Robert Joseph Fournier, Tyler C Marino, Stephanie Carlson, Albert Ruhi

Conservation scientists have long used population viability analysis (PVA) on species count data to quantify trends and critical decline risk, thereby informing conservation actions. These assessments typically focus on single species rather than assemblages and assume that risk is consistent within a given life stage (e.g., across the different seasons or months of a year). However, if risk is assessed at too broad a temporal or spatial scale, it may overlook diverging population declines between predators and prey that disrupt biotic interactions. In this study, we used time-series based PVA for age-0 forage fishes and their potential zooplankton prey for each month of the year in the San Francisco Estuary, over 1995-2023 (N = 175 time series). We used Multivariate Autoregressive (MAR) models that estimate long-term population trends and variability (i.e., process error) for each population. We found widespread negative population trends across fish species (56.6%) and observed that critical decline risk is often higher in months when species abundances peak compared to ‘shoulder’ months. Although current decline risk is somewhat balanced between predators and their prey (mean 21.8% for fish and 21.4% for zooplankton), our time-series models indicate trophic levels are poised to diverge over the next 10 years, with fish generally accumulating risk faster than their prey. Additionally, zooplankton showed 11.5% higher uncertainty about their near-term critical decline risk relative to fish. These observations suggest strong, previously unreported potential for future trophic mismatches. Our results underscore the need to assess risk over finer temporal scales within and across trophic levels to better understand vulnerability, and thus inform conservation of imperiled species. Our approach is transferable and highlights the benefits of time-series based PVA to understand risk of food-web collapse in the face of climate-induced phenological shifts.