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Here, we present the largest, global dataset of Lepidopteran traits, focusing initially on butterflies (ca. 12,500 species records). These traits are derived from field guides, taxonomic treatments, and other literature resources. We present traits on wing size, phenology,voltinism, diapause/overwintering stage, hostplant associations, and habitat affinities (canopy, edge, moisture, and disturbance). This dataset will facilitate comparative research on butterfly ecology and evolution and our goal is to inspire future research collaboration and the continued development of this dataset.

Data availability limits phenological research at broad temporal and spatial extents. Butterflies are among the few taxa with broad-scale occurrence data, from both incidental reports and formal surveys. Incidental reports have biases that are challenging to address, but structured surveys are often limited seasonally and may not span full flight phenologies. Thus, how these data source compare in phenological analyses is unclear. We modeled butterfly phenology in relation to traits and climate using parallel analyses of incidental and survey data, to explore their shared utility and potential for analytical integration. One workflow aggregated “Pollard” surveys, where sites are visited multiple times per year; the other aggregated incidental data from online portals: iNaturalist and eButterfly. For 40 species, we estimated early (10%) and mid (50%) flight period metrics, and compared the spatiotemporal patterns and drivers of phenology across species and between datasets. For both datasets, inter-annual variability was best explained by temperature, and seasonal emergence was earlier for resident species overwintering at more advanced stages. Other traits related to habitat, feeding, dispersal, and voltinism had mixed or no impacts. Our results suggest that data integration can improve phenological research, and leveraging traits may predict phenology in poorly studied species.

Large occurrence datasets provide a sizable resource for ecological analyses, but have substantial limitations. Phenological analyses in Fricet al. (2020) were misleading due to inadequate curation and improper statistics. Reanalysing 22 univoltine species with sufficient data for independent analysis, we found substantively different macroscale phenological patterns, including later onset at higher latitude for most species.

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.

Insect phenological lability is key for determining which species will adapt under environmental change. However, little is known about when adult insect activity terminates and overall activity duration. We used community‐science and museum specimen data to investigate the effects of climate and urbanisation on timing of adult insect activity for 101 species varying in life history traits. We found detritivores and species with aquatic larval stages extend activity periods most rapidly in response to increasing regional temperature. Conversely, species with subterranean larval stages have relatively constant durations regardless of regional temperature. Species extended their period of adult activity similarly in warmer conditions regardless of voltinism classification. Longer adult durations may represent a general response to warming, but voltinism data in subtropical environments are likely underreported. This effort provides a framework to address the drivers of adult insect phenology at continental scales and a basis for predicting species response to environmental change.

Recurring seasonal changes can lead to the evolution of phenological cues. For example, many arthropods undergo photoperiodic diapause, a programmed developmental arrest induced by short autumnal day length. The selective mechanisms that determine the timing of autumnal diapause initiation have not been empirically identified. We quantified latitudinal clines in genetically determined diapause timing of an invasive mosquito,Aedes albopictus,on two continents. We show that variation in diapause timing within and between continents is explained by a novel application of a growing degree day (GDD) model that delineates a location‐specific deadline after which it is not possible to complete an additional full life cycle. GDD models are widely used to predict spring phenology by modelling growth and development as physiological responses to ambient temperatures. Our results show that the energy accumulation dynamics represented by GDD models have also led to the evolution of an anticipatory life‐history cue in autumn.

Phenology is one of the most immediate responses to global climate change, but data limitations have made examining phenology patterns across greater taxonomic, spatial and temporal scales challenging. One significant opportunity is leveraging rapidly increasing data resources from digitized museum specimens and community science platforms, but this assumes reliable statistical methods are available to estimate phenology using presence‐only data. Estimating the onset or offset of key events is especially difficult with incidental data, as lower data densities occur towards the tails of an abundance distribution.

Understanding the correspondence between ambient temperature and insect development is necessary to forecast insect phenology under novel environments. In the face of climate change, both conservation and pest control efforts require accurate phenological predictions. Here, we compare a suite of degree‐day models to assess their ability to predict the phenology of a common, oligophagous butterfly, the silver‐spotted skipper,Epargyreus clarus(Cramer) (Lepidoptera: Hesperiidae). To estimate model parameters, we used development time of eggs and larvae reared in the laboratory at six constant temperatures ranging from 8 to 38 °C and on two host plants of contrasting quality (kudzu and wisteria). We employed three approaches to determine the base temperature to calculate degree days: linear regression, modified reduced major axis regression, and application of a generic base temperature value of 10 °C, which is commonly used in the absence of laboratory data. To calculate the number of degree days required to complete a developmental stage, we used data from caterpillars feeding on high‐ and low‐quality hosts, both in the field and in the laboratory. To test model accuracy, we predicted development time of seven generations of larvae reared in the field on the same host plants across 3 years (2014–2016). To compare performance among models, we regressed predicted vs. observed development time, and found that r²values were significantly larger when accounting for host plant quality. The accuracy of development time predictions varied across the season, with estimates of the first two generations being more accurate than estimates of the third generation, when ambient temperatures dropped outside the range in which development rate and temperature have a linear relationship. Overall, we show that accounting for variation in host plant quality when calculating development time in the field is more important than the choice of the base temperature for calculating degree days.

Thermal performance curves (TPCs) depict variation in vital rates in response to temperature and have been an important tool to understand ecological and evolutionary constraints on the thermal sensitivity of ectotherms. TPCs allow for the calculation of indicators of thermal tolerance, such as minimum, optimum, and maximum temperatures that allow for a given metabolic function. However, these indicators are computed using only responses from surviving individuals, which can lead to underestimation of deleterious effects of thermal stress, particularly at high temperatures. Here, we advocate for an integrative framework for assessing thermal sensitivity, which combines both vital rates and survival probabilities, and focuses on the temperature interval that allows for population persistence. Using a collated data set of Lepidopteran development rate and survival measured on the same individuals, we show that development rate is generally limiting at low temperatures, while survival is limiting at high temperatures. We also uncover differences between life stages and across latitudes, with extended survival at lower temperatures in temperate regions. Our combined performance metric demonstrates similar thermal breadth in temperate and tropical individuals, an effect that only emerges from integration of both development and survival trends. We discuss the benefits of using this framework in future predictive and management contexts.