The formation of marine kin structure : effects of dispersal, larval cohesion, and variable reproductive success

Author Posting. © The Author(s), 2018. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Ecology 99 (2018): 2374-2384, doi:10.1002/ecy.2480.The spatial distribution of relatives has profound e ects on kin interactions, inbreeding, and inclusive tness. Yet, in the marine environment, the processes that generate patterns of kin structure remain understudied because larval dispersal on ocean currents was historically assumed to disrupt kin associations. Recent genetic evidence of co-occurring siblings challenges this assumption and raises the intriguing question of how siblings are found together after a (potentially) disruptive larval phase. Here, we develop individual based models to explore how stochastic processes operating at the individual level a ect expected kinship at equilibrium. Speci cally, we predict how limited dispersal, sibling cohesion, and variability in reproductive success di erentially a ect patterns of kin structure. All three mechanisms increase mean kinship within populations, but their spatial e ects are markedly di erent. We nd that: (1) when dispersal is limited, kinship declines monotonically as a function of the distance between individuals; (2) when siblings disperse cohesively, kinship increases within a site relative to between sites; and (3) when reproductive success varies, kinship increases equally at all distances. The di erential e ects of these processes therefore only become apparent when individuals are sampled at multiple spatial scales. Notably, our models suggest that aggregative larval behaviors, such as sibling cohesion, are not necessary to explain documented levels of relatedness within marine populations. Together, these ndings establish a theoretical framework for disentangling the drivers of marine kin structure.CCD was supported by a Weston Howland Jr. Postdoctoral Scholarship from WHOI. MGN was supported by a grant from the US NSF (DEB-1558904)

The evolution of marine larval dispersal kernels in spatially structured habitats: Analytical models, individual-based simulations, and comparisons with empirical estimates.

Author Posting. © University of Chicago Press, 2019. This article is posted here by permission of University of Chicago Press for personal use, not for redistribution. The definitive version was published in Shaw, A. K., D'Aloia, C. C., & Buston, P. M. The evolution of marine larval dispersal kernels in spatially structured habitats: Analytical models, individual-based simulations, and comparisons with empirical estimates. American Naturalist, 193(3), (2019):424-435, doi:10.1086/701667.Understanding the causes of larval dispersal is a major goal of marine ecology, yet most research focuses on proximate causes. Here we ask how ultimate, evolutionary causes affect dispersal. Building on Hamilton and May’s classic 1977 article “Dispersal in Stable Habitats,” we develop analytic and simulation models for the evolution of dispersal kernels in spatially structured habitats. First, we investigate dispersal in a world without edges and find that most offspring disperse as far as possible, opposite the pattern of empirical data. Adding edges to our model world leads to nearly all offspring dispersing short distances, again a mismatch with empirical data. Adding resource heterogeneity improves our results: most offspring disperse short distances with some dispersing longer distances. Finally, we simulate dispersal evolution in a real seascape in Belize and find that the simulated dispersal kernel and an empirical dispersal kernel from that seascape both have the same shape, with a high level of short-distance dispersal and a low level of long-distance dispersal. The novel contributions of this work are to provide a spatially explicit analytic extension of Hamilton and May’s 1977 work, to demonstrate that our spatially explicit simulations and analytic models provide equivalent results, and to use simulation approaches to investigate the evolution of dispersal kernel shape in spatially complex habitats. Our model could be modified in various ways to investigate dispersal evolution in other species and seascapes, providing new insights into patterns of marine larval dispersal.We thank S. Levin, M. Neubert, S. Proulx, L. Sullivan, R. Warner, and several anonymous reviewers for helpful comments. This work was carried out in part using computing resources at the University of Minnesota Supercomputing Institute. The project was supported by a start-up award from the University of Minnesota to A.K.S. and a National Science Foundation award (OCE-1260424) to P.M.B. and colleagues; C.C.D. was supported by the Weston Howland Junior Postdoctoral Scholarship from the Woods Hole Oceanographic Institution.2020-01-1

Patterns, causes, and consequences of marine larval dispersal

Quantifying the probability of larval exchange among marine populations is key to predicting local population dynamics and optimizing networks of marine protected areas. The pattern of connectivity among populations can be described by the measurement of a dispersal kernel. However, a statistically robust, empirical dispersal kernel has been lacking for any marine species. Here, we use genetic parentage analysis to quantify a dispersal kernel for the reef fish Elacatinus lori, demonstrating that dispersal declines exponentially with distance. The spatial scale of dispersal is an order of magnitude less than previous estimates—the median dispersal distance is just 1.7 km and no dispersal events exceed 16.4 km despite intensive sampling out to 30 km from source. Overlaid on this strong pattern is subtle spatial variation, but neither pelagic larval duration nor direction is associated with the probability of successful dispersal. Given the strong relationship between distance and dispersal, we show that distance-driven logistic models have strong power to predict dispersal probabilities. Moreover, connectivity matrices generated from these models are congruent with empirical estimates of spatial genetic structure, suggesting that the pattern of dispersal we uncovered reflects long-term patterns of gene flow. These results challenge assumptions regarding the spatial scale and presumed predictors of marine population connectivity. We conclude that if marine reserve networks aim to connect whole communities of fishes and conserve biodiversity broadly, then reserves that are close in space (<10 km) will accommodate those members of the community that are short-distance dispersers.We thank Diana Acosta, Alben David, Kevin David, Alissa Rickborn, and Derek Scolaro for assistance with field work; Eliana Bondra for assistance with molecular work; and Peter Carlson for assistance with otolith work. We are grateful to Noel Anderson, David Lindo, Claire Paris, Robert Warner, Colleen Webb, and two anonymous reviewers for comments on this manuscript. This work was supported by National Science Foundation (NSF) Grant OCE-1260424, and C.C.D. was supported by NSF Graduate Research Fellowship DGE-1247312. All work was approved by Belize Fisheries and Boston University Institutional Animal Care and Use Committee. (OCE-1260424 - National Science Foundation (NSF); DGE-1247312 - NSF Graduate Research Fellowship)Published versio

Coupled Networks of Permanent Protected Areas and Dynamic Conservation Areas for Biodiversity Conservation Under Climate Change

The complexity of climate change impacts on ecological processes necessitates flexible and adaptive conservation strategies that cross traditional disciplines. Current strategies involving protected areas are predominantly fixed in space, and may on their own be inadequate under climate change. Here, we propose a novel approach to climate adaptation that combines permanent protected areas with temporary conservation areas to create flexible networks. Previous work has tended to consider permanent and dynamic protection as separate actions, but their integration could draw on the strengths of both approaches to improve biodiversity conservation and help manage for ecological uncertainty in the coming decades. As there are often time lags in the establishment of new permanent protected areas, the inclusion of dynamic conservation areas within permanent networks could provide critical transient protection to mitigate land-use changes and biodiversity redistributions. This integrated approach may be particularly useful in highly human-modified and fragmented landscapes where areas of conservation value are limited and long-term place-based protection is unfeasible. To determine when such an approach may be feasible, we propose the use of a decision framework. Under certain scenarios, these coupled networks have the potential to increase spatio-temporal network connectivity and help maintain biodiversity and ecological processes under climate change. Implementing these networks would require multidisciplinary scientific evidence, new policies, creative funding solutions, and broader acceptance of a dynamic approach to biodiversity conservation

Coupled networks of permanent protected areas and dynamic conservation areas for biodiversity conservation under climate change

The complexity of climate change impacts on ecological processes necessitates flexible and adaptive conservation strategies that cross traditional disciplines. Current strategies involving protected areas are predominantly fixed in space, and may on their own be inadequate under climate change. Here, we propose a novel approach to climate adaptation that combines permanent protected areas with temporary conservation areas to create flexible networks. Previous work has tended to consider permanent and dynamic protection as separate actions, but their integration could draw on the strengths of both approaches to improve biodiversity conservation and help manage for ecological uncertainty in the coming decades. As there are often time lags in the establishment of new permanent protected areas, the inclusion of dynamic conservation areas within permanent networks could provide critical transient protection to mitigate land-use changes and biodiversity redistributions. This integrated approach may be particularly useful in highly human-modified and fragmented landscapes where areas of conservation value are limited and long-term place-based protection is unfeasible. To determine when such an approach may be feasible, we propose the use of a decision framework. Under certain scenarios, these coupled networks have the potential to increase spatio-temporal network connectivity and help maintain biodiversity and ecological processes under climate change. Implementing these networks would require multidisciplinary scientific evidence, new policies, creative funding solutions, and broader acceptance of a dynamic approach to biodiversity conservation

Cytochrome b and radloci genotype identification data from fish sampled in the Belizean Barrier Reef in 2014.

Dataset: Goby genotypesCytochrome b and radloci genotype identification data from fish sampled in the Belizean Barrier Reef in 2014. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/738714NSF Division of Ocean Sciences (NSF OCE) OCE-126042

UTM coordinates for waypoint locations used to generate Elori raw data in 2006.

Dataset: Goby data geolocationsUTM coordinates for waypoint locations used to generate Elori raw data in 2006. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/704783NSF Division of Ocean Sciences (NSF OCE) OCE-126042

Data from fish genotyped at 14 and 20 loci at different life stages in the Belizean Barrier Reef in 2013.

Dataset: Data from fish genotyped at 14 and 20 lociData from fish genotyped at 14 and 20 loci at different life stages in the Belizean Barrier Reef in 2013. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/738724NSF Division of Ocean Sciences (NSF OCE) OCE-126042

Goby distribution and morphology data from Curlew Caye in the Belizean Barrier Reef collected in 2011.

Dataset: Goby distribution and abundanceGoby distribution and morphology data from Curlew Caye in the Belizean Barrier Reef collected in 2011. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/728230NSF Division of Ocean Sciences (NSF OCE) OCE-126042

Geolocation, abundance, and morphology data from Carrie Bow Caye in the Belizean Barrier Reef.

Dataset: Goby abundance and morphologyGeolocation, abundance, and morphology data from Carrie Bow Caye in the Belizean Barrier Reef. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/705432NSF Division of Ocean Sciences (NSF OCE) OCE-126042