DOI: 10.5061/dryad.4j0zpc8nd
title: Phenology-informed decline risk of estuarine fishes and their prey suggests potential for future trophic mismatches

description: 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, assessing risk at overly broad temporal or
 spatial scales may obscure diverging population declines between predators
 and prey, potentially disrupting 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 (56.8%) 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 23.7% for fish and 21.1%
 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 11.2% 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 the risk of food-web collapse in the
 face of climate-induced phenological shifts.
description: This work uses time-series based PVA to assess critical decline
 risk of estuarine fishes and their prey during phenologically-informed
 high abundance windows.
description: # Phenology-informed decline risk of estuarine fishes and their prey
 suggests potential for future trophic mismatches Dataset:
 "Fournier_et_al_monthly_risk.csv" Dataset description: This
 dataset contains monthly risk values for zooplankton and phytoplankton
 Columns: * Month_name: Name of month * Region: Region of the estuary *
 Taxa: Name of organism * Probability: Critical decline risk estimate * U:
 Deterministic trend * Q: Process error variance * Timesteps: Year of the
 projection * Best: Critical decline risk estimate for the case scenario *
 Worst: Critical decline risk estimate for the worst-case scenario * Month:
 Numerical month designation * Percenttot: Percent average abundance *
 Window: Designation for high-abundance windows * MonthName: Month
 abbreviation * Group: Taxonomic Group Dataset:
 "Fournier_et_al_mean_risk.csv" Dataset description: This dataset
 contains window-specific mean risk values * Region: Region of the estuary
 * Taxa: Name of organism * Probability: Mean critical decline risk across
 the key window * U: Deterministic trend * Q: Process error variance *
 Timesteps: Year of the projection * Best: Mean critical decline risk
 estimate for the best-case scenario * Worst: Mean critical decline risk
 estimate for the worst-case scenario * Group: Taxonomic Group * Predator:
 Predator for which high abundance windows were calculated Dataset:
 "Fournier_et_al_model_comparison.csv" Dataset description: This
 dataset contains the results of our supplementary analysis comparing the
 results from our MAR models to MARSS models * Region: Region of the
 estuary * Taxa: Name of organism * Probability: Mean critical decline risk
 across the key window * RU: Deterministic trend * RQ: Process error
 variance * Timesteps: Year of the projection * Best: Mean critical decline
 risk estimate for the case scenario * Worst: Mean critical decline risk
 estimate for the worst-case scenario * Group: Taxonomic Group *
 Convergence: MARSS package convergence code * R: Observation error
 estimate **Code/Software** Fournier_et_al._2025_Risk_code.Rmd: This code
 accesses and preprocesses zooplankton and fish time-series data from
 ecological databases, assigns sampling stations to regions, filters for
 data completeness, imputes missing values, and prepares the data for MAR
 (Multivariate Autoregressive) modeling by species, station, and region.