Abstract
Understanding animal movement behaviour is key to furthering our knowledge on intra- and inter-specific competition, group cohesion, energy expenditure, habitat use, the spread of zoonotic diseases or species management. We used a radial basis function surface approximation subject to minimum description length constraint to uncover the state-space dynamical systems from time series data. This approximation allowed us to infer structure from a mathematical model of the movement behaviour of sheep and red deer, and the effect of density, thermal stress and vegetation type. Animal movement was recorded using GPS collars deployed in sheep and deer grazing a large experimental plot in winter and summer. Information on the thermal stress to which animals were exposed was estimated using the power consumption of mechanical heated models and meteorological records of a network of stations in the plot. Thermal stress was higher in deer than in sheep, with less differences between species in summer. Deer travelled more distance than sheep, and both species travelled more in summer than in winter; deer travel distance showed less seasonal differences than sheep. Animal movement was better predicted in deer than in sheep and in winter than in summer; both species showed a swarming behaviour in group cohesion, stronger in deer. At shorter separation distances swarming repulsion was stronger between species than within species. At longer separation distances inter-specific attraction was weaker than intra-specific; there was a positive density-dependent effect on swarming, and stronger in deer than in sheep. There was not clear evidence which species attracted or repelled the other; attraction between deer at long separation distances was stronger when the model accounted for thermal stress, but in general the dynamic movement behaviour was hardly affected by the thermal stress. Vegetation type affected intra-species interactions but had little effect on inter-species interactions. Our modelling approach is useful in interpreting animal interactions, in order to unravel complex cooperative or competitive behaviours, and to the best of our knowledge is the first modelling attempt to make predictions of multi-species animal movement under different habitat mosaics and abiotic environmental conditions.
Highlights
There was not clear evidence which species attracted or repelled the other; attraction between deer at long separation distances was stronger when the model accounted for thermal stress, but in general the dynamic movement behaviour was hardly affected by the thermal stress
As we were interested in how vegetation type and thermal stress influenced species interaction, in addition to the vector v(si(t)), we considered additional information on these factors to predict the future positions of individuals
Our results indicate that (a) thermal stress was higher in deer than in sheep, with less differences between species in summer; (b) deer travelled more distance than sheep, and both species travelled more in summer than in winter; deer travel distance showed less seasonal differences than sheep; (c) animal movement was better predicted in deer than in sheep and in winter than in summer for both species; (d) both species showed a swarming behaviour, but this was stronger in deer; at shorter separation distances swarming repulsion was stronger between species doi:10.1371/journal.pone.0142707.g008
Summary
In developed countries the drastic reduction in extensive livestock farming and abandonment of traditional management practices has affected the condition of many habitats, some of which are hot-spots of biodiversity [4], and has been the cause of the rapid increase in population density and spatial distribution of some wild ungulate species [5]. This has had a negative effect on the rural economy [6,7], and altered the competitive interactions between wild species [8,9,10]. Animal movement is intrinsically stochastic and often affected by observation error, which make dynamic models of movement a major challenge for behavioural ecology [22]
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