Mayumi L. Arimitsu
Taal Levi
Gretchen Roffler
James T. Thorson
2022
<p><span>Diet analysis integrates a wide variety of visual, chemical, and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, for example, highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package </span><i>mvtweedie</i><span> that extends capabilities of widely used packages in R (</span><i>mgcv</i><span> and </span><i>ggplot2</i><span>) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted Puffins (</span><i>Fratercula cirrhata</i><span>) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980–2000) and then Pacific herring and capelin (2000–2020), while wolves (</span><i>Canis lupus ligoni</i><span>) in southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines.</span></p>
application/pdf
10.1002/ecy.3637
en
Ecological Society of America
Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie
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