Can Occupancy–Abundance Models Be Used to Monitor Wolf Abundance?

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.

Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data

“…The average wolves’ territory size vary between 100 and 400 km² in western and central Europe (Ciucci et al , Mech and Boitani , Duchamp et al ). Although these cells might not entirely cover wolves territories, Latham et al () studied the effect of grid size to assess wolf's occupancy and found that taking a large grid size may not be appropriate for areas with moderate to high wolf density as it can overestimate occupancy rate. On the other hand, if the size of the sampling unit is too small, then there is a risk of having very few detections within a year, which would make the estimation of the detection probability difficult.…”

Sampling scales define occupancy and underlying occupancy–abundance relationships in animals

“…For example, the relationship between wolf ( Canis lupus ) occupancy and density is curved and, furthermore, the curvature depends on sampling grid size (Latham et al. ).…”

Acoustic localization at large scales: a promising method for grey wolf monitoring

“…Thus, understanding and monitoring the expansion of the grey wolf’s range is important for preventing or mitigating conflicts as well as for conservation and management purposes. However, the monitoring of wolves is still challenging in the field because it is a wide-ranging habitat generalist, which lives at low densities and is often secretive and elusive [ 18 , 19 ]. Moreover, the conventional methods used for detecting the presence of grey wolves and estimating their number and population dynamics can be very time-consuming and costly.…”