Landscape Covariates Research Articles

Behavioral trade-offs and multitasking by elk in relation to predation risk from Mexican gray wolves.

Predator non-consumptive effects (NCE) can alter prey foraging time and habitat use, potentially reducing fitness. Prey can mitigate NCEs by increasing vigilance, chewing-vigilance synchronization, and spatiotemporal avoidance of predators. We quantified the relationship between Mexican wolf (Canis lupus baileyi) predation risk and elk (Cervus canadensis) behavior. We conducted behavioral observations on adult female elk and developed predation risk indices using GPS collar data from Mexican wolves, locations of elk killed by wolves, and landscape covariates. We compared a priori models to determine the best predictors of adult female behavior and multitasking. Metrics that quantified both spatial and temporal predation risk were the most predictive. Vigilance was positively associated with increased predation risk. The effect of predation risk on foraging and resting differed across diurnal periods. During midday when wolf activity was lower, the probability of foraging increased while resting decreased in high-risk areas. During crepuscular periods when elk and wolves were most active, increased predation risk was associated with increased vigilance and slight decreases in foraging. Our results suggest elk are temporally avoiding predation risk from Mexican wolves by trading resting for foraging, a trade-off often not evaluated in behavioral studies. Probability of multitasking depended on canopy openness and an interaction between maternal period and predation risk; multitasking decreased prior to parturition and increased post parturition in high-risk areas. Openness was inversely related to multitasking. These results suggest adult female elk are altering the type of vigilance used depending on resource availability/quality, current energetic needs, and predation risk. Our results highlight potentially important, but often-excluded behaviors and trade-offs prey species may use to reduce the indirect effects of predation and contribute additional context to our understanding of predator-prey dynamics.

Ground‐dwelling mammal and bird diversity in the southern Annamites: Exploring complex habitat associations and the ghost of past hunting pressure

AbstractThe Langbian Plateau, a biodiversity hotspot in the southern Annamites of Viet Nam, has undergone extensive hunting pressure. However, the limited information on the effects of overexploitation on the current status and community composition of wildlife hinders effective conservation efforts, including the implementation of targeted patrols to reduce snaring. In this study, we conducted a camera‐trapping survey across the Langbian Plateau, consisting of a broadleaf evergreen and coniferous habitat mosaic. We recorded 46 ground‐dwelling mammals and birds, including several threatened Annamite endemics. Using multi‐species Royle‐Nichols model and landscape covariates, we found higher richness in broadleaf evergreen forest located in more remote and less rugged areas. We then used species responses to covariates to predict species distribution and identify high‐priority areas for conservation. Furthermore, we constructed diversity profiles that indicated higher biodiversity in broadleaf evergreen forest compared to the coniferous forest. Finally, we used a dissimilarity index to assess the level of defaunation, revealing 16% of the community had been lost, with higher levels of defaunation for threatened and larger‐sized species. Our findings provide insights into the status, distribution, and occurrence of the ground‐dwelling mammal and bird communities in the Langbian Plateau, and can help stakeholders design more effective conservation strategies to protect existing populations.

Bat winter foraging habitat use in working forests: a multispecies spatial occupancy approach

AbstractInsectivorous bats in temperate zones have evolved strategies such as migration or hibernation to overcome challenges of reduced resource availability and increased energy demand during winter. In the southeastern United States Coastal Plain, bats are either year‐round residents and remain active during winter or are migrants from colder areas seeking milder temperatures. Southeastern Coastal Plain forests also may represent important areas for remnant populations of species impacted by white‐nose syndrome. Working pine (Pinus spp.) forests comprise a large proportion of southeastern Coastal Plain forests, yet winter bat habitat associations and how forest management affects bat use remain understudied. Hence, we used hierarchical multispecies spatial occupancy models to evaluate factors influencing winter bat occupancy and foraging habitat associations in working forests of the southeastern Coastal Plain. From January to March 2020–2022, we deployed Anabat Swift acoustic detectors and measured site‐ and landscape‐level covariates on six working landscapes. We detected five species of bats and three species groups at 93% (224/240) of sites. We observed higher species richness at sites with high proportions of contiguous forest and low levels of basal area. At the species level, occupancy patterns were influenced by site and landscape covariates, which had varying effects on species with distinct foraging strategies. Temperature was an important predictor of detectability. Our findings offer new insights into the ecology of bats in working forest landscapes during winter, where we highlight positive responses in occupancy with contiguous forests and lower levels of basal area, as in previous summer work. By providing valuable information on winter community composition and foraging habitat associations, we hope to guide management decisions for forest attributes important to these species, thus increasing conservation opportunities within working forests.

Spatial and temporal variability of corn response to nitrogen and seed rates

AbstractThe quantification of the spatial and temporal variability of crop response to controllable inputs and the relationship with their controlling factors is important for making input prescriptions. In this study, we (1) assessed the within‐field spatial variability of corn (Zea mays L.) response to nitrogen (NR) and seed (SR) rates using on‐farm precision experiments (OFPE) data and geographically weighted regression (GWR) models; (2) determined the spatial agreement between NR and SR response classes, and their temporal stability; and (3) modeled the effect of weather and site‐specific features on the spatial distribution of the responses. Response maps were estimated by fitting GWR models to yields and as‐applied NR and SR from 14 OFPE. Responses classified into positive and non‐positive response classes were spatially joined to weather, soil, and landscape covariates to train a random forest model. GWR models explained 30%–60% of yield variation. Positive responses to NR and SR represented about 46% and 33% of fields’ area, respectively, with large variation across field‐year combinations. Temporal agreement was about 50%–60%, for NR and SR, respectively. The spatial agreement between NR and SR response classes suggests that factors controlling the crop response to these inputs are similar. In both cases, weather variables were the most important predictors, followed by landscape and soil attributes. Although weather variables acted at a field level, that is, field‐year, the latter reflected the effect of within‐field variability on crop response. To reduce this weather uncertainty, future research should consider conducting larger numbers of OFPE across multiple seasons.

Factors driving bumble bee (Hymenoptera: Apidae: Bombus) and butterfly (Lepidoptera: Rhopalocera) use of sheared shrubland and young forest communities of the western Great Lakes.

In the northern Great Lakes region, the creation and maintenance of early-successional woody communities as wildlife habitat have increasingly become a conservation priority. The extent to which insect pollinators use these systems remains largely anecdotal. In summer (June-August) of 2021, we surveyed 49 early-successional sites in the western Great Lakes region treated with either shrub-shearing or silviculture (young forest) for bumble bees, butterflies, and habitat components (i.e., structural vegetation and floral resources). Hierarchical distance models predicted pollinator densities (λ^) to be, on average, λ^ = 84 bumble bees/ha and λ^ = 102 butterflies/ha. Although sheared shrubland and young forest communities supported comparable densities of bumble bees and butterflies, density was not equal across all sites. At the microhabitat scale, butterfly density and morphospecies richness were negatively associated with tall shrub cover and butterfly morphospecies richness (but not density) was driven by floral richness. Similarly, bumble bee density was positively associated with metrics of floral resources, underscoring the importance of blooming plants within these woody systems. Landscape covariates explained variation in butterfly density/richness but not bumble bee density. Ultimately, our results demonstrate that blooming plant abundance is an important driver of bumble bee and butterfly densities within these managed early-successional communities. Because early-successional woody communities are dynamic and their herbaceous openings are ephemeral, routine management would ensure that a variety of successional conditions exist on the landscape to meet the needs of bumble bees, butterflies, and potentially other insect pollinators.

White-tailed deer (Odocoileus virginianus) fawn survival and the influence of landscape characteristics on fawn predation risk in the Southern Appalachian Mountains, USA.

In the Southern Appalachian region of the United States, harvest data has indicated the occurrence of low deer densities while exposing a trend of declining white-tailed deer (Odocoileus virginianus) populations over the past several decades in northern Georgia. A triumvirate of increasing fawn predator populations reside in the Southern Appalachian Mountains including coyotes (Canis latrans), black bears (Ursus americanus) and bobcats (Lynx rufus). This region is also characterized by a homogenous landscape composed of mature forests and sparse understory vegetation, likely lacking adequate cover to offer fawns refugia from predators. Our objectives were to estimate survival and cause-specific mortality rates of fawns while assessing a possible link between mortality risk, intrinsic fawn characteristics (i.e., birth mass, Julian birth date, sibling status), and landscape features within fawn usage areas. During 2018-2020, we radio-collared 71 fawns within the Chattahoochee National Forest of northern Georgia, USA and monitored survival to 12 weeks of age. We observed low fawn survival (cumulative = 0.157, 95% CI = 0.091-0.273; vaginal implant transmitter = 0.196, 95% CI = 0.096-0.403) with predation as the leading cause of all known mortalities (45 of 55 mortalities; 82%) due primarily to coyotes (n = 22), black bears (n = 12), and bobcats (n = 7). Relationships between landscape features and fawn predation risk were minimal with only one informative covariate. Increasing amounts of early successional land cover within fawn usage areas decreased fawn mortality risk within the first 20 days of life, but elevated mortality risk thereafter. All fawns with any amount of early successional land cover in their usage areas died of predation (n = 13) at various time intervals, suggesting limited areas of potential fawning cover may be targeted by predators. However, fawn predation risk seemed to be high regardless of landscape covariates due to the limited number of surviving fawns. Coyote-caused mortality occurred over a longer period at a consistently higher magnitude than all other forms of mortality, indicating possible delayed prey-switching behavior and coyote predation as an important factor of fawn survival. The low recruitment of fawns influenced by high predation rates and homogenous habitat conditions is likely the cause of deer population declines in the region.

Daily stream temperature predictions for free-flowing streams in the Pacific Northwest, USA

Supporting sustainable lotic ecosystems and thermal habitats requires estimates of stream temperature that are high in scope and resolution across space and time. We combined and enhanced elements of existing stream temperature models to produce a new statistical model to address this need. Contrasting with previous models that estimated coarser metrics such as monthly or seasonal stream temperature or focused on individual watersheds, we modeled daily stream temperature across the entire calendar year for a broad geographic region. This model reflects mechanistic processes using publicly available climate and landscape covariates in a Generalized Additive Model framework. We allowed covariates to interact while accounting for nonlinear relationships between temporal and spatial covariates to better capture seasonal patterns. To represent variation in sensitivity to climate, we used a moving average of antecedent air temperatures over a variable duration linked to area-standardized streamflow. The moving average window size was longer for reaches having snow-dominated hydrology, especially at higher flows, whereas window size was relatively constant and low for reaches having rain-dominated hydrology. Our model’s ability to capture the temporally-variable impact of snowmelt improved its capacity to predict stream temperature across diverse geography for multiple years. We fit the model to stream temperatures from 1993–2013 and predicted daily stream temperatures for ~261,200 free-flowing stream reaches across the Pacific Northwest USA from 1990–2021. Our daily model fit well (RMSE = 1.76; MAE = 1.32°C). Cross-validation suggested that the model produced useful predictions at unsampled locations across diverse landscapes and climate conditions. These stream temperature predictions will be useful to natural resource practitioners for effective conservation planning in lotic ecosystems and for managing species such as Pacific salmon. Our approach is straightforward and can be adapted to new spatial regions, time periods, or scenarios such as the anticipated decline in snowmelt with climate change.

Spatial and Temporal Variability in Stream Thermal Regime Drivers for Three River Networks During the Summer Growing Season.

Many cold-water dependent aquatic organisms are experiencing habitat and population declines from increasing water temperatures. Identifying mechanisms which drive local and regional stream thermal regimes facilitates restoration at ecologically relevant scales. Stream temperatures vary spatially and temporally both within and among river basins. We developed a modeling process to identify statistical relationships between drivers of stream temperature and covariates representing landscape, climate, and management-related processes. The modeling process was tested in 3 study areas of the Pacific Northwest USA during the growing season (May [start], August [warmest], September [end]). Across all months and study systems, covariates with the highest relative importance represented the physical landscape (elevation [1st], catchment area [3rd], main channel slope [5th]) and climate covariates (mean monthly air temperature [2nd] and discharge [4th]). Two management covariates (ground water use [6th] and riparian shade [7th]) also had high relative importance. Across the growing season (for all basins) local reach slope had high relative importance in May, but transitioned to a regional main channel slope covariate in August and September. This modeling process identified regionally similar and locally unique relationships among drivers of stream temperature. High relative importance of management-related covariates suggested potential restoration actions for each system.

Testing umbrella species and food-web properties of large carnivores in the Rocky Mountains

Despite criticisms, the umbrella species concept remains a fundamental conservation tool for protecting biodiversity in the face of global change, yet it is rarely tested. Food web theory provides a tool to test both umbrella-species' suitability and their ecological function, which we investigate in a large-mammal food web. Using data from 698 camera trap locations in the Canadian Rockies, we develop hierarchical occupancy models to predict the co-occurrence of 16 large mammal species. We draw upon previous diet studies in the Canadian Rockies to describe the meta food-web (meta-web) for these species. Next, we filtered the meta-web using predicted occupancy to estimate realized food webs at each camera location. We tested the umbrella species concept using predicted occupancy across all 698 camera sites. We then tested for carnivore effects using realized food webs on 5 food-web properties: species richness, links, connectance, nestedness and modularity using generalized linear models while accounting for landscape covariates known to affect food web dynamics. Our multispecies occupancy models reflected factors previously demonstrated to affect large mammal occurrence. Our results also demonstrated that grizzly bear (Ursus horribilis), a generalist carnivore, was the best umbrella carivore species, and explained species richness the best. When considering food web properties, however, wolves (Canis lupus) and cougars (Felis concolor) served as better umbrellas that also captured food web properties such as connectance, links and nestedness that better reflect ecological interactions. Our results support the role of large carnivores as umbrella and ecologically interactive species in conservation planning.

Co-occurrence probabilities between mosquito vectors of West Nile and Eastern equine encephalitis viruses using Markov Random Fields (MRFcov)

Mosquito vectors of eastern equine encephalitis virus (EEEV) and West Nile virus (WNV) in the USA reside within broad multi-species assemblages that vary in spatial and temporal composition, relative abundances and vector competence. These variations impact the risk of pathogen transmission and the operational management of these species by local public health vector control districts. However, most models of mosquito vector dynamics focus on single species and do not account for co-occurrence probabilities between mosquito species pairs across environmental gradients. In this investigation, we use for the first time conditional Markov Random Fields (CRF) to evaluate spatial co-occurrence patterns between host-seeking mosquito vectors of EEEV and WNV around sampling sites in Manatee County, Florida. Specifically, we aimed to: (i) quantify correlations between mosquito vector species and other mosquito species; (ii) quantify correlations between mosquito vectors and landscape and climate variables; and (iii) investigate whether the strength of correlations between species pairs are conditional on landscape or climate variables. We hypothesized that either mosquito species pairs co-occur in patterns driven by the landscape and/or climate variables, or these vector species pairs are unconditionally dependent on each other regardless of the environmental variables. Our results indicated that landscape and bioclimatic covariates did not substantially improve the overall model performance and that the log abundances of the majority of WNV and EEEV vector species were positively dependent on other vector and non-vector mosquito species, unconditionally. Only five individual mosquito vectors were weakly dependent on environmental variables with one exception, Culiseta melanura, the primary vector for EEEV, which showed a strong correlation with woody wetland, precipitation seasonality and average temperature of driest quarter. Our analyses showed that majority of the studied mosquito species’ abundance and distribution are insignificantly better predicted by the biotic correlations than by environmental variables. Additionally, these mosquito vector species may be habitat generalists, as indicated by the unconditional correlation matrices between species pairs, which could have confounded our analysis, but also indicated that the approach could be operationalized to leverage species co-occurrences as indicators of vector abundances in unsampled areas, or under scenarios where environmental variables are not informative.Graphical

Identification of familial networks reveals sex-specific density dependence in the dispersal and reproductive success of an endangered ungulate

Density is an important demographic parameter that is commonly overlooked in studies of wild populations. Here, we examined the effects of variable spatially explicit density on a range of demographic parameters in a wild population of a cryptic ungulate, boreal woodland caribou (Rangifer tarandus caribou). Using non-invasive genetic sampling, we applied spatial capture–recapture methods with landscape covariates to estimate the density of boreal woodland caribou across a 108,806 km2 study area. We then created a familial network from the reconstructed parent–offspring relationships to determine whether spatial density influenced sex-specific individual reproductive success, female pregnancy status, and dispersal distance. We found that animal density varied greatly in response to land cover types and disturbance; animal density was most influenced by landscape composition and distance to roads varying from 0 in areas with >20% deciduous cover to 270 caribou per 1,000 km2 in areas presenting contiguous older coniferous cover. We found that both male and female reproductive success varied with density, with males showing a higher probability of having offspring in higher-density areas, and the opposite for females. No differences were found in female pregnancy rates occurring in high- and low-density areas. Dispersal distances varied with density, with offspring moving shorter distances when parents were found in higher-density areas. Familial networks showed lower-closeness centrality and lower-degree centrality for females in higher-density areas, indicating that females found in higher-density areas tend to be less broadly associated with animals across the range. Although high-density areas do reflect good-quality caribou habitat, the observed decreased closeness and degree centrality measures, dispersal rates, and lower female recruitment rates suggest that remnant habitat patches across the landscape may create population sinks.

Spatial-Statistical Analysis of Landscape-Level Wildfire Rate of Spread

The objectives of this study were to evaluate spatial sampling and statistical aspects of landscape-level wildfire rate of spread (ROS) estimates derived from airborne thermal infrared imagery (ATIR). Wildfire progression maps and ROS estimates were derived from repetitive ATIR image sequences collected during the 2017 Thomas and Detwiler wildfire events in California. Three separate landscape sampling unit (LSU) sizes were used to extract remotely sensed environmental covariates known to influence fire behavior. Statistical relationships between fire spread rates and landscape covariates were analyzed using (1) bivariate regression, (2) multiple stepwise regression, (3) geographically weighted regression (GWR), (4) eigenvector spatial filtering (ESF) regression, (5) regression trees (RT), and (6) and random forest (RF) regression. GWR and ESF regressions reveal that relationships between covariates and ROS estimates are substantially non-stationary and suggest that the global association of fire spread controls are locally differentiated on landscape scales. Directional slope is by far the most strongly associated covariate of ROS for the imaging sequences analyzed and the size of LSUs has little influence on any of the covariate relationships.

Occurrence patterns of wild turkeys altered by wild pigs

Eastern wild turkeys (Meleagris gallopavo silvestris) are an important game bird in the United States, particularly in the Southeast. The introduction of wild pigs (Sus scrofa) can negatively affect native wildlife, habitat quality, and ecosystem functions. To explore the potential effects that pig presence, through habitat degradation and interspecific competition, may have on wild turkeys, we evaluated changes in occupancy of co-occurring wild pigs and wild turkeys.We deployed camera-monitored bait stations on 3 wildlife management areas in Arkansas during January–April 2017–2019 and collected >680,000 images to determine turkey and wild pig occurrence. We evaluated presence/absence of turkeys and wild pigs in camera trap images using Timelapse2 image analyzer software® and then used program MARK to create 2-species occupancy models and determine the effect of wild pig presence and various landscape covariates on the occupancy and detectability of wild turkeys. The occupancy rate of wild pigs was 50.4% (95% CI = 46.9%–54.0%) and included positive relationships with the percent cover of deciduous forest and the number of wildlife openings within 500 m. The occupancy rate of turkeys increased from 45.5% (95% CI = 39.3%–51.6%) when wild pigs were not present to 59.4% (95% CI = 45.2%–73.5%, 95%) when wild pigs were present, indicating a tendency of the 2 species to select for similar environmental conditions (Species Interaction Factor = 1.13). Detectability of turkeys decreased when wild pigs occupied a site at any point during the season, regardless of whether or not the wild pigs were detected during the same trapping occasion. The decrease in detectability suggests a possible short-term displacement of turkeys by wild pigs. This displacement can have indirect effects through alteration of breeding behaviors and altered habitat use patterns, and these indirect effects represent an important topic for research moving forward.

Challenges and opportunities for estimating abundance of a low‐density moose population

AbstractMonitoring large herbivores across their core range has been readily accomplished using aerial surveys and traditional distance sampling. But for peripheral populations, where individuals may occur in patchy, low‐density populations, precise estimation of population size and trend remains logistically and statistically challenging. For moose (Alces alces) along their southern range margin in northern New York, USA, we sought robust estimates of moose distribution, abundance, and population trend (2016–2019) using a combination of aerial surveys (line transect distance‐sampling), repeated surveys in areas where moose were known to occur to boost the number of detections, and density surface modeling (DSM) with spatial covariates. We achieved a precise estimate of density (95% CI = 0.00–0.29 moose/km2) for this small population (656 moose, 95% CI = 501–859), which was patchily distributed across a large and heavily forested region (the 24,280‐km2Adirondack Park). Local moose abundance was positively related to active timber management, elevation, and snow cover, and negatively related to large bodies of water. As expected, moose abundance in this peripheral population was low relative to its core range in other northern forest states. Yet, in areas where abundance was greatest, moose densities in New York approached those where epizootics of winter tick (Dermacentor albipictus) have been reported, underscoring the need for effective and efficient monitoring. By incorporating autocorrelation in observations and landscape covariates, DSM provided spatially explicit estimates of moose density with greater precision and no additional field effort over traditional distance sampling. Combined with repeated surveys of areas with known moose occurrence to achieve viable sample sizes, DSM is a useful tool for effectively monitoring low density and patchy populations.

Use of predictive distribution models to describe habitat selection by bats in Colorado, USA

AbstractNumerous processes operating at landscape scales threaten bats (e.g., habitat loss, disease). Temperate bat species are rarely examined at commensurate scales because of logistical and modeling constraints. Recent modeling approaches now allow for presence‐only datasets, like those often available for bats, to assist with the development of predictive distribution models. We describe the use of presence‐only data and rigorous predictive distribution models to examine habitat selection by bats across Colorado, USA. We applied hierarchical Bayesian models to bat locations from 1906–2018 to examine relationships of 13 species with landscape covariates. We considered differences in type of activity (foraging, roosting, hibernation), seasonality (summer vs. winter), and scale (1, 5, 10, and 15‐km buffers). These findings generated statewide probability of use models to guide management of bat species in response to threats (e.g., white‐nose syndrome [WNS]). Analysis of buffers suggest selection of land cover and environmental covariates occurs at different scales depending on the species and activity. Pinyon (Pinus spp.)‐juniper (Juniperus spp.) appeared as a positive association in the highest number of models, followed by montane woodland, supporting the importance of these forest types to bats in Colorado. Other covariates commonly associated with bats in Colorado include westerly longitudes, and negative associations with montane shrubland. Mechanical treatments within pinyon‐juniper and montane woodlands should be conducted with caution to avoid harming bat communities. We developed hibernation models for only 2 species, making apparent the lack of winter records for bat species in the state. We also provide a composite predictive surface of small‐bodied bats in Colorado that delineates where these species, vulnerable to WNS, converge. This tool provides managers with focal points to apply surveillance and response strategies for the impending arrival of the disease.

Temporal scale of habitat selection for large carnivores: Balancing energetics, risk and finding prey.

When navigating heterogeneous landscapes, large carnivores must balance trade‐offs between multiple goals, including minimizing energetic expenditure, maintaining access to hunting opportunities and avoiding potential risk from humans. The relative importance of these goals in driving carnivore movement likely changes across temporal scales, but our understanding of these dynamics remains limited.Here we quantified how drivers of movement and habitat selection changed with temporal grain for two large carnivore species living in human‐dominated landscapes, providing insights into commonalities in carnivore movement strategies across regions.We used high‐resolution GPS collar data and integrated step selection analyses to model movement and habitat selection for African lions Panthera leo in Laikipia, Kenya and pumas Puma concolor in the Santa Cruz Mountains of California across eight temporal grains, ranging from 5 min to 12 hr. Analyses considered landscape covariates that are related to energetics, resource acquisition and anthropogenic risk.For both species, topographic slope, which strongly influences energetic expenditure, drove habitat selection and movement patterns over fine temporal grains but was less important at longer temporal grains. In contrast, avoiding anthropogenic risk during the day, when risk was highest, was consistently important across grains, but the degree to which carnivores relaxed this avoidance at night was strongest for longer term movements. Lions and pumas modified their movement behaviour differently in response to anthropogenic features: lions sped up while near humans at fine temporal grains, while pumas slowed down in more developed areas at coarse temporal grains. Finally, pumas experienced a trade‐off between energetically efficient movement and avoiding anthropogenic risk.Temporal grain is an important methodological consideration in habitat selection analyses, as drivers of both movement and habitat selection changed across temporal grain. Additionally, grain‐dependent patterns can reflect meaningful behavioural processes, including how fitness‐relevant goals influence behaviour over different periods of time. In applying multi‐scale analysis to fine‐resolution data, we showed that two large carnivore species in very different human‐dominated landscapes balanced competing energetic and safety demands in largely similar ways. These commonalities suggest general strategies of landscape use across large carnivore species.

Multiscale assessment of functional connectivity: Landscape genetics of eastern indigo snakes in an anthropogenically fragmented landscape in central Florida.

Landscape features can strongly influence gene flow and the strength and direction of these effects may vary across spatial scales. However, few studies have evaluated methodological approaches for selecting spatial scales in landscape genetics analyses, in part because of computational challenges associated with optimizing landscape resistance surfaces (LRS). We used the federally threatened eastern indigo snake (Drymarchon couperi) in central Florida as a case study with which to compare the importance of landscape features and their scales of effect in influencing gene flow. We used genetic algorithms (ResistanceGA) to empirically optimize LRS using categorical land cover surfaces, multiscale resource selection surfaces (RSS), and four combinations of landscape covariates measured at multiple spatial scales (multisurface multiscale LRS). We compared LRS where scale was selected using pseudo- and full optimization. Multisurface multiscale LRS received more empirical support than LRS optimized from categorical land cover surfaces or RSS. Multiscale LRS with scale selected using full optimization generally outperformed those with scale selected using pseudo-optimization. Multiscale LRS with large spatial scales (1200-1800m) received the most empirical support. Our results highlight the importance of considering landscape features across multiple spatial scales in landscape genetic analyses, particularly broad scales relative to species movement potential. Different effects of scale on home range-level movements and dispersal could explain weak associations between habitat suitability and gene flow in other studies. Our results also demonstrate the importance of large tracts of undeveloped upland habitat with heterogenous vegetation communities and low urbanization for promoting indigo snake connectivity.

Investigating niches and distribution of a rare species in a hierarchical framework: Virginia’s Warbler (Leiothlypis virginiae) at its northeastern range limit

Ensemble of small models (ESMs) is a technique to overcome the problem of few occurrence points. Applying the ESMs in a spatially hierarchical framework could increase the accuracy of predictions and conclusions by restricting available habitat at sequentially finer spatial scales. Our objective was to show how applying ESMs in a hierarchical habitat selection framework could help to understand rare species’ niches at various scales. We compared the accuracy of ESMs made by committee averaging and weighted averaging methods. We also compared the predictive power of ESMs made by various modeling techniques for Virginia’s warbler (Leiothlypis virginiae) at its northeastern range limit. We defined biologically relevant hierarchical orders of habitat selection for Virginia’s warbler in the Black Hills, U.S.A. We modeled habitat suitabity at the broadest scale as a function of bioclimatic covariates and at finer scales as functions of landcover, soil group and landscape covariates. The performance of modeling techniques varied among scales. Using the committee averaging method led to more accurate results than weighted averaging. At the broadest order, Virginia’s warbler had a narrow climatic niche. The importance of covariates changed across finer orders, such that at broader orders many covariates were important whereas at finer orders certain covariates became more important than others. We conclude that applying ESMs within a hierarchical framework can lead to detailed information about rare species’ niches, limiting factors at each habitat selection order, and potential distribution, which could help inform multiscale management.

Wild Turkey Nest Success in Pine‐Dominated Forests of the Southeastern United States

ABSTRACTNest success is a primary component of productivity for wild turkeys (Meleagris gallopavo; turkeys) and there is concern that turkey productivity is declining across the southeastern United States. We evaluated the influence of nest site and landscape characteristics on risk of nest failure for turkeys in pine (Pinus spp.)‐dominated forests across the southeastern United States. We used Cox proportional hazard models to evaluate daily hazard of nest failure associated with nest site and landscape metrics within 500‐m and 1‐km buffers centered on nests. Of 451 nests monitored (n = 320 females) between 2014 and 2018, 76% failed, with predation as the primary cause. Daily hazard of nest failure increased by 1.2% for each day that females delayed nest incubation following the mean nesting date (29 Apr; βday = 0.010 ± 0.002 [SE]; hazard ratio [HR] = 1.01, 95% CI = 1.006–1.015). Environmental covariates associated with risk of nest failure included the maximum enhanced vegetation index (EVI) and distance to the nearest ecotone. Daily hazard increased with increasing distance away from an ecotone (βecotone = 0.16 ± 0.06; HR = 1.17, CI = 1.03–1.32) and with lower EVI around the nest (βEVI = −0.30 ± 0.06; HR = 0.74, CI = 0.65–0.83). Additional nest site or landscape covariates were included in competitive models but did not influence risk of nest failure significantly. Our study highlights the importance of considering landscape context when designing and implementing land management actions intended to enhance wild turkey reproduction. Our findings suggest that landscape metrics thought to be important to turkeys in northern agro‐forested landscapes may not be relevant to turkeys in pine ecosystems of the southeastern United States. © 2021 The Wildlife Society.

Examining Landscape-Scale Fuel and Terrain Controls of Wildfire Spread Rates Using Repetitive Airborne Thermal Infrared (ATIR) Imagery

The objectives of this study are to evaluate landscape-scale fuel and terrain controls on fire rate of spread (ROS) estimates derived from repetitive airborne thermal infrared (ATIR) imagery sequences collected during the 2017 Thomas and Detwiler extreme wildfire events in California. Environmental covariate data were derived from prefire National Agriculture Imagery Program (NAIP) orthoimagery and USGS digital elevation models (DEMs). Active fronts and spread vectors of the expanding fires were delineated from ATIR imagery. Then, statistical relationships between fire spread rates and landscape covariates were analyzed using bivariate and multivariate regression. Directional slope is found to be the most statistically significant covariate with ROS for the five fire imagery sequences that were analyzed and its relationship with ROS is best characterized as an exponential growth function (adj. R2 max = 0.548, min = 0.075). Imaged-derived fuel covariates alone are statistically weak predictors of ROS (adj. R2 max = 0.363, min = 0.002) but, when included in multivariate models, increased ROS predictability and variance explanation (+14%) compared to models with directional slope alone.