Assumptions and gaps in population and reproduction data could lead to near local extinction for Wisconsin gray wolves according to a new study by University of Wisconsin-Madison researchers. The study, “Uncertainty and precaution in hunting wolves twice in a year” was published in PLOS ONE and was led by Nelson Institute Professor and Carnivore Co-existence Lab founder Adrian Treves and Nelson Institute doctoral candidate Naomi Louchouarn. Together, they evaluated current population data and how these data were used to inform policy decisions related to the 2021 wolf hunt in Wisconsin.
Specifically, the study investigated how uncertainty in population data can be better incorporated into modeling and policy. To study this, Treves and Louchouarn looked at population and reproduction data that were used to determine the legal thresholds, or number of wolves that could be hunted, during the first, and proposed second, wolf hunt in 2021. One of the challenges noted in the new study is that threshold numbers are based on very specific population numbers that may, or may not, accurately reflect the true population. For example, the threshold for the early 2021 wolf hunt was based on an assumption that there are 1,034 wolves in Wisconsin, when scientific studies indicate that there is a range between 937 and 1,364 wolves. Calculations in the study show that this discrepancy in population numbers could have a significant impact on the threshold number, indicating that when there is uncertainty in the numbers, more precautions should be taken to ensure that extinction does not occur.
“There is a lot of uncertainty around these numbers. And what scientists do with uncertainty is they account for it quantitatively,” Treves said. “We grapple with the uncertainty and ask ourselves what it means, and it makes us more cautious about our communications about our science. That’s what led to the title of the paper because we discovered in the literature on science and even on decision making, that when there’s high uncertainty, you also must have high precautions, because what we’re talking about here is any mistake means wiping out the state wolf population.”
To ensure that this does not happen moving forward, Treves and Louchouarn used their analysis to identify ways in which policy makers can more accurately represent legal thresholds and use science to guide policy decisions. One way that Treves and Louchouarn recommend addressing this challenge is through the use of predictive models such as the bell curve they generated as a part of this report. This graph shows how three different legal thresholds would impact the wolf population while accounting for a margin of error.
“When we quantify uncertainty, we can incorporate it into the policy decisions, which allows us to make decisions with greater understanding of the risks of those decisions and allows us to safeguard against value judgements dominating policy decisions,” shared Louchouarn.
Treves added that by using this method policymakers, and the public, can more accurately see how a population will be impacted by a legal threshold.
“We have science that tells us what the consequences are,” shared Treves. “That’s what those three bell curves demonstrate there’s still a chance of driving the population so that it goes below some of these thresholds. In conclusion, when one portrays uncertainty scientifically, one can see the need for precautions.”
Treves and Louchouarn shared that beyond improved modeling, their study also indicates a need for a peer reviewed, published method for counting wolves in the state as well as additional information on reproductive success for wolves. Both will inform the value judgements being made around legal thresholds.
“We learned that even the more moderate quota posed an unacceptable risk of state relisting as endangered or threatened under the statute,” Treves said. “We need to have predators that are important to ecosystem resilience and ecosystem health.”