DOI: 10.5061/dryad.dr7sqv9v9
title: Data from: Exploring movement decisions: can Bayesian movement-state models explain crop consumption behaviour in elephants (Loxodonta africana)?

description: 1. Animal movements towards goals or targets are based upon either
 maximization of resources or risk avoidance, and the way animals move can
 reveal information about their motivation for movement. 2. We use Bayesian
 movement models and hourly GPS-fixes to distinguish animal movements into
 movement states and analyse the influence of environmental variables on
 being in and switching to a state. Specifically, we apply our models to
 understand elephant movement decisions surrounding agricultural fields and
 crop consumption. As it is unclear what the role of habitat features are
 on this complex issue, we analyse whether elephants target agricultural
 crops for consumption, or simply pass through them in search of water. 3.
 Our Hidden-Markov models divide elephant movements into two states:
 exploratory movements that are fast and directional, and encamped
 movements that are slow and meandering. For each elephant, we ran 16
 models with each possible combination of habitat features (river, elephant
 corridor, agricultural field, trees), and repeated these analyses
 including interaction effects with both season and time of day. We used
 cross-validation to select the best performing model, and GLMMs to analyse
 the influence of habitat features on being in and switching to a state. 4.
 Our results show that in corridors, exploratory movements are dominant.
 Elephants mainly showed encamped movements at the river during the dry
 season, when temporary water sources have dried out and elephants rely on
 this permanent water source. In fields, males most often exhibited
 exploratory movements to and from the river, while females showed an
 increase in the frequency of encamped movements at night –when most crop
 consumption and movements through fields occur- and during the dry season.
 5. The predation-risk hypothesis could explain this behaviour, since
 foraging in fields might be less risky under the cover of darkness and
 during the dry season when farmers are absent from fields. This sexual
 segregation in elephant movement decisions highlights the importance of
 risk in movement patterns, while the increase in encamped movements in the
 dry season suggests the importance of agricultural timing. Taking this
 into account could increase efficiency of elephant crop consumption
 mitigation. 08-Jan-2020
description: <em>GPS
 location data</em> We collected GPS data
 from April 2014 to July 2016 for 6 male and 5 female elephants, each in
 different herds, collared with Vectronic GPS collars. The collars produced
 hourly fixes of the elephants’ locations, resulting in ca.19,000 fixes per
 elephant. We downloaded data using the Vectronic GPS PLUS X Collar
 Manager. When there were GPS reception issues resulting in missing data,
 we coded these as NA values. Each elephant included in the study produced
 data with less than 25% missing values. <i>Remote
 sensing data collection</i> To create habitat
 feature shape files, we used Landsat 8 band combinations (Data available
 from the U.S. Geological Survey) and the Semi-automatic Classification
 Plugin (Congedo, 2014) available in the open source Quantum GIS Geographic
 Information System program (Quantum GIS Development Team, 2016). We
 calculated overlaps between downloaded GPS coordinates and habitat
 features using the ‘NNJoin’ QGIS Plugin (Tveite, 2015), resulting in dummy
 variables for each of the covariates per hourly GPS fix. We created shape
 files for the Okavango river (‘river’), elephant corridors (determined by
 Songhurst et al., 2015), agricultural fields and tree agglomerations. For
 both of the river and field variables, we also calculated a buffer zone of
 each 300m, since research has shown that elephant movements change within
 this distance of these landscape features (Saj et al., 2001;
 Naughton-Treves & Treves, 2005; Chamaillé-Jammes et al., 2013). As
 some of the habitat features may overlap (e.g. corridor and field), an
 elephant can be in multiple habitats at once. We excluded other
 vegetation, such as grasses and herbs in the area, as they are highly
 dependent on rainfall and non-permanent. <i>Preparing
 the data </i> We combined the data
 from all elephants and converted the GPS coordinates into step sizes and
 turning angles using the adeHabitatLT package for R (Calenge et al. 2016;
 R Core Team, 2017). We discarded 11,731 fixes (of the total 213,301 fixes
 for all elephants) because these located the elephants to the same
 anomalous position, which was &gt;4000 km outside of their home
 ranges. We constructed a ‘season’ dummy variable considering the wet
 season to range from November-April and the dry season from May-October
 and a ‘time of day’ dummy with night occurring from 20.00-6.00, including
 dusk and dawn.