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Comparing isotopic niche widths among and within communities: SIBER – Stable Isotope Bayesian Ellipses in R
Author(s) -
Jackson Andrew L.,
Inger Richard,
Parnell Andrew C.,
Bearhop Stuart
Publication year - 2011
Publication title -
journal of animal ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.134
H-Index - 157
eISSN - 1365-2656
pISSN - 0021-8790
DOI - 10.1111/j.1365-2656.2011.01806.x
Subject(s) - ellipse , bayesian probability , computer science , convex hull , inference , niche , bayesian inference , data mining , statistical inference , statistics , multivariate statistics , econometrics , mathematics , ecology , artificial intelligence , machine learning , biology , regular polygon , geometry
Summary 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.