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ABSTRACT Landscape features can alter the transfer phase of dispersal and dispersal-mediated disease transmission and gene flow. The transfer phase is poorly understood, but improved understanding of landscape effects on dispersal distance and direction would allow better prediction and mitigation of disease spread and improved delineation of management zones. To investigate how ecological settings influence dispersal in white-tailed deer ( Odocoileus virginianus), we captured and radio-collared 409 juvenile male deer from 2 study areas in Wisconsin, USA, one dominated by public forest and another by row-crop agriculture. Dispersal directions were non-directed in the heavily forested study area, but there was a southeastern bias in the farmland study area. Individual dispersal distances were not related to forest cover, and study area average and maximum distances differed from expected, based on published relationships between forest cover and population-average dispersal distance. Roads, rivers, and cities were semipermeable barriers to dispersal, but effects of barriers differed with respect to study area, suggesting that natural and anthropogenic features influence dispersal-mediated disease transmission and gene flow. Our results suggest that dispersal models should consider movement barriers in more developed landscapes, and barriers can also be used to inform designation of biologically meaningful management units. © 2017 The Wildlife Society. [ABSTRACT FROM AUTHOR]

The article presents flawed analysis and unconvincing interpretation of impact of poaching on the conservation of carnivores. Topics discussed include analysis of hypothesis that culling will reduce poaching; impact of increasing availability of lethal depredation management on the reduction of illegal killing; and data on the same.

Habitat selection is a critical aspect of a species' ecology, requiring complex decision-making that is both hierarchical and scale-dependent, since factors that influence selection may be nested or unequal across scales. Elk ( Cervus canadensis ) ranged widely across diverse ecoregions in North America prior to European settlement and subsequent eastern extirpation. Most habitat selection studies have occurred within their contemporary western range, even after eastern reintroductions began. As habitat selection can vary by geographic location, available cover, season, and diel period, it is important to understand how a non-migratory, reintroduced population in northern Wisconsin, USA, is limited by the lack of variation in topography, elevation, and vegetation. We tested scale-dependent habitat selection on 79 adult elk from 2017 to 2020 using resource selection functions across temporal (i.e., seasonal) and spatial scales (i.e., landscape and home range). We found that selection varied both spatially and temporally, and elk selected areas with the greatest potential to influence fitness at larger scales, meaning elk selected areas closer to escape cover and further from "risky" features (e.g., annual wolf territory centers, county roads, and highways). We found stronger avoidance of annual wolf territory centers during spring, suggesting elk were selecting safer habitats during calving season. Elk selected habitats with less canopy cover across both spatial scales and all seasons, suggesting that elk selected areas with better access to forage as early seral stage stands have greater forage biomass than closed-canopy forests and direct solar radiation to provide warmth in the cooler seasons. This study provides insight into the complexity of hierarchical decision-making, such as how risky habitat features and land cover type influence habitat selection differently across seasons and spatial scales, influencing the decision-making of elk. Scale-dependent behavior is crucial to understand within specific geographic regions, as these decisions scale up to influence population dynamics., Competing Interests: The authors declare no conflicts of interest., (© 2024 The Author(s). Ecology and Evolution published by John Wiley & Sons Ltd.)

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., Competing Interests: The Wisconsin Department of Natural Resources is partially responsible for wolf management in the state of Wisconsin. The authors declare that no consequent competing interests exist. The analyses and conclusions presented here are those of the authors alone, and are not influenced by, nor represent official policy of any of the institutions or agencies with which the authors are affiliated., (Copyright: © 2024 Stauffer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Managing risk requires an adequate understanding of risk-factors that influence the likelihood of a particular event occurring in time and space. Risk maps can be valuable tools for natural resource managers, allowing them to better understand spatial characteristics of risk. Risk maps can also support risk-avoidance efforts by identifying which areas are relatively riskier than others. However, risks, such as human-carnivore conflict, can be diverse, multi-faceted, and overlapping in space. Yet, efforts to describe risk typically focus on only one aspect of risk. We examined wolf complaints investigated in Wisconsin, USA for the period of 1999-2011. We described the spatial patterns of four types of wolf-human conflict: livestock depredation, depredation on hunting hounds, depredation on non-hound dogs, and human health and safety concerns (HHSC). Using predictive landscape models and discriminant functions analysis, we visualized the landscape of risk as a continuous surface of overlapping risks. Each type of conflict had its own unique landscape signature; however, the probability of any type of conflict increased closer to the center of wolf pack territories and with increased forest cover. Hunting hound depredations tended to occur in areas considered to be highly suitable wolf habitat, while livestock depredations occurred more regularly in marginal wolf habitat. HHSC and non-hound dog depredations were less predictable spatially but tended to occur in areas with low housing density adjacent to large wildland areas. Similar to other research evaluating the risk of human-carnivore conflict, our data suggests that human-carnivore conflict is most likely to occur where humans or human property and large carnivores co-occur. However, identifying areas of co-occurrence is only marginally valuable from a conservation standpoint and could be described using spatially-explicit human and carnivore data without complex analytical approaches. These results challenge our traditional understanding of risk and the standard approach used in describing risk. We suggest that a more comprehensive understanding of the risk of human-carnivore conflict can be achieved by examining the spatial and non-spatial factors influencing risk within areas of co-occurrence and by describing the landscape of risk as a continuous surface of multiple overlapping risks., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Measurement or observation error is common in ecological data: as citizen scientists and automated algorithms play larger roles processing growing volumes of data to address problems at large scales, concerns about data quality and strategies for improving it have received greater focus. However, practical guidance pertaining to fundamental data quality questions for data users or managers-how accurate do data need to be and what is the best or most efficient way to improve it?-remains limited. We present a generalizable framework for evaluating data quality and identifying remediation practices, and demonstrate the framework using trail camera images classified using crowdsourcing to determine acceptable rates of misclassification and identify optimal remediation strategies for analysis using occupancy models. We used expert validation to estimate baseline classification accuracy and simulation to determine the sensitivity of two occupancy estimators (standard and false-positive extensions) to different empirical misclassification rates. We used regression techniques to identify important predictors of misclassification and prioritize remediation strategies. More than 93% of images were accurately classified, but simulation results suggested that most species were not identified accurately enough to permit distribution estimation at our predefined threshold for accuracy (<5% absolute bias). A model developed to screen incorrect classifications predicted misclassified images with >97% accuracy: enough to meet our accuracy threshold. Occupancy models that accounted for false-positive error provided even more accurate inference even at high rates of misclassification (30%). As simulation suggested occupancy models were less sensitive to additional false-negative error, screening models or fitting occupancy models accounting for false-positive error emerged as efficient data remediation solutions. Combining simulation-based sensitivity analysis with empirical estimation of baseline error and its variability allows users and managers of potentially error-prone data to identify and fix problematic data more efficiently. It may be particularly helpful for "big data" efforts dependent upon citizen scientists or automated classification algorithms with many downstream users, but given the ubiquity of observation or measurement error, even conventional studies may benefit from focusing more attention upon data quality., (© 2019 by the Ecological Society of America.)

Populations of large terrestrial carnivores are in various stages of recovery worldwide and the question of whether there is compensation in mortality sources is relevant to conservation. Here, we show variation in Wisconsin wolf survival from 1979 to 2013 by jointly estimating the hazard of wolves' radio-telemetry ending (endpoint) and endpoint cause. In previous analyses, wolves lost to radio-telemetry follow-up (collar loss) were censored from analysis, thereby assuming collar loss was unconfounded with mortality. Our approach allowed us to explicitly estimate hazard due to collar loss and did not require censoring these records from analysis. We found mean annual survival was 76% and mean annual causes of mortality were illegal killing (9.4%), natural and unknown causes (9.5%), and other human-caused mortality such as hunting, vehicle collisions and lethal control (5.1%). Illegal killing and natural mortality were highest during winter, causing wolf survival to decrease relative to summer. Mortality was highest during early recovery and lowest during a period of sustained population growth. Wolves again experienced higher risk of human-caused mortality relative to natural mortality as wolves expanded into areas with more human activity. We detected partial compensation in human- and natural-caused mortality since 2004 as the population saturated more available habitat. Prior to 2004, we detected additivity in mortality sources. Assessments of wolf survival and cause of mortality rates and the finding of partial compensation in mortality sources will inform wolf conservation and management efforts by identifying sources and sinks, finding areas of conservation need, and assessing management zone delineation.