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Copyright of Mammal Review is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In April 2019, the U.S. Fish and Wildlife Service (USFWS) released its recovery plan for the jaguar Panthera onca after several decades of discussion, litigation and controversy about the status of the species in the USA. The USFWS estimated that potential habitat, south of the Interstate-10 highway in Arizona and New Mexico, had a carrying capacity of c. six jaguars, and so focused its recovery programme on areas south of the USA–Mexico border. Here we present a systematic review of the modelling and assessment efforts over the last 25 years, with a focus on areas north of Interstate-10 in Arizona and New Mexico, outside the recovery unit considered by the USFWS. Despite differences in data inputs, methods, and analytical extent, the nine previous studies found support for potential suitable jaguar habitat in the central mountain ranges of Arizona and New Mexico. Applying slightly modified versions of the USFWS model and recalculating an Arizona-focused model over both states provided additional confirmation. Extending the area of consideration also substantially raised the carrying capacity of habitats in Arizona and New Mexico, from six to 90 or 151 adult jaguars, using the modified USFWS models. This review demonstrates the crucial ways in which choosing the extent of analysis influences the conclusions of a conservation plan. More importantly, it opens a new opportunity for jaguar conservation in North America that could help address threats from habitat losses, climate change and border infrastructure. [ABSTRACT FROM AUTHOR]

Reintroduction—defined here as the return of a species to a part of its range where it has been extirpated—is a critical pathway to conservation in the 21st century. As late as the 1960s, jaguars (Panthera onca) inhabited an expansive region in the central mountain ranges of Arizona and New Mexico in the United States, a habitat unique in all of jaguar range. Here, we make the case for reintroduction, building a rhetorical bridge between conservation science and practice. First, we present a rationale rooted in the philosophy of wildlife conservation. Second, we show that the species once occupied this territory and was extirpated by human actions that should no longer pose a threat. Third, we demonstrate that the proposed recovery area provides suitable ecological conditions. Fourth, we discuss how return of the species could be a net benefit to people, explicitly recognizing a diversity of values and concerns. Fifth, we show that reintroduction is practical and feasible over a realistic time horizon. Returning the jaguar to this area will enhance the recovery of an endangered species in the United States, further its range‐wide conservation, and restore an essential part of North America's cultural and natural heritage. [ABSTRACT FROM AUTHOR]

Conflict between livestock producers and wild predators is a central driver of large predator declines and simultaneously may imperil the lives and livelihoods of livestock producers. There is a growing recognition that livestock–predator conflict is a socio‐ecological problem, but few case studies exist to guide conflict research and management from this point of view. Here we present a case study of coyote‐sheep predation on a California ranch in which we combine methods from the rapidly growing field of predation risk modeling with participatory mapping of perceptions of predation risk. Our findings reveal an important selection bias that may occur when producer perceptions and decisions are excluded from ecological methods of studying conflict. We further demonstrate how producer inputs, participatory mapping, and ecological modeling of conflict can inform one another in understanding patterns, drivers, and management opportunities for livestock–predator conflict. Finally, we make recommendations for improving the interoperability of ecological and social data about predation risk. Collectively our methods offer a socio‐ecological approach that fills important research gaps and offers guidance to future research. [ABSTRACT FROM AUTHOR]

Copyright of Conservation Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Protected areas (PAs) play an important role in conserving biodiversity and providing ecosystem services, yet their effectiveness is undermined by funding shortfalls. Using lions (Panthera leo) as a proxy for PA health, we assessed available funding relative to budget requirements for PAs in Africa's savannahs. We compiled a dataset of 2015 funding for 282 state-owned PAs with lions. We applied three methods to estimate the minimum funding required for effective conservation of lions, and calculated deficits. We estimated minimum required funding as $978/km2 per year based on the cost of effectively managing lions in nine reserves by the African Parks Network; $1,271/km² based on modeled costs of managing lions at ≥50% carrying capacity across diverse conditions in 115 PAs; and $2,030/km² based on Packer et al.'s [Packer et al. (2013) Ecol Lett 16:635-641] cost of managing lions in 22 unfenced PAs. PAs with lions require a total of $1.2 to $2.4 billion annually, or ∼$1,000 to 2,000/km², yet received only $381 million annually, or a median of $200/km². Ninety-six percent of range countries had funding deficits in at least one PA, with 88 to 94% of PAs with lions funded insufficiently. In funding-deficit PAs, available funding satisfied just 10 to 20% of PA requirements on average, and deficits total $0.9 to $2.1 billion. African governments and the international community need to increase the funding available for management by three to six times if PAs are to effectively conserve lions and other species and provide vital ecological and economic benefits to neighboring communities. [ABSTRACT FROM AUTHOR]

Carnivore predation on livestock often leads people to retaliate. Persecution by humans has contributed strongly to global endangerment of carnivores. Preventing livestock losses would help to achieve three goals common to many human societies: preserve nature, protect animal welfare, and safeguard human livelihoods. Between 2016 and 2018, four independent reviews evaluated >40 years of research on lethal and nonlethal interventions for reducing predation on livestock. From 114 studies, we find a striking conclusion: scarce quantitative comparisons of interventions and scarce comparisons against experimental controls preclude strong inference about the effectiveness of methods. For wise investment of public resources in protecting livestock and carnivores, evidence of effectiveness should be a prerequisite to policy making or large-scale funding of any method or, at a minimum, should be measured during implementation. An appropriate evidence base is needed, and we recommend a coalition of scientists and managers be formed to establish and encourage use of consistent standards in future experimental evaluations. [ABSTRACT FROM AUTHOR]

Human-carnivore conflict is challenging to quantify because it is shaped by both the realities and people’s perceptions of carnivore threats. Whether perceptions align with realities can have implications for conflict mitigation: misalignments can lead to heightened and indiscriminant persecution of carnivores whereas alignments can offer deeper insights into human-carnivore interactions. We applied a landscape-scale spatial analysis of livestock killed by tigers and leopards in India to model and map observed attack risk, and surveyed owners of livestock killed by tigers and leopards for their rankings of threats across habitats to map perceived attack risk. Observed tiger risk to livestock was greatest near dense forests and at moderate distances from human activity while leopard risk was greatest near open vegetation. People accurately perceived spatial differences between tiger and leopard hunting patterns, expected greater threat in areas with high values of observed risk for both carnivores. Owners’ perception of threats largely did not depend on environmental conditions surrounding their village (spatial location, dominant land-use or observed carnivore risk). Surveys revealed that owners who previously lost livestock to carnivores used more livestock protection methods than those who had no prior losses, and that owners who had recently lost livestock for the first time expressed greater interest in changing their protection methods than those who experienced prior losses. Our findings suggest that in systems where realities and perceptions of carnivore risk align, conservation programs and policies can optimize conservation outcomes by (1) improving the effectiveness of livestock protection methods and (2) working with owners who have recently lost livestock and are most willing to invest effort in adapting protection strategies to mitigate human-carnivore conflict. [ABSTRACT FROM AUTHOR]

Innovative conservation tools are greatly needed to reduce livelihood losses and wildlife declines resulting from human-carnivore conflict. Spatial risk modeling is an emerging method for assessing the spatial patterns of predator-prey interactions, with applications for mitigating carnivore attacks on livestock. Large carnivores that ambush prey attack and kill over small areas, requiring models at fine spatial grains to predict livestock depredation hot spots. To detect the best resolution for predicting where carnivores access livestock, we examined the spatial attributes associated with livestock killed by tigers in Kanha Tiger Reserve, India, using risk models generated at 20, 100, and 200-m spatial grains. We analyzed land-use, human presence, and vegetation structure variables at 138 kill sites and 439 random sites to identify key landscape attributes where livestock were vulnerable to tigers. Land-use and human presence variables contributed strongly to predation risk models, with most variables showing high relative importance (≥0.85) at all spatial grains. The risk of a tiger killing livestock increased near dense forests and near the boundary of the park core zone where human presence is restricted. Risk was nonlinearly related to human infrastructure and open vegetation, with the greatest risk occurring 1.2 km from roads, 1.1 km from villages, and 8.0 km from scrubland. Kill sites were characterized by denser, patchier, and more complex vegetation with lower visibility than random sites. Risk maps revealed high-risk hot spots inside of the core zone boundary and in several patches in the human-dominated buffer zone. Validation against known kills revealed predictive accuracy for only the 20 m model, the resolution best representing the kill stage of hunting for large carnivores that ambush prey, like the tiger. Results demonstrate that risk models developed at fine spatial grains can offer accurate guidance on landscape attributes livestock should avoid to minimize human-carnivore conflict. [ABSTRACT FROM AUTHOR]

Zoonosis-based epidemics are inevitable unless we revisit our relationship with the natural world, protect habitats, and regulate wildlife trade, including live animals and non-sustenance products. To prevent future zoonoses, governments must establish effective legislation addressing wildlife trade, protection of habitats, and reduction of the wildlife-livestock-human interface., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Innovative conservation tools are greatly needed to reduce livelihood losses and wildlife declines resulting from human-carnivore conflict. Spatial risk modeling is an emerging method for assessing the spatial patterns of predator-prey interactions, with applications for mitigating carnivore attacks on livestock. Large carnivores that ambush prey attack and kill over small areas, requiring models at fine spatial grains to predict livestock depredation hot spots. To detect the best resolution for predicting where carnivores access livestock, we examined the spatial attributes associated with livestock killed by tigers in Kanha Tiger Reserve, India, using risk models generated at 20, 100, and 200-m spatial grains. We analyzed land-use, human presence, and vegetation structure variables at 138 kill sites and 439 random sites to identify key landscape attributes where livestock were vulnerable to tigers. Land-use and human presence variables contributed strongly to predation risk models, with most variables showing high relative importance (≥0.85) at all spatial grains. The risk of a tiger killing livestock increased near dense forests and near the boundary of the park core zone where human presence is restricted. Risk was nonlinearly related to human infrastructure and open vegetation, with the greatest risk occurring 1.2 km from roads, 1.1 km from villages, and 8.0 km from scrubland. Kill sites were characterized by denser, patchier, and more complex vegetation with lower visibility than random sites. Risk maps revealed high-risk hot spots inside of the core zone boundary and in several patches in the human-dominated buffer zone. Validation against known kills revealed predictive accuracy for only the 20 m model, the resolution best representing the kill stage of hunting for large carnivores that ambush prey, like the tiger. Results demonstrate that risk models developed at fine spatial grains can offer accurate guidance on landscape attributes livestock should avoid to minimize human-carnivore conflict.