Spatially dynamic abundance patterns for a rare fish species

Recovery of rare and imperiled species is often supported by targeting the dynamic drivers of abundance patterns. However, knowledge of these drivers can be obscured by challenges stemming from species detectability and autocorrelated data. Longfin Smelt (Spirinchus thaleichthys) in the San Francisco Bay-Delta have become rare as the result of dramatic declines in abundance. Abundance indices from a multitude of surveys in the Bay-Delta have documented these declines, but utilizing survey data to support recovery has been more challenging. To elucidate the spatiotemporal drivers of Longfin Smelt abundance patterns, we employed spatial multistate (“abundant”) dynamic occupancy models that integrated surveys across seasons from three fish monitoring programs. We found that species occupancy may be driven by broad-scale temporal and spatial processes not accounted for in the environmental covariates we used given the relative importance of day of year and spatial and year random effects. However, we also found that relatively high abundance may be driven in part by local habitat conditions. Our analysis approach allowed us to capture various sources of heterogeneity in the data and map seasonal distribution and abundance patterns for this rare species that can be used to inform policy and management decisions in the Bay-Delta.