Assessing the scope and severity of threats is necessary for evaluating impacts on populations to inform conservation planning. Quantitative threat assessment often requires monitoring programs that provide reliable data over relevant spatial and temporal scales, yet such programs can be difficult to justify until there is an apparent stressor. Leveraging efforts of wildlife management agencies to record winter counts of hibernating bats, we collated data for 5 species from over 200 sites across 27 U.S. states and 2 Canadian provinces from 1995 to 2018 to determine the impact of white‐nose syndrome (WNS), a deadly disease of hibernating bats. We estimated declines of winter counts of bat colonies at sites where the invasive fungus that causes WNS ( Pseudogymnoascus destructans ) had been detected to assess the threat impact of WNS. Three species undergoing species status assessment by the U.S. Fish and Wildlife Service ( Myotis septentrionalis , Myotis lucifugus , and Perimyotis subflavus ) declined by more than 90%, which warrants classifying the severity of the WNS threat as extreme based on criteria used by NatureServe. The scope of the WNS threat as defined by NatureServe criteria was large (36% of Myotis lucifugus range) to pervasive (79% of Myotis septentrionalis range) for these species. Declines for 2 other species ( Myotis sodalis and Eptesicus fuscus ) were less severe but still qualified as moderate to serious based on NatureServe criteria. Data‐sharing across jurisdictions provided a comprehensive evaluation of scope and severity of the threat of WNS and indicated regional differences that can inform response efforts at international, national, and state or provincial jurisdictions. We assessed the threat impact of an emerging infectious disease by uniting monitoring efforts across jurisdictional boundaries and demonstrated the importance of coordinated monitoring programs, such as the North American Bat Monitoring Program (NABat), for data‐driven conservation assessments and planning.

The monarch butterfly (Danaus plexippus) population in North America has sharply declined over the last two decades. Despite rising concern over the monarch butterfly's status, no comprehensive study of the factors driving this decline has been conducted. Using partial least-squares regressions and time-series analysis, we investigated climatic and habitat-related factors influencing monarch population size from 1993 to 2014. Potential threats included climatic factors, habitat loss (milkweed and overwinter forest), disease and agricultural insecticide use (neonicotinoids). While climatic factors, principally breeding season temperature, were important determinants of annual variation in abundance, our results indicated strong negative relationships between population size and habitat loss variables, principally glyphosate use, but also weaker negative effects from the loss of overwinter forest and breeding season use of neonicotinoids. Further declines in population size because of glyphosate application are not expected. Thus, if remaining threats to habitat are mitigated we expect climate-induced stochastic variation of the eastern migratory population of monarch butterfly around a relatively stationary population size.

The Eastern, migratory population of monarch butterflies (Danaus plexippus), an iconic North American insect, has declined by ~80% over the last decade. The monarch’s multi-generational migration between overwintering grounds in central Mexico and the summer breeding grounds in the northern U.S. and southern Canada is celebrated in all three countries and creates shared management responsibilities across North America. Here we present a novel Bayesian multivariate auto-regressive state-space model to assess quasi-extinction risk and aid in the establishment of a target population size for monarch conservation planning. We find that, given a range of plausible quasi-extinction thresholds, the population has a substantial probability of quasi-extinction, from 11–57% over 20 years, although uncertainty in these estimates is large. Exceptionally high population stochasticity, declining numbers and a small current population size act in concert to drive this risk. An approximately 5-fold increase of the monarch population size (relative to the winter of 2014–15) is necessary to halve the current risk of quasi-extinction across all thresholds considered. Conserving the monarch migration thus requires active management to reverse population declines and the establishment of an ambitious target population size goal to buffer against future environmentally driven variability.

The relationship between pesticides and pollinators, while attracting no shortage of attention from scientists, regulators, and the public, has proven resistant to scientific synthesis and fractious in matters of policy and public opinion. This is in part because the issue has been approached in a compartmentalized and intradisciplinary way, such that evaluations of organismal pesticide effects remain largely disjoint from their upstream drivers and downstream consequences. Here, we present a socioecological framework designed to synthesize the pesticide-pollinator system and inform future scholarship and action. Our framework consists of three interlocking domains-pesticide use, pesticide exposure, and pesticide effects-each consisting of causally linked patterns, processes, and states. We elaborate each of these domains and their linkages, reviewing relevant literature and providing empirical case studies. We then propose guidelines for future pesticide-pollinator scholarship and action agenda aimed at strengthening knowledge in neglected domains and integrating knowledge across domains to provide decision support for stakeholders and policymakers. Specifically, we emphasize (1) stakeholder engagement, (2) mechanistic study of pesticide exposure, (3) understanding the propagation of pesticide effects across levels of organization, and (4) full-cost accounting of the externalities of pesticide use and regulation. Addressing these items will require transdisciplinary collaborations within and beyond the scientific community, including the expertise of farmers, agrochemical developers, and policymakers in an extended peer community.

Weather and climate affect many ecological processes, making spatially continuous yet fine‐resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates? (2) Are there significant regional differences in accuracy among data sets? (3) How accurate are their mean values compared with extremes? (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.