Memory Effects on Movement Behavior in Animal Foraging

Abstract

An individual's choices are shaped by its experience, a fundamental property of behavior important to understanding complex processes. Learning and memory are observed across many taxa and can drive behaviors, including foraging behavior. To explore the conditions under which memory provides an advantage, we present a continuous-space, continuous-time model of animal movement that incorporates learning and memory. Using simulation models, we evaluate the benefit memory provides across several types of landscapes with variable-quality resources and compare the memory model within a nested hierarchy of simpler models (behavioral switching and random walk). We find that memory almost always leads to improved foraging success, but that this effect is most marked in landscapes containing sparse, contiguous patches of high-value resources that regenerate relatively fast and are located in an otherwise devoid landscape. In these cases, there is a large payoff for finding a resource patch, due to size, value, or locational difficulty. While memory-informed search is difficult to differentiate from other factors using solely movement data, our results suggest that disproportionate spatial use of higher value areas, higher consumption rates, and consumption variability all point to memory influencing the movement direction of animals in certain ecosystems.

Fig 1. Sample generated landscapes for different combinations of patch concentration from patchy to smooth and patch size from small to large.

Color indicates resource quality from none (white) to low (light green) to high (dark green). Total resources in each landscape are the same.

Fig 2. Sample trajectories for three movement models (left to right) on two different landscapes (top to bottom).

Trajectories start at the center with color changing through time (from green to light blue to dark blue). For memory and kinesis, thin lines indicate searching and thick lines indicate feeding behavior. Resources are shown at their undepleted level at the beginning of the simulation. Memory is parameterized with best overall parameters, ϕ _L = 1e − 05, ϕ _S = 0.01, ψ _M = 2, γ _Z = 10.

Fig 3. Consumption for the three movement models across different landscape parameters patch concentration μ _Q and size γ _Q for medium regeneration rate β _R = 0.01.

Bars show mean consumption values across replicates of landscape parameters while lines show minimum and maximum. Memory is parameterized with best overall parameters, ϕ _L = 1e − 05, ϕ _S = 0.01, ψ _M = 2, γ _Z = 10. In the figure, M = memory, K = kinesis, R = random walk.

Fig 4. Time spent in areas of different resource quality across different landscape parameters patch concentration μ _Q and size γ _Q for medium regeneration rate β _R = 0.01 compared to the distribution of resources on the landscape.

White represents zero resources while shades of gray from light to dark show quartiles of increasing quality. The memory model is parameterized with best overall parameters, ϕ _L = 1e − 05, ϕ _S = 0.01, ψ _M = 2, γ _Z = 10. In the figure, M = memory, K = kinesis, R = random walk, L = landscape.

Fig 5. Time spent searching (as opposed to feeding) for memory and kinesis models across different landscape parameter values for patch concentration μ _Q and size γ _Q for medium regeneration rate β _R = 0.01 as a violin plot showing median values and kernel density plot.

The memory model is parameterized with best overall parameters, ϕ _L = 1e − 05, ϕ _S = 0.01, ψ _M = 2, γ _Z = 10. In the figure, M = memory, K = kinesis.