Attitudes towards the Sri Lankan leopard 531


TABLE 1 Per cent of respondents who strongly agreed and strongly disagreed to eight statements relating to attitudes towards leopards in the Palatupana (n = 61) and Maskeliya (n = 52) regions, scored on a 5-point Likert scale. For complete results see Supplementary Tables 1 and 2.


Palatupana Statement


The nature & wildlife of Sri Lanka are a national treasure & should be conserved


I respect leopards for the economic value they bring to the country through wildlife tourism


My livelihood is more important than current leopard populations


It does not matter if a leopard kills a few of my cattle; they are wild animals trying to survive


At this farm we cannot tolerate leopards killing any cattle at any time I would be happier if there were fewer leopards where I live & raise my cattle I do not want to kill leopards, but if they kill my cattle I might have to


I would like to communicate & work together with scientists, government staff & organizations to find a solution that works for everyone


Strongly agree


68.9 50.8 91.8


26.2 68.9


65.6 72.1 52.5


Strongly disagree


0.0 3.3 0.0


31.2 0.0


11.5 8.2 3.3


Maskeliya


Strongly agree


13.5 9.6


57.7 0.0


75.0 11.5 15.4 86.5


Strongly disagree


0.0 0.0 0.0


76.9 1.9


9.6 3.8 0.0


emergent themes relevant in this context (Corbin & Strauss, 2008). Respondents were free to introduce additional topics they perceived as relevant. Wedid not ask questions regard- ing illegal activities such as killing of leopards, which could elicit distrust in respondents who may fear penalty; how- ever, if they volunteered such information it was noted. Interviews were transcribed and translated shortly after


speaking with each respondent. We examined translated transcripts collaboratively with local field assistants shortly after each interview, to ensure subtext and nuances were captured accurately. We focused our analysis on husbandry techniques and barriers to cattle rearing faced by respon- dents, following an inductive approach (Charmaz, 2006), to identify common themes across respondents.


Data analysis


We analysed all data using R 3.5.1 (R Core Team, 2018). We characterized attitudes towards leopards from the survey re- sults using χ² statistics and Fisher’s exact tests. We used ex- ploratory factor analysis to group survey item responses and attitude statement responses into a manageable number of variables, and to avoid over-parametrizing our models. Exploratory factor analysis identifies collinear variables and extracts factors by grouping strongly associated vari- ables together (DiStefano et al., 2009). For example, the variables ‘age’, ‘number of dependants’,and ‘length of time rearing cattle’ were grouped into a single factor, described as ‘socio-demographics’, which captures the relative influ- ence of each comprising variable, for use in subsequent modelling. We then created a weighted composite attitude score


for each respondent by multiplying the responses to each attitude statement (Table 1) by the associated factor load- ing (Supplementary Material 2). This does not mask the


individual effect of each question but weights each question separately. The resulting composite attitude score is con- tinuous and on a relative scale, interpretable in comparison with other respondents within the sample. Cronbach’s coef- ficient α was used to assess the reliability of this score, with standard requirements for internal consistency of α.0.70 for non-clinical studies (Bland & Altman, 1997). The result- ing α value in Palatupana was 0.76 and in Maskeliya 0.65, with the latter value still being considered acceptable, par- ticularly given the small sample size (van Griethuijsen et al., 2015) and the Likert-type nature of the survey ques- tions (Gadermann et al., 2012). We used generalized linear models to examine the rela-


tionships between the composite attitude score (response variable) and determinants of attitudes (predictor variables; Table 2). Continuous variables were standardized to amean of zero and a standard deviation of one prior to regression modelling, to allow for direct comparison of effect sizes. We ran generalized linear models with a gamma distribution and log link because of the continuous response variable and right-skew of the data (Ng & Cribbie, 2017). We ranked the performance of competing models that


explained variation in attitudes towards leopards using the Akaike information criterion, corrected for small sample size (AICc; Burnham & Anderson, 2002). We assessed the appropriateness of the top-ranked models using residual diagnostics in the DHARMa package in R (Hartig, 2019) and used the adjusted proportion of deviance explained as ameasure ofmodel fit (Crawley, 2007).Weran separate glo- bal models for each study site, including first-order additive effects of the full set of predictor variables (Table 2). We compared these global models with a set of candidate mod- els representing all possible first-order additive combina- tions of the variables.We ranked candidate models by their AICc score and considered all models with ΔAICc#2


Oryx, 2022, 56(4), 528–536 © The Author(s), 2021. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605321000247


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80  |  Page 81  |  Page 82  |  Page 83  |  Page 84  |  Page 85  |  Page 86  |  Page 87  |  Page 88  |  Page 89  |  Page 90  |  Page 91  |  Page 92  |  Page 93  |  Page 94  |  Page 95  |  Page 96  |  Page 97  |  Page 98  |  Page 99  |  Page 100  |  Page 101  |  Page 102  |  Page 103  |  Page 104  |  Page 105  |  Page 106  |  Page 107  |  Page 108  |  Page 109  |  Page 110  |  Page 111  |  Page 112  |  Page 113  |  Page 114  |  Page 115  |  Page 116  |  Page 117  |  Page 118  |  Page 119  |  Page 120  |  Page 121  |  Page 122  |  Page 123  |  Page 124  |  Page 125  |  Page 126  |  Page 127  |  Page 128  |  Page 129  |  Page 130  |  Page 131  |  Page 132  |  Page 133  |  Page 134  |  Page 135  |  Page 136  |  Page 137  |  Page 138  |  Page 139  |  Page 140  |  Page 141  |  Page 142  |  Page 143  |  Page 144  |  Page 145  |  Page 146  |  Page 147  |  Page 148  |  Page 149  |  Page 150  |  Page 151  |  Page 152  |  Page 153  |  Page 154  |  Page 155  |  Page 156  |  Page 157  |  Page 158  |  Page 159  |  Page 160  |  Page 161  |  Page 162  |  Page 163  |  Page 164