Wherever I may roam-Human activity alters movements of red deer (Cervus elaphus) and elk (Cervus canadensis) across two continents

Human activity and associated landscape modifications alter the movements of animals with consequences for populations and ecosystems worldwide. Species performing long-distance movements are thought to be particularly sensitive to human impact. Despite the increasing anthropogenic pressure, it remains challenging to understand and predict animals' responses to human activity. Here we address this knowledge gap using 1206 Global Positioning System movement trajectories of 815 individuals from 14 red deer (Cervus elaphus) and 14 elk (Cervus canadensis) populations spanning wide environmental gradients, namely the latitudinal range from the Alps to Scandinavia in Europe, and the Greater Yellowstone Ecosystem in North America. We measured individual-level movements relative to the environmental context, or movement expression, using the standardized metric Intensity of Use, reflecting both the directionality and extent of movements. We expected movement expression to be affected by resource (Normalized Difference Vegetation Index, NDVI) predictability and topography, but those factors to be superseded by human impact. Red deer and elk movement expression varied along a continuum, from highly segmented trajectories over relatively small areas (high intensity of use), to directed transitions through restricted corridors (low intensity of use). Human activity (Human Footprint Index, HFI) was the strongest driver of movement expression, with a steep increase in Intensity of Use as HFI increased, but only until a threshold was reached. After exceeding this level of impact, the Intensity of Use remained unchanged. These results indicate the overall sensitivity of Cervus movement expression to human activity and suggest a limitation of plastic responses under high human pressure, despite the species also occurring in human-dominated landscapes. Our work represents the first comparison of metric-based movement expression across widely distributed populations of a deer genus, contributing to the understanding and prediction of animals' responses to human activit

Wherever I may roam—Human activity alters movements of red deer (Cervus elaphus) and elk (Cervus canadensis) across two continents

Human activity and associated landscape modifications alter the movements of ani-mals with consequences for populations and ecosystems worldwide. Species perform-ing long-distance movements are thought to be particularly sensitive to human impact. Despite the increasing anthropogenic pressure, it remains challenging to understand and predict animals' responses to human activity. Here we address this knowledge gap using 1206 Global Positioning System movement trajectories of 815 individuals from 14 red deer (Cervus elaphus) and 14 elk (Cervus canadensis) populations spanning wide environmental gradients, namely the latitudinal range from the Alps to Scandinavia in Europe, and the Greater Yellowstone Ecosystem in North America. We measured individual-level movements relative to the environmental context, or movement ex-pression, using the standardized metric Intensity of Use, reflecting both the directional-ity and extent of movements. We expected movement expression to be affected by resource (Normalized Difference Vegetation Index, NDVI) predictability and topogra-phy, but those factors to be superseded by human impact. Red deer and elk movement expression varied along a continuum, from highly segmented trajectories over relatively small areas (high intensity of use), to directed transitions through restricted corridors (low intensity of use). Human activity (Human Footprint Index, HFI) was the strong-est driver of movement expression, with a steep increase in Intensity of Use as HFI increased, but only until a threshold was reached. After exceeding this level of impact, the Intensity of Use remained unchanged. These results indicate the overall sensitivity of Cervus movement expression to human activity and suggest a limitation of plastic responses under high human pressure, despite the species also occurring in human-dominated landscapes. Our work represents the first comparison of metric- based movement expression across widely distributed populations of a deer genus, contribut-ing to the understanding and prediction of animals' responses to human activity.publishedVersio

Weather conditions associated with autumn migration by mule deer in Wyoming

Maintaining ecological integrity necessitates a proactive approach of identifying and acquiring lands to conserve unfragmented landscapes, as well as evaluating existing mitigation strategies to increase connectivity in fragmented landscapes. The increased use of highway underpasses and overpasses to restore connectivity for wildlife species offers clear conservation benefits, yet also presents a unique opportunity to understand how weather conditions may impact movement of wildlife species. We used remote camera observations (19,480) from an existing wildlife highway underpass in Wyoming and daily meteorological observations to quantify weather conditions associated with autumn migration of mule deer in 2009 and 2010. We identified minimal daily temperature and snow depth as proximate cues associated with mule deer migration to winter range. These weather cues were consistent across does and bucks, but differed slightly by year. Additionally, extreme early season snow depth or cold temperature events appear to be associated with onset of migration. This information will assist wildlife managers and transportation officials as they plan future projects to maintain and enhance migration routes for mule deer

Successful Capture and Relocation of Mourning Doves: A Multi-Agency Endeavor

Capture and relocation has been successfully used for decades as a means of wildlife damage control. USDA, APHIS, Wildlife Services (WS), Missouri Department of Conservation (MDC) and the University of Missouri Department of Fisheries and Wildlife Sciences (MU) were involved in a collaborative project which produced benefits for the cooperator, and all agencies involved. At an industrial site located in northern Kansas City, mourning dove (Zenaida macroura) droppings accumulated under the roosting area. In addition to being unsanitary, the droppings also ran the risk of causing equipment to malfunction in a secondary chemical containment system. Benefits of our interagency capture and relocation program included a non-lethal solution to resolve the wildlife problem, positive public relations opportunities for the cooperator and provided data to a long-term mourning dove banding study. Mourning doves were trapped, banded, and relocated from an industrial area in eastern Kansas City, Missouri. The banded doves were released 31.4 km to the southeast (153°) at the James A. Reed Memorial Wildlife Area (JARMWA), near Lee\u27s Summit, Missouri. We captured and relocated 566 (499 hatching year, 36 after hatching year and 31 unknown age) doves from July 12 to August 11. During that period there were no recaptures at the problem roost site, however birds were recaptured at the JARMWA at a rate similar to that of birds captured at the release site (3% JARMWA; 4% industrial site). During the opening 2 days of mourning dove hunting season birds released on the JARMWA from the industrial area were harvested at a slightly lower rate than birds caught and released on JARMWA (18% industrial site and 23% JARMWA). Results from our study indicate that capture and relocation of problem mourning doves can be successfully completed

Across scales, pronghorn select sagebrush, avoid fences, and show negative responses to anthropogenic features in winter

Abstract Pronghorn (Antilocapra americana) are endemic to western North America where they occupy expanses of grassland and sagebrush (Artemisia spp.) habitats. The Red Desert region in south‐central Wyoming, USA, has historically served as a stronghold for pronghorn populations, but many herds there have experienced declining population trends over the last two decades, concurrent with oil and natural gas development. These demographic changes and the potential for such energy development, its associated infrastructure, and other anthropogenic features including roads and fences to influence pronghorn habitat selection were the impetuses for our study. We sought to evaluate the potential effect of human‐induced disturbance on multi‐scale seasonal resource selection of 142 adult female pronghorn from 2013 to 2016 using 442 unique animal‐season‐year datasets. We utilized a traditional resource selection function to evaluate seasonal home‐range selection and a step‐selection function to assess fine‐scale, patch‐level seasonal selection. We also compared resource selection during daytime and nighttime hours with step‐selection analyses. At the seasonal home‐range scale, pronghorn selected for areas with more sagebrush during both seasons and areas farther from fences during summer. This trend was also apparent at the patch‐scale level, where pronghorn selected sagebrush‐dominant habitats and avoided crossing fences in all seasons during both day and night. Additionally at this scale, pronghorn selected areas farther from fences during daytime in summer. At the broader, home‐range scale, pronghorn selected areas with greater road density during summer, but with lower road densities and farther from wells during winter. Avoidance of anthropogenic features during winter was also observed at the finer, patch‐scale, with pronghorn selecting for increased density of roads and oil and natural gas wells during daytime in summer, but selecting areas farther from these features during daytime in winter. We recommend minimizing fencing and other forms of anthropogenic disturbance in high‐quality seasonal pronghorn habitats with high proportions of sagebrush, particularly during winter when risk‐avoidance responses may be amplified

Heterogeneity in risk‐sensitive allocation of somatic reserves in a long‐lived mammal

Abstract Food quality and availability, when combined with energetic demands in seasonal environments, shape resource acquisition and allocation by animals and hold consequences for life‐history strategies. In long‐lived species with extensive maternal care, regulation of somatic reserves of energy and protein can occur in a risk‐sensitive manner, wherein resources are preferentially allocated to support survival at the cost of investment in reproduction. We investigated how Rocky Mountain bighorn sheep (Ovis canadensis), an alpine mammal in a highly seasonal environment, allocates somatic reserves across seasons. In accordance with the hypothesis of risk‐sensitive resource allocation, we expected accretion and catabolism of somatic reserves to be regulated relative to preseason nutritional state, reproductive state, and variation among populations in accordance with local environmental conditions. To test that hypothesis, we monitored seasonal changes in percent ingesta‐free body fat (IFBFat) and ingesta‐free, fat‐free body mass (IFFFBMass) in three populations of bighorn sheep in northwest Wyoming between 2015 and 2019 through repeated captures of female sheep in December and March of each year in a longitudinal study design. Regulation of somatic reserves was risk‐sensitive and varied relative to the amount of somatic reserves an animal had at the beginning of the season. Regulation of fat reserves was sensitive to reproductive state and differed by population, particularly over the summer. In one population with low rates of recruitment of young, sheep that recruited offspring lost fat over the summer in contrast to the other two populations where sheep that recruited gained fat. And yet, all populations exhibited similar changes in fat catabolism and risk sensitivity over winter. The magnitude of body fat and mass change across seasons may be indicative of sufficiency of seasonal ranges to meet energetic demands of survival and reproduction. Risk‐sensitive allocation of resources was pervasive, suggesting nutritional underpinnings are foundational to behavior, vital rates, and, ultimately, population dynamics. For species living in alpine environments, risk‐sensitive resource allocation may be essential to balance investment in reproduction with ensuring survival

Diverse migratory portfolios drive inter‐annual switching behavior of elk across the Greater Yellowstone Ecosystem

Abstract A growing body of evidence shows that some ungulates alternate between migratory and nonmigratory behaviors over time. Yet it remains unclear whether such short‐term behavioral changes can help explain reported declines in ungulate migration worldwide, as opposed to long‐term demographic changes. Furthermore, advances in tracking technology reveal that a simple distinction between migration and nonmigration may not sufficiently describe all individual behaviors. To better understand the dynamics and drivers of ungulate switching behavior, we investigated 14 years of movement data from 361 elk in 20 herds across the Greater Yellowstone Ecosystem (GYE). First, we categorized yearly individual behaviors using a clustering algorithm that identified similar migratory tactics across a continuum of behaviors. Then, we tested seven hypotheses to explain why some ungulates switch behaviors, and we evaluated how behavioral changes affected the proportions of different behaviors across the system. We identified four distinct behavioral tactics: residents (4.8% of elk‐years), short‐distance migrants (53.7%), elevational migrants (21.9%) and long‐distance migrants (19.6%). Of the 20 herds, 18 were partially migratory, and 5 had all four movement tactics present. We observed switches between migratory tactics in all sets of consecutive years during our study period, with an average of 22.5% of individual elk changing movement tactics from one year to the next. Elk in herds with higher movement tactic diversity were significantly more likely to switch tactics and often responded more effectively to adverse environmental conditions, compared to those in herds with low movement tactic diversity. During our study period, switching increased the prevalence of both short‐ and long‐distance migrants, decreased the prevalence of elevational migrants, and had no effect on the prevalence of residents. Our findings suggest that rather than contributing to the declining migratory behavior found in the GYE, switching behavior may enable greater resiliency to continuously changing environmental and anthropogenic conditions

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.

BackgroundChest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists.Methods and findingsWe developed CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs. CheXNeXt was trained and internally validated on the ChestX-ray8 dataset, with a held-out validation set consisting of 420 images, sampled to contain at least 50 cases of each of the original pathology labels. On this validation set, the majority vote of a panel of 3 board-certified cardiothoracic specialist radiologists served as reference standard. We compared CheXNeXt's discriminative performance on the validation set to the performance of 9 radiologists using the area under the receiver operating characteristic curve (AUC). The radiologists included 6 board-certified radiologists (average experience 12 years, range 4-28 years) and 3 senior radiology residents, from 3 academic institutions. We found that CheXNeXt achieved radiologist-level performance on 11 pathologies and did not achieve radiologist-level performance on 3 pathologies. The radiologists achieved statistically significantly higher AUC performance on cardiomegaly, emphysema, and hiatal hernia, with AUCs of 0.888 (95% confidence interval [CI] 0.863-0.910), 0.911 (95% CI 0.866-0.947), and 0.985 (95% CI 0.974-0.991), respectively, whereas CheXNeXt's AUCs were 0.831 (95% CI 0.790-0.870), 0.704 (95% CI 0.567-0.833), and 0.851 (95% CI 0.785-0.909), respectively. CheXNeXt performed better than radiologists in detecting atelectasis, with an AUC of 0.862 (95% CI 0.825-0.895), statistically significantly higher than radiologists' AUC of 0.808 (95% CI 0.777-0.838); there were no statistically significant differences in AUCs for the other 10 pathologies. The average time to interpret the 420 images in the validation set was substantially longer for the radiologists (240 minutes) than for CheXNeXt (1.5 minutes). The main limitations of our study are that neither CheXNeXt nor the radiologists were permitted to use patient history or review prior examinations and that evaluation was limited to a dataset from a single institution.ConclusionsIn this study, we developed and validated a deep learning algorithm that classified clinically important abnormalities in chest radiographs at a performance level comparable to practicing radiologists. Once tested prospectively in clinical settings, the algorithm could have the potential to expand patient access to chest radiograph diagnostics