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., highl...
Main Authors: | , , , |
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Format: | Dataset |
Language: | English |
Published: |
Dryad
2021
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Subjects: | |
Online Access: | https://dx.doi.org/10.5061/dryad.08kprr53h https://datadryad.org/stash/dataset/doi:10.5061/dryad.08kprr53h |
Summary: | 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) ... : File list Reproducible_script_R1.R Wolf.csv Seabird.csv MDO.seabirdforagingarea.SST.csv Description Reproducible_script_R1.R – R script used to replicate all analysis and figures in main text and appendices. See comments at top for directions prior to running. Wolf.csv - CSV file containing four columns used in the wolf metabarcoding case-study in Fig. 3 of the main text: “Latitude” -- Latitude of scat sample in Degree-decimals; “Longitude” -- Longitude of scat sample; “group” -- prey taxonomic group used in analysis; “Response” -- metabarcoding read count used as response variable. Seabird.csv - CSV file containing three columns used in the seabird bill-load case-study in Fig. 2 of the main text: “Year” – Year AD for bill-load sample; “group” -- prey taxonomic group used in analysis; “Response” – bill-load count used as response variable. MDO.seabirdforagingarea.SST.csv - CSV file containing two additional columns used in the seabird bill-load case-study in Fig. 2 of the main text: “Year” – Year AD, ... |
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