Wolf population regulation revisited-Again

Differential provisioning roles, prey size, and prey abundance shape the dynamic feeding behavior of gray wolves

The demands of raising dependent young can influence the feeding behaviors of social carnivores, especially for individuals that are primarily responsible for provisioning young. We investigated how the feeding and provisioning behavior of a social carnivore, gray wolves (Canis lupus), are connected and shaped by extrinsic and intrinsic factors, and whether and how these patterns changed throughout the pup-rearing season (April-August). We found breeding wolves had shorter handling times of prey, lower probability of returning to kills, and greater probability of returning to homesites after kills compared to subordinate individuals. However, the feeding and provisioning behaviors of breeding individuals changed considerably over the pup-rearing season. Wolves had longer handling times and returned to provision pups directly after kills less frequently as annual prey abundance decreased. These patterns indicate that adult wolves prioritize meeting their own energetic demands over those of their pups when prey abundance decreases. We suggest that differential provisioning of offspring based on prey abundance is a behavioral mechanism by which group size adjusts to available resources via changes in neonate survival.

Estimating Wolf Population Size and Dynamics by Field Monitoring and Demographic Models: Implications for Management and Conservation

We estimated the current size and dynamics of the wolf population in Tuscany and investigated the trends and demographic drivers of population changes. Estimates were obtained by two different approaches: (i) mixed-technique field monitoring (from 2014 to 2016) that found the minimum observed pack number and estimated population size, and (ii) an individual-based model (run by Vortex software v. 10.3.8.0) with demographic inputs derived from a local intensive study area and historic data on population size. Field monitoring showed a minimum population size of 558 wolves (SE = 12.005) in 2016, with a density of 2.74 individuals/100 km2. The population model described an increasing trend with an average annual rate of increase λ = 1.075 (SE = 0.014), an estimated population size of about 882 individuals (SE = 9.397) in 2016, and a density of 4.29 wolves/100 km2. Previously published estimates of wolf population were as low as 56.2% compared to our field monitoring estimation and 34.6% in comparison to our model estimation. We conducted sensitivity tests to analyze the key parameters driving population changes based on juvenile and adult mortality rates, female breeding success, and litter size. Mortality rates played a major role in determining intrinsic growth rate changes, with adult mortality accounting for 62.5% of the total variance explained by the four parameters. Juvenile mortality was responsible for 35.8% of the variance, while female breeding success and litter size had weak or negligible effects. We concluded that reliable estimates of population abundance and a deeper understanding of the role of different demographic parameters in determining population dynamics are crucial to define and carry out appropriate conservation and management strategies to address human-wildlife conflicts.

Estimation of pack density in grey wolf (Canis lupus) by applying spatially explicit capture-recapture models to camera trap data supported by genetic monitoring

Background

Density estimation is a key issue in wildlife management but is particularly challenging and labour-intensive for elusive species. Recently developed approaches based on remotely collected data and capture-recapture models, though representing a valid alternative to more traditional methods, have found little application to species with limited morphological variation. We implemented a camera trap capture-recapture study to survey wolf packs in a 560-km² area of Central Italy. Individual recognition of focal animals (alpha) in the packs was possible by relying on morphological and behavioural traits and was validated by non-invasive genotyping and inter-observer agreement tests. Two types (Bayesian and likelihood-based) of spatially explicit capture-recapture (SCR) models were fitted on wolf pack capture histories, thus obtaining an estimation of pack density in the area.

Results

In two sessions of camera trapping surveys (2014 and 2015), we detected a maximum of 12 wolf packs. A Bayesian model implementing a half-normal detection function without a trap-specific response provided the most robust result, corresponding to a density of 1.21 ± 0.27 packs/100 km² in 2015. Average pack size varied from 3.40 (summer 2014, excluding pups and lone-transient wolves) to 4.17 (late winter-spring 2015, excluding lone-transient wolves).

Conclusions

We applied for the first time a camera-based SCR approach in wolves, providing the first robust estimate of wolf pack density for an area of Italy. We showed that this method is applicable to wolves under the following conditions: i) the existence of sufficient phenotypic/behavioural variation and the recognition of focal individuals (i.e. alpha, verified by non-invasive genotyping); ii) the investigated area is sufficiently large to include a minimum number of packs (ideally 10); iii) a pilot study is carried out to pursue an adequate sampling design and to train operators on individual wolf recognition. We believe that replicating this approach in other areas can allow for an assessment of density variation across the wolf range and would provide a reliable reference parameter for ecological studies.

Yellowstone wolf (Canis lupus) density predicted by elk (Cervus elaphus) biomass

The Northern Range (NR) of Yellowstone National Park (YNP) hosts a higher prey biomass density in the form of elk (Cervus elaphus L., 1758) than any other system of gray wolves (Canis lupus L., 1758) and prey reported. Therefore, it is important to determine whether that wolf–prey system fits a long-standing model relating wolf density to prey biomass. Using data from 2005 to 2012 after elk population fluctuations dampened 10 years subsequent to wolf reintroduction, we found that NR prey biomass predicted wolf density. This finding and the trajectory of the regression extend the validity of the model to prey densities 19% higher than previous data and suggest that the model would apply to wolf–prey systems of even higher prey biomass.