Substantial global data show that many taxa are shifting their phenologies in response to climate change. For birds, migration arrival dates in breeding regions have been shifting earlier, and there is evidence that both evolutionary adaptation and behavioural flexibility influence these shifts. As more efficient flyers may be able to demonstrate more flexibility to respond to changing conditions during migratory flight, we hypothesize that differences among passerine species in flight efficiency, as reflected by morphology, may be associated with the magnitude of shifts in arrival date in response to climate warming. We applied a logistic model to 18 years of eBird data to estimate mean arrival date for 44 common passerines migrating to northeast North America. We then used linear mixed‐effects models to estimate changes in mean arrival date and compared these changes to morphological proxies for flight efficiency and migratory distance using phylogenetic generalized least squares models. On average, passerine species shifted their arrival dates 0.120 days earlier each year, with 27 of the 44 species shifting to significantly earlier arrival times, and two shifting to significantly later ones. Of the 15 species with non‐significant shifts, 13 trended toward earlier arrivals. Longer migration distances and higher wing aspect ratios were associated with greater shifts toward earlier arrivals. Migration distance and aspect ratio were also significantly correlated to each other. This suggests that changes in arrival date are affected by factors pertaining to migratory flight over long distances namely, flight efficiency and migration distance. These traits may be able predict the magnitude of arrival date shift, and by extension identify species that are most at risk to climate change due to inflexible arrival timing.

Temperature profoundly affects the physical, chemical, and biological attributes of lakes, and is influenced by several abiotic factors. Lake temperature modelling permits regional estimates of seasonal fish thermal habitat availability; however, this requires models that are accurate across large spatial scales. To address this, we fit a semi-mechanistic seasonal temperature-profile model (STM) to 369 morphometrically diverse North American lakes with data spanning 1971-2016. STM with a fixed-depth thermocline formula accurately modelled lake temperature (median pseudo <i>R</i>²: 0.95, median lake-year-specific RMSE: 1.13 ºC). We used random forests to select candidate predictors, then used linear mixed-effects modelling, based on these predictors, to create empirical equations to predict STM parameters from lake-specific morphometric and climate measures. We tested the accuracy of our equations by predicting thermal profiles in 776 Ontario lakes, finding good agreement between predicted and observed temperatures (median lake-year-specific RMSE: 2.38 ºC) and stratification occurrence (91.7%). These findings enhance our understanding of the factors that influence lake temperatures and can be used to identify lake types and regions that may be especially susceptible to climate change.