Abstract
Estimating the abundance of wild carnivores is of foremost importance for conservation and management. However, given their elusive habits, direct observations of these animals are difficult to obtain, so abundance is more commonly estimated from sign surveys or radio-marked individuals. These methods can be costly and difficult, particularly in large areas with heavy forest cover. As an alternative, recent research has suggested that wolf abundance can be estimated from occupancy–abundance curves derived from “virtual” surveys of simulated wolf track networks. Although potentially more cost-effective, the utility of this approach hinges on its robustness to violations of its assumptions. We assessed the sensitivity of the occupancy–abundance approach to four assumptions: variation in wolf movement rates, changes in pack cohesion, presence of lone wolves, and size of survey units. Our simulations showed that occupancy rates and wolf pack abundances were biased high if track surveys were conducted when wolves made long compared to short movements, wolf packs were moving as multiple hunting units as opposed to a cohesive pack, and lone wolves were moving throughout the surveyed landscape. We also found that larger survey units (400 and 576 km2) were more robust to changes in these factors than smaller survey units (36 and 144 km2). However, occupancy rates derived from large survey units rapidly reached an asymptote at 100% occupancy, suggesting that these large units are inappropriate for areas with moderate to high wolf densities (>15 wolves/1,000 km2). Virtually-derived occupancy–abundance relationships can be a useful method for monitoring wolves and other elusive wildlife if applied within certain constraints, in particular biological knowledge of the surveyed species needs to be incorporated into the design of the occupancy surveys. Further, we suggest that the applicability of this method could be extended by directly incorporating some of its assumptions into the modelling framework.
Highlights
Large mammalian carnivores provide conservation biologists and resource managers with complex challenges, such as endangered carnivore species recovery, human-carnivore conflict, and the management of carnivore species to conserve threatened and endangered prey
We found that the occupancy–abundance relationship estimated from the two smaller survey units (36 km2 and 144 km2) was sensitive to variation in wolf movement parameters (Figure 1a, b)
Field occupancy surveys based on tracks seen in snow, combined with occupancy–abundance relationships developed from simulated wolf paths [13], can be a useful method to monitor wolves if biological knowledge of the species is incorporated in the design of such a sign survey
Summary
Large mammalian carnivores provide conservation biologists and resource managers with complex challenges, such as endangered carnivore species recovery, human-carnivore conflict, and the management of carnivore species to conserve threatened and endangered prey. Wolves (Canis lupus) are an excellent example because they are wide-ranging habitat generalists, exist at low densities, and are often secretive and reclusive, making surveys that rely on direct sightings difficult or impossible. Been estimated in many locations across North America and are becoming increasingly common in the literature (e.g., [6]). These estimates are usually obtained by intensive aerial snow-tracking or radiotelemetry [7], [8]. In landscapes with ongoing wolf harvest seasons, it can be difficult to maintain an adequate sample of marked animals
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