010), is a long-dispersing species (5– 7 months pelagic larval duration; Goldstein, Matsuda, Take-

nouchi, & Butler, 2008), and is genetically panmictic throughout the region (Naro-Maciel et al., 2011). Management plans presume that individual subpopulations are largely resupplied by larvae from elsewhere in the region, complicating harvest decisions (e.g., SEDAR, 2010). Using a biophysical larval dispersal model, Kough, Paris, and Butler (2013) identified “source” and “sink” locations of spiny lobster in the Caribbean region and made international management recommendations accordingly. However, this analysis does not account for replacement along multiple generations or postrecruitment processes and cannot actually assess demographic replacement and population persistence, limiting its relevance for marine spatial planning. To identify appropriate spatial scales for management of Caribbean spiny lobster, we couple large-scale connectivity information to a dynamic population model. We evaluate the patterns of persistence in the metapopulation across multiple generations, both within and across national boundaries, to determine the networks governing the overall population dynamics. In contrast to earlier analyses concluding that the metapopulation is dominated by source–sink dynamics (Kough et al., 2013), we identify multiple persistent networks, and propose transnational neighborhoods for spiny lobster management.

2 2.1

M AT E R I A L A N D M E T H O D S Spatial domain

Using coral reef habitat location (Andréfouët et al., 2005), 2,930 polygons representing both larval release and settlement locations were designed in the Caribbean region

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describing 35 nations (Figure 1). The release areas were defined as 80 km2 polygons characterized by the proportion of coral reef habitat (i.e., available habitat for spiny lobster) within each polygon (Supporting Information).

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Deterministic population model

To calculate population persistence, we developed two versions of a population model, one deterministic and the other stochastic. The deterministic version follows the approach used by Hastings and Botsford (2006); the stochastic version adds two potentially important processes (Watson, Kendall, Siegel, & Mitarai, 2012): time-varying dispersal and spatially variable fishing effort. The deterministic model was developed to model the lobster population dynamics assuming temporally constant dispersal patterns and spatially constant fishing effort. The model was discrete time, spatially explicit, and age-structured (see Supporting Information for full description). In this model, ̄ an averlarvae dispersed according to the dispersal matrix 𝑫, age of the three dispersal matrices Di obtained from each of 3 years of simulation of the larval dispersal model (see Supporting Information for details). Three harvest rates (F) were tested in the model: a null harvest rate F0 = 0/y, a harvest rate of FMSY = 0.34/y (SEDAR, 2010), and F1 = 1.0/y. We assumed that fishing was the same in every habitat patch. Persistence was estimated by focusing on the scenario in which population density is equally low across all patches (so density-dependence can be ignored) and asking whether the population will increase in size. We constructed a demographic connectivity matrix 𝑪 that includes the dispersal matrix, reproductive output, survival of recruits at low density, and scaling of larval production by available habitat area. Persistence was then assessed for each nation and multination subnetwork by determining whether the eigenvalues of the corresponding submatrices of C were > 1 (Supporting Information).

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Stochastic population model

To assess the influence of interannual variability in ocean circulation and dispersal on lobster population dynamics, we included those parameters in a stochastic version of the population model. In this model, we actually tracked the age structure of the population at each time step and patch, but used the same parameter values for age-specific size, fecundity, and mortality and the same density-dependent recruitment function as in the deterministic model. To simulate variability in larval connectivity, in each annual time step of the model, we randomly selected one of the three dispersal matrices Di to use (obtained from the dispersal model). To simulate a spatially dynamic fishing fleet, we assumed that fishing effort followed lobster abundance within each nation. Within a nation, fishing

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effort in each patch was set to be proportional to the biomass yield calculated in that patch in the prior year. In a deterministic model, this would result in an equilibrium distribution of effort with equal (and thus optimized) catch per unit effort in each patch; in a stochastic model, fishing effort shifts slightly each time step. The average harvest rate across all patches was held constant, and the same harvest rates were tested as in the deterministic model (F = 0/y, F = 0.34/y, F = 1.0/y). The stochastic model is based on the one developed by White et al. (2013) and detailed methods are given in the Supporting Information. We simulated dynamics for each nation (or network) independently, making 100 simulation runs of 500 years each. It can be difficult to determine if such a model is persistent, because a flat trajectory at a zero equilibrium can be difficult to distinguish from a slow decline to extinction. Therefore after 450 years, we turned off the density dependence and calculated the return trajectory over the following 50 years; the slope of that trajectory was our estimate of persistence. Because testing all the combinations of network for persistence would be time consuming and not necessarily informative, we decided to test networks based on marine ecoregions described in the Caribbean Sea (Spalding et al., 2007; Supporting Information).

3 3.1

RESULTS Larval connectivity patterns

Caribbean spiny lobster habitats are potentially highly connected through larval dispersal (Figure 2A). Broadly, larvae released from the western Caribbean (Colombia, Panama, Costa Rica, Nicaragua, Honduras, Belize, Mexico, United States) had a low probability of settling in the eastern Caribbean (Leeward and Windward Islands). However, there were high probabilities of larval dispersal from the Leeward and Windward islands to Colombia, Jamaica, and the British Virgin Islands. The raw dispersal probabilities in Figure 2A are only meaningful indicators of overall connectivity if the rate of larval production is equal in all patches; this is not the case once habitat area is accounted for. In the deterministic model, lack of available habitat generated low connectivity in the central (Jamaica, Cayman Islands, Haiti) and eastern Caribbean parts (Leeward Islands; Figure 2B). Lower larval connectivity in the western Caribbean was also observed for the stochastic model (Figure 2C). In all three models, higher values of local retention and self-recruitment were observed in Mexico and United States (Figure S1). Increasing the harvest rate consistently reduced connectivity rates for both the deterministic and stochastic models (Figure S2).

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FIGURE 2

Connectivity matrix representing the transport success of spiny lobster larvae from release areas to settlement areas for the dispersal model (A), the deterministic population model at harvest rate FMSY (B), and the stochastic population model at harvest rate FMSY (C) Note. Areas are grouped per nation. VEN, Venezuela; COL, Colombia; PAN, Panama; CRI, Costa Rica; NIC, Nicaragua; HND, Honduras; BLZ, Belize; MEX, Mexico; USA, United States of America; BHS, The Bahamas; TCA, Turks and Caicos; CUB, Cuba; CYM, Cayman Islands; JAM, Jamaica; HTI, Haiti; DOM, Dominican Republic; PRI, Puerto Rico; VGB, British Virgin Islands; SES, Saint Eustatius, Saba; MAF, Saint Martin; AIA, Anguilla; KNA, Saint Kitts and Nevis; ATG, Antigua and Barbuda; MSR, Montserrat; GP, Guadeloupe; DMA, Dominica; MQ, Martinique; BRB, Barbados; LCA, Saint Lucia; TTO, Trinidad and Tobago; GRD, Grenada; VCT, Saint Vincent and Grenadines; BON, Bonaire; CW, Curacao; ABW, Aruba. The actual matrices used in the model simulations and persistence calculations were higher resolution (2,390 × 2,390 cells); cells corresponding to each nation have been averaged here for ease of presentation.

3.2

Population persistence

The overall metapopulation of spiny lobster was found to be persistent in the Caribbean region for all the harvest rates tested (deterministic population model: 𝜆 = 10 for F0 , 𝜆 = 3.26 for FMSY , 𝜆 = 1.34 for F1 ; stochastic population model: 𝜆 = 1.32 for F0 , 𝜆 = 1.14 for FMSY , 𝜆 = 1.00 for F1 ). Persistence estimates were qualitatively similar between the deterministic and stochastic models, with growth rates (𝜆) consistently slightly lower in the stochastic model. Therefore, we focus on the results from the stochastic model; results from the deterministic model are shown in the Supporting Information. For the stochastic population model, persistence decreased with the increase of harvest rate (Figures 3 and 4). For the no-

harvest rate scenario, there was a gradient of self-persistence between the northern and southern parts of the Caribbean region with Mexico, United States, Cuba, the Bahamas, and Turks and Caicos being more self-persistent than Honduras, Jamaica, Haiti, British Virgin Islands, Colombia, and Venezuela (Figure 3A). At FMSY , some nations remained self-persistent, such as Mexico, United States, Cuba, and the Bahamas (Figure 3B). At the higher harvest rate F1 , the population was only persistent in Mexico (Figure 3C) and the growth rate was almost zero in large parts of the Leeward Islands and Curacao. In the deterministic model (Figure S4), there was a wider range of values of 𝜆. As in the stochastic model, most of the smaller nations (Leeward Islands, Windward Islands, Costa Rica, and Nicaragua) had very low

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Gradient of spiny lobster population self-persistence in the Caribbean region for the stochastic population model for three harvest rates: F0 (A), FMSY (B), and F1 (C) Notes. The color represents the value of the population growth rate estimated by simulation for each nation. A value > 1 indicates persistence. The line in the color bar indicates a change of value interval (interval 0.5 from 0 to 1 and interval 0.1 from 1).

persistence even without harvest, while Mexico and also Cuba were self-persistent for all higher harvest rates. In the stochastic model, all the ecoregional networks were found to be persistent for the no-harvest rate scenario (Figure 4A). At FMSY , the Southern Gulf of Mexico had the highest growth rate (𝜆) followed by the Greater Antilles, the Floridian, and the Bahamian networks (Figure 4B). The inclusion of Haiti, Dominican Republic, Cayman Islands, Jamaica, and Puerto Rico in the Greater Antilles network led them to be part of a persistent network although they were not selfpersistent at FMSY (Figure 3B). Similarly, Turks and Caicos became persistent when it was included in the Bahamian network. The division of Mexico into two networks (Southern Gulf of Mexico and Eastern Mexico) revealed that only the Southern Gulf of Mexico was always persistent regardless of the harvest rates tested. The Bahamian network was also persistent at F1 , whereas neither the Bahamas nor Turks and Caicos were self-persistent on their own (Figures 3C, 4C). Few differences were observed in the deterministic model (Figure S5); when there was no harvest, the Eastern Caribbean and the Northern Gulf of Mexico networks were not found to be persistent and, at F1 , only the Southern Gulf of Mexico and the Greater Antilles networks were persistent.

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DIS CUS S IO N

Our study is the first—to our knowledge—to assess spatial patterns of persistence of a marine metapopulation by coupling large-scale connectivity information to a dynamic population model. Although spiny lobster populations are highly connected among Caribbean nations via larval dispersal, several nations and ecoregional networks that could persist independently of the larger basin-wide metapopulation—even at high exploitation rates—were identified. Consequently, the dynamics and management of those subpopulations are not intrinsically dependent on “upstream” connectivity. By contrast, the persistence of the remaining subpopulations was highly dependent on transnational connectivity. In both population models, spiny lobster population persistence depended strongly on the exploitation rate. The overall metapopulation was always persistent, primarily because the highest harvest rate considered was just below the maximum sustainable harvest rate possible in the deterministic model. Within that range of harvest rates, we identified shifting patterns of persistence in the individual nations and ecoregions. Few nations were self-persistent in the case of an intermediate (FMSY ) and high (F1 ) harvest rate. Considering the

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Gradient of spiny lobster population networks persistence in the Caribbean region based on marine ecoregions (Spalding et al., 2007) for the stochastic model for three harvest rates: F0 (A), FMSY (B), and F1 (C) Notes. The color represents the value of the population growth rate estimated by simulation for each ecoregion. A value > 1 indicates persistence. The line in the color bar indicates a change of value interval (interval 0.5 from 0 to 1 and interval 0.1 from 1).

intermediate fishing rate as a hypothetical reference point for the current state of exploitation in the Caribbean region, Mexico, United States, Cuba, and the Bahamas are the only selfpersistent nations (as predicted by the stochastic model). In case of a high exploitation of the resource, Mexico would be the only nation to self-persist. These results suggest that spiny lobster management at national scales is currently possible in those nations, despite high larval inputs from the southern and eastern Caribbean region, unlike previously thought based on larval transport information alone (Kough et al., 2013; SEDAR, 2010). Our analysis of persistence highlights different facets of the population dynamics of spiny lobster. Self-persistent nations can support persistence of networks that include non-selfpersistent nations. This was the case for all the networks in the unharvested scenario and for the network-persistent Bahamian and Greater Antilles ecoregions at the intermediate harvest rate, in which the Bahamas and Cuba, respectively, were also self-persistent. However, there were cases in which connectivity led to the persistence of networks even when all of the constituent nations were not self-persistent. This occurred for the Bahamian network at the highest harvest rate, which was network persistent despite neither the

Bahamas nor Turks and Caicos being self-persistent at that harvest rate. By contrast, several nations in the southern and eastern Caribbean were never found to be self-persistent or members of a network-persistent ecoregion when harvest was included in our model. For those nations, the populations are sustained only by demographic replacement along the lowprobability dispersal pathways from persistent ecoregions in the north and west (in agreement with Kough et al., 2013). In the context of spatial fisheries management, the appropriate spatial scale of managed areas strongly depends on the patterns of larval connectivity (Botsford, Hastings, & Gaines, 2001; Kaplan, Botsford, & Jorgensen, 2006; Watson et al., 2011; White et al., 2010). Moreover, areas wherein populations persist should be identified and targeted to ensure effective management. For example, areas with high larval retention and the potential for self-persistence areas are well suited for the placement of no-take reserves, which could support self-persistent populations even in the face of high levels of extraction outside of the reserve boundaries (Costello et al., 2010; White et al., 2010; the trade-off inherent in this planning advice is that extremely high larval retention will limit the supply of larvae from the reserve to harvested areas). Additionally, accounting for patterns of network persistence

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can improve overall fishery biomass, yield, and resilience to disturbance in marine spatial management (Hopf, Jones, Williamson, & Connolly, 2016; Magris et al., 2018). Successful spatial planning would also benefit from other metrics of spatial demographic connectivity, such as eigenvector centrality, which identifies high-contribution subpopulations (Magris et al., 2018; White et al., 2014). No-take marine reserves may boost the persistence of spiny lobster in the southern and eastern Caribbean ecoregions, because the dispersal network from within those ecoregions is sufficient to sustain populations in the absence of harvest. Chollett et al. (2017) showed that including a reserve network protecting 20% of fishing grounds in Honduras allowed the persistence of spiny lobster population, while we found that Honduras was self-persistent only when the resource was nonexploited (although we explored a limited range of harvest rates). Assessing the effect of implementing reserves for fisheries management could be useful in nations or ecoregions that are currently predicted to be non-persistent when harvested. It is important to recognize that reserves are not a universally applicable management tool, and may not be advisable in nations or ecoregions with very low larval retention (Kough, Paris, & Butler, 2018). We propose a new vision of the spiny lobster management in the Caribbean, with the implementation of regional management scheme in South Gulf of Mexico and Florida and transnational management in the Greater Antilles (Cuba, Haiti, Dominican Republic, Cayman Islands, Jamaica, Puerto Rico) and the Bahamian ecoregions (The Bahamas and Turks and Caicos). The persistence in the latter two networks is largely driven by the self-persistence in Cuba and the Bahamas, respectively, where separate management measures at national scale could be established. In the Caribbean region, the Western Central Atlantic Fishery Commission has gathered together fishery organizations and countries sharing the spiny lobster resource. A first meeting pointed to a need of a regional management plan to address the fishery issues (FAO, 2015). In 2016, several measures followed (seasonal closure, minimum harvest size), but no regional management plan was proposed (FAO, 2016). At this time, Cuba, Mexico, and United States were mentioned as having existing national plans to manage the fishery. Nation-scale management for those nations aligns with our self-persistence calculations (though the US management plan currently assumes that all settling larvae arrive from upstream populations; SEDAR, 2010). Our proposed vision for transnational spiny lobster management in the Caribbean region could inform the development of an actual regional management plan. Our results necessarily depended on simplifying assumptions about overall spiny lobster harvest being the same among nations (Lett, Nguyen-Huu, Cuif, Saenz-Agudelo, & Kaplan, 2015). This assumption is largely conservative (e.g., we did not assume that harvest was particularly low or better man-

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aged in any one nation) and merely reflect the current state of biological and management knowledge—the analysis could be updated as new information becomes available. However, more importantly, this analysis illustrates the need to account for both spatial variations in larval production and population dynamics when assessing patterns of metapopulation connectivity for management (Burgess et al., 2014; Watson et al., 2010, 2012). Our study emphasizes that accounting for patterns of persistence can reveal the spatial scale of dynamics within metapopulation networks. By simulating the demographic feedbacks inherent in population dynamics, we identified locations that are likely to be self-sustaining and could persist without upstream larval supply, even though current rates of upstream larval supply are very high. Just because a subpopulation receives many immigrants does not mean that it is dependent on immigration. This population dynamic perspective can lead to different conclusions regarding the appropriate spatial scales of management than looking at larval dispersal probabilities alone. ACKNOW LEDGMENTS We thank Philippe Verley for his help with the ontogenetic vertical migration module in Ichthyop and Tom Matthews for providing inputs on spiny lobster fisheries in the Caribbean region. We are grateful to two anonymous reviewers whose constructive comments significantly improved the quality of the manuscript. LG and LC are supported by the Harbor Branch Oceanographic Institute Foundation. This is contribution 482 from PISCO, the Partnership for Interdisciplinary Studies of Coastal Oceans. This is contribution number 2152 from Harbor Branch Oceanographic Institute at Florida Atlantic University. REFERENCES Andréfouët, S., Muller-Karger, F. E., Robinson, J. A., Kranenburg, C. J., Torres-Pulliza, D., Spraggins, S. A., & Murch, B. (2005). Global assessment of modern coral reef extent and diversity for regional science and management applications: A view from space. Proceedings of the 10th International Coral Reef Symposium, Okinawa, Japan, 1732−1745. Botsford, L. W., Hastings, A., & Gaines, S. D. (2001). Dependence of sustainability on the configuration of marine reserves and larval dispersal distance. Ecology Letters, 4, 144−150. Burgess, S. C., Nickols, K. J., Griesemer, C. D., Barnett, L. A. K., Dedrick, A. G., Satterthwaite, E. V., … Botsford, L. W. (2014). Beyond connectivity: How empirical methods can quantify population persistence to improve marine protected-area design. Ecological Applications, 24, 257−270. Chollett, I., Garavelli, L., O'Farrell, S., Chérubin, L., Matthews, T. R., Mumby, J., & Box, S. J. (2017). A genuine win-win: Resolving the “Conserve or Catch” conflict in marine reserves network design. Conservation Letters. https://doi.org/10.1111/conl.12318

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S U P P O RT I NG IN FO R M AT I O N Additional supporting information may be found online in the Supporting Information section at the end of the article. How to cite this article: Garavelli L, White JW, Chollett I, Chérubin LM. Population models reveal unexpected patterns of local persistence despite widespread larval dispersal in a highly exploited species. Conservation Letters. 2018;e12567. https://doi.org/10.1111/ conl.12567