Human–elephant interactions in Gabon 263
every hour, using the following steps: (1) imported the geo- graphical locations of the two elephants during April 2017– April 2019 (representing 27,228 recorded locations) to ArcMap 10.3 (Esri, Redlands, USA); (2) computed the home ranges of the two elephants, and used the villages within these ranges as focal villages; (3) drewa 500-m buffer around each of the focal villages (the distance within which almost all plantations occurred), using the houses visible from satellite imagery to define the village extent; and (4) intersected the geographical locations with the village polygons (including the 500-m buffer), providing a filtered data set of elephant presence, a hybrid metric representing a combination of the frequency and duration of elephant approaches to villages (e.g. three locations could represent either three separate approaches or one approach lasting 3 h). We also extracted the time of day and month and identified the nearest village to each presence location. To estimate the home ranges of both elephants, we cre-
ated a 100% minimum convex polygon of all of their loca- tion points. To identify the area where they spent 50%of their time, we created a 50% kernel density estimate (Seaman & Powell, 1996; Mohaymany et al., 2013; Bonnier et al., 2019) using the adehabitat package (Calenge, 2006) in R 4.2.1 (R Core Team, 2022).
Perceptions surveys
We identified seven villages that were visited by one or both of the elephants during the study period and were accessible by the survey team. In July 2019, we administered a semi- structured standardized survey (Hill et al., 2002; Treves et al., 2006; Fairet, 2012)to 101 households (one adult from each) across each of the seven villages to determine: (1) what crops each farmer grows and when; (2) when farm- ers perceive crop use by elephants to be high; (3) which crops farmers perceive elephants to prefer; (4) what farmers currently do to reduce crop damage; and (5) what farmers perceive as being principal drivers of human–elephant con- flict. Our survey contained 53 questions collecting both qualitative and quantitative data of respondent perceptions (Supplementary Material 1). We first conducted a pilot study in the village of Mbes (18 respondents), and sub- sequently adjusted the survey as necessary by editing questions to ensure clarity. We included the pilot data in the data analysis. We administered the surveys using a random
procedure in all study villages. We first divided the village into two parts separated by the national road (north and south) and flipped a coin to determine where to begin the survey. We then flipped a coin again to determine whether to begin the survey at the eastern or western end of the vil- lage. After surveying 25% of households in each of the four sections (e.g. north-west), we moved to the opposite section, and we repeated this process for the remaining two sections.
The data from this survey include information on the plantations, their distance from the village, the type of crop, the period of planting, the time of harvest and the crops preferred by elephants (in order of preference). Other questions included whether the presence of elephants was a problem, the level of impact of the problem, the causes of the problem, the financial valuation of losses, the distance from the village to the crops where elephants foraged and any mitigation strategies used. We categorized and codified the survey transcripts, col-
lapsing similar response categories into broader ones. For example, ‘loggers’, ‘deforestation because of all the noise [from machinery] in the forest’ and ‘they followthe food be- cause loggers have cut everything [fruit-bearing trees]’ were all grouped under the category ‘logging’.
Data analysis
To characterize the daily and monthly trends in elephant visitation events, we used the activity package (Rowcliffe et al., 2014)in R. In both instances we used the number of elephant visitation events as the response variable and month of the year or time of day as explanatory variables. We also characterized temporal patterns in crop availability and perceived patterns in elephant visitation from the sur- vey data using the same technique as described above. For this, we used the number of respondents reporting elephant visitation, crop planting and crop harvest as the response variables and month of the year as the explanatory variable. In all instances we converted time of day or month of the year into radians and then used the default settings of the fitact() function to estimate a kernel density distribution for each response term. We used correlation coefficients to explore how the
empirical elephant visitation data and perceptions surveys were linked. In each instance we used monthly elephant vis- itation data or the perceptions of survey respondents report- ing elephant visits each month as the response variable. The significance of the correlation coefficient was deter- mined using the F-statistic from linear models in R, for which we deemed P,0.05 as strong evidence of a link be- tween actual/perceived visits and crop availability.
Results
Elephant movements and patterns of village visitation There was considerable difference between the ranging be- haviours of the two focal elephants: Amelia had a 100%min- imum convex polygon of 148 km2 and a 50% kernel density estimate of 25 km2, whereas Nzamba had a 100%minimum convex polygon of 742 km2 and a 50% kernel density esti- mate of 113 km2 (Fig. 1). Amelia approached within 500 m
Oryx, 2024, 58(2), 261–268 © The Author(s), 2023. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605323000704
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
