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In North America, winters are becoming more variable such that warm and cold extremes are increasingly common. Refugia (in time or space) can reduce the exposure animals experience to extreme temperatures. However, animals must be able to adjust their behavior to capitalize on refugia. Our goal was to identify the behavioral mechanisms that grant access to refugia in time and space, focusing on a northern ungulate (white-tailed deer, Odocoileus virginianus) as a model. We drew upon an extensive camera trap network in Wisconsin, USA, over two winters that experienced both warm and cold extremes. To understand the use of temporal refugia, we modeled deer activity (at daily resolution) during night, dawn, day, and dusk as a function of weather predictors. To understand the use of spatial refugia, we modeled deer activity at camera locations (at daily resolution) as a function of landscape characteristics, weather conditions, and landscape–weather interactions. During anomalously cold temperatures, deer became more diurnal; conversely, on anomalously warm days, deer were more nocturnal. Deer were more active in conifer-dominated landscapes on cold days. Conversely, during warm extremes, deer increased activity in deciduous-dominated landscapes. Finally, deer showed multiple modes of behavioral flexibility (activity in time as well as space) and demonstrated stronger responses to temperature anomalies later in the winter, suggesting that the effects of extreme events are dependent upon their seasonal timing. Behavioral shifts presumably reduce exposure to extremes and may render species more resilient to increasingly variable winter climates.

Animal behaviors are often modified in urban settings due to changes in species assemblages and interactions. The ability of prey to respond to a predator is a critical behavior, but urban populations may experience altered predation pressure, food supplementation, and other human-mediated disturbances that modify their responsiveness to predation risk and promote habituation.Citizen-science programs generally focus on the collection and analysis of observational data (e.g., bird checklists), but there has been increasing interest in the engagement of citizen scientists for ecological experimentation.Our goal was to implement a behavioral experiment in which citizen scientists recorded antipredator behaviors in wild birds occupying urban areas. In North America, increasing populations of

Grassland birds have exhibited dramatic and widespread declines since the mid‐20th century. Greater prairie chickens (Tympanuchus cupido pinnatus) are considered an umbrella species for grassland conservation and are frequent targets of management, but their responses to land use and management can be quite variable. We used data collected during 2007–2009 and 2014–2015 to investigate effects of land use and grassland management practices on habitat selection and survival rates of greater prairie chickens in central Wisconsin, USA. We examined habitat, nest‐site, and brood‐rearing site selection by hens and modeled effects of land cover and management on survival rates of hens, nests, and broods. Prairie chickens consistently selected grassland over other cover types, but selection or avoidance of management practices varied among life‐history stages. Hen, nest, and brood survival rates were influenced by different land cover types and management practices. At the landscape scale, hens selected areas where brush and trees had been removed during the previous year, which increased hen survival. Hens selected nest sites in hay fields and brood‐rearing sites in burned areas, but prescribed fire had a negative influence on hen survival. Brood survival rates were positively associated with grazing and were highest when home ranges contained ≈15%–20% shrub/tree cover. The effects of landscape composition on nest survival were ambiguous. Collectively, our results highlight the importance of evaluating responses to management efforts across a range of life‐history stages and suggest that a variety of management practices are likely necessary to provide structurally heterogeneous, high‐quality habitat for greater prairie chickens. Brush and tree removal, grazing, hay cultivation, and prescribed fire may be especially beneficial for prairie chickens in central Wisconsin, but trade‐offs among life‐history stages and the timing of management practices must be considered carefully., Greater prairie chickens (Tympanuchus cupido pinnatus) are considered an umbrella species for grassland conservation and are frequent targets of management, but their responses to land use and management can be quite variable. We examined habitat, nest‐site, and brood‐rearing site selection by hens and modeled effects of land cover and management on hen, nest, and brood survival. Collectively, our results highlight the importance of evaluating responses to management efforts across a range of life‐history stages and suggest that a variety of management practices are likely necessary to provide structurally heterogeneous, high‐quality habitat for greater prairie chickens.

Winter climate warming is rapidly leading to changes in snow depth and soil temperatures across mid- and high-latitude ecosystems, with important implications for survival and distribution of species that overwinter beneath the snow. Amphibians are a particularly vulnerable group to winter climate change because of the tight coupling between their body temperature and metabolic rate. Here, we used a mechanistic microclimate model coupled to an animal biophysics model to predict the spatially explicit effects of future climate change on the wintering energetics of a freeze-tolerant amphibian, the Wood Frog (Lithobates sylvaticus), across its distributional range in the eastern United States. Our below-the-snow microclimate simulations were driven by dynamically downscaled climate projections from a regional climate model coupled to a one-dimensional model of the Laurentian Great Lakes. We found that warming soil temperatures and decreasing winter length have opposing effects on Wood Frog winter energy requirements, leading to geographically heterogeneous implications for Wood Frogs. While energy expenditures and peak body ice content were predicted to decline in Wood Frogs across most of our study region, we identified an area of heightened energetic risk in the northwestern part of the Great Lakes region where energy requirements were predicted to increase. Because Wood Frogs rely on body stores acquired in fall to fuel winter survival and spring breeding, increased winter energy requirements have the potential to impact local survival and reproduction. Given the geographically variable and intertwined drivers of future under-snow conditions (e.g., declining snow depths, rising air temperatures, shortening winters), spatially explicit assessments of species energetics and risk will be important to understanding the vulnerability of subnivium-adapted species.

The recovery of piscivorous birds around the world is touted as one of the great conservation successes of the 21st century, but for some species, this success was short-lived. Bald eagles, ospreys and great blue herons began repatriating Voyageurs National Park, USA, in the mid-20th century. However, after 1990, only eagles continued their recovery, while osprey and heron recovery failed for unknown reasons. We aimed to evaluate whether top-down effects of bald eagles and bottom-up effects of inclement weather, habitat quality and fish resources contributed to the failed recovery of ospreys and herons in a protected area. We quantified the relative influence of top-down and bottom-up factors on nest colonization, persistence (i.e., nest reuse) and success for ospreys, and occurrence and size of heronries using 26 years (1986-2012) of spatially explicit monitoring data coupled with multi-response hierarchical models and Bayesian variable selection approaches. Bald eagles were previously shown to recover faster due to intensive nest protection and management. Increased numbers of eagles were associated with a reduction in the numbers of osprey nests, their nesting success and heronry size, while higher local densities of nesting eagles deterred heronries nearby. We found little evidence of bottom-up limitations on the failed recovery of herons and ospreys. We present a conservation conundrum: bald eagles are top predators and a flagship species of conservation that have benefited from intensive protection, but this likely hindered the recovery of ospreys and herons. Returning top predators, or rewilding, is widely promoted as a conservation strategy for top-down ecosystem recovery, but managing top predators in isolation of jointly recovering species can halt or reverse ecosystem recovery. Previous studies warn of the potential consequences of ignoring biotic interactions amongst recovering species, but we go further by quantifying how these interactions contributed to failed recoveries via impacts on the nesting demography of jointly recovering species. Multi-species management is paramount to realizing the ecosystem benefits of top predator recovery.

Grassland birds are among the most globally threatened bird groups due to substantial degradation of native grassland habitats. However, the current network of grassland conservation areas may not be adequate for halting population declines and biodiversity loss. Here, we evaluate a network of grassland conservation areas within Wisconsin, U.S.A., that includes both large Focal Landscapes and smaller targeted conservation areas (e.g., Grassland Bird Conservation Areas, GBCAs) established within them. To date, this conservation network has lacked baseline information to assess whether the current placement of these conservation areas aligns with population hot spots of grassland‐dependent taxa. To do so, we fitted data from thousands of avian point‐count surveys collected by citizen scientists as part of Wisconsin's Breeding Bird Atlas II with multinomial N‐mixture models to estimate habitat–abundance relationships, develop spatially explicit predictions of abundance, and establish ecological baselines within priority conservation areas for a suite of obligate grassland songbirds. Next, we developed spatial randomization tests to evaluate the placement of this conservation network relative to randomly placed conservation networks. Overall, less than 20% of species statewide populations were found within the current grassland conservation network. Spatial tests demonstrated a high representation of this bird assemblage within the entire conservation network, but with a bias toward birds associated with moderately tallgrasses relative to those associated with shortgrasses or tallgrasses. We also found that GBCAs had higher representation at Focal Landscape rather than statewide scales. Here, we demonstrated how combining citizen science data with hierarchical modeling is a powerful tool for estimating ecological baselines and conducting large‐scale evaluations of an existing conservation network for multiple grassland birds. Our flexible spatial randomization approach offers the potential to be applied to other protected area networks and serves as a complementary tool for conservation planning efforts globally., Grassland birds are rapidly declining globally and therefore are of conservation concern. Here, we integrated citizen science data, hierarchical occupancy models, and novel spatial simulations to evaluate the spatial configuration of an existing conservation network for the conservation of 9 obligate grassland birds. We found representation of grassland birds within the conservation network to be high for species that specialize in moderately tall grassland habitats but not shortgrass or tallgrass specialists.

Detection/non-detection data are widely collected by ecologists interested in estimating species distributions, abundances, and phenology, and are often subject to imperfect detection. Recent model development has focused on accounting for both false positive and false negative errors given evidence that misclassification is common across many sampling protocols. To date, however, model-based solutions to false positive error have largely addressed occupancy estimation. We describe a generalized model structure that allows investigators to account for false positive error in detection/non-detection data across a broad range of ecological parameters and model classes, and demonstrate that previously developed model-based solutions are special cases of the generalized model. Simulation results demonstrate that estimators for abundance and migratory arrival time ignoring false positive error exhibit severe (20-70%) relative bias even when only 5-10% of detections are false positives. Bias increased when false positive detections were more likely to occur at sites or within occasions in which true positive detections were unlikely to occur. Models accounting for false positive error following the site confirmation or observation confirmation designs generally reduced bias substantially, even when few detections were confirmed as true or false positives or when the process model for false positive error was misspecified. Results from an empirical example focusing on gray fox (Urocyon cinereoargenteus) in Wisconsin, USA reinforce concerns that biases induced by false positive error can also distort spatial predictions often used to guide decision-making. Model sensitivity to false positive error extends well beyond occupancy estimation, but encouragingly, model-based solutions developed for occupancy estimators are generalizable and effective across a range of models widely used in ecological research.

The emergence of citizen science, passive sensors (e.g., trail cameras and acoustic monitoring), and satellite remote sensing have enabled biological data to be collected at unprecedented spatial and temporal scales. There is growing interest in networking these datastreams to expedite the collection and synthesis of environmental and biological data to improve broad-scale ecological monitoring, but there are no examples of such networks being developed to directly inform decision-making by managing agencies. Here, we present the implementation of one such jurisdictional observation network (JON), Snapshot Wisconsin (SW), that links satellite remote sensing (RS) with a volunteer-based trail camera network to generate new insights into wildlife distributions and improve their management by the state agency. SW relies on citizen scientists to deploy trail cameras across the state and classify images of wildlife. As of early 2020 SW comprises nearly 1800 volunteers hosting >2100 active cameras recording >37 million images across a sampling effort of >2000 combined trap-years at >3300 distinct camera locations. We use a set of case studies to demonstrate the potential power of a JON to monitor wildlife with unprecedented combinations of spatial, temporal, and biological resolution and extent. Specifically, we demonstrate that SW markedly improves the spatial and temporal resolution with which black bear distributions can be monitored or forecast, in turn improving the resolution of decision-making. Enhancing the biological resolution of monitoring (e.g., monitoring the distribution of species traits or behaviors) may provide new insights into population drivers, such as the connection between vegetation productivity and white-tailed deer foraging behaviors. Enhanced taxonomic extent provided by trail cameras and other passive sensor networks provide managers new information for a wide range of species and communities that are not otherwise monitored. Our cases further show that JONs synergize existing monitoring practices by serving as a complementary and independent line of evidence or as a tool to enhance the extent and precision of existing models through integrated modeling approaches. SW and other JONS are a powerful new tool for agencies to better achieve their missions and reshape the nature of environmental decision-making.

Recent advancements in information technology and data acquisition have created both new research opportunities and new challenges for using big data in ornithology. We provide an overview of the past, present, and future of big data in ornithology, and explore the rewards and risks associated with their application. Structured data resources (e.g., North American Breeding Bird Survey) continue to play an important role in advancing our understanding of bird population ecology, and the recent advent of semistructured (e.g., eBird) and unstructured (e.g., weather surveillance radar) big data resources has promoted the development of new empirical perspectives that are generating novel insights. For example, big data have been used to study and model bird diversity and distributions across space and time, explore the patterns and determinants of broad-scale migration strategies, and examine the dynamics and mechanisms associated with geographic and phenological responses to global change. The appli...

Grassland birds are declining faster than any other bird guild across North America. Shrinking ranges and population declines are attributed to widespread habitat loss and increasingly fragmented landscapes of agriculture and other land uses that are misaligned with grassland bird conservation. Concurrent with habitat loss and degradation, temperate grasslands have been disproportionally affected by climate change relative to most other terrestrial biomes. Distributions of grassland birds often correlate with gradients in climate, but few researchers have explored the consequences of weather on the demography of grassland birds inhabiting a range of grassland fragments. To do so, we modeled the effects of temperature and precipitation on nesting success rates of 12 grassland bird species inhabiting a range of grassland patches across North America (21,000 nests from 81 individual studies). Higher amounts of precipitation in the preceding year were associated with higher nesting success, but wetter conditions during the active breeding season reduced nesting success. Extremely cold or hot conditions during the early breeding season were associated with lower rates of nesting success. The direct and indirect influence of temperature and precipitation on nesting success was moderated by grassland patch size. The positive effects of precipitation in the preceding year on nesting success were strongest in relatively small grassland patches and had little effect in large patches. Conversely, warm temperatures reduced nesting success in small grassland patches but increased nesting success in large patches. Mechanisms underlying these differences may be patch-size-induced variation in microclimates and predator activity. Although the exact cause is unclear, large grassland patches, the most common metric of grassland conservation, appears to moderate the effects of weather on grassland-bird demography and could be an effective component of climate-change adaptation.

In regions where snowfall historically has been a defining seasonal characteristic of the landscape, warming winters have reduced the depth, duration, and extent of snowpack. However, most management and conservation has focused on how aboveground wildlife will be affected by altered snow conditions, even though the majority of species that persist through the winter do so under the snowpack in a thermally stable refugium: the subnivium. Shortened winters, forest management practices, and winter recreation can alter subnivium conditions by increasing snow compaction and compromising thermal stability at the soil-snow interface. To help slow the loss of the subnivium in the face of rapidly changing winter conditions, we suggest managers adopt regional conservation plans for identifying threatened snow-covered environments; measure and predict the effects land cover and habitat management has on local subnivium conditions; and control the timing and distribution of activities that disturb and compact snow cover (e.g., silvicultural practices, snow recreation, and road and trail maintenance). As a case study, we developed a spatially explicit model of subnivium presence in a working landscape of the Chequamegon National Forest, Wisconsin. We identified landscapes where winter recreation and management practices could threaten potentially important areas for subnivium persistence. Similar modeling approaches could inform management decisions related to subnivium conservation. Current climate projections predict that snow seasons will change rapidly in many regions, and as result, we advocate for the immediate recognition, conservation, and management of the subnivium and its dependent species.

Allen's rule predicts that homeotherms inhabiting cooler climates will have smaller appendages, while those inhabiting warmer climates will have larger appendages relative to body size. Birds’ bills tend to be larger at lower latitudes, but few studies have tested whether modern climate change and urbanization affect bill size. Our study explored whether bill size in a wide‐ranging bird would be larger in warmer, drier regions and increase with rising temperatures. Furthermore, we predicted that bill size would be larger in densely populated areas, due to urban heat island effects and the higher concentration of supplementary foods. Using measurements from 605 museum specimens, we explored the effects of climate and housing density on northern cardinal bill size over an 85‐year period across the Linnaean subspecies’ range. We quantified the geographic relationships between bill surface area, housing density, and minimum temperature using linear mixed effect models and geographically weighted regression. We then tested whether bill surface area changed due to housing density and temperature in three subregions (Chicago, IL., Washington, D.C., and Ithaca, NY). Across North America, cardinals occupying drier regions had larger bills, a pattern strongest in males. This relationship was mediated by temperature such that birds in warm, dry areas had larger bills than those in cool, dry areas. Over time, female cardinals’ bill size increased with warming temperatures in Washington, D.C., and Ithaca. Bill size was smaller in developed areas of Chicago, but larger in Washington, D.C., while there was no pattern in Ithaca, NY. We found that climate and urbanization were strongly associated with bill size for a wide‐ranging bird. These biogeographic relationships were characterized by sex‐specific differences, varying relationships with housing density, and geographic variability. It is likely that anthropogenic pressures will continue to influence species, potentially promoting microevolutionary changes over space and time.

Extreme weather, including heat waves, droughts, and high rainfall, is becoming more common and affecting a diversity of species and taxa. However, researchers lack a framework that can anticipate how diverse species will respond to weather extremes spanning weeks to months. Here we used high-resolution occurrence data from eBird, a global citizen science initiative, and dynamic species distribution models to examine how 109 North American bird species ranging in migration distance, diet, body size, habitat preference, and prevalence (commonness) respond to extreme heat, drought, and rainfall across a wide range of temporal scales. Across species, temperature influenced species' distributions more than precipitation at weekly and monthly scales, while precipitation was more important at seasonal scales. Phylogenetically controlled multivariate models revealed that migration distance was the most important factor mediating responses to extremely hot or dry weeks; residents and short-distance migrants occurred less often following extreme heat. At monthly or seasonal scales, less common birds experienced decreases in occurrence following drought-like conditions, while widespread species were unaffected. Spatial predictions demonstrated variation in responses to extreme weather across species' ranges, with predicted decreases in occurrence up to 40% in parts of ranges. Our results highlight that extreme weather has variable and potentially strong implications for birds at different time scales, but these responses are mediated by life-history characteristics. As weather once considered extreme occurs more frequently, researchers and managers require a better understanding of how diverse species respond to extreme conditions.

The rapid improvement of camera traps in recent decades has revolutionized biodiversity monitoring. Despite clear applications in conservation science, camera traps have seldom been used to model the abundance of unmarked animal populations. We sought to summarize the challenges facing abundance estimation of unmarked animals, compile an overview of existing analytical frameworks, and provide guidance for practitioners seeking a suitable method. When a camera records multiple detections of an unmarked animal, one cannot determine whether the images represent multiple mobile individuals or a single individual repeatedly entering the camera viewshed. Furthermore, animal movement obfuscates a clear definition of the sampling area and, as a result, the area to which an abundance estimate corresponds. Recognizing these challenges, we identified 6 analytical approaches and reviewed 927 camera-trap studies published from 2014 to 2019 to assess the use and prevalence of each method. Only about 5% of the studies used any of the abundance-estimation methods we identified. Most of these studies estimated local abundance or covariate relationships rather than predicting abundance or density over broader areas. Next, for each analytical approach, we compiled the data requirements, assumptions, advantages, and disadvantages to help practitioners navigate the landscape of abundance estimation methods. When seeking an appropriate method, practitioners should evaluate the life history of the focal taxa, carefully define the area of the sampling frame, and consider what types of data collection are possible. The challenge of estimating abundance of unmarked animal populations persists; although multiple methods exist, no one method is optimal for camera-trap data under all circumstances. As analytical frameworks continue to evolve and abundance estimation of unmarked animals becomes increasingly common, camera traps will become even more important for informing conservation decision-making.Estimación de la Abundancia de Animales No Marcados con Base en Datos de Cámaras Trampa Resumen La rápida mejoría de las cámaras trampa en las décadas recientes ha revolucionado el monitoreo de la biodiversidad. A pesar de su clara aplicación en las ciencias de la conservación, las cámaras trampa han sido utilizadas pocas veces para modelar la abundancia de las poblaciones de animales no marcados. Buscamos resumir los retos que enfrenta la estimación de la abundancia de animales no marcados, compilar una perspectiva general de los marcos analíticos de trabajo existentes y proporcionar una guía para aquellos practicantes que buscan un método adecuado. Cuando una cámara registra múltiples detecciones de animales no marcados, no se puede determinar si las imágenes representan a diferentes individuos en movimiento o a un solo individuo que entra repetidamente a la zona de visión de la cámara. Sumado a esto, el movimiento animal ofusca una definición clara del área de muestreo y, como resultado, del área a la cual corresponde un estimado de abundancia. Después de reconocer estos retos, identificamos seis estrategias analíticas y revisamos 927 estudios con cámaras trampa publicados entre 2014 y 2019 para evaluar el uso y la prevalencia de cada método. Solamente en el 5% de los estudios se usó cualquiera de los métodos de estimación de abundancia que identificamos. La mayoría de estos estudios estimaron la abundancia local o las relaciones de covarianza en lugar de predecir la abundancia o la densidad a lo largo de áreas más amplias. Después, para cada estrategia analítica, recopilamos los requerimientos de datos, suposiciones, ventajas y desventajas para ayudar a los practicantes a navegar el paisaje de los métodos de estimación de abundancia. Cuando los practicantes busquen un método apropiado deberán evaluar la historia de vida del taxón focal, definir cuidadosamente el área del marco de muestreo y considerar cuáles tipos de recolección de datos son posibles. El reto de estimar la abundancia de poblaciones de animales no marcados persiste; aunque existan muchos métodos, no hay método único óptimo para los datos de las cámaras trampa que cumpla con todas las circunstancias. Mientras los marcos analíticos de trabajo sigan evolucionando y la estimación de la abundancia de animales no marcados sea cada vez más común, las cámaras trampa serán todavía más importantes para informar la toma de decisiones de conservación.近几十年来红外相机陷阱技术的快速发展已经彻底改变了生物多样性监测的现状。尽管红外相机陷阱法在动物保护科学中有明确的应用, 但它很少被用来模拟无标记动物的种群数量。本研究旨在总结无标记动物的丰度估计所面临的挑战, 总结现有的分析框架并为寻求合适方法的实践者提供指导意见。当红外相机多次记录到无标记的动物时, 人们无法确定这些图像代表的是多个个体还是一个重复进入相机拍摄范围的个体。此外, 动物的运动导致不能清晰地划定采样区域, 因此也模糊了所对应区域的丰度估计。面对这些挑战, 我们确定了六种分析方法, 并综述了 2014 年至 2019 年发表的 927 项红外相机陷阱研究, 以评估每种方法的使用情况和流行程度。结果发现, 只有约 5% 的研究使用了至少一种我们确定的丰度估计方法。这些研究大多是估计局部丰度或协变量关系, 而不是预测更大范围内的动物丰度或密度。接下来, 我们总结了每种分析方法的数据需求、假设、优点和缺点, 以帮助实践者了解丰度估计方法的总体情况。实践者在寻找合适的方法时, 应评估研究所关注类群的生活史, 谨慎地确定采样范围, 并考虑可能收集到的数据类型。无标记动物的种群数量估计仍面临挑战, 虽然已存在多种方法, 但没有一种方法对于所有红外相机陷阱数据都是最优的。随着分析框架的不断发展和对无标记动物数量估计变得越来越普遍, 红外相机陷阱法在为指导保护决策中也将更加重要。【翻译: 胡怡思; 审校: 聂永刚】.

Temperate grasslands and their dependent species are exposed to high variability in weather and climate due to the lack of natural buffers such as forests. Grassland birds are particularly vulnerable to this variability, yet have failed to shift poleward in response to recent climate change like other bird species in North America. However, there have been few studies examining the effect of weather on grassland bird demography and consequent influence of climate change on population persistence and distributional shifts. The goal of this study was to estimate the vulnerability of Henslow’s Sparrow (Ammodramus henslowii), an obligate grassland bird that has been declining throughout much of its range, to past and future climatic variability. We conducted a demographic meta-analysis from published studies and quantified the relationship between nest success rates and variability in breeding season climate. We projected the climate-demography relationships spatially, throughout the breeding range, and temporally, from 1981 to 2050. These projections were used to evaluate population dynamics by implementing a spatially explicit population model. We uncovered a climate-demography linkage for Henslow’s Sparrow with summer precipitation, and to a lesser degree, temperature positively affecting nest success. We found that future climatic conditions—primarily changes in precipitation—will likely contribute to reduced population persistence and a southwestward range contraction. Future distributional shifts in response to climate change may not always be poleward and assessing projected changes in precipitation is critical for grassland bird conservation and climate change adaptation.

With over half of earth's terrestrial biota living beneath forest canopies, our ability to accurately capture organism–climate relationships in forested ecosystems is imperative for predicting species' vulnerability to future climate change. Assessing the vulnerability of forest dependent species, however, hinges on quantifying microclimates that exist below the forest canopy and might be influenced by varying levels of disturbance in human-modified landscapes. The goal of our study was to examine the multi-scaled predictors of subcanopy microclimate variability across a heterogeneous landscape in Midwestern USA during winter, and to further evaluate whether a widely available interpolated climate model accurately captures this variability. By deploying a network of temperature sensors along a fragmentation gradient, we found that forests in more fragmented landscapes with greater amounts of forest edge and increasing distances between forest patches, experienced colder minimum and average daily temperatures throughout the winter than forests in less fragmented landscapes. We found that greater tree densities and higher elevations led to warmer microclimates while increasing distances from urban centers led to colder microclimates. The negative effect of forest edge on minimum temperatures was lessened by the effect of increasing basal area, highlighting the importance of local- and landscape-scale features on microclimate heterogeneity. Temperature discrepancies between subcanopy microclimates and climate interpolations were influenced by many of the same features, and could be of a similar magnitude as those predicted by future climate change scenarios. Using a biological threshold based on metabolic and demographic constraints for winter birds, we found that the variability in microclimates along our forest fragmentation gradient (50 km) was comparable to the magnitude captured by weather stations across a latitudinal gradient spanning more than 650 km. Our results suggest that biophysical properties of landscapes can alter spatial gradients of microclimates and should be considered when assessing species' vulnerabilities to future climate change.

Climate change is not occurring over a homogeneous landscape and the quantity and quality of available land cover will likely affect the way species respond to climate change. The influence of land cover on species’ responses to climate change, however, is likely to differ depending on habitat type and composition. Our goal was to investigate responses of forest and grassland breeding birds to over 20 years of climate change across varying gradients of forest and grassland habitat. Specifically, we investigated whether (i) increasing amounts of available land cover modify responses of forest and grassland-dependent birds to changing climate and (ii) the effect of increasing land cover amount differs for forest and grassland birds. We used Bayesian spatially-varying intercept models to evaluate species- and community-level responses of 30 forest and 10 grassland birds to climate change across varying amounts of their associated land cover types. Responses of forest birds to climate change were weak and constant across a gradient of forest cover. Conversely, grassland birds responded strongly to changing climatic conditions. Specifically, increasing temperatures led to higher probabilities of localized extinctions for grassland birds, and this effect was intensified in regions with low amounts of grassland cover. Within the context of northeastern forests and grasslands, we conclude that forests serve as a possible buffer to the impacts of climate change on birds. Conversely, species occupying open, fragmented grassland areas might be particularly at risk of a changing climate due to the diminished buffering capacity of these ecosystems.

The seasonal dynamics of snow cover strongly affect ecosystem processes and winter habitat, making them an important driver of terrestrial biodiversity patterns. Snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra satellites can capture these dynamics over large spatiotemporal scales, allowing for the development of indices with specific application in ecological research and predicting biodiversity. Here, our primary objective was to derive winter habitat indices (WHIs) from MODIS that quantify snow season length, snow cover variability, and the prevalence of frozen ground without snow as a proxy for subnivium conditions. We calculated the WHIs for the full snow year (Aug-Jul) and winter months (Dec-Feb) across the contiguous US from 2003/04 to 2017/18 and validated them with ground-based data from 797 meteorological stations. To demonstrate the potential of the WHIs for biodiversity assessments, we modeled their relationships with winter bird species richness derived from eBird observations. The WHIs had clear spatial patterns reflecting both altitudinal and latitudinal gradients in snow cover. Snow season length was generally longer at higher latitudes and elevations, while snow cover variability and frozen ground without snow were highest across low elevations of the mid latitudes. Variability in the WHIs was largely driven by elevation in the West and by latitude in the East. Snow season length and frozen ground without snow were most accurately mapped, and had correlations with station data across all years of 0.91 and 0.85, respectively. Snow cover variability was accurately mapped for winter (r = 0.79), but not for the full snow year (r = −0.21). The model containing all three WHIs used to predict winter bird species richness patterns across the contiguous US was by far the best, demonstrating the individual value of each index. Regions with longer snow seasons generally supported fewer species. Species richness increased steadily up to moderate levels of snow cover variability and frozen ground without snow, after which it steeply declined. Our results show that the MODIS WHIs accurately characterized unique gradients of snow cover dynamics and provided important information on winter habitat conditions for birds, highlighting their potential for ecological research and conservation planning.

Changes in land use and climate are among the main drivers of range shifts for multiple species and taxa. While such distributional changes are often observed at broad scales, the processes driving them – colonization and extinction along range boundaries – occur at local scales. There is a need then, to identify whether these local drivers are consistent with patterns at broader scales. Snowshoe hares (Lepus americanus), an important prey species in northern forest ecosystems, have experienced range contraction along much of their southern boundary. Population declines are linked to climate change via camouflage mismatch, an inability to match seasonal coat color molts with declining snow cover duration. We used data from camera traps to monitor the spatial extent and habitat associations of hares before, during and after an experimental translocation, and multi-season occupancy models to model the effects of habitat and climate on colonization and extinction patterns. We compared our contemporary occupancy models to historical patterns of extirpation and simulated future effects of timber harvest on occupancy. Colonization by hares was strongly associated with distance from source population and patch size of aspen-alder. Historical patterns of extirpation were consistent with contemporary patterns, and simulations revealed that increased frequency and magnitude of aspen harvest increased hare occupancy by creating large patches of early-successional habitat. As global change alters ecological communities, identifying mechanisms that drive range-shifts and targeting landscape features that can be manipulated to buffer those effects can be used to promote the persistence of individual species and ecological communities.

Conservation for species impacted by climate change often occurs at scales impractical for local land managers. Snowshoe hares (Lepus americanus) are one of the most well‐documented species declining from climate change–specifically a reduction in snowcover–yet clear management strategies have yet to emerge. To test whether camouflage mismatch is reducing hare survival we translocated 96 hares to a site recently extirpated of snowshoe hares, and monitored coat color change, mismatch with snow, habitat use, and weekly survival in winter‐spring of 2017. Hare survival was low during periods of camouflage mismatch, and mismatched hares were 3.2 × less likely to survive, but this pattern varied by habitat. We found that aspen‐alder stands >5 hectares negated the mortality costs of mismatch. We provide experimental evidence that mismatch is driving the range contraction of snowshoe hares, and identify specific habitats to buffer the consequences of climate change on this declining winter specialist.

Urbanization causes the simplification of natural habitats, resulting in animal communities dominated by exotic species with few top predators. In recent years, however, many predators such as hawks, and in the US coyotes and cougars, have become increasingly common in urban environments. Hawks in the Accipiter genus, especially, are recovering from widespread population declines and are increasingly common in urbanizing landscapes. Our goal was to identify factors that determine the occupancy, colonization and persistence of Accipiter hawks in a major metropolitan area. Through a novel combination of citizen science and advanced remote sensing, we quantified how urban features facilitate the dynamics and long-term establishment of Accipiter hawks. Based on data from Project FeederWatch, we quantified 21 years (1996–2016) of changes in the spatio-temporal dynamics of Accipiter hawks in Chicago, IL, USA. Using a multi-season occupancy model, we estimated Cooper's ( Accipiter cooperii ) and sharp-shinned ( A. striatus ) hawk occupancy dynamics as a function of tree canopy cover, impervious surface cover and prey availability. In the late 1990s, hawks occupied 26% of sites around Chicago, but after two decades, their occupancy fluctuated close to 67% of sites and they colonized increasingly urbanized areas. Once established, hawks persisted in areas with high levels of impervious surfaces as long as those areas supported high abundances of prey birds. Urban areas represent increasingly habitable environments for recovering predators, and understanding the precise urban features that drive colonization and persistence is important for wildlife conservation in an urbanizing world.

Spatially-indexed repeated detection-nondetection data is widely collected by ecologists interested in estimating parameters associated with species distribution, relative abundance, phenology, and more while accounting for imperfect detection. Recent model development has focused on accounting for false positive error as well, given growing recognition that misclassification is common across many sampling protocols. To date, however, the development of model-based solutions to false positive error has been largely restricted to occupancy models. We describe a general form of the observation confirmation protocol originally described for occupancy estimation that permits investigators to flexibly and intuitively extend several models for detection-nondetection data to account for false positive error. Simulation results demonstrate that estimators for relative abundance and arrival time exhibit relative bias greater than 20% under realistic levels of false positive prevalence (e.g., 5% of detections are false positive). Bias increases as true and false positives occur in more distinct places or times, but can also be sensitive to the values of the state variables of interest, sampling design, and sampling efficiency. Results from an empirical study focusing on patterns of gray fox relative abundance across Wisconsin, USA suggest that false positive error can also distort estimated spatial patterns often used to guide decision-making. The extended estimators described within typically improve performance at any level of confirmation, and when false positive error occurs at random and constitutes less than 10% of all detections, the estimators are essentially unbiased when more than 50 observations can be confirmed as true or false positives. The generalized form of the observation-confirmation protocol is a flexible model-based solution to false positive error useful for researchers collecting data with sampling devices like trail or smartphone cameras, acoustic recorders, or other techniques where classifications can be reviewed post-hoc.

Measurement or observation error is common in ecological data: as citizen scientists and automated algorithms play larger roles processing growing volumes of data to address problems at large scales, concerns about data quality and strategies for improving it have received greater focus. However, practical guidance pertaining to fundamental data quality questions for data users or managers-how accurate do data need to be and what is the best or most efficient way to improve it?-remains limited. We present a generalizable framework for evaluating data quality and identifying remediation practices, and demonstrate the framework using trail camera images classified using crowdsourcing to determine acceptable rates of misclassification and identify optimal remediation strategies for analysis using occupancy models. We used expert validation to estimate baseline classification accuracy and simulation to determine the sensitivity of two occupancy estimators (standard and false-positive extensions) to different empirical misclassification rates. We used regression techniques to identify important predictors of misclassification and prioritize remediation strategies. More than 93% of images were accurately classified, but simulation results suggested that most species were not identified accurately enough to permit distribution estimation at our predefined threshold for accuracy (5% absolute bias). A model developed to screen incorrect classifications predicted misclassified images with97% accuracy: enough to meet our accuracy threshold. Occupancy models that accounted for false-positive error provided even more accurate inference even at high rates of misclassification (30%). As simulation suggested occupancy models were less sensitive to additional false-negative error, screening models or fitting occupancy models accounting for false-positive error emerged as efficient data remediation solutions. Combining simulation-based sensitivity analysis with empirical estimation of baseline error and its variability allows users and managers of potentially error-prone data to identify and fix problematic data more efficiently. It may be particularly helpful for "big data" efforts dependent upon citizen scientists or automated classification algorithms with many downstream users, but given the ubiquity of observation or measurement error, even conventional studies may benefit from focusing more attention upon data quality.