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Climate change is contributing to declines of insects through rising temperatures, altered precipitation patterns, and an increasing frequency of extreme events. The impacts of both gradual and sudden shifts in weather patterns are realized directly on insect physiology and indirectly through impacts on other trophic levels. Here, we investigated direct effects of seasonal weather on butterfly occurrences and indirect effects mediated by plant productivity using a temporally intensive butterfly monitoring dataset, in combination with high‐resolution climate data and a remotely sensed indicator of plant primary productivity. Specifically, we used Bayesian hierarchical path analysis to quantify relationships between weather and weather‐driven plant productivity on the occurrence of 94 butterfly species from three localities distributed across an elevational gradient. We found that snow pack exerted a strong direct positive effect on butterfly occurrence and that low snow pack was the primary driver of reductions during drought. Additionally, we found that plant primary productivity had a consistently negative effect on butterfly occurrence. These results highlight mechanisms of weather‐driven declines in insect populations and the nuances of climate change effects involving snow melt, which have implications for ecological theories linking topographic complexity to ecological resilience in montane systems.

Ongoing declines in insect populations have led to substantial concern and calls for conservation action. However, even for relatively well studied groups, like butterflies, information relevant to species‐specific status and risk is scattered across field guides, the scientific literature, and agency reports. Consequently, attention and resources have been spent on a minuscule fraction of insect diversity, including a few well studied butterflies. Here we bring together heterogeneous sources of information for 396 butterfly species to provide the first regional assessment of butterflies for the 11 western US states. For 184 species, we use monitoring data to characterize historical and projected trends in population abundance. For another 212 species (for which monitoring data are not available, but other types of information can be collected), we use exposure to climate change, development, geographic range, number of host plants, and other factors to rank species for conservation concern. A phylogenetic signal is apparent, with concentrations of declining and at‐risk species in the families Lycaenidae and Hesperiidae. A geographic bias exists in that many species that lack monitoring data occur in the more southern states where we expect that impacts of warming and drying trends will be most severe. Legal protection is rare among the taxa with the highest risk values: of the top 100 species, one is listed as threatened under the US Endangered Species Act and one is a candidate for listing. Among the many taxa not currently protected, we highlight a short list of species in decline, includingVanessa annabella,Thorybes mexicanus,Euchloe ausonides, andPholisora catullus. Notably, many of these species have broad geographic ranges, which perhaps highlights a new era of insect conservation in which small or fragmented ranges will not be the only red flags that attract conservation attention.

Certain general facets of biotic response to climate change, such as shifts in phenology and geographic distribution, are well characterized; however, it is not clear whether the observed similarity of responses across taxa will extend to variation in other population‐level processes. We examined population response to climatic variation using long‐term incidence data (collected over 42 years) encompassing 149 butterfly species and considerable habitat diversity (10 sites along an elevational gradient from sea level to over 2,700 m in California). Population responses were characterized by extreme heterogeneity that was not attributable to differences in species composition among sites. These results indicate that habitat heterogeneity might be a buffer against climate change and highlight important questions about mechanisms maintaining interpopulation differences in responses to weather. Despite overall heterogeneity of response, population dynamics were accurately predicted by our model for many species at each site. However, the overall correlation between observed and predicted incidence in a cross validation analysis was moderate (Pearson'sr = 0.23,SE0.01), and 97% of observed data fell within the predicted 95% credible intervals. Prediction was most successful for more abundant species as well as for sites with lower annual turnover. Population‐level heterogeneity in response to climate variation and the limits of our predictive power highlight the challenges for a future of increasing climatic variability.