Estimating wolf densities in forested areas using network sampling of tracks in snow

Abstract

Few reliable methods exist for estimating population size of large terrestrial carnivores. This is particularly true in forested areas where sightability is low and when radiocollared individuals are unavailable in the target population. We used stratified network sampling to sample wolf (Canis lycaon) tracks in the snow to estimate density in western Algonquin Park, Ontario in February 2002. We partitioned our 3,425-km2 study area into 137 5 times 5-km sample units (SU) and stratified SUs as having a high (n = 61) or low (n = 76) probability of containing detectable wolf tracks based on the relative amount of watercourses and conifer cover within each block. We used a Bell 206B helicopter to survey 28 high (46%) and 17 low (22%) SUs. When fresh tracks were found in a block, we followed the tracks forward to the wolves themselves and then backward until the tracks were no longer considered “fresh.” We observed 17 “fresh” track networks within 45 SUs. The average pack size in the area we surveyed was 4.2 ± 0.4 (SE). These observations resulted in an estimate of 87 ± 11.4 (90% CI) wolves in the study area, for a density of 2.5 ± 0.3 wolves/100 km2. We detected no violations of the assumptions of this survey design and obtained a similar density estimate (2.3 wolves/100 km2) in 2003 using location data from 24 radiocollared wolves in 10 packs from an area that overlapped our 2002 survey area. The sampling unit probability estimator (SUPE) provides an objective, accurate, and repeatable means of estimating wolf density with an associated measure of precision. However, tracking wolves in forested cover was time-consuming, so costs will be considerably higher per unit area in forested areas relative to the more open cover types where this technique was originally developed.