Phenology-informed decline risk of estuarine fishes and their prey suggests potential for future trophic mismatches

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 tha..., This work uses time-series based PVA to assess critical decline risk of estuarine fishes and their prey during phenologically-informed high abundance windows.
, , # 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 cr...,

保护科学家长期以来一直借助种群生存力分析（Population Viability Analysis, PVA）对物种种群计数数据进行分析，以量化种群变化趋势与临界衰退风险，进而为保护实践提供决策支撑。此类评估通常聚焦于单一物种而非物种集合，且假设同一生活史阶段内的风险水平保持一致（例如，一年中的不同季节或月份）。然而，在过于宽泛的时间或空间尺度上开展风险评估，可能会掩盖捕食者与猎物之间种群衰退的分化趋势，进而潜在破坏生物群落间的相互作用。本研究针对1995年至2023年间旧金山河口（San Francisco Estuary）内每年各月份的0龄饵料鱼类及其潜在浮游动物猎物，开展了基于时间序列的种群生存力分析（共包含175条时间序列）。我们采用多变量自回归（Multivariate Autoregressive, MAR）模型，估算各物种种群的长期趋势与变异性（即过程误差）。研究发现，超半数鱼类种群呈现显著负增长趋势（占比达56.8%），且观测到……本研究利用基于时间序列的种群生存力分析，对河口鱼类及其猎物在物候学匹配的高丰度窗口期内的临界衰退风险进行了评估。

# 基于物候学的河口鱼类及其猎物衰退风险研究揭示未来营养级错配潜力

## 数据集："Fournier_et_al_monthly_risk.csv"
数据集说明：本数据集包含浮游动物与浮游植物的月度风险值

字段说明：
* Month_name：月份全称
* Region：河口区域
* Taxa：生物类群名称
* Probability：临界衰退风险估算值
* U：确定性趋势
* Q：过程误差方差
* Timesteps：预测年份
* Best：基准情景下的临界衰退风险估算值
* Worst：最坏情景下的临界衰退风险估算值
* Month：月份数字编号
* Percenttot：平均丰度占比
* Window：高丰度窗口期标识
* MonthName：月份缩写
* Group：分类学类群

## 数据集："Fournier_et_al_mean_risk.csv"
数据集说明：本数据集包含各窗口期的平均风险值

字段说明：
* Region：河口区域
* Taxa：生物类群名称
* Probability：平均临界……