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. 2021 Jul 5:9:e11666.
doi: 10.7717/peerj.11666. eCollection 2021.

Quantifying the effects of delisting wolves after the first state began lethal management

Affiliations

Quantifying the effects of delisting wolves after the first state began lethal management

Adrian Treves et al. PeerJ. .

Abstract

Predators and their protection are controversial worldwide. Gray wolves, Canis lupus, lost U.S. federal protection (delisting) and the State of Wisconsin began lethal management first among all states and tribes that regained authority over wolves. Here we evaluated the initial success of reaching the state's explicit objective, "…to allow for a sustainable harvest that neither increases nor decreases the state's wolf population…" We used official state figures for hunter-killed wolves, population estimates from April 2017-2020, and the latest peer-reviewed model of individual wolf survival to estimate additional deaths resulting from federal delisting. More than half of the additional deaths were predicted to be cryptic poaching under the assumption that this period resembled past periods of liberalized wolf-killing in Wisconsin. We used a precautionary approach to construct three conservative scenarios to predict the current status of this wolf population and a minimum estimate of population decline since April 2020. From our scenarios that vary in growth rates and additional mortality estimates, we expect a maximum of 695-751 wolves to be alive in Wisconsin by 15 April 2021, a minimum 27-33% decline in the preceding 12 months. This contradicts the state expectation of no change in the population size. We draw a conclusion about the adequacy of regulatory mechanisms under state control of wolves and discuss the particular governance conditions met in Wisconsin. We recommend greater rigor and independent review of the science used by agencies to plan wolf hunting quotas and methods. We recommend clearer division of duties between state wildlife agencies, legislatures, and courts. We recommend federal governments reconsider the practice of sudden deregulation of wolf management and instead recommend they consider protecting predators as non-game or transition more slowly to subnational authority, to avoid the need for emergency relisting.

Keywords: Anthropogenic mortality; Canis lupus; Human-caused mortality; Hunting; Illegal take; Poaching; Policy; Regulated an dunregulated off-take; U.S. endangered species act; Wisconsin gray wolf.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. Cumulative Incidence of endpoints by protection period.
Cumulative incidence functions (CIFs) for 499 monitored, adult wolves in Wisconsin during two policy periods (gray: reduced ESA protections; black: full ESA protections) for all deaths and disappearances (Panel A: n = 499), and disappearances only (Panel B: n = 243) from 1979–2012. Coordinates (x, y) represent the cumulative incidence or proportion of monitored wolves experiencing an endpoint (y-axis); showing all deaths in (A) or all disappearances in (B), over time (x-axis) in days. Time zero is set to 16 April 2020, a conservative step because death or disappearance increases with time, by definition. CIFs modeled with semi-parametric Fine-Gray models (Santiago-Ávila, Chappell & Treves, 2020). The first period of 201 days runs from 15 April 2020 to 3 November 2020 when delisting was announced in the Federal Register (USFWS, 2020) and the period of reduced ESA protection began. Day 312 marks the start of the Wisconsin wolf-hunt on 22 Feb 2021, and day 365 marks the end of the wolf-year on 14 April 2021. Finally, day 566 marks the approximate start date of the putative, next wolf-hunt, to illustrate further increases in the CIFs of mortalities and disappearances. We used the increment between the period of full ESA protection (black markers) to the corresponding value on the upper curve of reduced ESA protection (gray markers) to estimate the additional wolves deducted from the population by any endpoint (A) or LTF (B) which we predict would have survived if delisting had not occurred.

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