Individual-level patterns of resource selection do not predict hotspots of contact

Contact among animals is crucial for various ecological processes, including social behaviors, disease transmission, and predator-prey interactions. However, the distribution of contact events across time and space is heterogeneous, influenced by environmental factors and biological purposes. Previous studies have assumed that areas with abundant resources and preferred habitats attract more individuals and, therefore, lead to more contact. To examine the accuracy of this assumption, we used a use-available framework to compare landscape factors influencing the location of contacts between wild pigs (Sus scrofa) in two study areas in Florida and Texas (USA) from those influencing non-contact space use. We employed a contact-resource selection function (RSF) model, where contact locations were defined as used points and locations without contact as available points. By comparing outputs from this contact RSF with a general, population-level RSF, we assessed the factors driving both habitat selection and contact. We found that the landscape predictors (e.g., wetland, linear features, and food resources) played different roles in habitat selection from contact processes for wild pigs in both study areas. This indicated that pigs interacted with their landscapes differently when choosing habitats compared to when they encountered other individuals. Consequently, relying solely on the spatial overlap of individual or population-level RSF models may lead to a misleading understanding of contact-related ecology. Our findings challenge prevailing assumptions about contact and introduce innovative approaches to better understand the ecological drivers of spatially explicit contact. By accurately predicting the spatial distribution of contact events, we can enhance our understanding of contact based ecological processes and their spatial dynamics.

Accounting for central place foraging constraints in habitat selection studies

Habitat selection studies contrast actual space use with the expected use under the null hypothesis of no selection (hereafter neutral use). Neutral use is most often equated to the relative frequencies with which environmental features occur. This generates a considerable bias when studying habitat selection by foragers that perform numerous trips back and forth to a central place (CP). Indeed, the increased space use close to the CP with respect to distant places reflects a mechanical effect, rather than a true selection for the closest habitats. Yet, correctly estimating habitat selection by CP foragers is of paramount importance for a better understanding of their ecology and to properly plan conservation actions. We show that including the distance to the CP as a covariate in unconditional Resource Selection Functions, as applied in several studies, is ineffective to correct for the bias. This bias can be eliminated only by contrasting the actual use to an appropriate neutral use that considers the CP forager behavior. We also show that the need to specify an appropriate neutral use overall distribution can be bypassed by relying on a conditional approach, where the neutral use is assessed locally regardless of the distance to the CP.

Changes in movement, habitat use, and response to human disturbance accompany parturition events in bighorn sheep (Ovis canadensis)

Parturition and the early neonatal period are critical life history stages in ungulates with considerable implications for population growth and persistence. Understanding the changes in behaviour induced by ungulate parturition is important for supporting effective population management, but reliably identifying birth sites and dates presents a challenge for managers. Rocky Mountain bighorn sheep (Ovis canadensis canadensis) are one such highly valued and ecologically important species in montane and subalpine ecosystems of Western North America. In the face of changing patterns of anthropogenic land use, wildlife managers increasingly require site-specific knowledge of the movement and habitat selection characteristics of periparturient sheep to better inform land use planning initiatives and ensure adequate protections for lambing habitat. We used movement data from GPS collared parturient (n = 13) and non-parturient (n = 8) bighorn sheep in Banff National Park, Canada to (1) identify lambing events based on changes in key movement metrics, and (2) investigate how resource selection and responses to human use change during the periparturient period. We fit a hidden Markov model (HMM) to a multivariate characterization of sheep movement (step length, daily home range area, residence time) to predict realistic lambing dates for the animals in our study system. Leave-one-out cross validation of our model resulted in a 93% success rate for parturient females. Our model, which we parameterized using data from known parturient females, also predicted lambing events in 25% of known non-parturient ewes in a test dataset. Using a latent selection difference function and resource selection functions, we tested for postpartum changes in habitat use, as well as seasonal differences in habitat selection. Immediately following lambing, ewes preferentially selected high-elevation sites on solar aspects that were more rugged, closer to escape terrain, and further from roads. Within-home range habitat selection was similar between individuals in different reproductive states, but parturient ewes had stronger selection for low snow depth, sites closer to barren ground, and sites further from trails. We propose that movement-based approaches such as HMMs are a valuable tool for identifying critical parturition habitat in species with complex movement patterns and may have particular utility in study areas without access to extensive field observations or vaginal implant transmitters. Furthermore, our results suggest that managers should minimize human disturbance in lambing areas to avoid interfering with maternal behaviour and ensure access to a broad range of suitable habitat in the periparturient period.

Association of Leopard Cat Occurrence with Environmental Factors in Chungnam Province, South Korea

This study was conducted to investigate the association of leopard cat (Prionailurus bengalensis) occurrences and environmental factors in Chungnam Province, South Korea, using two different analytical approaches for binomial responses: boosted regression trees and logistic regression. The extensive field survey data collected through the Chungnam Biotope Project were used to model construction and analysis. Five major influential factors identified by the boosted regression tree analysis were elevation, distance to road, distance to water channel/body, slope and population density. Logistic regression analysis indicated that distance to forest, population density, distance to water, and diameter class of the forest were the significant explanatory variables. The results showed that the leopard cats prefer the areas with higher accessibility of food resources (e.g., abundance and catchability) and avoid the areas adjacent to human-populated areas. The results also implied that boosted regression and logistic regression models could be used in a complementary manner for evaluating wildlife distribution and management.

Habitat use by mixed-species bird flocks in tropical forests of the Western Ghats, India

While mixed-species flocks of birds (hereafter ‘flocks’) have been widely studied, few studies have looked at the effect of habitat structure on flock presence and flocking propensity within a site. Here, we employ a use-availability approach in locations with flocks and random locations to ask whether habitat characteristics influence the presence of flocks, and whether structurally similar microhabitats support compositionally similar flocks. We also examine the effect of habitat on flock size and species richness, and the effect of intraspecifically gregarious flock participants on habitat selection. We find that flocks use a narrow subset of available tree density and canopy cover variation and prefer relatively less-dense areas with large trees and a complex foliage structure. Similar microhabitats do not result in compositionally similar flocks, and while foliage complexity was associated with flock size, no habitat characteristics influenced species richness. Flocks led by the intraspecifically gregarious western crowned warbler (Phylloscopus occipitalis), a potential nuclear species, showed preference for high foliage complexity and tree density. Thus, habitat preferences of intraspecifically gregarious species, which are followed by other species, could play a strong role in habitat selection in flocks. This suggests that degraded forests that cannot provide a suitable range of tree density, canopy cover, and/or complex vegetation structure may not support some core flock species around which flocks form, which may lead to decreased flocking in those patches.

A machine learning method for estimating the probability of presence using presence-background data

Estimating the prevalence or the absolute probability of the presence of a species from presence-background data has become a controversial topic in species distribution modelling. In this paper, we propose a new method by combining both statistics and machine learning algorithms that helps overcome some of the known existing problems. We have also revisited the popular but highly controversial Lele and Keim (LK) method by evaluating its performance and assessing the RSPF condition it relies on. Simulations show that the LK method with the RSPF assumptions would render fragile estimation/prediction of the desired probabilities. Rather, we propose the local knowledge condition, which relaxes the predetermined population prevalence condition that has so often been used in much of the existing literature. Simulations demonstrate the performance of the new method utilizing the local knowledge assumption to successfully estimate the probability of presence. The local knowledge extends the local certainty or the prototypical presence location assumption, and has significant implications for demonstrating the necessary condition for identifying absolute (rather than relative) probability of presence from presence background without absence data in species distribution modelling.

A Data-driven Horizon Scan of Bacterial Pathogens at the Wildlife-livestock Interface

Many livestock diseases rely on wildlife for the transmission or maintenance of the pathogen, and the wildlife-livestock interface represents a potential site of disease emergence for novel pathogens in livestock. Predicting which pathogen species are most likely to emerge in the future is an important challenge for infectious disease surveillance and intelligence. We used a machine learning approach to conduct a data-driven horizon scan of bacterial associations at the wildlife-livestock interface for cows, sheep, and pigs. Our model identified and ranked from 76 to 189 potential novel bacterial species that might associate with each livestock species. Wildlife reservoirs of known and novel bacteria were shared among all three species, suggesting that targeting surveillance and/or control efforts towards these reservoirs could contribute disproportionately to reducing spillover risk to livestock. By predicting pathogen-host associations at the wildlife-livestock interface, we demonstrate one way to plan for and prevent disease emergence in livestock.

Puma concolor potential distribution and connectivity in the Colombian Llanos

Understanding species distribution to target biodiversity conservation actions in countries with high biodiversity, scarce data availability and low study sites accessibility is very challenging. These issues limit management and conservation actions even on charismatic and potentially conflictive species like large carnivores. We developed a geographic assessment of the potential distribution, core patches and connectivity areas for Puma concolor in the Colombian Llanos (Orinoco region). To create this geographic representation, we used methodological approaches that work with scarce information and still provide a spatially-explicit distribution that could be used by stakeholders. Our results show the importance that the Llanos region has for the conservation of the species and the potential role that can have to ensure a resident long-term population. Based on our approach, more than 50,000 km2 (near to 30% of the study area) can be still considered as core habitats for the species, and most of them are still connected, with spatial gaps lower than the estimated dispersal distance of the species and several remaining steppingstones. Further research is required to validate our analysis and we expect our results can target research and conservation priorities within the region.

Habitat selection and density of common pheasant (Phasianus colchicus) in Northern Italy: effects of land use cover and landscape configuration

Knowing the ecology of game species is important to define sustainable hunting pressure and to plan management actions aimed to maintain viable populations. Common pheasant (Phasianus colchicus) is one of the main gamebird species in Europe and North America, despite its native range extending from the Caucasus to Eastern China. This research aimed to define the environmental variables shaping the spatial distribution of male pheasants and to estimate their breeding density in an agroecosystem of northern Italy. During the breeding season, 2015, we carried out 372 point counts with unlimited distances, randomly placed following a stratified sampling survey design. The habitat requirements of the pheasant were evaluated following a presence vs. availability approach, using environmental variables related to land use cover and landscape configuration. We built generalized linear models with a binary distribution, selecting variables following an information-theoretic approach. Densities were estimated through both conventional and multiple-covariate distance sampling. We estimated a density of 1.45 males/km2, with 4.26 males/km2 in suitable areas and 0.91 males/km2 in unsuitable ones. We found pheasants in areas with meadows and tree plantations, which were used to find food and refuges from predators and bad weather conditions. Similarly, woodlands have a positive effect on species occurrence, whereas arable lands were avoided, specifically maize and paddy fields. We found little evidence that landscape configuration affects pheasant occurrence. We found pheasants to be negatively affected by the length of edges between woodlands and arable lands, whereas edges between woodlands and grasslands seem to be beneficial for the species. These findings could help landscape and wildlife managers to plan habitat improvement actions useful to maintain self-sustaining populations of this species, by increasing cover of woodlands, meadows, and tree plantations.

Searching for seadragons: predicting micro-habitat use for the common (weedy) seadragon (Phyllopteryx taeniolatus) based on habitat and prey

Habitat associations can be critical predictors of larger-scale organism distributions and range shifts. Here the authors consider how a critical habitat, kelp (Ecklonia radiata) and prey (mysid crustacean swarms), can influence small- and large-scale distribution on the iconic common (weedy) seadragon (Phyllopteryx taeniolatus:Syngnathidae). P. taeniolatus are charismatic fish endemic to the temperate reefs of southern Australia, reported to range from Geraldton, Western Australia (28.7667°S, 114.6167°E) around southern Australia to Port Stephens, New South Wales (32.614369°S, 152.325676°E). The authors test a previously developed model of seadragon habitat preferences to predict P. taeniolatus occurrence within four sites from Sydney to the northern limit of their range in eastern Australia. They determined that P. taeniolatus associations with Ecklonia and mysid shrimp can be extrapolated across multiple sites to predict the occurrence of individual P. taeniolatus within a location/site. For instance, the authors demonstrated a significant positive relationship between the density of mysid swarms and the density of P. taeniolatus, evident across all sites despite large differences in the density of mysid swarms among sites. The findings are the first to model P. taeniolatus habitat associations across multiple sites to the northern limit of their range and have applications in protecting P. taeniolatus populations and how they may respond under climate change scenarios, such as poleward kelp retractions.

Dynamic Forecast of Desert Locust Presence Using Machine Learning with a Multivariate Time Lag Sliding Window Technique

Desert locust plagues can easily cause a regional food crisis and thus affect social stability. Preventive control of the disaster highlights the early detection of hopper gregarization before they form devastating swarms. However, the response of hopper band emergence to environmental fluctuation exhibits a time lag. To realize the dynamic forecast of band occurrence with optimal temporal predictors, we proposed an SVM-based model with a temporal sliding window technique by coupling multisource time-series imagery with historical locust ground survey observations from between 2000–2020. The sliding window method was based on a lagging variable importance ranking used to analyze the temporal organization of environmental indicators in band-forming sequences and eventually facilitate the early prediction of band emergence. Statistical results show that hopper bands are more likely to occur within 41–64 days after increased rainfall; soil moisture dynamics increasing by approximately 0.05 m³/m³ then decreasing may enhance the chance of observing bands after 73–80 days. While sparse vegetation areas with NDVI increasing from 0.18 to 0.25 tend to witness bands after 17–40 days. The forecast model combining the optimal time lags of these dynamic indicators with other static indicators allows for a 16-day extended outlook of band presence in Somalia, Ethiopia, and Kenya. Monthly predictions from February to December 2020 display an overall accuracy of 77.46%, with an average ROC-AUC of 0.767 and a mean F-score close to 0.772. The multivariate forecast framework based on the lagging effect can realize the early warning of band presence in different spatiotemporal scenarios, supporting early decisions and response strategies for desert locust preventive management.

Conceptual and methodological advances in habitat-selection modeling: guidelines for ecology and evolution

Habitat selection is a fundamental animal behavior that shapes a wide range of ecological processes, including animal movement, nutrient transfer, trophic dynamics and population distribution. Although habitat selection has been a focus of ecological studies for decades, technological, conceptual and methodological advances over the last 20 yr have led to a surge in studies addressing this process. Despite the substantial literature focused on quantifying the habitat-selection patterns of animals, there is a marked lack of guidance on best analytical practices. The conceptual foundations of the most commonly applied modeling frameworks can be confusing even to those well versed in their application. Furthermore, there has yet to be a synthesis of the advances made over the last 20 yr. Therefore, there is a need for both synthesis of the current state of knowledge on habitat selection, and guidance for those seeking to study this process. Here, we provide an approachable overview and synthesis of the literature on habitat-selection analyses (HSAs) conducted using selection functions, which are by far the most applied modeling framework for understanding the habitat-selection process. This review is purposefully non-technical and focused on understanding without heavy mathematical and statistical notation, which can confuse many practitioners. We offer an overview and history of HSAs, describing the tortuous conceptual path to our current understanding. Through this overview, we also aim to address the areas of greatest confusion in the literature. We synthesize the literature outlining the most exciting conceptual advances in the field of habitat-selection modeling, discussing the substantial ecological and evolutionary inference that can be made using contemporary techniques. We aim for this paper to provide clarity for those navigating the complex literature on HSAs while acting as a reference and best practices guide for practitioners.

Understanding environmental patterns of canid predation on white-tailed deer (Odocoileus virginianus)

The outcome of encounters between predators and prey affects predation rates and ultimately population dynamics. Determining how environmental features influence predation rates helps guide conservation and management efforts. We studied where gray wolves (Canis lupus Linnaeus, 1758) and coyotes (Canis latrans Say, 1823) killed white-tailed deer (Odocoileus virginianus (Zimmermann, 1780)) in northern Wisconsin, USA. We monitored 499 white-tailed deer for cause-specific mortality between 2011 and 2014 using VHF radio collars. We investigated the locations of 125 deer mortalities and determined that 63 were canid (wolf or coyote) kill sites. We analyzed spatial patterns of kill sites using resource selection functions in a model selection framework, incorporating environmental variables including vegetative cover, human development, snow depth, and water. We found no evidence that vegetative cover or human development affected predation risk; however, we did find that increasing snow depth resulted in increased relative predation risk. This finding is consistent with existing research on the influence of snow cover on white-tailed deer survival. Our results suggest that understanding the spatial and temporal patterns of white-tailed deer predation requires a better understanding of snow depth variation in space and time. As climate change scenarios predict changes in snowfall throughout the northern hemisphere, understanding the effect on predator–prey spatial dynamics will be important for management and conservation efforts.

Fine-scale foraging habitat selection by two diving central place foragers in the Northeast Atlantic

Habitat selection and spatial usage are important components of animal behavior influencing fitness and population dynamic. Understanding the animal-habitat relationship is crucial in ecology, particularly in developing strategies for wildlife management and conservation. As this relationship is governed by environmental features and intra- and interspecific interactions, habitat selection of a population may vary locally between its core and edges. This is particularly true for central place foragers such as gray and harbor seals, where, in the Northeast Atlantic, the availability of habitat and prey around colonies vary at local scale. Here, we study how foraging habitat selection may vary locally under the influence of physical habitat features. Using GPS/GSM tags deployed at different gray and harbor seals' colonies, we investigated spatial patterns and foraging habitat selection by comparing trip characteristics and home-range similarities and fitting GAMMs to seal foraging locations and environmental data. To highlight the importance of modeling habitat selection at local scale, we fitted individual models to colonies as well as a global model. The global model suffered from issues of homogenization, while colony models showed that foraging habitat selection differed markedly between regions for both species. Despite being capable of undertaking far-ranging trips, both gray and harbor seals selected their foraging habitat depending on local availability, mainly based on distance from the last haul-out and bathymetry. Distance from shore and tidal current also influenced habitat preferences. Results suggest that local conditions have a strong influence on population spatial ecology, highlighting the relevance of processes occurring at fine geographical scale consistent with management within regional units.

Plotting receiver operating characteristic and precision-recall curves from presence and background data

The receiver operating characteristic (ROC) and precision-recall (PR) plots have been widely used to evaluate the performance of species distribution models. Plotting the ROC/PR curves requires a traditional test set with both presence and absence data (namely PA approach), but species absence data are usually not available in reality. Plotting the ROC/PR curves from presence-only data while treating background data as pseudo absence data (namely PO approach) may provide misleading results.In this study, we propose a new approach to calibrate the ROC/PR curves from presence and background data with user-provided information on a constant c, namely PB approach. Here, c defines the probability that species occurrence is detected (labeled), and an estimate of c can also be derived from the PB-based ROC/PR plots given that a model with good ability of discrimination is available. We used five virtual species and a real aerial photography to test the effectiveness of the proposed PB-based ROC/PR plots. Different models (or classifiers) were trained from presence and background data with various sample sizes. The ROC/PR curves plotted by PA approach were used to benchmark the curves plotted by PO and PB approaches.Experimental results show that the curves and areas under curves by PB approach are more similar to that by PA approach as compared with PO approach. The PB-based ROC/PR plots also provide highly accurate estimations of c in our experiment.We conclude that the proposed PB-based ROC/PR plots can provide valuable complements to the existing model assessment methods, and they also provide an additional way to estimate the constant c (or species prevalence) from presence and background data.

A 'How to' guide for interpreting parameters in habitat-selection analyses

Habitat‐selection analyses allow researchers to link animals to their environment via habitat‐selection or step‐selection functions, and are commonly used to address questions related to wildlife management and conservation efforts. Habitat‐selection analyses that incorporate movement characteristics, referred to as integrated step‐selection analyses, are particularly appealing because they allow modelling of both movement and habitat‐selection processes.

Despite their popularity, many users struggle with interpreting parameters in habitat‐selection and step‐selection functions. Integrated step‐selection analyses also require several additional steps to translate model parameters into a full‐fledged movement model, and the mathematics supporting this approach can be challenging for many to understand.

Using simple examples, we demonstrate how weighted distribution theory and the inhomogeneous Poisson point process can facilitate parameter interpretation in habitat‐selection analyses. Furthermore, we provide a ‘how to’ guide illustrating the steps required to implement integrated step‐selection analyses using the amt package

By providing clear examples with open‐source code, we hope to make habitat‐selection analyses more understandable and accessible to end users.

Behavior-specific occurrence patterns of Pinyon Jays (Gymnorhinus cyanocephalus) in three Great Basin study areas and significance for pinyon-juniper woodland management

The Pinyon Jay is a highly social, year-round inhabitant of pinyon-juniper and other coniferous woodlands in the western United States. Range-wide, Pinyon Jays have declined ~ 3–4% per year for at least the last half-century. Occurrence patterns and habitat use of Pinyon Jays have not been well characterized across much of the species’ range, and obtaining this information is necessary for better understanding the causes of ongoing declines and determining useful conservation strategies. Additionally, it is important to better understand if and how targeted removal of pinyon-juniper woodland, a common and widespread vegetation management practice, affects Pinyon Jays. The goal of this study was to identify the characteristics of areas used by Pinyon Jays for several critical life history components in the Great Basin, which is home to nearly half of the species’ global population, and to thereby facilitate the inclusion of Pinyon Jay conservation measures in the design of vegetation management projects. To accomplish this, we studied Pinyon Jays in three widely separated study areas using radio telemetry and direct observation and measured key attributes of their locations and a separate set of randomly-selected control sites using the U. S. Forest Service’s Forest Inventory Analysis protocol. Data visualizations, principle components analysis, and logistic regressions of the resulting data indicated that Pinyon Jays used a distinct subset of available pinyon-juniper woodland habitat, and further suggested that Pinyon Jays used different but overlapping habitats for seed caching, foraging, and nesting. Caching was concentrated in low-elevation, relatively flat areas with low tree cover; foraging occurred at slightly higher elevations with generally moderate but variable tree cover; and nesting was concentrated in slightly higher areas with high tree and vegetation cover. All three of these Pinyon Jay behavior types were highly concentrated within the lower-elevation band of pinyon-juniper woodland close to the woodland-shrubland ecotone. Woodland removal projects in the Great Basin are often concentrated in these same areas, so it is potentially important to incorporate conservation measures informed by Pinyon Jay occurrence patterns into existing woodland management paradigms, protocols, and practices.

Predicting greater sage-grouse habitat selection at the southern periphery of their range

Mapping suitable habitat is an important process in wildlife conservation planning. Species distribution reflects habitat selection processes occurring across multiple spatio-temporal scales. Because habitat selection may be driven by different factors at different scales, conservation planners require information at the scale of the intervention to plan effective management actions. Previous research has described habitat selection processes shaping the distribution of greater sage-grouse (Centrocercus urophasianus; sage-grouse) at the range-wide scale. Finer-scale information for applications within jurisdictional units inside the species range is lacking, yet necessary, because state wildlife agencies are the management authority for sage-grouse in the United States. We quantified seasonal second-order habitat selection for sage-grouse across the state of Utah to produce spatio-temporal predictions of their distribution at the southern periphery of the species range. We used location data obtained from sage-grouse marked with very-high-frequency radio-transmitters and lek location data collected between 1998 and 2013 to quantify species habitat selection in relation to a suite of topographic, edaphic, climatic, and anthropogenic variables using random forest algorithms. Sage-grouse selected for greater sagebrush (Artemisia spp.) cover, higher elevations, and gentler slopes and avoided lower precipitations and higher temperatures. The strength of responses to habitat variables varied across seasons. Anthropogenic variables previously reported as affecting their range-wide distribution (i.e., roads, powerlines, communication towers, and agricultural development) were not ranked as top predictors at our focal scale. Other than strong selection for sagebrush cover, the responses we observed differed from what has been reported at the range-wide scale. These differences likely reflect the unique climatic, geographic, and topographic context found in the southern peripheral area of the species distribution compared to range-wide environmental gradients. Our results highlight the importance of considering appropriateness of scale when planning conservation actions for wide-ranging species.

Pronounced Fidelity and Selection for Average Conditions of Calving Area Suggestive of Spatial Memory in a Highly Migratory Ungulate

A distinguishing characteristic of many migratory animals is their annual return to distinct calving (birthing) areas in the spring, yet the navigational mechanisms employed during migration that result in this pattern are poorly understood. Effective conservation of these species requires reliable delineation of such areas, quantifying the factors that influence their selection, and understanding the underlying mechanisms resulting in use of calving areas. We used barren-ground caribou (Rangifer tarandus granti) as a study species and identified calving sites of the Western Arctic Herd in Alaska using GPS collar data from 2010–2017. We assessed variability in calving areas by comparing spatial delineations across all combinations of years. To understand calving area selection at a landscape scale, we performed a resource selection analysis comparing calving sites to available locations across the herd’s range and incorporated time-varying, remotely sensed metrics of vegetation quality and quantity. We found that whereas calving areas varied from year to year, this annual variation was centered on an area of recurring attraction consistent with previous studies covering the last six decades. Calving sites were characterized by high-quality forage at the average time of calving, but not peak calving that year, and by a narrow range of distinct physiographic factors. Each year, calving sites were located on areas of above-average conditions based on our predictive model. Our findings indicate that the pattern of spring migration for pregnant females was to migrate to areas that consistently provide high-quality forage when averaged across years, and then upon arriving at this calving ground, refine selection using their perception of annually varying conditions that are driven by environmental stochasticity. We suggest that the well-documented and widespread pattern of fidelity to calving grounds by caribou is supportive of a navigational mechanism based on spatial memory at a broad scale to optimize foraging and energy acquisition at a critical life-history stage. The extent to which migrants depend on memory to reach their spring destinations has implications for the adaptability of populations to changing climate and human impacts.

Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining

By 29 May 2020, the coronavirus disease (COVID-19) caused by SARS-CoV-2 had spread to 188 countries, infecting more than 5.9 million people, and causing 361,249 deaths. Governments issued travel restrictions, gatherings of institutions were cancelled, and citizens were ordered to socially distance themselves in an effort to limit the spread of the virus. Fear of being infected by the virus and panic over job losses and missed education opportunities have increased people’s stress levels. Psychological studies using traditional surveys are time-consuming and contain cognitive and sampling biases, and therefore cannot be used to build large datasets for a real-time depression analysis. In this article, we propose a CorExQ9 algorithm that integrates a Correlation Explanation (CorEx) learning algorithm and clinical Patient Health Questionnaire (PHQ) lexicon to detect COVID-19 related stress symptoms at a spatiotemporal scale in the United States. The proposed algorithm overcomes the common limitations of traditional topic detection models and minimizes the ambiguity that is caused by human interventions in social media data mining. The results show a strong correlation between stress symptoms and the number of increased COVID-19 cases for major U.S. cities such as Chicago, San Francisco, Seattle, New York, and Miami. The results also show that people’s risk perception is sensitive to the release of COVID-19 related public news and media messages. Between January and March, fear of infection and unpredictability of the virus caused widespread panic and people began stockpiling supplies, but later in April, concerns shifted as financial worries in western and eastern coastal areas of the U.S. left people uncertain of the long-term effects of COVID-19 on their lives.

Habitat association in the critically endangered Mangshan pit viper (Protobothrops mangshanensis), a species endemic to China

Habitat directly affects the population size and geographical distribution of wildlife species, including the Mangshan pit viper (Protobothrops mangshanensis), a critically endangered snake species endemic to China. We searched for Mangshan pit viper using randomly arranged transects in their area of distribution and assessed their habitat association using plots, with the goals of gaining a better understanding of the habitat features associated with P. mangshanensis detection and determining if the association with these features varies across season. We conducted transect surveys, found 48 individual snakes, and measured 11 habitat variables seasonally in used and random plots in Hunan Mangshan National Nature Reserve over a period of 5 years (2012–2016). The important habitat variables for predicting Mangshan pit viper detection were fallen log density, shrub density, leaf litter cover, herb cover and distance to water. In spring, summer and autumn, Mangshan pit viper detection was always positively associated with fallen log density. In summer, Mangshan pit viper detection was related to such habitats with high canopy cover, high shrub density and high herb cover. In autumn, snakes generally occurred in habitats near water in areas with high fallen log density and tall shrubs height. Our study is the first to demonstrate the relationship between Mangshan pit viper detection and specific habitat components. Mangshan pit viper detection was associated with habitat features such as with a relatively high fallen log density and shrub density, moderately high leaf litter cover, sites near stream, and with lower herb cover. The pattern of the relationship between snakes and habitats was not consistent across the seasons. Identifying the habitat features associated with Mangshan pit viper detection can better inform the forestry department on managing natural reserves to meet the habitat requirements for this critically endangered snake species.

Development and evaluation of habitat suitability models for nesting white-headed woodpecker (Dryobates albolarvatus) in burned forest

Salvage logging in burned forests can negatively affect habitat for white-headed woodpeckers (Dryobates albolarvatus), a species of conservation concern, but also meets socioeconomic demands for timber and human safety. Habitat suitability index (HSI) models can inform forest management activities to help meet habitat conservation objectives. Informing post-fire forest management, however, involves model application at new locations as wildfires occur, requiring evaluation of predictive performance across locations. We developed HSI models for white-headed woodpeckers using nest sites from two burned-forest locations in Oregon, the Toolbox (2002) and Canyon Creek (2015) fires. We measured predictive performance by developing one model at each of the two locations and quantifying discrimination of nest from reference sites at two other wildfire locations where the model had not been developed (either Toolbox or Canyon Creek, and the Barry Point Fire [2011]). We developed and evaluated Maxent models based on remotely sensed environmental metrics to support habitat mapping, and weighted logistic regression (WLR) models that combined remotely sensed and field-collected metrics to inform management prescriptions. Both Maxent and WLR models developed either at Canyon Creek or Toolbox performed adequately to inform management when applied at the alternate Toolbox or Canyon Creek location, respectively (area under the receiver-operating-characteristic curve [AUC] range = 0.61-0.72) but poorly when applied at Barry Point (AUC = 0.53-0.57). The final HSI models fitted to Toolbox and Canyon Creek data quantified suitable nesting habitat as severely burned or open sites adjacent to lower severity and closed canopy sites, where foraging presumably occurs. We suggest these models are applicable at locations similar to development locations but not at locations resembling Barry Point, which were characterized by more (pre-fire) canopy openings, larger diameter trees, less ponderosa pine (Pinus ponderosa), and more juniper (Juniperus occidentalis). Considering our results, we recommend caution when applying HSI models developed at individual wildfire locations to inform post-fire management at new locations without first evaluating predictive performance.

Retracing the contours of the early angiosperm environmental niche

BACKGROUND AND AIMS

Our aim was to understand the environmental conditions of the emergence and radiation of early angiosperms. Such a question has long remained controversial because various approaches applied in the past have drawn conflicting images of early angiosperm ecology.

METHODS

We provided a new perspective on the question by using support vector machines to model the environmental niche of 51 species belonging to ten genera of extant lineages that diverged early during angiosperm evolution (basal angiosperms). Then, we analysed the resulting pattern of niche overlap and determined whether this pattern deviates from what would be expected on the basis of a null model or whether it might mirror a legacy of a common primitive niche based on a phylogenetic reconstruction.

KEY RESULTS

The niche of three-quarters of the species and all genera converged towards tropical montane cloud forests (TMCFs). The latitudinal pattern of basal angiosperm richness indeed culminated in the tropics, and the elevational pattern revealed a humpback curve peaking between 2000 m and 3500 m when accounting for the effect of area. At first glance, this diversity pattern does not significantly differ from null predictions. However, we revealed a tendency for the basal-most taxa to occur in TMCFs so that phylogenetic reconstructions indicated that the niche of the common ancestor of the sampled basal angiosperms had a probability of 0.85-0.93 to overlap with TMCFs.

CONCLUSIONS

Our new approach indicates that the environmental convergence of extant basal angiosperms towards TMCFs would reflect a legacy of an ancestral niche from which the least basal taxa would have diverged following a random pattern under geometric constraints.

Winter home range and habitat selection differs among breeding populations of herring gulls in eastern North America

BACKGROUND

Recognizing the factors influencing migratory individuals throughout their annual cycle is important for understanding the drivers of population dynamics. Previous studies have found that Herring Gulls (Larus argentatus) in the Atlantic region have lower survival rates than those in the Great Lakes and the Arctic. One possible explanation for divergent survival rates among these populations is differences in their non-breeding habitats.

METHODS

We tracked Herring Gulls from five populations, breeding in the eastern Arctic, the Great Lakes, Newfoundland, Sable Island, and the Bay of Fundy. We assessed the extent of migratory connectivity between breeding and wintering sites, and tested if there were differences in home range size or habitat selection among these populations during the winter.

RESULTS

The tracked Herring Gulls had strong migratory connectivity between their breeding and wintering areas. We found that Herring Gulls from the Arctic spent most of the winter in marine habitats, while the other populations used a wider variety of habitats. However, the Newfoundland and Sable Island populations selected for urban habitats, and almost all individuals the specialized in urban habitats came from one of the three Atlantic populations.

CONCLUSIONS

Our results suggest that there could potentially be a link between urban habitat use during the winter and reduced adult survival in Atlantic Canada Herring Gulls.