Predicting riverine fish production using empirical models and the metabolic theory of ecology

Fish production integrates many different measures of community performance, such as abundance, biomass, growth, and reproduction, into one valuable quantitative metric but requires resource intensive data for empirical estimation. While published empirical models and the metabolic theory of ecology (MTE) represent alternative methods to estimate fish production, few studies have focused on productivity models for stream fish assemblages. The goal of our study was to determine whether existing empirical models and elements of the metabolic theory of ecology can reliably estimate stream fish productivity. We used production estimates from the literature (n = 107) to parameterize models based on the metabolic theory of ecology and new estimates of stream fish production from North America (n = 78) to compare and validate all models. Using major axis regression, we determined that while all models had strongly correlated production estimates relative to the observed values (r² range: [0.496, 0.815]), not all the models produced accurate estimates. The MTE model with the temperature component had a poorer predictive performance (RMSE = 0.502) relative to models based solely on allometric scaling (RMSE range: [0.299, 0.380]). We conclude that standard production models can generate relative estimates of production using general fish sample data, however, the accuracy and precision of the estimates can vary among the models. Our study highlights the need for productivity estimates for stream fish assemblages from different geographic regions, to test empirical models with novel datasets, and for further investigation of temperature effects on fish productivity.