DOI: 10.6078/d1qt4k
title: Long-term data reveal widespread phenological change across major U.S. estuarine food webs

description: Climate change is shifting the timing of organismal life-history events.
 Although consequential food-web mismatches can emerge if predators and
 prey shift at different rates, research on phenological shifts has
 traditionally focused on single trophic levels. Here, we analyzed
 &gt;2000 long-term, monthly time series of phytoplankton, zooplankton,
 and fish abundance or biomass for the San Francisco, Chesapeake, and
 Massachusetts bays. Phenological shifts occurred in over a quarter (28%)
 of the combined series across all three estuaries. However, phenological
 trends for many (~29%-68%) taxa did not track the changing environment.
 While planktonic taxa largely advanced their phenologies, fishes displayed
 broad patterns of both advanced and delayed timing of peak abundance.
 Overall, these divergent patterns illustrate the potential for
 climate-driven trophic mismatches. Our results suggest that even if
 signatures of global climate change differ locally, widespread
 phenological change has the potential to disrupt estuarine food webs.
description: We compiled and filtered long-term data for fishes, zooplankton,
 phytoplankton, temperature, and salinity from multiple monitoring surveys
 in the San Francisco, Chesapeake, Massachusetts Bays. For San Francisco
 Bay, we obtained fish and water quality data from the California
 Department of Fish and Wildlife’s San Francisco Bay Study (CDFW 2023a),
 and zooplankton and phytoplankton data from the Environmental Monitoring
 Program (CDFW 2023b). For Chesapeake Bay, we obtained fish and water
 quality data from the Virginia Institute of Marine Science’s Chesapeake
 Multispecies Monitoring and Assessment Program (Latour et al. 2023); and
 zooplankton, phytoplankton, and associated water quality from the
 Chesapeake Bay Program (CBP 2023). For Massachusetts Bay, we obtained
 phytoplankton, zooplankton, and water quality data from the Massachusetts
 Water Resources Authority’s Water Column Monitoring Program (MWRA 2023).
 All data were monthly, spanned at least 10 years, and were examined for
 quality.
description: Analyses were performed using R. The code is provided.
description: # Long-term data reveal widespread phenological change across major U.S.
 estuarine food webs Widespread climate-induced phenological change across
 major U.S. estuaries Dataset: Fournier et al correlations.csv Dataset
 description: This dataset contains all input data for trivariate meta
 regression model Columns: Estuary:Designation for our three estuary
 systems. SFE=San Francisco, CHE=Chesapeake, MAS=Massachusetts
 Station:Station names as provided by collecting agency Taxa:Name of taxa
 Start: Start date of time series for that taxa/station combination End:
 End date of time series for that taxa/station combination Length: Length
 of the time series (in years) Latitude: Latitude of the sampling location
 Longitude: Longitude of the sampling location Mean.temp: Mean temperature
 for sampling location Mean.sa: Mean salinity for sampling location
 Test:Name of the correlation test performed with covariate.
 PTTemp=Phenology vs Time for temperature;PCTemp=Phenology vs Covariate for
 temperature;TCTemp=Time vs Covariate for temperature; PTSal=Phenology vs
 Time for salinity; PCSal=Phenology vs Covariate for salinity; TCSal=Time
 vs Covariate for salinity Value:Pearson correlation coefficient for that
 relationship var_1:First variable in correlation. P=Phenology,
 C=Covariate, T=Time var_2:Second variable in correlation. P=Phenology,
 C=Covariate, T=Time cor_ID: Name of the correlation test performed without
 covariate Variable:Environmental covariate Group:Taxonomic group Size:
 Size/age class Scale: Seasonal or annual model Dataset: Fournier et al
 trivariate results.csv. Dataset description: All model outputs for
 trivariate meta regression models Taxa: Name of taxa Estimate: Maximum
 likelihood slope estimate Se: standard error Zvalue: Z score value Pval: P
 value ci.lb: Lower band of confidence interval ci.ub: Upper band of
 confidence interval ID: Type of slope Variable: Covariate in that test
 Estuary: Code foe each estuary Region: Name of region (if regional model)
 Whole if representing entire estuary. Scale: Annual or seasonal model
 Dataset: Fournier et al abundance trends.csv. Dataset description: All
 tracking classifications with abundance trends Status: Name of
 classification Count: Number in that classification Group: Either all taxa
 or a specified group of taxa Percentageold: Percentage of taxa in that
 classification Estuary: Acronym of the estuary Variable: Temperature or
 salinity Total: Total number of modeled taxa Percent: Modified percent
 column where non-tracking percentages are negative (used to make divergent
 barplot) Tracking: Whether a group is tracking or not Dataset: Fournier et
 al classifications.csv. Dataset description: All tracking classifications
 Status: Name of classification Count: Number in that classification Group:
 Either all taxa or a specified group of taxa Percentageold: Percentage of
 taxa in that classification Estuary: Acronym of the estuary Variable:
 Temperature or salinity Total: Total number of modeled taxa Percent:
 Modified percent column (used to make divergent barplot) Tracking: Whether
 a group is tracking or not Dataset: Fournier et al regional trends.csv.
 Dataset description: Used to calculate relative abundance Taxa: Name of
 taxa Mean: Mean abundance Mag: Order of magnitude Estimates: Slope of
 correlation test pvalues: P value of correlation test Region: Name of
 region RegionMean: Region-specific mean abundance RegionMag:
 Region-specific order of magnitude Estuary: Estuary designation Dataset:
 Fournier et al peak dates.csv. Dataset description: Dataframe of all dates
 of peak abundance Taxa: Name of taxa Year: Year Station: Station
 designation Julian: Julian date of peak Date: Date of peak Count:
 Abundance at peak Group: Taxonomic group Size: Age or size class Estuary:
 Estuary designation