| Abstract |
Incorporating memory (i.e., some notion of familiarity or experience with the landscape) into models of animal movement is a rising challenge in the field of movement ecology. The recent proliferation of new methods offers new opportunities to understand how memory influences movement. However, there are no clear guidelines for practitioners wishing to parameterize the effects of memory on moving animals. We review approaches for incorporating memory into step-selection analyses (SSAs), a frequently used movement modeling framework. Memory-informed SSAs can be constructed by including spatial-temporal covariates (or maps) that define some aspect of familiarity (e.g., whether, how often, or how long ago the animal visited different spatial locations) derived from long-term telemetry data. We demonstrate how various familiarity covariates can be included in SSAs using a series of coded examples in which we fit models to wildlife tracking data from a wide range of taxa. We discuss how these different approaches can be used to address questions related to whether and how animals use information from past experiences to inform their future movements. We also highlight challenges and decisions that the user must make when applying these methods to their tracking data. By reviewing different approaches and providing code templates for their implementation, we hope to inspire practitioners to investigate further the importance of memory in animal movements using wildlife tracking data. |
| Authors |
Dongmin Kim  , Peter R. Thompson , David W. Wolfson , Jerod A. Merkle  , Luiz Gustavo Rodrigues Oliveira‐Santos , James D. Forester , Tal Avgar , Mark A. Lewis , John Fieberg
|
| Journal Info |
BioMed Central | Movement Ecology , vol: 12
, iss: 1
|
| Publication Date |
11/18/2024 |
| ISSN |
2051-3933 |
Type |
review |
| Open Access |
gold
|
| DOI |
https://doi.org/10.1186/s40462-024-00510-9 |
| Keywords |
Animal ecology (Score: 0.7957016) , Behavioural sciences (Score: 0.47699028)
|
