782 L. Von Hagen et al.


construct validity, the facilitator orally defined the concepts of crop damage (i.e. the act of any animal entering a farm and consuming or trampling crops) and deterrents (i.e. any method used to prevent entry or frighten wildlife away from farms). These methods could involve both active deterrence such as yelling, waving a torch (flashlight) or pa- trolling, or passive methods such as any type of fencing. We subsequently transferred the data from the hardcopy sur- veys into a database for analysis. We edited the data to create groupings for survey ques-


tions and to prepare the data for analysis (Supplementary Material 2), and we then selected models to evaluate our hy- potheses (Table 2). We evaluated each variable of interest (age, education level, years farming, gender, exposure to


TABLE 2 A priori models used to test hypotheses related to the use of deterrents to prevent crop damage by elephants in the six study villages. Note that farm size is a quadratic term (area size; indicated by superscript 2), so the relationship is not linear as for the other terms.


Model Description 1


Null 2 Constant + age


3 Constant + education level 4 Constant + years farming 5 Constant + gender 6 Constant + exposure 7 Constant + farm size2 8 Constant + village


9 Constant + age + education level 10 Constant + age + exposure 11 Constant + age + farm size2


15 Constant + age + education level + exposure


16 Constant + age + exposure + farm size2


17 Constant + education level + exposure + farm size2


18 Constant + age + education level + exposure + farm size2


19 Constant + age + education level + years farming


20 Constant + age + education level + years farming + gender


21 Constant + age + education level + years farming + gender + exposure


22 Constant + age + education level + years farming + gender + exposure + farm size2


23 Constant + age + education level + years farming + gender + exposure + farm size2 + village


24 Constant + age + education level + years farming + gender + farm size2 + village


Hypotheses1 All


DU, EB All


DU, EB DU, EB DU, EB DU, EB DU, EB DU, EB DU DU


12 Constant + education level + exposure DU, EB 13 Constant + education level + farm size2 14 Constant + exposure + farm size2


DU, EB DU


DU, EB DU


DU, EB DU, EB DU, EB DU, EB DU, EB DU, EB DU, EB


DE


1DE, deterrent-exposure hypothesis; DU, deterrent-use hypothesis; EB, economic-barriers hypothesis.


deterrents, farm size and village) for collinearity with a ro- bust variance inflation factor, and all factors were near 1.0, signifying no collinearity between these variables. We ana- lysed the models using a generalized linear model that ac- counts for the non-normal distribution of response variables.Weused a binomial distribution when creating lo- gistic regression models. Wecompared our results using the Akaike information criterion corrected for small sample sizes (AICc) as a measure of fit. We also report adjusted pseudo-r2 values, following a previously proposed approach (Zhang, 2017). We considered models with ΔAICc#2 (compared to the best model; Burnham & Anderson, 2002) to be competitive and evaluated them using the ex- planatory values of model weights and adjusted pseudo-r2 values. Topmodels are reported, but these are not indicators of hypothesis support as we evaluated each model inde- pendently according to respective model metrics, effect sizes and sociological meaning. For each top model we re- port effect sizes with 95% confidence intervals (CI) of sig- nificant coefficients (P,0.150; Arnold, 2010) to further describe the significance of our data. We conducted all ana- lyses in R 4.0.2 (R Core Team, 2020).


Results


Of the 206 respondents that completed the survey, the num- ber of participants per village ranged from 29 to 37. The ratio of female to male participantswas 53:47, although this varied by village (Supplementary Table 1). Respondents ranged in age from 18 to 85 years, with a mean age of 46 ± SD 14 years and household size ranged from 2 to 34 with a mean of 8 ± SD 4.Most respondents (64%) had a primary educa- tion level, 22% had completed secondary education, 7% had completed tertiary education and the remaining 7% had no formal education. The main source of income for 92%of respondents was farming. For the deterrent-use hypothesis, model 12 (education


level + exposure to deterrents) best described which farmers used deterrents (Table 3). Individuals exposed to informa- tion about deterrents were 3.65 (95% CI: 1.65–8.63) times more likely to use deterrents (P = 0.002). Respondents with secondary levels of education were 4.64 (95% CI: 1.21–20.75) times more likely to use deterrents compared to those with no education (P = 0.031). We also found that individuals with primary education were 1.94 (95%CI: 0.55– 7.87) times more likely to use deterrents compared to those with no education, but this was not significant (P = 0.310). Education level alone (model 3) was the best-fitting model for farmers who had received information on deterrents (deterrent-exposure hypothesis). We found that re- spondents with tertiary education were 5.00 (95% CI: 0.89–40.97) times more likely to have received information on deterrents compared to those with no education (P = 0.087). For information specifically on fencing


Oryx, 2024, 58(6), 779–787 © The Author(s), 2024. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605323001795


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