Estimating abundance of a cryptic social carnivore using spatially explicit capture–recapture

Abstract

Estimating population abundance of wolves (Canis lupus) in densely forested landscapes is challenging because reduced visibility lowers the success of methods such as aerial surveys and enumeration of group size using radiotelemetry. However, regular population estimates of wolves are necessary for population monitoring and sustainable management. We used noninvasive hair snaring and spatially explicit capture-recapture (SECR) to estimate wolf abundance on Prince of Wales Island (POW), Alaska, USA, during 2012-2015. We monitored 36-82 hair-snare stations weekly for 9-11 weeks during autumn. The noninvasive study area covered 1,683 km 2 during 2012-2013 and was expanded to 3,281 km 2 during 2014-2015. We identified 57 individual wolves during the study period using DNA from hair follicles genotyped at 10 microsatellite loci. We used population density estimates using SECR (2013: 24.5 wolves/1,000 km 2 [95% CI ¼ 14.4-41.9 wolves/1,000 km 2 ], 2014: 9.9 wolves/1,000 km 2 [95% CI ¼ 5.5-17.7/1,000 km 2 ], 2015: 11.9 wolves/1,000 km 2 [95% CI ¼ 7.7-18.5 wolves/1,000 km 2 ]) to predict the autumn population for the POW management unit (2013: 221.1 wolves [95% CI ¼ 130-378]; 2014: 89.1 wolves [95% CI ¼ 49.8-159.4]; 2015: 107.5 wolves [95% CI ¼ 69-167]). We detected and redetected more wolves and increased the precision of the density estimate after increasing the hair sampling intensity and sampling area in 2014-2015.Our results demonstrate that estimating wolf abundance using noninvasive sampling and SECR was feasible and reliably applied producing a statistically robust population estimate for monitoring wolf populations in densely forested areas. These methods have promise for application to widely ranging carnivores at population-level scales and may be especially useful when regular density estimates are necessary for management and conservation. Ó

Estimating Coyote Densities with Local, Discrete Bayesian Capture‐Recapture Models

“…Although SCR models were initially developed for use with traditional marking methods such as live‐trapping (Efford 2004), physical capture can be dangerous for the researchers and animals and often results in small sample sizes (Marucco et al 2011, Roffler et al 2019). Noninvasive sampling presents a viable alternative to live‐trapping and generally increases the number of individuals detected and recapture rates (Taberlet et al 1999, Schwartz et al 2006, Solberg et al 2006).…”

“…Collection of fecal DNA is particularly useful for detecting cryptic species that scent mark with feces and urine such as carnivores (MacKay et al 2008, Stenglein et al 2010 b , Kelly et al 2012). These techniques are increasingly being used in SCR models to monitor changes in population abundance across a variety of carnivore species over large spatial extents (Mumma et al 2015, Lonsinger et al 2018, Roffler et al 2019).…”

Comparison of grizzly bear hair‐snag and scat sampling along roads to inform wildlife population monitoring

“…Since the first DNA extractions from wild animal hair (Taberlet andBouvet 1992, Woods et al 1999), researchers have been developing techniques and applications to study a variety of wildlife using hair samples (Paetkau 2003, Beja-Pereira et al 2009, Proctor et al 2010. Hair is often collected through hair snares that, for example, use barbed wire or other adhesive alternatives at hair-snag sites or rub pads (Beja-Pereira et al 2009, Mumma et al 2015, Roffler et al 2019. While hair samples provide high quality data that can be used to identify species and individuals, field protocols are time and cost intensive (Boulanger et al 2006, Croose et al 2019.…”

Dhole pack size variation: Assessing the effect of Prey availability and Apex predator

“…This resulted in the identification of intensive use areas by dholes in the study sites based on number of captures at each camera location. Being territorial and social species these intensive use areas are mutually exclusive for each pack and the probability of capture decreases as you move away from the core area (Roffler Waite et al, 2019). After identification of dhole intensive use areas across the study sites, an effort was made to actively track dholes on foot and using a vehicle.…”