Attitudes towards the snow leopard 785


TABLE 1 The eight predictors used in model construction to assess variables contributing to positive and negative attitudes towards the snow leopard Panthera uncia.


Predictor


Covariate Number of animals lost


Years of formal education


Factor Snow leopard abundance


Insurance


Important to religion


Loss to snow leopards


Explanation


Number of livestock lost to snow leopards in previous 5 years


Number of years of formal education completed by herder


Perception of snow leopard abundance in study area


Dog ownership Herder owns one or more dogs Guarding


Herders actively guard their livestock to deter predators


Herders possess livestock insurance to offset financial burden of livestock depredation


Herders consider snow leopards important to their religion


Herder experienced a livestock depredation event they believe was by a snow leopard


scale values of 1, 2 and 3 were collapsed into a Disagree category, 4 assigned to a Neutral category, and 5, 6 and 7 were collapsed into an Agree category. Cronbach’s alpha was used to ensure internal consistency within all five atti- tude statements (Cronbach, 1951). A principal component analysis (PCA) with varimax rotation and pairwise exclu- sion of cases was used to identify variables contributing most to variation in attitudes (Kaiser, 1958; Jolliffe, 2002). Components with eigenvalues .1 were selected for inter- pretation (Kaiser, 1960) and internal consistency of state- ments in separate components assessed. Likert type scores for statements in each component were averaged to give an overall agreement score. A Wilcoxon signed-rank test was completed to assess statistical differences between PCA components (Wilcoxon, 1945). Relationships among vari- ables were examined using Spearman’s rho (Spearman, 1904). Those exhibiting multi-collinearity with high signifi- cance (P,0.01) had one variable removed based on author expertise and number of other highly significant correlations (Dormann et al., 2013). Generalized estimating equations were used to determine influential predictors of positive and negative attitudes. These equations are appropriate for datasets with non-normal distributions, without having to make data corrections (Kowalski & Tu, 2008;Tangetal., 2012). A series of general models were constructed based on eight predictor variables (Table 1). The first model was built using all variables. The least impactful variable was then removed in a stepwise fashion for each sequential model. The quasi-likelihood under the independence model crite- rion was used to rank models, with the lowest quasi- likelihood value deemed most appropriate (Cui, 2007; Hardin & Hilbe, 2003). SPSS 25.0 (SPSS, Chicago, USA) was used for all analyses, with significance set at P,0.05.


Variable Level


Numeric Numeric


Ordinal. Coded as 0 if reported 1–4 (low abundance) & 1 if reported 5–7 (high abundance) on Likert scale Ordinal. Coded as 0 for no dogs, 1 if$1 dogs owned


Ordinal. Coded as 0 if herders do not guard livestock, 1 if herders do guard livestock


Ordinal. Coded as 0 if herders do not possess livestock insurance, 1 if herders do possess livestock insurance


Ordinal. Coded as 0 if reported 1–4 (not important to religion) & 1 if reported 5–7 (important to religion) on Likert scale Ordinal. Coded as 0 for no reported loss, 1 for loss of$1 animals


Results


Interview responses We completed interviews with 73 herders (67 men, six women), over an area of c. 476 km2,of whom 94.5% reported pastoralism and 5.5% reported civil service employment as being their primary income source. The greatest Euclidian distance between sites where inter- views were conducted was 231 km. No retaliatory killings of snow leopards were reported. The results of the other interview questions are presented in Table 2.


Principal component analysis and correlation coefficients Reliability statistics showed internal consistency (â = 0.72). There were two components with eigenvalues.1, account- ing together for 67.3% of variance observed (Table 3). The highest scores for component 1 were associated with positive attitudes towards snow leopards, which we refer to as Snow Leopard Positive (â = 0.70). The highest scores for compo- nent 2 were associated with negative attitudes, which we refer to as Snow Leopard Negative (â = 0.53). Agreement was significantly higher for positive than for negative statements (P,0.001). The mean scores for each attitude statement and per cent of agreement, neutrality and dis- agreement were evaluated and correlation coefficients for predictor variable relationships resulted in the removal of four variables (Table 4).


Attitude correlates The model with the lowest quasi- likelihood score for Snow Leopard Positive included number


of animals lost (−0.125, 95%CI −0.221–−0.028,P = 0.012) and years of education (0.077, 95%CI 0.18–0.135,P = 0.011). The model with the lowest quasi-likelihood score for Snow Leopard Negative included number of animals lost (−0.154,


Oryx, 2021, 55(5), 783–790 © The Author(s), 2020. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605319001315


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