Exploring movement decisions: Can Bayesian movement‐state models explain crop consumption behaviour in elephants (Loxodonta africana)?

Abstract

Animal movements towards goals or targets are based upon either maximization of resource acquisition or risk avoidance, and the way animals move can reveal information about their motivation. We use hidden Markov models (HMMs) fitted in a Bayesian framework and hourly Global Positioning System fixes to distinguish animal movements into distinct states and analyse the influence of environmental variables on being in, and switching to, a particular state. Specifically, we apply our models to understand elephant movement decisions around agricultural fields, and crop consumption. As it is unclear what the role of habitat features are on this complex process, we analyse whether elephants target agricultural crops for consumption, or simply pass through them in search of water. Our HMMs separate 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 selected 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 model. 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 relied 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 behaviour during the dry season and at night—the times when most crop consumption and movements through fields occur. Adaptation to risk could explain this behaviour, since foraging in fields is likely less risky under the cover of darkness and during the dry season when farmers are absent. This sex segregation in elephant movement decisions highlights the importance of predation risk in shaping movement patterns, which can result in sex segregation in responses to mitigation methods. The increase in encamped movements in the dry season suggests the importance of agricultural timing, and shows the potential for early ploughing and early‐harvest crop types in order to reduce elephant crop consumption. Taking this into account could increase efficiency of elephant crop consumption mitigation.

African forest elephant movements depend on time scale and individual behavior

“…1 ). They have recently been used to elucidate spatial memory 44 , detect musth-related movement behavior 45 and explore crop raiding decisions in savannah elephants 30 . We used a gamma distribution to describe the step lengths, a von Mises distribution to describe turning angles, and the Viterbi algorithm to estimate the most likely sequence of movement states to have generated the observations.…”

“…Finally, ‘exploratory movements’ are long, directionally persistent movements to new locations. The relative amount of time individuals spend in exploratory movements versus resting/foraging (shorter movements with low directional persistence) is important for understanding individual foraging decisions, social interactions and responses to anthropogenic disturbances 29 , 30 . Characterizing these movement behaviors and the factors that influence them is important for effective conservation; for example, to ensure that reserves or corridors are large enough, properly configured, and allow for seasonal and/or anthropogenically-induced behavioral changes.…”

Are elephants attracted by deforested areas in miombo woodlands?

“…Indeed, fields are more likely to be visited at the end of the growing season when the quality of crops supersedes natural vegetation (Osborn, 2004). Elephant bulls have a greater tendency to raid crops than females (Branco et al, 2019;Vogel et al, 2020;Von Gerhardt et al, 2014) as shown by M1's clear preference for recently deforested patches (Figure 3). There is a notable individual variability related to crop-raiding, whereas M1 and F2 selection patterns are compatible with crop-raiding, F1 and F3 patterns are less clear as they do appear to select these patches but no clear seasonal pattern can be discerned (Figure 3).…”

Prospects for the Analysis and Reduction of Damaging Behaviour in Group-Housed Livestock, With Application to Pig Breeding

“…Parameter estimations can be performed by the (restricted) maximum likelihood method (Dedieu et al, 2014). However, Bayesian approaches are often preferred for complex models (Vogel et al, 2020). Outputs of the model that would be of interest in genetic studies could include the subject-specific probabilities of hidden states at each time point (these could be replaced by the most likely state for simplicity; Leos-Barajas and Michelot, 2018) and/or the subject-specific initial state and transition probabilities that depict the dynamic change of the animal's behaviour over time.…”

“…Capture-recapture analysis (CRA) was originally developed to study survival dynamics in wild animal populations (Pradel et al, 1997(Pradel et al, , 2005. Nowadays CRA has applications in many fields of research, including animal activity patterns (Langrock et al, 2012) and movements (Vogel et al, 2020). The principle of this method of survival analysis in wild species is to use data from several independent but overlapping samples to estimate a probability of survival despite the fact that there is missing information.…”