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      Ontogeny drives allochthonous trophic support of snappers: Seascape connectivity along the mangrove-seagrass-coral reef continuum of a tropical marine protected area

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            Comparing isotopic niche widths among and within communities: SIBER - Stable Isotope Bayesian Ellipses in R.

            1. The use of stable isotope data to infer characteristics of community structure and niche width of community members has become increasingly common. Although these developments have provided ecologists with new perspectives, their full impact has been hampered by an inability to statistically compare individual communities using descriptive metrics. 2. We solve these issues by reformulating the metrics in a Bayesian framework. This reformulation takes account of uncertainty in the sampled data and naturally incorporates error arising from the sampling process, propagating it through to the derived metrics. 3. Furthermore, we develop novel multivariate ellipse-based metrics as an alternative to the currently employed Convex Hull methods when applied to single community members. We show that unlike Convex Hulls, the ellipses are unbiased with respect to sample size, and their estimation via Bayesian inference allows robust comparison to be made among data sets comprising different sample sizes. 4. These new metrics, which we call SIBER (Stable Isotope Bayesian Ellipses in R), open up more avenues for direct comparison of isotopic niches across communities. The computational code to calculate the new metrics is implemented in the free-to-download package Stable Isotope Analysis for the R statistical environment. © 2011 The Authors. Journal of Animal Ecology © 2011 British Ecological Society.
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              Best practices for use of stable isotope mixing models in food-web studies

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                Author and article information

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                Journal
                Estuarine, Coastal and Shelf Science
                Estuarine, Coastal and Shelf Science
                Elsevier BV
                02727714
                January 2022
                January 2022
                : 264
                : 107591
                Article
                10.1016/j.ecss.2021.107591
                1c70892a-ed5e-474a-814d-f3e38715da55
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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