Using multi‐scale spatial models of dendritic ecosystems to infer abundance of a stream salmonid

Abstract

Abstract Understanding patterns of species abundance is essential for planning landscape‐level conservation. The complex hierarchies of dendritic ecosystems result in different levels of heterogeneity at distinct geographic scales. Species responses to dynamic environmental drivers may also vary spatially depending on their interactions with landscape features. Monitoring abundance by explicitly quantifying their spatial and temporal variation is important for strategic management. We analysed brook trout (Salvelinus fontinalis) count data collected from 173 sites in western North Carolina between 1989 and 2015. We developed a Bayesian hierarchical model that used single‐ and multi‐pass electro‐fishing data and characterized their respective capture probabilities. We quantified spatial variation using a multi‐scale process model representative of the nested stream habitats, and we investigated differences in population temporal trends and responses to seasonal weather patterns by space and life stage. Trout abundance was lower on the Atlantic slope of the Eastern Continental Divide than in the interior, on average, and the Atlantic slope juveniles were more adversely affected by high winter flows. However, Atlantic slope populations of both lifestages demonstrated positive temporal trends, whereas Interior juveniles demonstrated a negative trend. We found higher spatial variation than temporal variation in abundance when conditioned on the covariates, where the primary source of spatial heterogeneity was revealed at the segment level, compared to watershed or network levels. Our multi‐scale spatial model outperformed simpler models in abundance estimation and out‐of‐sample prediction. The inferred per‐pass capture probabilities indicated that single‐pass surveys were as efficient as multi‐pass surveys. Synthesis and applications. Our study suggested conservation priority should involve multiple criteria, including present‐day abundance, temporal trend and sensitivity to environmental drivers. Based on the inferred scale‐specific variations in trout abundance, we recommend that future surveys strategically combine single‐pass surveys with multi‐pass surveys to optimize abundance estimation. Our approach is widely applicable to other species and ecosystems occupying dendritic habitats.