Skip to main content

page search

Library Incorporating animal spatial memory in step selection functions

Incorporating animal spatial memory in step selection functions

Incorporating animal spatial memory in step selection functions

Resource information

Date of publication
December 2016
Resource Language
ISBN / Resource ID

Memory is among the most important and neglected forces that shapes animal movement patterns. Research on the movement–memory interface is crucial to understand how animals use spatial learning to navigate across space because memory‐based navigation is directly linked to animals' space use and home range behaviour; however, because memory cannot be measured directly, it is difficult to account for. Here, we incorporated spatial memory into step selection functions (SSF) to understand how resource selection and spatial memory affect space use of feral hogs (Sus scrofa). We used Biased Random Bridge kernel estimates linked to residence time as a surrogate for memory and tested four conceptually different dynamic maps of spatial memory. We applied this memory‐based SSF to a data set of hog relocations to evaluate the importance of land cover type, time of day and spatial memory on the animals' space use. Our approach has shown how the incorporation of spatial memory into animal movement models can improve estimates of habitat selection. Memory‐based SSF provided a feasible way to gain insight into how animals use spatial learning to guide their movement decisions. We found that while hogs selected forested areas and water bodies and avoided grasslands during the day (primarily at noon), they had a strong tendency to select previously visited areas, mainly those held in recent memory. Beyond actively updating their memory with recent experiences, hogs were able to discriminate among spatial memories encoded at different circadian phases of their activity. Even though hogs are thought to have long memory retention, they likely relied on recent experiences because the local food resources are quickly depleted and slowly renewed, yielding an uncertain spatial distribution of resources.

Share on RLBI navigator

Authors and Publishers

Author(s), editor(s), contributor(s)

Oliveira‐Santos, Luiz Gustavo R.
Forester, James D.
Piovezan, Ubiratan
Tomas, Walfrido M.
Fernandez, Fernando A. S.
Fryxell, John

Data Provider