Data and code to replicate: Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie

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, e.g., 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 mvtweedie that
extends capabilities of widely used packages in R (mgcv and ggplot2) by
transforming output to calculate prey compositions.  We
demonstrate this approach and software using two examples. Tufted puffins
(Fratercula cirrhata) 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 (Canis lupus ligoni) in Southeast Alaska forage on mountain
goats and marmots in northern uplands and marine mammals in seaward island
coastlines.