Edge thermal niche conservation
We measured the edge thermal niche—the temperatures found at the range edge—by predicting annual warm and cold temperature extremes at the range edge position using region-specific GAMs (see Temperature and distribution data sources ). We then fitted Bayesian linear regressions to test whether either the warm or cold extreme temperature at a species’ range edge had changed over time (n = 306, 153 range edges for each of two temperature extremes). Single-species Bayesian linear regressions were fitted using the “rstanarm package” (Goodrich et al. 2018) with four chains, 40,000 iterations including 10,000 burn-in draws, and a target average proposal acceptance probability of 0.99. We selected a normally distributed vague prior with a mean of 0 and standard deviation of 0.1 °C/year; this standard deviation was chosen to exceed the largest rates of SST change across all study regions (see Results ). Models were weighted by GAM-estimated standard errors (\(\frac{1}{\text{SE}^{2}}\)), so that estimated temperatures with higher associated uncertainties were less influential. Models converged for all range edges (Gelman-Rubin convergence statistic below 1.1). We calculated the mean and 90% Bayesian credible interval from each single-species posterior distribution of the year coefficient for either warm or cold temperature extremes.
If at least one of the two temperature metrics we measured at a range edge—cold or warm extremes—was constant over time, the range edge could be tracking that temperature and exhibiting edge thermal niche conservatism (Hutchins 1947). We categorized range edges according to whether the range edge maintained a constant warm extreme temperature at the edge over time, a constant cold extreme temperature, both, or neither, based on 90% Bayesian credible intervals (Figure 2). In this method, edge thermal niche conservatism could arise either from the range edge shifting in space to track temperature, or the range edge remaining stationary in a location where temperatures have not changed over time. To disentangle these processes, we compared changes in the edge thermal niche to changes in the range edge position (Figure 2).
RESULTS
From 1967 to 2018, minimum, mean, and maximum SST in the Northeast all increased (Figure 1A), translating to more than one degree Celsius of warming in every SST metric over the fifty years measured (respectively, 0.023 ± 0.007 °C/year, p = 1.4 × 10-3; 0.03 ± 0.004 °C/year, p = 3.7 × 10-9; 0.033 ± 0.006 °C/year, p = 4.4 × 10-7). On the West Coast (Figure 1B), no significant trends occurred in any temperature metric from 1976-2018, despite variation of ± 2 °C for individual years (minimum SST 0.004 ± 0.008 °C/year, p = 0.65; mean SST 0.002 ± 0.007 °C/year, p = 0.77; maximum SST 0.003 ± 0.009 °C/year,p = 0.77). In the Eastern Bering Sea (Figure 1C), warming was evident in maximum SST change from 1988-2018, which increased 0.038 ± 0.018 °C/year (p = 0.049). Neither minimum nor mean SST increased significantly in the Eastern Bering Sea, although both had a positive relationship with year (minimum 0.008 ± 0.01 °C/year, p = 0.48; 0.022 ± 0.013 °C/year, p = 0.10).