Thu, Aug 18, 2022: 5:00 PM-6:30 PM
ESA Exhibit Hall
Background/Question/Methods: Biomass production is a key ecosystem process and reflects how energy is distributed within an ecosystem by integrating multiple components of community performance. Previous studies have considered a wide variety of community performance measures for aquatic systems (e.g., indices of biotic integrity, trout density), but few have investigated how total fish productivity of riverine systems is affected by environmental variables. We investigated the influence of multiple abiotic and biotic variables on total stream fish productivity in wadeable stream reaches (n = 2,294) across a broad region of Ontario, Canada using variance-partitioning and model selection.
Results/Conclusions: We found that biotic predictors explained more variation in productivity relative to the abiotic variables (biotic R2partial = 0.277; abiotic R2partial = 0.061). The best predictive model included species richness, salmonid presence/absence, centrarchid presence/absence, upstream catchment area, latitude, mean annual precipitation, growing degree days, and ecozone (R2marginal = 0.385; R2conditional = 0.512). These findings indicate that biotic variables are key to understanding stream fish production patterns, however, both types of variables can be used to predict stream fish productivity.
Results/Conclusions: We found that biotic predictors explained more variation in productivity relative to the abiotic variables (biotic R2partial = 0.277; abiotic R2partial = 0.061). The best predictive model included species richness, salmonid presence/absence, centrarchid presence/absence, upstream catchment area, latitude, mean annual precipitation, growing degree days, and ecozone (R2marginal = 0.385; R2conditional = 0.512). These findings indicate that biotic variables are key to understanding stream fish production patterns, however, both types of variables can be used to predict stream fish productivity.