Search

The black‐tailed prairie dog (Cynomys ludovicianus) is a keystone species of North American short‐ and mixed‐grass temperate grasslands with critical importance for multiple species of conservation concern. Low population numbers due to habitat and social constraints as well as disease require conservation and management actions. To identify potential habitat and future relocation sites, we evaluated 6 statistical techniques to predict the optimal resource selection function model for black‐tailed prairie dog habitat in 3 Native nations of Montana. Random forest models performed best across our study area and included grass coverage and climatic and topographic variables as the most important predictors of prairie dog occupancy. Using predictive habitat models in conjunction with land‐use designations can refine conservation planning to support wildlife population objectives where there are foundations for long‐term success. Our analysis also serves as a guide for assessing habitat suitability, connectivity, and restoration for additional species dependent or interconnected with black‐tailed prairie dogs. [ABSTRACT FROM AUTHOR]

Understanding spatial and temporal variation in beaver abundance is a central goal for a wide range of management issues, ranging from species reintroductions to mitigation of environmental and economic impacts. Yet due to high costs associated with surveys, many studies are limited to a single regional estimate, or a complete census of a smaller study area extrapolated to the surrounding landscape. We present a survey design that allows for predicting beaver abundance across the broader landscape through interpolation. In October 2019, we conducted an aerial survey in a 15,000 km2 study area around Algonquin Provincial Park in Ontario, Canada. We counted 145 colonies on 73 plots that averaged 4.5 km2 (+/−3.27 SD). Our regional estimate for beaver abundance of 0.55 (95% CI = +/−0.18) colonies/km2 is comparable to historical surveys conducted in the region in the 1970s. We then predicted beaver abundance in unsampled plots using a Poisson generalized additive model (adjusted R2 = 0.81, deviance explained = 55.9%) that included non‐linear responses to elevation (P < 0.001), shoreline complexity (P = 0.003), and availability of shade‐tolerant hardwoods (P = 0.001). Our species distribution model predicted strong east‐west patterns in beaver abundance across the study region associated with spatial patterns in elevation and forest composition. [ABSTRACT FROM AUTHOR]

Increasing use of hair to survey carnivore distribution and identify carnivore scavengers or predators at prey kill sites requires methods for cost‐efficient identification of predator hair. Although DNA analysis can be used to identify species‐specific hairs, reliability depends on quality of hair, collection method, and environmental conditions, with cost that can exceed CAD$35/hair. In contrast, features of guard hairs including hair length, banding, and macro‐ and microscopic characteristics of the hair cuticle and medulla offer an alternative approach when hair quality is poor or funding is limited. Past keys focused on hair identification of prey species (e.g., ungulates, rodents) in predator scat analysis or were general because they contained all mammals in a region, thus complicating the focus on dichotomous keys for large carnivores. We used Random Forest (RF) to identify features that best classified known‐origin guard hairs (n = 175) and used these characteristics to develop a dichotomous key for hair identification of the 7 major, large carnivore species common to the Rocky Mountains of Alberta, Canada. We found relative medulla width and pattern, cuticle‐scale characteristics, and hair length provided the greatest probability of correctly distinguishing among hairs of different carnivore species. Correct classification of within sample hairs with RF based on Area Under the Curve (AUC) averaged 0.95 ± 0.10, with coyote (Canis latrans) hairs having the lowest classification accuracy. Blind trials classifying 21 hairs using the dichotomous key yielded correct classifications of 88% ± 7% to the family level and 60% ± 10% to the species level. Hair preparation and identification by a trained technician was estimated at 30 ± 15 min/hair and CAD$8/hair. Our carnivore hair key provides an alternative approach to DNA hair analysis when either funds are limited, or hair samples are not of sufficient quality to be successfully sequenced. © 2018 The Wildlife Society. Past dichotomous hair keys focus on identification of prey species or contain all mammals in a region, with a lack of carnivore‐specific hair keys. We developed a dichotomous key specifically for identifying guard hairs of the 7 major, large carnivore species common to the Rocky Mountains of Canada. [ABSTRACT FROM AUTHOR]

In Flanders (northern Belgium), wild boar (Sus scrofa) returned in 2006 after 50 years of absence and the population is increasing, both in abundance and geographic extent. In the absence of wild boar, Flanders' landscape structure changed into a dense, mosaic‐like pattern of agricultural, natural, and urban areas. The return of the wild boar increasingly leads to human–wildlife conflicts, mainly linked to damage in agriculture. Hence, there is a growing need for a time‐efficient, standardized, and accurate method to assess crop damage. We present an Unmanned Aerial Vehicle‐based method, using Geographic Object‐Based Image Analysis and Random Forests to estimate the damaged area and associated yield losses, between 2015 and 2017, due to wild boar in individual fields in Flanders. Our approach resulted in an 84.50% overall accuracy in calculating damaged area for maize fields and 94.40% for grasslands. Damage levels ranged between 14.3% and 20.2% in maize fields and 16.5% to 25.4% in grasslands. Our method can provide objective base data for compensation schemes and guide management strategies based on damage assessments. © 2018 The Wildlife Society. We used drones, Geographic Object‐Based Image Analysis and Random Forest models to assess crop damage by wild boar. [ABSTRACT FROM AUTHOR]