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      Divergent responses of pelagic and benthic fish body-size structure to remoteness and protection from humans

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          Abstract

          Animal body-size variation influences multiple processes in marine ecosystems, but habitat heterogeneity has prevented a comprehensive assessment of size across pelagic (midwater) and benthic (seabed) systems along anthropic gradients. In this work, we derive fish size indicators from 17,411 stereo baited-video deployments to test for differences between pelagic and benthic responses to remoteness from human pressures and effectiveness of marine protected areas (MPAs). From records of 823,849 individual fish, we report divergent responses between systems, with pelagic size structure more profoundly eroded near human markets than benthic size structure, signifying greater vulnerability of pelagic systems to human pressure. Effective protection of benthic size structure can be achieved through MPAs placed near markets, thereby contributing to benthic habitat restoration and the recovery of associated fishes. By contrast, recovery of the world’s largest and most endangered fishes in pelagic systems requires the creation of highly protected areas in remote locations, including on the High Seas, where protection efforts lag.

          Editor’s summary

          Marine megafauna are increasingly threatened and are difficult to protect. Understanding the influence of humans on body size in fishes is also challenging given that data on marine species often come from fishery-based activities. Letessier et al . deployed more than 17,000 remotely operated baited devices to collect data on fish size and abundance as related to habitat (pelagic or benthic), human activities, and marine protected areas. Pelagic species were strongly influenced by human pressures and protection. The authors concluded that benthic species could be effectively protected even near markets, whereas only more remote protected areas will effectively safeguard large pelagic species. —Sacha Vignieri

          Abstract

          Fish size structure is more affected by human pressure in midwater than in seabed ecosystems.

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          Most cited references72

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          Fishing down marine food webs

          The mean trophic level of the species groups reported in Food and Agricultural Organization global fisheries statistics declined from 1950 to 1994. This reflects a gradual transition in landings from long-lived, high trophic level, piscivorous bottom fish toward short-lived, low trophic level invertebrates and planktivorous pelagic fish. This effect, also found to be occurring in inland fisheries, is most pronounced in the Northern Hemisphere. Fishing down food webs (that is, at lower trophic levels) leads at first to increasing catches, then to a phase transition associated with stagnating or declining catches. These results indicate that present exploitation patterns are unsustainable.
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            Is Open Access

            The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded

            The coefficient of determination R 2 quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest. However, estimating R 2 for generalized linear mixed models (GLMMs) remains challenging. We have previously introduced a version of R 2 that we called for Poisson and binomial GLMMs, but not for other distributional families. Similarly, we earlier discussed how to estimate intra-class correlation coefficients (ICCs) using Poisson and binomial GLMMs. In this paper, we generalize our methods to all other non-Gaussian distributions, in particular to negative binomial and gamma distributions that are commonly used for modelling biological data. While expanding our approach, we highlight two useful concepts for biologists, Jensen's inequality and the delta method, both of which help us in understanding the properties of GLMMs. Jensen's inequality has important implications for biologically meaningful interpretation of GLMMs, whereas the delta method allows a general derivation of variance associated with non-Gaussian distributions. We also discuss some special considerations for binomial GLMMs with binary or proportion data. We illustrate the implementation of our extension by worked examples from the field of ecology and evolution in the R environment. However, our method can be used across disciplines and regardless of statistical environments.
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              Is Open Access

              ggeffects: Tidy Data Frames of Marginal Effects from Regression Models

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                Author and article information

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                Journal
                Science
                Science
                0036-8075
                1095-9203
                March 2024
                March 2024
                : 383
                : 6686
                : 976-982
                Affiliations
                [1 ]CESAB – FRB, Montpellier, France.
                [2 ]Institute of Zoology, Zoological Society of London, Regent’s Park, London, UK.
                [3 ]Marine Futures Lab, School of Biological Sciences, University of Western Australia, Perth, WA, Australia.
                [4 ]MARBEC, Université de Montpellier, CNRS, Ifremer, IRD, Montpellier, France.
                [5 ]Red Sea Fisheries Research Station, P.O. Box 730, Port Sudan, Red Sea State, Sudan.
                [6 ]Faculty of Marine Science and Fisheries, Red Sea State University, P.O. Box 24, Port Sudan, Red Sea State, Sudan.
                [7 ]National Geographic Society, Washington, DC 20036, USA.
                [8 ]Hawai‘i Institute of Marine Biology, University of Hawai‘i, Kāne‘ohe, Hawai‘i, USA.
                [9 ]Galapagos Science Center, Universidad San Francisco de Quito, Quito, Ecuador.
                [10 ]MigraMar, Olema, CA, USA.
                [11 ]ENTROPIE, Institut de Recherche pour le Développement, IRD-UR-UNC-IFREMER-CNRS, Centre IRD de Nouméa, Nouméa Cedex, New-Caledonia, France.
                [12 ]Institute of Marine Research, Nye Flødevigveien 20, 4817 His, Norway.
                [13 ]Centre for Coastal Research (CCR), Department of Natural Sciences, University of Agder, P.O. Box 422, N-4604 Kristiansand, Norway.
                [14 ]Oceans Institute, University of Western Australia, Perth, WA, Australia.
                Article
                10.1126/science.adi7562
                38422147
                77b31317-8a8a-4b58-abae-7864f4b5c5c2
                © 2024

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