Spatio‐temporal analyses of marine predator diets from data‐rich and data‐limited systems

Abstract

Accounting for variation in prey mortality and predator metabolic potential arising from spatial variation in consumption is an important task in ecology and resource management. However, there is no statistical method for processing stomach content data that accounts for fine-scale spatio-temporal structure while expanding individual stomach samples to population-level estimates of predation. Therefore, we developed an approach that fits a spatio-temporal model to both prey-biomass-perpredator-biomass data (i.e. the ratio of prey biomass in stomachs to predator weight) and predator biomass survey data, to predict "predator-expanded-stomach-contents" (PESCs). PESC estimates can be used to visualize either the annual landscape of PESCs (spatio-temporal variation), or can be aggregated across space to calculate annual variation in diet proportions (variation among prey items and among years).We demonstrated our approach in two contrasting scenarios: a data-rich situation involving eastern Bering Sea (EBS) large-size walleye pollock (Gadus chalcogrammus, Gadidae) for 1992-2015; and a data-limited situation involving West Florida Shelf red grouper (Epinephelus morio, Epinephelidae) for 2011-2015. Large walleye pollock PESC was predicted to be higher in very warm years on the Middle Shelf of the EBS, where food is abundant. Red grouper PESC was variable in north-western Florida waters, presumably due to spatio-temporal variation in harmful algal bloom severity.Our approach can be employed to parameterize or validate diverse ecosystem models, and can serve to address many fundamental ecological questions, such as providing an improved understanding of how climate-driven changes in spatial overlap between predator and prey distributions might influence predation pressure. K E Y W O R D S diet proportions, Poisson-link delta model, predation pressure, predator-expanded-stomachcontents, spatio-temporal model, stomach content data | 719 GRÜSS et al.1. INTRODUCTION 719 2. METHODS 721 2.1 Model overview 721 2.2 Model details 721 2.2.1 Poisson-link delta modelling framework 721 2.2.2 Parameter estimation 722 2.2.3 Estimation of predator-expanded-stomach-contents 722 2.2.4 Estimation of diet proportions and input sample sizes 723 2.3 Data-rich case-study 723 2.4 Data-limited case-study 725 2.5 Comparisons with other approaches to estimating diet proportions 726 3 RESULTS 727 3.1 Data-rich case-study 727 3.2 Data-limited case-study 729 3.3 Comparisons with other approaches to estimating diet proportions 730 4 DISCUSSION 731 ACKNOWLEDGMENTS 735

Understanding patterns of distribution shifts and range expansion/contraction for small yellow croaker (Larimichthys polyactis) in the Yellow Sea

“…In other words, we first fitted a binomial GLMM to encounter/non‐encounter data for yellow croaker, then fitted a Gamma GLMM to positive biomass catch rate data, and finally multiplied the predictions of the binomial and Gamma GLMMs to obtain final biomass density estimates for yellow croaker (Grüss, Walter, et al, 2019; Lo, Jacobson, & Squire, 1992). The spatio‐temporal delta‐Gamma GLMMs were implemented using R package “VAST” (Grüss, Rose, Justić, & Wang, 2020; Grüss, Thorson, et al, 2020; Thorson, 2019a), which is publicly available online (https://github.com/James-Thorson-NOAA/VAST). We employed the spatio‐temporal delta‐Gamma not only to estimate spatio‐temporal patterns of biomass density for yellow croaker, but also to understand how the northward and eastward COGs and effective area occupied of the yellow croaker population may have changed over the period 2001–2017, as described in detail below.…”

Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie

“…Analysts increasingly use diets of generalist predators to understand changes in prey availability across time and space when such information may be otherwise limited (Clare et al, 2014 ; Grüss et al, 2020 ). Developing exploratory models with covariates (as facilitated by using common packages such as mgcv ) can improve the accuracy of predictions across space and time, and provide greater understanding of factors regulating trophic interactions including numerical and functional responses.…”

Forecasting community reassembly using climate‐linked spatio‐temporal ecosystem models

“…For example, stomach-content analysis can identify predation between age-0 pollock, demersal pollock and arrowtooth flounder (Livingston et al 2017), discrete-choice models fitted to fishery catch rates or revenue can identify fishery performance (Haynie et al 2009), and seabird observers record variability in seabird bycatch (Anderson et al 2011). Importantly, these analyses are increasingly feasible within a spatio-temporal modelling framework (Grüss et al 2020), such that physiological and process research can be incorporated into spatial ecosystem models such as this. We therefore recommend further research to fit both distributional data (like those used here) and process data (growth, predation, maturity and reproduction) to improve forecasts of community reassembly.…”