Using Bayesian stable isotope mixing models and generalized additive models to resolve diet changes for fish-eating killer whales Orcinus orca

Abstract

Understanding diet composition is fundamental to making conservation and management decisions about depleted species, particularly when nutritional stress is a potential threat hindering recovery. Diet in free-ranging marine mammals is challenging to study, but stable isotope mixing models are a powerful means of estimating the contribution of prey species to diet and can improve precision by leveraging information from multiple data sources. We evaluated diet composition of a fish-eating killer whale population (Southern Resident killer whales, Orcinus orca) using 2 approaches. First, we fit generalized additive models to evaluate seasonal and interannual patterns in isotopic values across age, sex, and pod, which revealed seasonal carbon enrichment for certain pods and a recent increased nitrogen enrichment that could suggest increased Chinook salmon consumption, changing isotopic values of prey, or nutritional stress. Second, we developed a Bayesian stable isotope mixing model that accounts for the different integration times represented by bulk stable isotopes and fecal samples. Results showed that estimated prey contributions are similar between prey data sources, though the precision of estimates from periods with smaller sample sizes was improved by using an informative prior to account for the different consumption windows of the data. This study illustrates the importance of improving our understanding of how killer whale diets vary over time (both seasonally and across years) and uses a novel approach to resolve 2 sources of diet information (stable isotope, fecal samples) with different consumption windows.