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Phenological shifts and mismatch with marine productivity vary among Pacific salmon species and populations

Abstract

Global climate change is shifting the timing of life-cycle events, sometimes resulting in phenological mismatches between predators and prey. Phenological shifts and subsequent mismatches may be consistent across populations, or they could vary unpredictably across populations within the same species. For anadromous Pacific salmon (Oncorhynchus spp.), juveniles from thousands of locally adapted populations migrate from diverse freshwater habitats to the Pacific Ocean every year. Both the timing of freshwater migration and ocean arrival, relative to nearshore prey (phenological match/mismatch), can control marine survival and population dynamics. Here we examined phenological change of 66 populations across six anadromous Pacific salmon species throughout their range in western North America with the longest time series spanning 1951–2019. We show that different salmon species have different rates of phenological change but that there was substantial within-species variation that was not correlated with changing environmental conditions or geographic patterns. Moreover, outmigration phenologies have not tracked shifts in the timing of marine primary productivity, potentially increasing the frequency of future phenological mismatches. Understanding population responses to mismatches with prey are an important part of characterizing overall population-specific climate vulnerability.

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Fig. 1: Species-specific shifts in outmigration phenology.
Fig. 2: Population-specific outmigration phenology in six Pacific salmon species.
Fig. 3: Mismatch between the rate of change in peak smolt outmigration phenology and the rate of change in the spring phytoplankton bloom.
Fig. 4: Response of salmon outmigration phenology to air temperature changes.

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Data availability

Data are available on Dryad (https://doi.org/10.5061/dryad.dfn2z356g). Data provided are calculated peak-change and peak-range data. Source data are provided with this paper.

Code availability

Model code is available as an R package ‘phenomix’ by Eric Ward on Github at ‘ericward-noaa/phenomix’.

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Acknowledgements

This project would not have been possible without the dedication and fortitude of scientists and technicians from Alaska Department of Fish and Game, Fisheries and Oceans Canada, Washington Department of Fish and Wildlife, Oregon Department of Fish and Wildlife, University of Washington, University of Oregon, Confederated Tribes of Warm Springs and the US Forest Service that collected the 41 long-term datasets used in this project. Please see the extended acknowledgement in the Supplementary Information for a detailed list of acknowledgements. We also thank the Chelan County Public Utility District, King County Cooperative Watershed Management grant programme, the WRIA 8 technical committee, Seattle Public Utilities, Puget Sound Energy, Bonneville Power Administration, Dingell–Johnson Sportfish Restoration Act, Washington State Salmon Recovery Funding Board, Washington State General Fund, Seattle City Light and Habitat Conservation Trust Foundation for supporting these monitoring projects. Funding for S.M.W. was provided by Vanier Canada Graduate Scholarship, Weston Family Scholarship and Steven Berkeley Marine Conservation Fellowship. Additional funding from the Liber Ero Foundation was for J.W.M. We also thank T. D. Williams, L. Crozier, A. Dufault, N. Dulvy, N. Mantua, J. Reynolds and members of the Salmon Watersheds Lab for feedback on the early paper.

Author information

Authors and Affiliations

Authors

Contributions

S.M.W. collated data and completed analysis. S.M.W. and J.W.M. designed the study and wrote the paper. E.J.W. developed models. S.M.W., J.W.M., E.J.W., C.W.K., J.H.A., T.W.B., C.N.C.-H., P.C.C., T.D.D., M.R.D., L.G., P.J.L., M.N.C.L., D.A.P., D.T.S., M.R.S., E.J.S., I.A.T. and G.J.W. contributed to data collection and writing.

Corresponding author

Correspondence to Samantha M. Wilson.

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Nature Ecology & Evolution thanks Andrea Reid, Xingli Giam and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Rate of change in peak outmigration timing modelled with increasing numbers of sequential years of data.

Rate of change was calculated beginning with using only the five most recent years of outmigration data and re-running the analysis again for each successive year added. Thus, each point above represents a model run, beginning with five which included the most recent five years of data, and ending with the complete dataset. Species are represented by colours with coho (green), pink (pink), sockeye (vermillion), Chinook (black), chum (blue) and steelhead (orange). Vertical lines (error bars) represent 95% confidence interval, point represents mean. When models did not converge, confidence intervals were not produced. More information on sites is located in Supplementary Table 1.

Source data

Extended Data Fig. 2 Top model parameter estimates for shifts in peak outmigration phenology.

Top model parameter estimates (left) and the relationship between log trap elevation and change in peak phenology for Chinook salmon (right, top) and steelhead trout only (right, bottom). Left panel: black bars are parameters for which confidence intervals do not overlap with zero, indicating a significant effect; grey bars overlap zero and are not significant. Point is the mean, horizontal error bars represent the 95% confidence interval. Right panel: colours indicate the salmon species (coho = green, pink = pink, chum = blue, steelhead = orange, sockeye = vermillion, Chinook = black), grey background indicates 95% confidence region for relationship between log trap elevation and rate of change in peak outmigration timing. More information on sites is located in Supplementary Table 1.

Source data

Extended Data Fig. 3 Comparison of the rate of change in peak outmigration timing for salmon and spring phytoplankton phenology.

Rate of change in peak outmigration timing for salmon (coho (green), pink (pink), sockeye (vermillion), Chinook (black), chum (blue) salmon and steelhead (orange) trout) and spring phytoplankton phenology (dark green) between 1999 and 2019 (truncated salmon time series). Where curve (95% confidence interval) overlaps 0 (horizontal dashed line) species phenologies are not shifting. Overlap between spring phytoplankton phenology and salmon phenology curves indicates that they are shifting at the same rate.

Source data

Extended Data Fig. 4 Map of satellite derived chlorophyll-a 2 × 2 degree sections 1–29, with trap locations (black triangles).

Inset is the rate of change in initial peak of chlorophyll-a (first day above the 5% of the annual mean chlorophyll-a) time period spans from 1999–2019 (n = 20 for all sections). Points represent mean rate of change, horizontal error bars represent 95% confidence intervals.

Source data

Extended Data Fig. 5 Comparison of rate of change in peak migration timing for full vs. truncated time series.

Full time series includes all years when data were collected (closed circles), whereas truncated time series includes only smolt data collected from 1999–2019 (open circles). Colours indicate species where orange = steelhead trout, green = coho, black = Chinook, vermillion = sockeye, blue = chum, light pink = odd-year pink, dark pink = even year pink salmon. Ordered by difference in change of peak from negative to positive. More information on sites is located in Supplementary Table 1.

Source data

Extended Data Table 1 Top 10 models based on AICc ranking predicting change in peak outmigration day for full length dataset
Extended Data Table 2 P values of Bonferroni post hoc pairwise comparisons of the rate of change in peak phenology across species for full dataset
Extended Data Table 3 Model results of weighted linear model of time series length on the rate of shift in migration timing
Extended Data Table 4 Top model (<2 ∆AIC) and geographic, environmental, and biological predictor coefficients for change in peak outmigration day for truncated time series (1999–2019)

Supplementary information

Supplementary Information

Supplementary Table 1, Methods and Results for supporting analyses.

Reporting Summary

Source data

Source Data

Statistical source data for Figs. 1–4, Extended Data Figs. 1–5 and Extended Data Tables 1–4.

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Wilson, S.M., Moore, J.W., Ward, E.J. et al. Phenological shifts and mismatch with marine productivity vary among Pacific salmon species and populations. Nat Ecol Evol 7, 852–861 (2023). https://doi.org/10.1038/s41559-023-02057-1

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