DOI: 10.5061/dryad.ns1rn8q20
title: Data from: Elephant pathway use in a human-dominated landscape

description: Habitat loss and fragmentation are one of the biggest threats facing
 wildlife today. Understanding the role of wildlife pathways in connecting
 resource areas is key to maintain landscape connectivity, reduce the
 impacts of habitat loss and help address human-wildlife conflict. In this
 study, we used sign surveys and camera trapping to understand the fine
 scale movement of elephants moving between a protected area and
 agricultural zone in the Masai Mara, Kenya. We used Generalised Linear
 Models to determine factors driving high frequency of pathway use by
 elephants. Our results showed strong seasonal trends in pathway use, with
 peaks coinciding with the dry season. However, no correlations between
 rainfall and pathway use were found. Temporal patterns of pathway use
 indicate that elephants use risk avoidance strategies by moving between
 the two areas at times of low human disturbance. Spatial analysis revealed
 that the most frequently used pathways were closer to farms, saltlicks and
 forest and those that had a higher percentage of forest cover. Our models
 also showed a positive relationship between pathway use and the number of
 elephant crop raiding incidents, highlighting that pathways can play a
 role in human-elephant conflict. As habitat loss continues, pathways may
 become more important for linking resources. However, they are also likely
 to facilitate movement into farmland. The results from this study provide
 an opportunity for planned management activities to ensure connectivity
 and to mitigated conflict.
description: We identified active pathways along the escarpment with the
 assistance of local rangers and farmers (Figure 2). We assumed pathways
 were in use if the path was devoid of vegetation (Blake and Inkamba-Nkulu,
 2004), marked with elephant dung or footprints and showed signs of
 elephant browsing on the bordering vegetation (Von Gerhardt et al., 2014).
 Pathways that did not show any of these signs were not included in this
 study. We then mapped each pathway using a Garmin Etrek30 Global
 Positioning System (GPS). The GPS track was taken from the bottom of the
 escarpment on the border of the Masai Mara to the top of the escarpment.
 The end of the pathway was determined by the point at which the pathway
 widened and became open habitat. Habitat type was also recorded on each
 pathway using a classification system from Kindt et al., (2011). As each
 pathway went through a number of different habitats, we used a GPS to
 record the co-ordinate at which there was a change in habitat type. To
 determine seasonal pathway use, we conducted bi-weekly elephant dung
 surveys on each pathway from September 2014 to August 2015. During these
 surveys, on each pathway we counted dung piles along two predefined
 transects (one going down the pathway and one going down the pathway) to
 ensure we covered the pathway. Each dung pile represents a single
 individual. Dung was removed after each count to avoid
 recounting. To determine temporal patterns of pathway
 use and elephant group type using the pathways, we placed 32 heat and
 motion camera traps (Bushnell Trophy Cam HD 2013) on 14 pathways during
 two sampling periods: September 2014 – October 2014 and February 2015 –
 August 2015 (Figure 2). We were unable to place cameras on all the
 pathways due to limited camera availability and the unsuitability of some
 pathways for camera trap placement; i.e. some pathways were too wide or
 too open to place camera traps. To ensure elephants were captured on the
 14 chosen pathways, we placed cameras on the narrowest part of the pathway
 or sections where we knew elephants would cross (e.g. by small water
 bodies). To obtain suitable photographs of elephants for group type
 identification, the camera traps were mounted on trees or erected posts at
 varying heights between 1 – 3 metres depending on the pathway slope. The
 height of the camera &gt; 1 metre was to ensure the best capture of
 the head, pinnae, and tusks of elephants (Smit et al., 2017). Each pathway
 had at least one camera facing up the escarpment and one facing down the
 escarpment to capture the frontal area of the elephant to aid
 identification of the elephants. If pathways split, two cameras were
 placed on each sub-pathway to ensure elephants were captured. Cameras were
 set at a 5-second trigger interval with three colour images taken per
 trigger event. At night time the camera used infrared and had the same
 trigger speed. We downloaded the images from memory cards and changed the
 camera batteries every three weeks. We then created a
 database of the camera trap images and recorded the site (pathway name),
 the position of the camera trap (up or down), the type of photo (e.g.
 wildlife, people or false trigger), the wildlife species in the photo and
 the date and time the image was taken. Specifically, for the elephant
 images, we recorded additional information including: (1) the direction in
 which the elephant was travelling; (2) the number of elephants in the
 image and; (3) the group type. Group type was classified as (i) bull
 groups, (ii) female-led family groups or (ii) family + bull groups. Group
 type was determined by sexing elephants based on their genitalia (if
 visible), body size, shape of their head and length and configuration of
 tusk size (Moss, 1996). During the period in which an elephant group
 crossed a camera, depending on the size of the group, many images were
 captured. Thus, to avoid double counting elephant groups, we developed a
 Python script to select images from our database that were taken more than
 15 minutes apart. This time marker was determined after reviewing all the
 images and calculating the average time between each independent group.
 Group type was then determined by reviewing the series of images within
 the 15-minute time frame. To understand the
 relationship between pathway use and crop raiding, we collected data on
 elephant incursions into agricultural fields in the Trans Mara District
 from September 2014 to September 2015 (see Tiller et al., 2021).
 Analysing patterns of pathway usage All the
 data analysis was carried out using the statistical software R (R Core
 Team, 2016). We assessed the seasonal patterns of pathway use by totalling
 the number of dung piles counted across all pathways for each month and
 averaging rainfall readings from weather stations across the Trans Mara
 and Masai Mara National Reserve. We then carried out a Spearman’s Rank
 Correlation test with dung counts and rainfall. Due to the potentially
 delayed effects of rainfall on the ripening of crops and greening of
 vegetation, we also ran correlations between dung counts and rainfall from
 the previous month. To look at the temporal patterns of
 elephant groups travelling up the pathways into the Trans Mara and down
 the pathways into the Masai Mara, we sorted the camera trap data into time
 and direction. Images were grouped into time stamps of 24 one-hour
 intervals so that we had a frequency distribution of each elephants
 travelling up and elephants travelling down the pathways.
 Analysing the factors determining pathway use
 To determine whether high pathway activity predicted high
 human-elephant conflict, we fitted a linear model based on the number of
 crop raiding incidents per month as our response variable and the total
 number of dung piles across all pathways as our predictor
 variable. We used descriptive statistics to summarise
 the number of elephant detections on each pathway. To investigate the
 factors driving high elephant pathway use, we used four predictor
 variables: distance of pathway to nearest farm, continuous forest outside
 the pathway, saltlicks and the percentage of forest cover along pathways.
 To measure the percentage of forest on each pathway, we calculated the
 length of the pathway and then used the GPS co-ordinates from the habitat
 survey to work out the proportion of the pathway that we had classified as
 forest. We mapped farmland and forest cover and locations of saltlicks by
 on-screen digitising of 5 m CNES/Astrium satellite imagery from 2015 using
 QGIS (QGIS Development Team, 2015). We then calculated the distance from
 the end of each pathway to the nearest farm, continuous forest in the
 Trans Mara and saltlicks using the Generate Near Table function in ArcMap
 10.4. We then carried out exploratory analysis
 including graphical inspection, correlation matrices and bivariate tests.
 Variance inflation factors (VIFs) and Pearson correlation coefficient
 (<em>r</em>) were used to test for collinearity amongst the
 predictor variables and we found no evidence of collinearity between our
 predictor variables (VIF &lt; 5; <em>r</em> &lt; 0.7;
 Dormann et al., 2013). Exploratory modelling identified persistent over
 dispersion between the response and predictor variables. Thus, to overcome
 this problem, we fitted a GLM with a negative binomial error structure and
 all predictor variables were scaled to have a mean of zero and a standard
 deviation of 0.5 (Gelman, 2008). For model selection, we used a model
 averaging approach (Burnham and Anderson, 2002) using the MuMin package
 (Barton, 2016), which examines average parameter estimates, standard
 errors and confidence intervals of the predictor variables. We constructed
 a global model containing all predictor variables and applied the Dredge
 function to produce a model set containing all possible model
 permutations. Models were then ranked based on their AICc (Akaike
 Information Criterion) score, where the lowest score signifies the most
 parsimonious model, and calculated the delta AICc (the difference in AICc
 between each model and the best preforming model). We then averaged
 parameter estimates for models with a delta AICc &lt;2, as this
 suggests a similar level of support among models (Burnham and Anderson,
 2002). The relative importance (RI) of explanatory variables was then
 calculated by summing the Akaike weights across all models in which the
 variable was present, resulting in an estimate of probability that the
 variable of interest features in the best model. Finally, we applied a
 goodness of fit test to the model set to determine if the models fitted
 the data well.
description: # Elephant pathway use in a human-dominated landscape
 [https://doi.org/10.5061/dryad.ns1rn8q20](https://doi.org/10.5061/dryad.ns1rn8q20) Data includes the final clean Excel sheets containing all the variable data that was imported into R for analysis. This data was used for Spearman’s Rank Correlation tests, a linear model and descriptive statistics. ## Description of the data and file structure The files 'SURVEY A_results' and 'SURVEY B_results' are Excel spreadsheets with a summary of the camera trap images from the pathways. Each row is one camera trap image with the processed data of the date, time, photo label, elephant group type, number of elephants and whether the elephants were traveling up or down the pathway. The file 'Data_Analysis_1' is an Excel spreadsheet that has all the data used in the papers models. This dataset has the different pathway use variables that were tested. For example, distance to farmland, slope etc.  The file 'conflict' is an Excel spreadsheet with a summary of the number of human-elephant conflict incidents each month during the study period and the number of dung piles recorded on the pathways. The file 'rain' is an Excel spreadsheet with a summary of monthly average rainfall and number of elephant dung piles recorded. ## Code/Software All R Code used in this study can be found in the file 'Rcode'.