The management objectives of many protected areas must meet the dual mandates of protecting biodiversity while providing recreational opportunities. Balancing these mandates is made difficult by constraints on monitoring trends in the status of biodiversity and impacts of recreation. Using detections from 45 camera traps deployed between July 2019 and September 2021, we assessed the potential impacts of recreation on spatial and temporal habitat use for 8 medium- and large-bodied terrestrial mammals in an isolated alpine protected area: Cathedral Provincial Park, Canada. We hypothesized that some wildlife perceive a level of threat from people, such that they avoid ‘risky times’ or ‘risky places’ associated with human activity. Other species may benefit from associating with people, be it through access to anthropogenic resource subsidies or filtering of competitors/predators that are more human-averse (i.e., human shield hypothesis). Specifically, we predicted that large carnivores would show the greatest segregation from people while mesocarnivores and ungulates would associate spatially with people. We found spatial co-occurrence between ungulates and recreation, consistent with the human shield hypothesis, but did not see the predicted negative relationship between larger carnivores and humans, except for coyotes (Canis latrans). Temporally, all species other than cougars (Puma concolor) had activity patterns significantly different from that of recreationists, suggesting potential displacement in the temporal niche. Wolves (Canis lupus) and mountain goats (Oreamnos americanus) showed shifts in temporal activity away from people on recreation trails relative to off-trail areas, providing further evidence of potential displacement. Our results highlight the importance of monitoring spatial and temporal interactions between recreation activities and wildlife communities, in order to ensure the effectiveness of protected areas in an era of increasing human impacts.

Wildlife populations can be unmarked, meaning individuals lack visually distinguishing features for identification; populations may also exhibit non-independent movements, meaning individuals move together. For either unmarked or non-independent individuals, models based on spatial capture-recapture (SCR) approaches estimate abundance, density, and other population parameters critical for monitoring, management, and conservation. However, when individuals are both unmarked and non-independent, few model options exist. One approach has been to apply unmarked models and not address the non-independence despite unquantified impacts of overdispersion on bias, precision, and the ability to make robust ecological inferences. We conducted a simulation study to quantify the impact of non-independence on the performance of spatial count (SC) and spatial partial identity models (SPIM), two SCR-based unmarked modeling approaches, and used the performance of fully marked and independent SCR as a reference. We varied the levels of non-independence (aggregation and cohesion), detection probability, and the number of partial identity covariates used to resolve identities in SPIM estimation. We expected estimates of abundance and sigma (the spatial scale of individual movement) to be increasingly biased and less precise as aggregation and cohesion increased. Results showed that models indeed became less robust to increasing non-independence, especially for abundance, but importantly suggested that only SPIM could be reliably applied under low levels of cohesion when sufficient partial identity covariates are available. SC yielded consistently biased estimates with inflated precision that could not be corrected to nominal levels of coverage. SCR was the most robust across all combinations of aggregation and cohesion, as expected. We therefore advise against the use of SC models for estimating population parameters when individuals are known to be non-independent, caution that SPIM may be used under narrow ecological conditions, and encourage continued investigations into sampling design and methods development for populations of unmarked and non-independent individuals.

Human disturbance directly affects animal populations but indirect effects of disturbance on species behaviors are less well understood. Camera traps provide an opportunity to investigate variation in animal behaviors across gradients of disturbance. We used camera trap data to test predictions about predator-sensitive behavior in three ungulate species (caribou Rangifer tarandus; white-tailed deer, Odocoileus virginianus; moose, Alces alces) across two boreal forest landscapes varying in disturbance. We quantified behavior as the number of camera trap photos per detection event and tested its relationship to predation risk between a landscape with greater industrial disturbance and predator abundance (Algar) and a “control” landscape with lower human and predator activity (Richardson). We also assessed the influence of predation risk and habitat on behavior across camera sites within the disturbed Algar landscape. We predicted that animals in areas with greater predation risk (more wolf activity, less cover) would travel faster and generate fewer photos per event, while animals in areas with less predation risk would linger (rest, forage), generating more photos per event. Consistent with predictions, caribou and moose had more photos per event in the landscape where predation risk was reduced. Within the disturbed landscape, no prey species showed a significant behavioral response to wolf activity, but the number of photos per event decreased for white-tailed deer with increasing line of sight (m) along seismic lines (i.e. decreasing visual cover), consistent with a predator-sensitive response. The presence of juveniles was associated with shorter behavioral events for caribou and moose, suggesting greater predator sensitivity for females with calves. Only moose demonstrated a positive association with vegetation productivity (NDVI), suggesting that for other species influences of forage availability were generally weaker than those from predation risk. Behavioral insights can be gleaned from camera trap surveys and provide information about animal responses to predation risk and the indirect impacts of human disturbances.

Density estimation is a key goal in ecology but accurate estimates remain elusive, especially for unmarked animals. Data from camera-trap networks combined with new density estimation models can bridge this gap but recent research has shown marked variability in accuracy, precision, and concordance among estimators. We extend this work by comparing estimates from two different classes of models: unmarked spatial capture-recapture (spatial count, SC) models, and Time In Front of Camera (TIFC) models, a class of random encounter model. We estimated density for four large mammal species with different movement rates, behaviours, and sociality, as these traits directly relate to model assumptions. TIFC density estimates were typically higher than SC model estimates for all species. Black bear TIFC estimates were ~ 10-fold greater than SC estimates. Caribou TIFC estimates were 2-10 fold greater than SC estimates. White-tailed deer TIFC estimates were up to 100-fold greater than SC estimates. Differences of 2-5 fold were common for other species in other years. SC estimates were annually stable except for one social species; TIFC estimates were highly annually variable in some cases and consistent in others. Tests against densities obtained from DNA surveys and aerial surveys also showed variable concordance and divergence. For gregarious animals TIFC may outperform SC due to the latter model’s assumption of independent activity centres. For curious animals likely to investigate camera traps, SC may outperform TIFC, which assumes animal behavior is unaffected by cameras. Unmarked models offer great possibilities, but a pragmatic approach employs multiple estimators where possible, considers the ecological plausibility of assumptions, and uses an informed multi-inference approach to seek estimates from models with assumptions best fitting a species’ biology.

1. Landscape change is a key driver of biodiversity declines due to habitat loss and fragmentation, but spatially shifting resources can also facilitate range expansion and invasion. Invasive populations are reproductively successful, and landscape change may buoy this success. 2. We show how modelling the spatial structure of reproductive success can elucidate the mechanisms of range shifts and sustained invasions for mammalian species with attendant young. We use an example of white-tailed deer (deer; Odocoileus virginianus) expansion in the Nearctic boreal forest, a North American phenomenon implicated in severe declines of threatened woodland caribou (Rangifer tarandus). 3. We hypothesized that deer reproductive success is linked to forage subsidies provided by extensive landscape change via resource extraction. We measured deer occurrence using data from 62 camera-traps in northern Alberta, Canada, over three years. We weighed support for multiple competing hypotheses about deer reproductive success using multi-state occupancy models and generalized linear models in an AIC-based model selection framework. 4. Spatial patterns of reproductive success were best explained by features associated with petroleum exploration and extraction, which offer early seral vegetation resource subsidies. Effect sizes of anthropogenic features eclipsed natural heterogeneity by two orders of magnitude. We conclude that deer populations are likely buffered from overwinter mortality by landscape change, wherein early seral forage subsidies support high springtime reproductive success to offset or exceed winter losses. 5. Synthesis and Applications. Modelling spatial structuring in reproductive success can become a key goal of remote camera-based global networks, yielding ecological insights into mechanisms of invasion and range shifts to inform effective decision-making for global biodiversity conservation.