Disentangling the contributions of density dependence and independence to population growth rates

Separately quantifying the effects of density-dependent and density-independent factors on vital rates is necessary to determine their contribution to changes in population growth rates and better inform management and conservation. State space modeling and recently developed transient life table response experiments (tLTRE) at the level of vital rate predictors provide a powerful quantitative framework to disentangle the effects of environmental conditions and density dependence on population dynamics. We applied these approaches to assess the dynamics of a stream-dwelling rainbow trout (Oncorhynchus mykiss) population in the Stellako River in northern British Columbia from 1988 to 2022. Using an integrated, size class-structured dynamic N-mixture model with snorkel count and length-at-age data, we assessed the effects of mean summer air temperature, discharge, sockeye salmon returns, and density dependence on productivity, survival, and size class transition probabilities. In addition, we used tLTRE analysis to quantify the contribution of density dependence and environmental conditions to the dynamics of the study population over the study period. Productivity and survival of all size classes exhibited density dependence. The mean summer air temperature had a strong negative relationship with size class 1 (10-30 cm) fish survival, whereas the survival of size class 3 (50+ cm) was strongly positively related to sockeye salmon returns. Environmental stochasticity in productivity and temperature-driven survival of size class 1 fish were the most important contributors to population dynamics. A reduction in the survival of size class 1 due to warming summer temperatures was the primary driver of the population decline starting in the early 2000s. Our findings underscore the utility of integrated, dynamic N-mixture modeling combined with tLTRE at the level of vital rate predictors to identify environmental and demographic drivers of population growth rates to direct management and conservation efforts.