Integrated spatial analysis for human–wildlife coexistence in the American West

Carter, Neil; Williamson, Matthew A.; Gilbert, Sophie L.; Lischka, Stacy A.; Prugh, Laura R.; Lawler, Joshua J.; Metcalf, Alexander L.; Jacob, Aerin L.; Beltrán, Bray J.; Castro, Antonio Arjona; Sage, Abigail; Burnham, Morey

doi:10.1088/1748-9326/ab60e1

Cited by 26 publications

(29 citation statements)

References 24 publications

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“…As discussed in relation to variable range sizes found in areas of high human impact, understanding the tolerance of local people to elephants is critical to understanding ranging behavior, but such spatially explicit information on human perception is rare. 64 While several systems in this study have fenced boundaries, a factor known to restrict normative elephant movement behavior and space use, we lacked systematic information on fences across all studied populations. We also lacked high quality layers on the vegetation community, including tree fruiting phenology, relying on a coarse tree cover layer, which we found had a positive impact on range size.…”

Section: Elliptical Time-density Rangesmentioning

confidence: 99%

Human footprint and protected areas shape elephant range across Africa

et al. 2021

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Human footprint and protected areas shape elephant range across Africa Highlights d 229 African elephants were tracked across forest, Sahel, savanna, and bushveld sites d Protected areas and the human footprint both constrain elephant range size d 62% of the African continent is potential elephant habit, of which elephants use 17% d 85% of potential elephant habitat lies outside current protected areas

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Section: Elliptical Time-density Rangesmentioning

confidence: 99%

Human footprint and protected areas shape elephant range across Africa

et al. 2021

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“…Although past studies have analyzed spatial variation of HEC, conceptual obstacles still exist due to the complex nature of the relationship between HEC and its occurrence in a specific spatial context. Controlling elephant depredation requires proper understanding of the conflict distribution patterns across time and space scales (Gastineau et al, 2019;Williamson et al, 2020) and spatial risk assessment can be a valuable mechanism in locating the HEC hotspots.…”

Section: Discussionmentioning

confidence: 99%

Descriptive Spatial Analysis of Human-Elephant Conflict (HEC) Distribution and Mapping HEC Hotspots in Keonjhar Forest Division, India

et al. 2021

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Escalation of human-elephant conflict (HEC) in India threatens its Asian elephant (Elephas maximus) population and victimizes local communities. India supports 60% of the total Asian elephant population in the world. Understanding HEC spatial patterns will ensure targeted mitigation efforts and efficient resource allocation to high-risk regions. This study deals with the spatial aspects of HEC in Keonjhar forest division, where 345 people were killed and 5,145 hectares of croplands were destroyed by elephant attacks during 2001–2018. We classified the data into three temporal phases (HEC1: 2001–2006, HEC2: 2007–2012, and HEC3: 2013–2018), in order to (1) derive spatial patterns of HEC; (2) identify the hotspots of HEC and its different types along with the number of people living in the high-risk zones; and (3) assess the temporal change in the spatial risk of HEC. Significantly dense clusters of HEC were identified in Keonjhar and Ghatgaon forest ranges throughout the 18 years, whereas Champua forest range became a prominent hotspot since HEC2. The number of people under HEC risk escalated from 14,724 during HEC1 and 34,288 in HEC2, to 65,444 people during HEC3. Crop damage was the most frequent form of HEC in the study area followed by house damage and loss of human lives. Risk mapping of HEC types and high priority regions that are vulnerable to HEC, provides a contextual background for researchers, policy makers and managers.

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“…Increased emphasis on strategic selection of private lands to achieve conservation goals requires spatially explicit characterizations of conservation opportunity (i.e., the probability that conservation will occur) on private lands. Although there has been an increasing interest in exploring how social and ecological interactions affect the spatial arrangement of conservation actions (e.g., Carter et al 2015;Baldwin & Leonard 2015;Williamson et al 2018), a number of conceptual and methodological hurdles exist to ensure that conservation practitioners do not waste limited resources on strategies based on spurious models (Carter et al 2020). We have highlighted the potential for incomplete spatial data to bias empirical evaluations of previous conservation actions that may lead conservation planners to incorrectly target locations and interventions.…”

Section: Discussionmentioning

confidence: 99%

Improving inferences about private land conservation by accounting for incomplete reporting

Williamson

Dickson

Hooten

et al. 2021

Conservation Biology

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Private lands provide key habitat for imperiled species and are core components of function protectected area networks; yet, their incorporation into national and regional conservation planning has been challenging. Identifying locations where private landowners are likely to participate in conservation initiatives can help avoid conflict and clarify trade-offs between ecological benefits and sociopolitical costs. Empirical, spatially explicit assessment of the factors associated with conservation on private land is an emerging tool for identifying future conservation opportunities. However, most data on private land conservation are voluntarily reported and incomplete, which complicates these assessments. We used a novel application of occupancy models to analyze the occurrence of conservation easements on private land. We compared multiple formulations of occupancy models with a logistic regression model to predict the locations of conservation easements based on a spatially explicit social-ecological systems framework. We combined a simulation experiment with a case study of easement data in Idaho and Montana (United States) to illustrate the utility of the occupancy framework for modeling conservation on private land. Occupancy models that explicitly accounted for variation in reporting produced estimates of predictors that were substantially less biased than estimates produced by logistic regression under all simulated conditions. Occupancy models produced estimates for the 6 predictors we evaluated in our case study that were larger in magnitude, but less certain than those produced by logistic regression. These results suggest that occupancy models result in qualitatively different inferences regarding the effects of predictors on conservation easement occurrence than logistic regression and highlight the importance of integrating variable and incomplete reporting of participation in empirical analysis of conservation initiatives. Failure to do so can lead to emphasizing the wrong social, institutional, and environmental factors that enable conservation and underestimating conservation opportunities in landscapes where social norms or institutional constraints inhibit reporting.

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Integrated spatial analysis for human–wildlife coexistence in the American West

Cited by 26 publications

References 24 publications

Human footprint and protected areas shape elephant range across Africa

Human footprint and protected areas shape elephant range across Africa

Descriptive Spatial Analysis of Human-Elephant Conflict (HEC) Distribution and Mapping HEC Hotspots in Keonjhar Forest Division, India

Improving inferences about private land conservation by accounting for incomplete reporting

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