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      Reverse engineering field-derived vertical distribution profiles to infer larval swimming behaviors

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          Abstract

          Biophysical models are well-used tools for predicting the dispersal of marine larvae. Larval behavior has been shown to influence dispersal, but how to incorporate behavior effectively within dispersal models remains a challenge. Mechanisms of behavior are often derived from laboratory-based studies and therefore, may not reflect behavior in situ. Here, using state-of-the-art models, we explore the movements that larvae must undertake to achieve the vertical distribution patterns observed in nature. Results suggest that behaviors are not consistent with those described under the tidally synchronized vertical migration (TVM) hypothesis. Instead, we show ( i) a need for swimming speed and direction to vary over the tidal cycle and ( ii) that, in some instances, larval swimming cannot explain observed vertical patterns. We argue that current methods of behavioral parameterization are limited in their capacity to replicate in situ observations of vertical distribution, which may cause dispersal error to propagate over time, due to advective differences over depth and demonstrate an alternative to laboratory-based behavioral parameterization that encompasses the range of environmental cues that may be acting on planktic organisms.

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          Most cited references48

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          Recent progress in understanding larval dispersal: new directions and digressions.

          Larvae have been difficult to study because their small size limits our ability to understand their behavior and the conditions they experience. Questions about larval transport focus largely on (a) where they go [dispersal] and (b) where they come from [connectivity]. Mechanisms of transport have been intensively studied in recent decades. As our ability to identify larval sources develops, the consequences of connectivity are garnering more consideration. Attention to transport and connectivity issues has increased dramatically in the past decade, fueled by changing motivations that now include management of fisheries resources, understanding of the spread of invasive species, conservation through the design of marine reserves, and prediction of climate-change effects. Current progress involves both technological advances and the integration of disciplines and approaches. This review focuses on insights gained from physical modeling, chemical tracking, and genetic approaches. I consider how new findings are motivating paradigm shifts concerning (1) life-history consequences; (2) the openness of marine populations, self-recruitment, and population connectivity; (3) the role of behavior; and (4) the significance of variability in space and time. A challenge for the future will be to integrate methods that address dispersal on short (intragenerational) timescales such as elemental fingerprinting and numerical simulations with those that reflect longer timescales such as gene flow estimates and demographic modeling. Recognition and treatment of the continuum between ecological and evolutionary timescales will be necessary to advance the mechanistic understanding of larval and population dynamics.
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            Connectivity and resilience of coral reef metapopulations in marine protected areas: matching empirical efforts to predictive needs.

            Design and decision-making for marine protected areas (MPAs) on coral reefs require prediction of MPA effects with population models. Modeling of MPAs has shown how the persistence of metapopulations in systems of MPAs depends on the size and spacing of MPAs, and levels of fishing outside the MPAs. However, the pattern of demographic connectivity produced by larval dispersal is a key uncertainty in those modeling studies. The information required to assess population persistence is a dispersal matrix containing the fraction of larvae traveling to each location from each location, not just the current number of larvae exchanged among locations. Recent metapopulation modeling research with hypothetical dispersal matrices has shown how the spatial scale of dispersal, degree of advection versus diffusion, total larval output, and temporal and spatial variability in dispersal influence population persistence. Recent empirical studies using population genetics, parentage analysis, and geochemical and artificial marks in calcified structures have improved the understanding of dispersal. However, many such studies report current self-recruitment (locally produced settlement/settlement from elsewhere), which is not as directly useful as local retention (locally produced settlement/total locally released), which is a component of the dispersal matrix. Modeling of biophysical circulation with larval particle tracking can provide the required elements of dispersal matrices and assess their sensitivity to flows and larval behavior, but it requires more assumptions than direct empirical methods. To make rapid progress in understanding the scales and patterns of connectivity, greater communication between empiricists and population modelers will be needed. Empiricists need to focus more on identifying the characteristics of the dispersal matrix, while population modelers need to track and assimilate evolving empirical results.
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              Connectivity Modeling System: A probabilistic modeling tool for the multi-scale tracking of biotic and abiotic variability in the ocean

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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                May 23 2019
                : 201900238
                Article
                10.1073/pnas.1900238116
                6575233
                31123143
                9bef615e-cba2-41d0-a463-729eb69a97d0
                © 2019

                Free to read

                https://www.pnas.org/site/aboutpnas/licenses.xhtml

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