Human–tiger conflict in the Khata Corridor, Nepal 809


TABLE 1 The four settlements (Fig. 1), with the number of households in each, the number of households surveyed, economic benefits received from tiger tourism, distance from Bardiya National Park, Nepal (Fig. 2), and enforcement of provisions on forest use, ordered by distance from the Park. Economic benefit is categorized based on the number of tourism-related businesses, including hotels, home- stays, shops and other small-scale enterprises in each settlement, and designated as low (,5 businesses), moderate (5–20) or high (.20). We measured distance of each settlement from Bardiya National Park headquarters to the midpoint of the respective settlement. Enforcement of provisions on forest use indicates the extent to which laws and regulations are implemented to control and manage the use of forest resources near to the settlement, as observed by surveyors during field visits and whilst interacting with respondents. High enforcement refers to regular patrolling and well-maintained fences, moderate enforcement reflects no patrolling but some main- tenance of fences, and low enforcement indicates no patrolling and no maintenance of fences.


Settlement


Thakurdwara Dalla


Neulapur Pattharbhuji


Number of households


620 330 560 210


Number of households surveyed


63 34 58 22


respondent, age, monthly family income and livestock herd size owned by each household (Supplementary Table 1, Supplementary Material 2). ForH2, to assess the factors underlying the risk of a person


encountering a tiger, we examined which independent vari- ableswere significantly associated with tiger records, using bin- ary logistic regression. The dependent variable consisted of binary data in which we coded all 33 recorded tiger records as 1 and an equal number of pseudo-absences as 0.We gener- ated pseudo-absence points using the create random points feature in ArcGIS 10.8 (Esri, 2022), distributing them equally amongst the three settlement wards (i.e. 11 in each ward; Barbet-Massin et al., 2012).We validated these by confirming that none of them overlapped with the presence data, and that all were within the same human settlement boundaries as the tiger records. Independent variables were human settle- ment as a categorical variable (to which settlement each pres- ence and each random pseudo-absence was most proximate) and the nearest distance of each presence and pseudo- absence to the forest edge, water feature, grazing land, house- hold androad(Supplementary Table 1). Given the strong positive correlations between nearest distance to the forest edge, grazing land and water feature (r.0.5) and between nearest distance to the household and road (r.0.4; Supple- mentary Fig. 1), we selected nearest distances to forest and household for the analysis. For both the multinomial and binary logistic regression


we first built a single-effect model for each predictor vari- able, in R 4.2.0 (R Core Team, 2023) using the glm() (binary) and multinom() (multinomial) functions in the nnet pack- age (Ripley & Venables, 2023). We compared all single- effect models based on the Akaike information criterion corrected for small samples sizes (AICc) and identified the most influential variable based on the lowest AICc value (variable human settlement types in the multinomial logistic regression and nearest distance to forest edge in the binary logistic regression; Supplementary Table 2). Subsequently, we developed additional models by


Economic benefit


High


Moderate Low Low


Distance from Park (km)


1.0 5.0 6.0 8.5


Forest use enforcement


High


Moderate Moderate Low


combining these influential variables with other predictor variables. In addition, we constructed models that included either no predictor variables (null model) or all variables. After developing candidate models we considered the mod- els with ΔAICc values #2 as the best competing models. We chose the model with the lowest AICc value but highest Akaike weight as the best explanatory model. We also performed χ2 goodness-of-fit tests to assess whether responses to survey questions were statistically different considering in which season and at which time respondents visited forests, sighted a tiger or witnessed a tiger attack or the killing of either a person or livestock.


Results


Forest visitation by people Frequency of forest visitation varied, but all respondents indi- cated they visit the forest at least occasionally. Some respon- dents visited the forest only once per year (37%), followed by weekly (25%) andmonthly (22%),with daily visits uncommon (16%). The frequency of forest visitation correlated strongly with economic benefit from tourism, with the majority of re- spondents from Pattharbhuji (low benefit) visiting the forest daily, whereas respondents from Thakurdwara (high benefit) visited forests only annually, and respondents fromDalla and Neulapur (moderate benefit) reported a medium frequency of visits (Fig. 3). Based on the AICc values in themultinomial regression, no single variable best explained forest visitation. The best-fitting model for explaining the frequency of forest visitations included settlement type, age, gender and live- stock herd size (Supplementary Table 2). Amodel including settlement type, age and livestock herd size was also well sup- ported but was less parsimonious based on its slightly higher AICc and lower Akaikeweight values than the best-fit model. The best-fit model indicated that frequency of forest visits (using yearly visits as the baseline for comparison with more frequent visit categories) was significantly dependent on


Oryx, 2024, 58(6), 806–814 © The Author(s), 2024. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605323001849


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