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Declines in the abundance and diversity of insects pose a substantial threat to terrestrial ecosystems worldwide. Yet, identifying the causes of these declines has proved difficult, even for well-studied species like monarch butterflies, whose eastern North American population has decreased markedly over the last three decades. Three hypotheses have been proposed to explain the changes observed in the eastern monarch population: loss of milkweed host plants from increased herbicide use, mortality during autumn migration and/or early-winter resettlement and changes in breeding-season climate. Here, we use a hierarchical modelling approach, combining data from >18,000 systematic surveys to evaluate support for each of these hypotheses over a 25-yr period. Between 2004 and 2018, breeding-season weather was nearly seven times more important than other factors in explaining variation in summer population size, which was positively associated with the size of the subsequent overwintering population. Although data limitations prevent definitive evaluation of the factors governing population size between 1994 and 2003 (the period of the steepest monarch decline coinciding with a widespread increase in herbicide use), breeding-season weather was similarly identified as an important driver of monarch population size. If observed changes in spring and summer climate continue, portions of the current breeding range may become inhospitable for monarchs. Our results highlight the increasingly important contribution of a changing climate to insect declines. 

Abstract AimSpecies distribution models (SDMs) are increasingly applied across macroscales using detection‐nondetection data. These models typically assume that a single set of regression coefficients can adequately describe species–environment relationships and/or population trends. However, such relationships often show nonlinear and/or spatially varying patterns that arise from complex interactions with abiotic and biotic processes that operate at different scales. Spatially varying coefficient (SVC) models can readily account for variability in the effects of environmental covariates. Yet, their use in ecology is relatively scarce due to gaps in understanding the inferential benefits that SVC models can provide compared to simpler frameworks. InnovationHere we demonstrate the inferential benefits of SVC SDMs, with a particular focus on how this approach can be used to generate and test ecological hypotheses regarding the drivers of spatial variability in population trends and species–environment relationships. We illustrate the inferential benefits of SVC SDMs with simulations and two case studies: one that assesses spatially varying trends of 51 forest bird species in the eastern United States over two decades and a second that evaluates spatial variability in the effects of five decades of land cover change on grasshopper sparrow (Ammodramus savannarum) occurrence across the continental United States. Main conclusionsWe found strong support for SVC SDMs compared to simpler alternatives in both empirical case studies. Factors operating at fine spatial scales, accounted for by the SVCs, were the primary divers of spatial variability in forest bird occurrence trends. Additionally, SVCs revealed complex species–habitat relationships with grassland and cropland area for grasshopper sparrow, providing nuanced insights into how future land use change may shape its distribution. These applications display the utility of SVC SDMs to help reveal the environmental factors that drive species distributions across both local and broad scales. We conclude by discussing the potential applications of SVC SDMs in ecology and conservation. 

Abstract Projecting species’ responses to future climate conditions is critical for anticipating conservation challenges and informing proactive policy and management decisions. However, best practices for choosing climate models for projection ensembles are currently in flux. We compared including a maximum number of models against trimming ensembles based on model validation. This was done within the emerging practice of ensemble building using an increasingly larger number of global climate models (GCMs) for future projections. We used recently reported estimates on primary drivers of population fluctuations for the migratory monarch butterfly (Danaus plexippus) to examine how multiple sources of uncertainty impact population forecasts for a well‐studied species. We compared mean spring temperature and precipitation observed in Texas from 1980 to 2005 with predictions from 16 GCMs to determine which of the models performed best. We then built tailored climate projections accumulating both temperature (in the form of growing degree days) and rainfall using both “complete” (all 16) and “trimmed” (best‐performing) ensembles based on three emission scenarios. We built the tailored projections of spring growing conditions to assess the range of possible climate outcomes and their potential impacts on monarch development. Results were similar when mean predictions were compared between trimmed and complete ensembles. However, when daily projections and uncertainty were accumulated over the entire spring season, we showed substantial differences between ensembles in terms of possible ecological outcomes. Ensembles that used all 16 GCMs included so much uncertainty that projections for future spring conditions ranged from being too cold to too hot for monarch development. GCMs based on best‐performing metrics provided much more useful information, projecting higher spring temperatures for developing monarch larvae in the future which could lead to larger summer populations but also suggesting risk from excessive heat. When there is a strong basis for identifying mechanistic drivers of population dynamics, our results support using a smaller subset of validated GCMs to bracket a range of the most defensible future environmental conditions tailored to the species of interest. Yet understating uncertainty remains a risk, and we recommend clearly articulating the rationale and consequences of selecting GCMs for long‐term projections. 

Monarch butterflies in eastern North America have declined by 84% on Mexican wintering grounds since the observed peak in 1996. However, coarse-scale population indices from northern US breeding grounds do not show a consistent downward trend. This discrepancy has led to speculation that autumn migration may be a critical limiting period. We address this hypothesis by examining the role of multiscale processes impacting monarchs during autumn, assessed using arrival abundances at all known winter colony sites over a 12-y period (2004–2015). We quantified effects of continental-scale (climate, landscape greenness, and disease) and local-scale (colony habitat quality) drivers of spatiotemporal trends in winter colony sizes. We also included effects of peak summer and migratory population indices. Our results demonstrate that higher summer abundance on northern breeding grounds led to larger winter colonies as did greener autumns, a proxy for increased nectar availability in southern US floral corridors. Colony sizes were also positively correlated with the amount of local dense forest cover and whether they were located within the Monarch Butterfly Biosphere Reserve, but were not influenced by disease rates. Although we demonstrate a demographic link between summer and fine-scale winter population sizes, we also reveal that conditions experienced during, and at the culmination of, autumn migration impact annual dynamics. Monarchs face a growing threat if floral resources and winter habitat availability diminish under climate change. Our study tackles a long-standing gap in the monarch’s annual cycle and highlights the importance of evaluating migratory conditions to understand mechanisms governing long-term population trends.