DOI: 10.6078/d19t3g
title: Four decades of climatic fluctuations and fish recruitment stability across a marine-freshwater gradient

description: Investigating the effects of climatic variability on biological diversity,
 productivity, and stability is key to understanding possible futures for
 ecosystems under accelerating climate change. A critical question is, how
 does climatic variability influence juvenile recruitment of different fish
 species and life histories that use estuaries as nursery habitats? Here we
 examined spatiotemporal abundance trends and environmental responses of 18
 fish species that frequently spend the juvenile stage rearing in the San
 Francisco Estuary, CA, USA. First, we constructed multivariate
 autoregressive state-space models using age-0 fish abundance, freshwater
 flow (Flow), and sea surface temperature (SST) data collected over four
 decades. Next, we calculated coefficients of variation (CV) to assess
 portfolio effects (1) within and among species, life histories
 (anadromous, marine opportunist, estuarine dependent species), and the
 whole community, and (2) within and among regions of the estuary. We found
 that species abundances varied over space, across time (increasing,
 decreasing, dynamically stable), and in 83% of cases, in response to
 environmental conditions (wet/dry, cool/warm). Anadromous species
 responded strongly to flow in the upper estuary; marine opportunist
 species responded to flow and/or SST in the lower estuary; and estuarine
 dependent species had diverse responses across the estuary. Overall, the
 whole community when considered across the entire estuary had the lowest
 CV, and aggregate life histories and species provided strong biological
 insurance to the portfolio (2.4 to 3.5-fold increases in stability,
 respectively). Spatial insurance also increased stability, although to a
 lesser extent (up to 1.6-fold increases). Our study advances the notion
 that fish recruitment stability in estuaries is controlled by
 biocomplexity – life history diversity and spatiotemporal variation in the
 environment. However, under climate change, intensified drought and marine
 heatwaves may increase the risk of multiple consecutive recruitment
 failures by synchronizing species dynamics and trajectories via Moran
 effects and ultimately erode estuarine nursery function.
description: These data were derived from the following resources available in
 the public domain: (1) Department of California Fish and Game San
 Francisco Bay Study (URL:
 https://filelib.wildlife.ca.gov/Public/BayStudy/); (2) NOAA/UCSD Shore
 Stations Farallon Islands station (URL:
 https://shorestations.ucsd.edu/data-farallon/); and (3) California
 Department of Water Resources dayflow (URL:
 https://data.cnra.ca.gov/dataset/dayflow). All datasets were filtered to
 the same time periods (April to October 1980 to 2018) and summarized as
 mean annual values: (1) San Francisco Bay Study: log-transformed mean
 annual catch per unit effort of 18 age-0 fishes at 35 core stations
 ('CPUE'); (2) zscore-transformed Farallon Islands: mean annual
 sea surface temperature ('SST') that incorporates interpolated
 missing values from seasonal autoregressive integrated moving average
 (ARIMA) models-- see associated script; (3) Dayflow: zscore-transformed
 mean annual Net Delta Outflow ('Flow').
description: Metadata is available in the public domain: (1) Department of
 California Fish and Game San Francisco Bay Study (URL: https://filelib.wildlife.ca.gov/Public/BayStudy/); (2) NOAA Shore Stations Farallon Islands station (URL: https://shorestations.ucsd.edu/data-farallon/); and (3) California Department of Water Resources dayflow (URL: https://data.cnra.ca.gov/dataset/dayflow). README files are available for each data type. R Markdown scripts are provided to reproduce model results based on the derived datasets: (1) Multivariate autoregressive state space (MARSS) model script specifies species-level model structures, fits, diagnostics, AICc comparisons, and coefficients; (2) Autoregressive integrated moving average (ARIMA) script specifies seasonal model structure and results, to impute missing daily SST values before summarizing into annual means from April-October 1980-2018.