Uncertainty and precaution in hunting wolves twice in a year: Reanalysis of Treves and Louchouarn.

Management of wolves is controversial in many jurisdictions where wolves live, which underscores the importance of rigor, transparency, and reproducibility when evaluating outcomes of management actions. Treves and Louchouarn 2022 (hereafter TL) predicted outcomes for various fall 2021 hunting scenarios following Wisconsin's judicially mandated hunting and trapping season in spring 2021, and concluded that even a zero harvest scenario could result in the wolf population declining below the population goal of 350 wolves specified in the 1999 Wisconsin wolf management plan. TL further concluded that with a fall harvest of > 16 wolves there was a "better than average possibility" that the wolf population size would decline below that 350-wolf threshold. We show that these conclusions are incorrect and that they resulted from mathematical errors and selected parameterizations that were consistently biased in the direction that maximized mortality and minimized reproduction (i.e., positively biased adult mortality, negatively biased pup survival, further halving pup survival to November, negatively biased number of breeding packs, and counting harvested wolves twice among the dead). These errors systematically exaggerated declines in predicted population size and resulted in erroneous conclusions that were not based on the best available or unbiased science. Corrected mathematical calculations and more rigorous parameterization resulted in predicted outcomes for the zero harvest scenario that more closely coincided with the empirical population estimates in 2022 following a judicially prevented fall hunt in 2021. Only in scenarios with simulated harvest of 300 or more wolves did probability of crossing the 350-wolf population threshold exceed zero. TL suggested that proponents of some policy positions bear a greater burden of proof than proponents of other positions to show that "their estimates are accurate, precise, and reproducible". In their analysis, TL failed to meet this standard that they demanded of others.

Relationships of catch-per-unit-effort metrics with abundance vary depending on sampling method and population trajectory.

Catch-per-unit-effort (CPUE) is often used to monitor wildlife populations and to develop statistical population models. Animals caught and released are often not included in CPUE metrics and their inclusion may create more accurate indices of abundance. We used 21 years of detailed harvest records for bobcat (Lynx rufus) in Wisconsin, U.S.A., to calculate CPUE and 'actual CPUE' (ACPUE; including animals caught and released) from bobcat hunters and trappers. We calibrated these metrics to an independent estimate of bobcat abundance and attempted to create simple but effective models to estimate CPUE and ACPUE using harvest success data (i.e., bobcats harvested/available permits). CPUE showed virtually no relationship with bobcat abundance across all years, but both CPUE and ACPUE had stronger, non-linear, and negative relationships with abundance during the periods when the population was decreasing. Annual harvest success strongly predicted composite ACPUE and CPUE from hunters and trappers and hunter ACPUE and CPUE but was a poorer predictor of trapper ACPUE and CPUE. The non-linear, and sometimes weak, relationships with bobcat abundance likely reflect the increasing selectivity of bobcat hunters for trophy animals. Studies calibrating per-unit-effort metrics against abundance should account for population trajectories and different harvest methods (e.g., hunting and trapping). Our results also highlight the potential for estimating per-unit-effort metrics from relatively simple and inexpensive data sources and we encourage additional research into the use of per-unit-effort metrics for population estimation.

Hunter selection for larger and older male bobcats affects annual harvest demography.

Wildlife researchers often rely on demographic data collected from harvested animals to estimate population dynamics. But demographic data from harvested animals may be non-representative if hunters/trappers have the ability and motivation to preferentially select for certain physical traits. Hunter preference is well demonstrated for ungulates, but less so for other wildlife species such as furbearers. We used data from bobcats harvested in Wisconsin (1983-2014) to determine if harvest method and demographics (mass, male:female sex ratio and age) have changed over time, and if bobcat hunters/trappers exhibited selection. Each trait of harvested bobcats that we tested changed over time, and because these selected traits were interrelated, we inferred that harvest selection for larger size biased harvests in favour of older, male bobcats. The selection of older, male bobcats appears primarily driven by hound hunters (hereafter hunters) compared to trappers, with hunters more frequently creating taxidermy mounts from their harvested bobcats. We found an increase in the proportion of bobcats that were harvested by hunting compared to trapping over time, and this was associated with increased selectivity and substantial changes in the characteristics of harvested bobcats. Selection by hunters may bias population models that are based on the demography of harvested bobcats, and accounting for biases that may occur, including from different harvest methods, is critical when using harvest-dependent data.

A Bayesian state-space model using age-at-harvest data for estimating the population of black bears (Ursus americanus) in Wisconsin.

Population estimation is essential for the conservation and management of fish and wildlife, but accurate estimates are often difficult or expensive to obtain for cryptic species across large geographical scales. Accurate statistical models with manageable financial costs and field efforts are needed for hunted populations and using age-at-harvest data may be the most practical foundation for these models. Several rigorous statistical approaches that use age-at-harvest and other data to accurately estimate populations have recently been developed, but these are often dependent on (a) accurate prior knowledge about demographic parameters of the population, (b) auxiliary data, and (c) initial population size. We developed a two-stage state-space Bayesian model for a black bear (Ursus americanus) population with age-at-harvest data, but little demographic data and no auxiliary data available, to create a statewide population estimate and test the sensitivity of the model to bias in the prior distributions of parameters and initial population size. The posterior abundance estimate from our model was similar to an independent capture-recapture estimate from tetracycline sampling and the population trend was similar to the catch-per-unit-effort for the state. Our model was also robust to bias in the prior distributions for all parameters, including initial population size, except for reporting rate. Our state-space model created a precise estimate of the black bear population in Wisconsin based on age-at-harvest data and potentially improves on previous models by using little demographic data, no auxiliary data, and not being sensitive to initial population size.

Trapping and furbearer management in North American wildlife conservation.

Furbearer Management in North America maintains wild furbearer populations at sustainably harvestable, scientifically determined and socially acceptable levels. Furbearer management impacts numerous wildlife populations and habitats, and human health, safety and property. Achieving balance in the management of furbearers is not always an easy task partly because regulated trapping, a controversial management technique, plays a critical role in this balance. Steps have been taken by wildlife professionals to improve the humaneness of trapping through the development of international standards used to evaluate traps. These efforts will ideally preserve trapping and the many roles it plays in furbearer management and wildlife management in general.

Serologic evidence of avian influenza (H4N6) exposure in a wild-caught raccoon.

Growing concerns about avian influenza, and its effect on agriculture and human health, have highlighted the need to understand the role of wildlife in maintaining and spreading the virus. We surveyed the wildlife inhabiting a poultry farm with recent H3N6 and H4N6 avian influenza virus exposure in Pennsylvania, U.S.A. One raccoon (Procyon lotor) tested positive for H4N6 antibodies. This is the first recorded incident of avian influenza exposure in a wild raccoon. We suggest that raccoons may play a role in the transmission of avian influenza viruses and in compromising biosecurity efforts at poultry operations.