ArticleEcological applications : a publication of the Ecological Society of America2025
Phenology-informed decline risk of estuarine fishes and their prey suggests potential for future trophic mismatches.
Article in Ecological applications : a publication of the Ecological Society of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Time-varying flow-ecology relationships for an endangered fish population: Longfin Smelt in the San Francisco Estuary.Ecological applications : a publication of the Ecological Society of America · 2026Article
- Phenology-informed decline risk of estuarine fishes and their prey suggests potential for future trophic mismatches.Ecological applications : a publication of the Ecological Society of America · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Conservation scientists have long used population viability analysis (PVA) on species count data to quantify critical decline risk, thereby informing conservation actions. These assessments typically focus on a 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). The PVA were parameterized using 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 peak in abundance 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 risk of food-web collapse in the face of climate-induced phenological shifts.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.