In the face of a rapidly changing climate, mechanistic population models are a powerful tool for predicting the impacts of environmental conditions, such as temperature and disturbance, on specific taxa. However, population models typically focus solely on abundance, overlooking biomass, a critical link between populations and the broader food web. Here, we present a time-varying stage-structured matrix population model to predict the abundance and biomass of aquatic macroinvertebrates, a diverse set of organisms that are the food base for riparian and aquatic consumers. Forecasting the effects of temperature regime, high flow event timing, and load-following on aquatic macroinvertebrate abundances and biomass in regulated rivers provides insights into food web implications of various climate scenarios.
We apply this model to the Colorado River below Glen Canyon Dam, modelling five aquatic macroinvertebrate taxa that represent a range of life history strategies and include the native net-spinning caddisfly, the introduced New Zealand mudsnail, the introduced amphipod, a midge from the family Chironomidae, and a mayfly. Preliminary results demonstrate that increasing water temperatures leads to higher population abundances but reduced per-capita biomass. This inverse relationship creates a unimodal response in standing stock biomass, with a decline at warmer temperatures, potentially reducing food availability for higher trophic levels.
By integrating flow, temperature, and load-following predictions with mechanistic modeling, this work provides a framework for anticipating population-level responses to novel flow regimes driven by climate change or management actions. These modeling tools enable researchers and managers to develop adaptive strategies to preserve biodiversity and ecosystem function in dammed river systems, ensuring the resilience of these ecosystems in the face of a changing climate.