Estimating abundance of mountain lions from unstructured spatial sampling

Abstract

Mountain lions (Puma concolor) are often difficult to monitor because of their low capture probabilities, extensive movements, and large territories. Methods for estimating the abundance of this species are needed to assess population status, determine harvest levels, evaluate the impacts of management actions on populations, and derive conservation and management strategies. Traditional mark–recapture methods do not explicitly account for differences in individual capture probabilities due to the spatial distribution of individuals in relation to survey effort (or trap locations). However, recent advances in the analysis of capture–recapture data have produced methods estimating abundance and density of animals from spatially explicit capture–recapture data that account for heterogeneity in capture probabilities due to the spatial organization of individuals and traps. We adapt recently developed spatial capture–recapture models to estimate density and abundance of mountain lions in western Montana. Volunteers and state agency personnel collected mountain lion DNA samples in portions of the Blackfoot drainage (7,908 km2) in west‐central Montana using 2 methods: snow back‐tracking mountain lion tracks to collect hair samples and biopsy darting treed mountain lions to obtain tissue samples. Overall, we recorded 72 individual capture events, including captures both with and without tissue sample collection and hair samples resulting in the identification of 50 individual mountain lions (30 females, 19 males, and 1 unknown sex individual). We estimated lion densities from 8 models containing effects of distance, sex, and survey effort on detection probability. Our population density estimates ranged from a minimum of 3.7 mountain lions/100 km2 (95% CI 2.3–5.7) under the distance only model (including only an effect of distance on detection probability) to 6.7 (95% CI 3.1–11.0) under the full model (including effects of distance, sex, survey effort, and distance × sex on detection probability). These numbers translate to a total estimate of 293 mountain lions (95% CI 182–451) to 529 (95% CI 245–870) within the Blackfoot drainage. Results from the distance model are similar to previous estimates of 3.6 mountain lions/100 km2 for the study area; however, results from all other models indicated greater numbers of mountain lions. Our results indicate that unstructured spatial sampling combined with spatial capture–recapture analysis can be an effective method for estimating large carnivore densities. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.

Linking resource selection and mortality modeling for population estimation of mountain lions in Montana

“…It is noteworthy that the predicted effect on population growth was only 2.6% at a statewide scale, given the high starting population was almost double the low starting population (total density of 4.04/100 km 2 vs. 2.19/100 km 2 ), (Tables 8 and 9). Two recent DNA mark recapture studies in Montana have suggested that densities of mountain may be higher than those presented here, perhaps as high as 6.7 mountain lions per 100 km 2 compared to our high density of 4.04/100 km 2 (Russell et al, 2012;Proffitt et al, in review). Unfortunately neither of these studies produced population estimates of resident adults which are needed to estimate survival in our Eq.…”

“…In adaptive management, predictions about the effects of harvest must be paired with monitoring to evaluate those predictions and the effects of the management action (Nichols and Williams, 2006). Although existing methods for estimating lion population size and trend are cost-and time-prohibitive for widespread use (McKinney, 2011), new developments in the use of genetic identification of individuals in a spatial mark-recapture framework offer promise (Russell et al, 2012). These methods allow for the estimation of population size using samples from one season, and are far cheaper than other alternatives.…”

Potential for camera-traps and spatial mark-resight models to improve monitoring of the critically endangered West African lion (Panthera leo)

“…Density is derived by dividing the sum of activity centers within the state space by the total area of the state space. While the model assumes independent activity centers, the model is known to be robust to this assumption (Russell et al 2012). Moreover, an equivalent view of the uniformity assumption is that it implies a lack of specific information about each activity center, similar to an uninformed prior.…”

Genetic and genomic monitoring with minimally invasive sampling methods

“…Using genetic monitoring to estimate abundance spans methods from the enumeration of the number of genotypes in a region (Taberlet et al, 1997), to single-session models (Miller et al, 2005;Petit & Valière, 2006), to occupancy (Lonsinger et al, 2017;Marucco et al, 2012), to complex mark recapture models that integrate sex, age and reproductive status information (Carroll et al, 2013;Woodruff et al, 2016). The advent of spatial mark recapture models (Efford et al, 2004(Efford et al, , 2011Royle & Young, 2008) greatly improved analytical tools for density estimates using genetic monitoring approaches (Mollet et al, 2015;Russell et al, 2012;Thompson et al, 2012).…”