A mechanistic home range model for optimal use of spatially distributed resources

2004 ◽  
Vol 177 (1-2) ◽  
pp. 209-232 ◽  
Author(s):  
Michael S. Mitchell ◽  
Roger A. Powell
2020 ◽  
Vol 60 (4) ◽  
pp. 1007-1023 ◽  
Author(s):  
Yuxiang Liu ◽  
Corbin D Jones ◽  
Lainy B Day ◽  
Kyle Summers ◽  
Sabrina S Burmeister

Synopsis The complexity of an animal’s interaction with its physical and/or social environment is thought to be associated with behavioral flexibility and cognitive phenotype, though we know little about this relationship in amphibians. We examined differences in cognitive phenotype in two species of frog with divergent natural histories. The green-and-black poison frog (Dendrobates auratus) is diurnal, displays enduring social interactions, and uses spatially distributed resources during parental care. Túngara frogs (Physalaemus=Engystomops pustulosus) are nocturnal, express only fleeting social interactions, and use ephemeral puddles to breed in a lek-type mating system. Comparing performance in identical discrimination tasks, we find that D. auratus made fewer errors when learning and displayed greater behavioral flexibility in reversal learning tasks than túngara frogs. Further, túngara frogs preferred to learn beacons that can be used in direct guidance whereas D. auratus preferred position cues that could be used to spatially orient relative to the goal. Behavioral flexibility and spatial cognition are associated with hippocampal function in mammals. Accordingly, we examined differential gene expression in the medial pallium, the amphibian homolog of the hippocampus. Our preliminary data indicate that genes related to learning and memory, synaptic plasticity, and neurogenesis were upregulated in D. auratus, while genes related to apoptosis were upregulated in túngara frogs, suggesting that these cellular processes could contribute to the differences in behavioral flexibility and spatial learning we observed between poison frogs and túngara frogs.


2021 ◽  
Vol 118 (15) ◽  
pp. e2014856118
Author(s):  
Nathan Ranc ◽  
Paul R. Moorcroft ◽  
Federico Ossi ◽  
Francesca Cagnacci

Many animals restrict their movements to a characteristic home range. This constrained pattern of space use is thought to result from the foraging benefits of memorizing the locations and quality of heterogeneously distributed resources. However, due to the confounding effects of sensory perception, the role of memory in home-range movement behavior lacks definitive evidence in the wild. Here, we analyze the foraging decisions of a large mammal during a field resource manipulation experiment designed to disentangle the effects of memory and perception. We parametrize a mechanistic model of spatial transitions using experimental data to quantify the cognitive processes underlying animal foraging behavior and to predict how individuals respond to resource heterogeneity in space and time. We demonstrate that roe deer (Capreolus capreolus) rely on memory, not perception, to track the spatiotemporal dynamics of resources within their home range. Roe deer foraging decisions were primarily based on recent experience (half-lives of 0.9 and 5.6 d for attribute and spatial memory, respectively), enabling them to adapt to sudden changes in resource availability. The proposed memory-based model was able to both quantify the cognitive processes underlying roe deer behavior and accurately predict how they shifted resource use during the experiment. Our study highlights the fact that animal foraging decisions are based on incomplete information on the locations of available resources, a factor that is critical to developing accurate predictions of animal spatial behavior but is typically not accounted for in analyses of animal movement in the wild.


Author(s):  
Sebastian Schmoll ◽  
Matthias Schubert

We show that the task of collecting stochastic, spatially distributed resources (Stochastic Resource Collection, SRC) may be considered as a Semi-Markov-Decision-Process. Our Deep-Q-Network (DQN) based approach uses a novel scalable and transferable artificial neural network architecture. The concrete use-case of the SRC is an officer (single agent) trying to maximize the amount of fined parking violations in his area. We evaluate our approach on a environment based on the real-world parking data of the city of Melbourne. In small, hence simple, settings with short distances between resources and few simultaneous violations, our approach is comparable to previous work. When the size of the network grows (and hence the amount of resources) our solution significantly outperforms preceding methods. Moreover, applying a trained agent to a non-overlapping new area outperforms existing approaches.


2020 ◽  
Author(s):  
Giorgio Fabbri ◽  
Silvia Faggian ◽  
Giuseppe Freni

Author(s):  
Nathan Ranc ◽  
Paul R. Moorcroft ◽  
Federico Ossi ◽  
Francesca Cagnacci

AbstractMany animals restrict their movements to a characteristic home range. This pattern of constrained space-use is thought to result from the foraging benefits of memorizing the locations and quality of heterogeneously distributed resources. However, due to the confounding effects of sensory perception, the role of memory in home range movement behavior lacks unequivocal evidence in the wild. Here, we analyze the foraging decisions of a large mammal during a field resource manipulation experiment designed to disentangle the effects of memory and perception. Using a cognitive movement model, we demonstrate that roe deer (Capreolus capreolus) rely on memory, not perception, to track the spatio-temporal dynamics of resources within their home range. Our findings show a memory-based spatial transition model parametrized with experimental data can successfully be used to quantify cognitive processes and to predict how animals respond to resource heterogeneity in space and time.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ketika Garg ◽  
Christopher T Kello

AbstractEfficient foraging depends on decisions that account for the costs and benefits of various activities like movement, perception, and planning. We conducted a virtual foraging experiment set in the foothills of the Himalayas to examine how time and energy are expended to forage efficiently, and how foraging changes when constrained to a home range. Two hundred players foraged the human-scale landscape with simulated energy expenditure in search of naturally distributed resources. Results showed that efficient foragers produced periods of locomotion interleaved with perception and planning that approached theoretical expectations for Lévy walks, regardless of the home-range constraint. Despite this constancy, efficient home-range foraging trajectories were less diffusive by virtue of restricting locomotive search and spending more time instead scanning the environment to plan movement and detect far-away resources. Altogether, results demonstrate that humans can forage efficiently by arranging and adjusting Lévy-distributed search activities in response to environmental and task constraints.


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