DOI: 10.6078/d1fd73
title: Migration in drought: Receding streams contract the seaward migration window of endangered salmon

description: Prolonged migration windows buffer migratory animal populations against
 uncertainty in resource availability. Understanding how intensifying
 droughts from climate change influence the migration window is critical
 for biodiversity conservation in a warming world. We explored how drought
 affects the seaward migration of endangered coho salmon (Oncorhynchus
 kisutch) near the southern extent of their range in California, USA. We
 tracked stream departures of juvenile coho, measuring streamflow and
 temperature in 7 streams over 13 years, spanning an historic drought with
 extreme dry and warm conditions. Linear mixed effects models indicate
 that, over the range of observations, a decrease in seasonal streamflow
 (from 4.5 to 0.5 mm/day seasonal runoff) contracted the migration window
 by 31% (from 11 to 7 weeks). An increase from 10.2 to 12.8 ℃ in mean
 seasonal water temperature hastened the migration window by three weeks.
 Pacific salmon have evolved to synchronize ocean arrival with productive
 ocean upwelling. However, earlier and shorter migration windows during
 drought could lead to mismatches, decreasing fitness and population
 stability. Our study demonstrates that drought-induced low flows and
 warming threaten coho salmon in California and suggests that environmental
 flow protections will be needed to support the seaward migration of
 Pacific salmon in a changing climate.
description: <strong>Runoff and water
 temperature data</strong> We estimated mean annual precipitation, averaged
 across each drainage area, using Google Climate Engine, March 2011 -
 February 2021. Where multiple temperature loggers were present in
 a study stream, we selected a single location based on the completeness of
 data in the study season and proximity to the PIT antenna. Hourly
 temperature measurements were converted into mean daily
 values.  <strong>Analysis</strong>
 <strong> </strong> For data analysis and modeling, we excluded streams
 that had less than 3 years of biological data, leaving 47 stream-years. We
 conducted all analyses in R (version 4.0.4, R Core Team, 2018). We tested
 outmigration timing data for normal distribution among streams, years, and
 stream-years, using the <em>shapiro.test</em> function of the
 <em>broom </em>package. The Shapiro-Wilk test showed that all
 distributions were unlikely to be normally distributed (i.e. among years,
 p = 5.5 × 10<sup>-9</sup> – 7.6 × 10<sup>-39</sup>
 and W = 0.88 – 0.98). However, the Shapiro-Wilk test can provide small
 p-values for large samples and consequently provide a false negative,
 regarding normal distribution (among years, sample size range was 485 –
 3453). Therefore, we could not rule out the possibility of
 assumptions being met to perform ANOVA (Analysis of Variance) tests. We
 did so, using the <em>aov </em>function of the
 <em>AICcmodavg </em>package: i) one-way, by stream, ii) a
 one-way, by year, iii) a two-way, by stream and year, and iv) a two-way
 with stream-year interaction. To isolate the effects of stream and year on
 variance, we performed the ANOVA tests on the maximum subset of data for
 which each stream had the same years of outmigration (four streams, each
 with the same six years of data, totaling 24 stream-years). The aictab
 function of the AICcmodavg package demonstrated that the two-way model
 with stream-year interaction was the highest performing (lowest AICc
 value), followed by: the two-way model, one-way by year model, and one-way
 by stream model. In both ANOVA tests, the year, stream, and year-stream
 interaction terms each had “Pr(&gt;F)” values &lt; 2 ×
 10<sup>-16</sup>. The “2-way ANOVA with interaction” (year
 F-value 646.58, stream F-value 349.85, year-stream interaction F-value
 29.31, residuals 4.11 × 10<sup>-16</sup>) had higher F values
 and lower residuals than the 2-way ANOVA (year F-value 629.3, stream
 F-value 340.5, residuals 4.22 × 10<sup>-16</sup>). We used the
 TukeyHSD function of the <em>AICcmodavg</em> package to
 conduct pairwise tests for significant differences in outmigration timing
 distributions. Among streams, five of six pairwise differences were highly
 significant (p &lt; 0.0001). Among years, all 15 pairwise comparisons
 were highly significant (p &lt; 0.001). Among stream-years, 216 of 277
 pair-wise comparisons were significant (p &lt; 0.05). We checked for
 homoscedasticity in the interaction model, using the <em>leveneTest
 </em>function of the car library, and we found evidence that the
 variance across groups is significantly different. Consequently, we cannot
 assume homogeneity of variances in the different groups, which is
 typically a required assumption for conducting ANOVA
 tests. Since the normal distribution assumption of the
 one-way ANOVA was not met, we applied the Kruskal-Wallis test, as a
 non-parametric alternative to test for variance among streams and years,
 using the package rstatix. As with the ANOVA tests, we performed
 Kruskal-Wallis tests on the maximum subset of data for which each stream
 had the same years of outmigration (24 stream-years), using the functions
 <em>kruskal_test</em>, <em>kruskal_effsize</em>,
 <em>dunn_test</em>, and <em>wilcox_test</em>.
 Among streams, we found significant variance (p = 2.16 ×
 10<sup>-143</sup>), with a “small” effect size (eta-squared
 measure = 0.04) (Tomczak and Tomczak 2014), and 5 of 6 pairwise
 differences were highly significant (Dunn’s test & Wilcoxon’s
 test: p &lt; 0.0001). Among years, we found significant variance (p =
 0), with a “large” effect size (eta-squared measure = 0.17) (Tomczak and
 Tomczak 2014), and 13 of 15 pairwise differences were highly significant
 (Dunn’s test & Wilcoxon’s test: p &lt;
 0.0001). <strong>Modeling the effects of streamflow and water
 temperature on outmigration timing</strong>
 Modeling was limited to the 42
 stream-years for which water temperature and outmigration timing data were
 collected. For the outmigration start date model, the runoff date range
 was March-April and the degree-days date range was March-April. For the
 outmigration end date and duration models, the runoff date range was
 March-June and the degree-days date range was March-April. Coefficient
 units are “days per daily runoff (mm)” and “days per 100 degree-days”.
 In
 identifying top model(s), we did not consider degree-days to influence
 outmigration duration because: i) the AIC value of the runoff-only model
 was 1.99 less than the additive model, ii) the degree-days in the additive
 model had a p-value &gt; 0.05, and iii) Mar-Jun runoff had similar
 coefficient effect sizes in the additive model and run-off only model
 (Appendix S1: Table S3). We calculated conditional coefficients (including
 stream, as a random effect) and marginal coefficients (excluding stream,
 as a random effect) of determination (R<sup>2</sup>)
 (Nakagawa and Schielzeth
 2013), using the
 r.<em>squaredGLMM </em>function of the <em>MuMIn
 </em>package (Barton` 2020). We also reported the model coefficients
 and 95% confidence intervals, as measures of effect size, and generated
 partial dependence plots for using the <em>plot_model
 </em>function of the <em>sjPlot </em>package (Lüdecke
 2021). <strong>Literature
 cited</strong> Barton`, K. (2020). MuMIn: Multi-Model Inference. R
 package version 1.43.17. Lüdecke, D. (2021). sjPlot: Data Visualization for
 Statistics in Social Science. R package version
 2.8.9. Nakagawa, S., and H. Schielzeth. 2013. A general
 and simple method for obtaining R 2
 from generalized linear mixed-effects models. Methods in Ecology and
 Evolution 4:133–142. Tomczak, M., and E. Tomczak. 2014. The need to
 report effect size estimates revisited. An overview of some recommended
 measures of effect size 1:7.
description: Please see
 <em>DataS1/data/README_Metadata.pdf</em>.