An overwinter protocol for detecting wolverines and other carnivores at camera traps paired with automated scent dispensers

Camera traps deployed with olfactory attractants are used to survey rare and elusive carnivores. Study areas with deep snowpack and rugged terrain present challenges and risks to field personnel, who traditionally must revisit camera stations regularly to refresh attractants. In such locations, alternative overwinter survey protocols that include a persistent attractant would improve both the safety and efficiency of camera-trap surveys. We present a protocol for installing camera traps and automated scent dispensers on trees at above-average maximum snow depth to eliminate the need for interim service visits and to enable standardized surveys to be conducted throughout the year. Our protocol proved to be effective at attracting and detecting numerous and repeated visits by wolverines, fishers, and other carnivores in two montane regions of the western contiguous United States. The volume, timing, and composition of liquid scent lure released by automated scent dispensers can be varied to target multiple species of interest, and the dispenser can be used in situations where bait rewards may influence the behavior of target species and/or pose human safety concerns.

The method matters. A comparative study of biologging and camera traps as data sources with which to describe wildlife habitat selection

Habitat use is a virtually universal activity among animals and is highly relevant as regards designing wildlife management and conservation actions. This has led to the development of a great variety of methods to study it, of which resource selection functions combined with biologging-derived data (RSF) is the most widely used for this purpose. However this approach has some constraints, such as its invasiveness and high costs. Analytical approaches taking into consideration imperfect detection coupled with camera trap data (IDM) have, therefore, emerged as a non-invasive cost-effective alternative. However, despite the fact that both approaches (RSF and IDM) have been used in habitat selection studies, they should also be comparatively assessed. The objective of this work is consequently to assess them from two perspectives: explanatory and predictive. This has been done by analyzing data obtained from camera traps (60 sampling sites) and biologging (17 animals monitored: 7 red deer Cervus elaphus, 6 fallow deer Dama dama and 4 wild boar Sus scrofa) in the same periods using IDM and RSF, respectively, in Doñana National Park (southern Spain) in order to explain and predict habitat use patterns for three studied species. Our results showed discrepancies between the two approaches, as they identified different predictors as being the most relevant to determine species intensity of use, and they predicted spatial patterns of habitat use with a contrasted level of concordance, depending on species and scale. Given these results and the characteristics of each approach, we suggested that although partly comparable interpretations can be obtained with both approaches, they are not equivalent but rather complementary. The combination of data from biologging and camera traps would, therefore, appear to be suitable for the development of an analytical framework with which to describe and characterise the habitat use processes of wildlife.

Occupancy of the Ethiopian endemic Moorland Francolin in pristine and degraded Afroalpine biome using a camera trap approach

Occupancy modeling is an essential tool for understanding species-habitat associations, thereby helping to plan the conservation of rare and threatened wildlife species. The conservation status and ecology of several avian species, particularly ground-dwelling birds, are poorly known in Ethiopia. We used camera trap-based occupancy modeling to investigate habitat covariate influence on occupancy (Ψ) and detection probability (ρ) estimates of Moorland Francolins Scleroptila psilolaema from spatially replicated surveys across both relatively pristine and disturbed landscapes in the Afroalpine biome of Ethiopia. Model-averaged estimate of ψ ^ across all sites was 0.76 (SD = 0.28) and ρ ^ was 0.77 (SD = 0.13) in the pristine landscape. The ψ ^ of the species in the disturbed landscape was 0.56 (SD = 0.19) and ρ ^ was 0.48 (SD = 0.06). As hypothesized, based on our model-averaged beta coefficient estimates (βmean ± SE), predators significantly negatively influenced the occupancy of Moorland Francolins in pristine habitat. We also found a significant positive association of occupancy with herb species richness. Contrary to our prediction, distance to road significantly negatively influence the occupancy of the species, suggesting that occupancy probability was highest in proximity to roadsides and trails in the pristine habitat. There was no significant influence of habitat covariates on the occupancy of the species in the disturbed habitat. The most important covariates that significantly influence the detectability of the species in pristine habitat included sampling occasion and precipitation. The greater occupancy and detectability of this endemic species in the pristine habitat could be linked with the particular conservation status and management of this biodiversity hotspot in the central highlands of Ethiopia. Our results suggest that strict legal enforcement is required to sustainably preserve Moorland Francolins and the ecological integrity of the entire Afroalpine biome. We recommend using camera traps in order to develop realistic and effective conservation and management strategies for rare, sensitive, cryptic, and ground-dwelling animals in the region.

Targeting burrows improves detection in giant pangolin Smutsia gigantea camera-trap surveys

The Endangered giant pangolin Smutsia gigantea is rare and elusive across its Central African range. Because of its solitary and nocturnal nature, the species is difficult to study and so its ecology is little known. Pangolins are considered the most trafficked mammals in the world. Therefore, confirming presence accurately and monitoring trends in distribution and abundance are essential to inform and prioritize conservation efforts. Camera traps are popular tools for surveying rare and cryptic species. However, non-targeted camera-trap surveys yield low camera-trapping rates for pangolins. Here we use camera-trap data from surveys conducted within three protected areas in Uganda to test whether targeted placement of cameras improves giant pangolin detection probability in occupancy models. The results indicate that giant pangolin detection probability is highest when camera traps are targeted on burrows. The median number of days from camera deployment to first giant pangolin detection event was 12, with the majority of events captured within 32 days from deployment. The median interval between giant pangolin events at a camera-trap site was 33 days. We demonstrate that camera-trap surveys can be designed to improve the detection of giant pangolins and we outline a set of recommendations to maximize the effectiveness of efforts to survey and monitor the species.

Occupancy data improves parameter precision in spatial capture-recapture models

Population size is one of the basic demographic parameters for species management and conservation. Among different estimation methods, spatially explicit capture-recapture (SCR) models allow the estimation of population density in a framework that has been greatly developed in recent years. The use of automated detection devices, such as camera traps, has impressively extended SCR studies for individually identifiable species. However, its application to unmarked/partially marked species remains challenging, and no specific method has been widely used. We fitted an SCR-integrated model (SCR-IM) to stone marten Martes foina data, a species for which only some individuals are individually recognizable by natural marks, and estimate population size based on integration of three submodels: (1) individual capture histories from live capture and transponder tagging; (2) detection/nondetection or "occupancy" data using camera traps in a bigger area to extend the geographic scope of capture-recapture data; and (3) telemetry data from a set of tagged individuals. We estimated a stone marten density of 0.352 (SD: 0.081) individuals/km2. We simulated four dilution scenarios of occupancy data to study the variation in the coefficient of variation in population size estimates. We also used simulations with similar characteristics as the stone marten case study, comparing the accuracy and precision obtained from SCR-IM and SCR, to understand how submodels' integration affects the posterior distributions of estimated parameters. Based on our simulations, we found that population size estimates using SCR-IM are more accurate and precise. In our stone marten case study, the SCR-IM density estimation increased the precision by 37% when compared to the standard SCR model as regards to the coefficient of variation. This model has high potential to be used for species in which individual recognition by natural markings is not possible, therefore limiting the need to rely on invasive sampling procedures.

Methodological approaches for estimating populations of the endangered dhole Cuon alpinus

Large carnivores are important for maintaining ecosystem integrity and attract much research and conservation interest. For most carnivore species, estimating population density or abundance is challenging because they do not have unique markings for individual identification. This hinders status assessments for many threatened species, and calls for testing new methodological approaches. We examined past efforts to assess the population status of the endangered dhole (Cuon alpinus), and explored the application of a suite of recently developed models for estimating their populations using camera-trap data from India's Western Ghats. We compared the performance of Site-Based Abundance (SBA), Space-to-Event (STE), and Time-to-Event (TTE) models against current knowledge of their population size in the area. We also applied two of these models (TTE and STE) to the co-occurring leopard (Panthera pardus), for which density estimates were available from Spatially Explicit Capture-Recapture (SECR) models, so as to simultaneously validate the accuracy of estimates for one marked and one unmarked species. Our review of literature (n = 38) showed that most assessments of dhole populations involved crude indices (relative abundance index; RAI) or estimates of occupancy and area of suitable habitat; very few studies attempted to estimate populations. Based on empirical data from our field surveys, the TTE and SBA models overestimated dhole population size beyond ecologically plausible limits, but the STE model produced reliable estimates for both the species. Our findings suggest that it is difficult to estimate population sizes of unmarked species when model assumptions are not fully met and data are sparse, which are commonplace for most ecological surveys in the tropics. Based on our assessment, we propose that practitioners who have access to photo-encounter data on dholes across Asia test old and new analytical approaches to increase the overall knowledge-base on the species, and contribute towards conservation monitoring of this endangered carnivore.