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Introduction: Terrestrial species in riverine ecosystems face unique constraints leading to diverging patterns of population structure, connectivity, and disease dynamics. Desert bighorn sheep (Ovis canadensis nelsoni) in Grand Canyon National Park, a large native population in the southwestern USA, offer a unique opportunity to evaluate population patterns and processes in a remote riverine system with ongoing anthropogenic impacts. We integrated non-invasive, invasive, and citizen-science methods to address questions on abundance, distribution, disease status, genetic structure, and habitat fragmentation. Methods: We compiled bighorn sightings collected during river trips by park staff, commercial guides, and private citizens from 2000-2018 and captured bighorn in 2010-2016 to deploy GPS collars and test for disease. From 2011-2015, we noninvasively collected fecal samples and genotyped them at 9-16 microsatellite loci for individual identification and genetic structure. We used assignment tests to evaluate genetic structure and identify subpopulations, then estimated gene flow and recent migration to evaluate fragmentation. We used spatial capturerecapture to estimate annual population size, distribution, and trends after accounting for spatial variation in detection with a resource selection function model. Results and discussion: From 2010-2018, 3,176 sightings of bighorn were reported, with sightings of 56-145 bighorn annually on formal surveys. From 2012-2016, bighorn exhibiting signs of respiratory disease were observed along the river throughout the park. Of 25 captured individuals, 56% were infected by Mycoplasma ovipneumoniae, a key respiratory pathogen, and 81% were recently exposed. Pellet sampling for population estimation from 2011-2015 yielded 1,250 genotypes and 453 individuals. We detected 6 genetic clusters that exhibited mild to moderate genetic structure (FST 0.022-0.126). The river, distance, and likely topography restricted recent gene flow, but we detected cross-river movements in one section via genetic recaptures, no subpopulation appeared completely isolated, and genetic diversity was among the highest reported. Recolonization of one large stretch of currently empty habitat appears limited by the constrained topology of this system. Annual population estimates ranged 536-552 (95% CrI range 451-647), lamb:ewe ratios varied, and no significant population decline was detected. We provide a multi-method sampling framework useful for sampling other wildlife in remote riverine systems. [ABSTRACT FROM AUTHOR]

Highway construction and expansion through bear habitat can negatively affect brown bear populations. Highway structures can decrease habitat availability through habitat loss and restricted access, roads often displace animals and cause re-direction of natural movements, and highways can act as barriers to decrease gene flow. Lastly, highway traffic can cause direct bear and human mortality through car-animal collisions. We examined the spatial and temporal distribution of brown bear crossings of the Sterling and Seward Highways on the Kenai Peninsula, Alaska. Data were collected between 1995 and 2001 as part of an ongoing population study. We created random walking bears within each bear’s home range and compared the geographic distribution of each bear’s highway crossing locations to the random crossing locations to assess whether the number and pattern of crossing locations were different than expected. An information theoretic approach comparing logistic regression models was used to determine if traffic volume, distance to cover across the highway, road density, and distance to the closest stream crossing were related to locations bears crossed the highway. We conducted a second set of analyses comparing models based on the temporal factors of daylight versus darkness, mean bear movement per hour, and traffic volume. Most bears crossed the highway less frequently than expected. While locations where bears crossed the highway were clustered, none of the spatial models tested strongly explained the observed clustering. Bears were more likely to cross the highway during nighttime. Additional research will be necessary to identify the cues bears use to choose locations to cross the highway.

Many human activities affect how bears use habitat. The effects of motorized recreational vehicle use on trails have not been formally assessed previously. Potential effects include displacement from and avoidance of high quality habitat, either temporally or spatially, which could affect reproduction and survival and result in fewer bears. Focusing on displacement, we used hourly locations from four GPS-collared female bears in the Badger-Two Medicine area in the Lewis and Clark National Forest to assess spatial and temporal distributions of bears relative to trail locations and to recreational use on trails. When availability was defined as circles equal to 95 percent of move distances around the previous bear location, all bears used areas near trails less than expected. We iteratively reclassified trail habitat versus non-trail habitat as increasing buffers in 50m increments around trails until we reached a buffer-width at which bears used areas near trails in proportion to availability. Compositional analysis results showed that bears selected against areas within 250 - 900m from ATV trails and within 450 - 600m from single-track trails, which had some motorbike use. The distance from trails at which bear use approximated availability varied by individual bear, by time of day, and by type of trail. Log-ratio differences were used to assess selection. Bears were less likely to spend time near trails with high (~5 trips/day average) motorized use than trails with low motorized use. We used an information-theoretic approach to select between nonlinear regression models with variables that included motorized use estimates, non-motorized use estimates, and trail density.

Novel approaches to quantifying density and distributions could help biologists adaptively manage wildlife populations, particularly if methods are accurate, consistent, cost‐effective, rapid, and sensitive to change. Such approaches may also improve research on interactions between density and processes of interest, such as disease transmission across multiple populations. We assess how satellite imagery, unmanned aerial system (UAS) imagery, and Global Positioning System (GPS) collar data vary in characterizing elk density, distribution, and count patterns across times with and without supplemental feeding at the National Elk Refuge (NER) in the US state of Wyoming. We also present the first comparison of satellite imagery data with traditional counts for ungulates in a temperate system. We further evaluate seven different aggregation metrics to identify the most consistent and sensitive metrics for comparing density and distribution across time and populations. All three data sources detected higher densities and aggregation locations of elk during supplemental feeding than non‐feeding at the NER. Kernel density estimates (KDEs), KDE polygon areas, and the first quantile of interelk distances detected differences with the highest sensitivity and were most highly correlated across data sources. Both UAS and satellite imagery provide snapshots of density and distribution patterns of most animals in the area at lower cost than GPS collars. While satellite‐based counts were lower than traditional counts, aggregation metrics matched those from UAS and GPS data sources when animals appeared in high contrast to the landscape, including brown elk against new snow in open areas. UAS counts of elk were similar to traditional ground‐based counts on feed grounds and are the best data source for assessing changes in small spatial extents. Satellite, UAS, or GPS data can provide appropriate data for assessing density and changes in density from adaptive management actions. For the NER, where high elk densities are beneath controlled airspace, GPS collar data will be most useful for evaluating how management actions, including changes in the dates of supplemental feeding, influence elk density and aggregation across large spatial extents. Using consistent and sensitive measures of density may improve research on the drivers and effects of density within and across a wide range of species. [ABSTRACT FROM AUTHOR]

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