Modelling large herbivore movement decisions: Beyond food availability as a predictor of ranging patterns


Boult VL, Sibly RM, Quaife T, Fishlock V, Moss C & Lee PC (2019) Modelling large herbivore movement decisions: Beyond food availability as a predictor of ranging patterns. African Journal of Ecology, 57 (1), pp. 10-19.

The ability of animals to adapt to their changing environment will depend in part on shifts in their ranging patterns, but when and why individuals choose to move requires detailed understanding of their decision-making processes. We develop a simple decision-making model accounting for resource availability in habitually used ranges. We suggest that disparities between model predictions and animal tracking data indicate additional factors influencing movement decisions, which may be identified given detailed system-specific knowledge. The model was evaluated using movement data from satellite-tracked elephants (Loxodonta africana) inhabiting the Amboseli Ecosystem in Kenya, moving from savannah areas with low quality but constant resource availability, to areas with temporally-constrained higher nutrient availability. Overall, the model fit the data well: there was a good correlation between predicted and observed locations for the combined data from all elephants, but variation between individuals in how well the model fits. For those elephants where model predictions were less successful, additional factors likely to affect movement decisions, reproduction, anthropogenic threats, memory and perception, are suggested. This protocol for building and testing decision-making models should contribute to success in attempts to preserve sufficient space for large herbivores in their increasingly human-dominated ecosystems.

Decision-making; Large herbivores; Movement ecology; NDVI; Optimal foraging; Remote sensing

African Journal of Ecology: Volume 57, Issue 1

FundersNatural Environment Research Council
Publication date31/03/2019
Publication date online17/09/2018
Date accepted by journal17/08/2018


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