Innovations in movement and behavioural ecology from camera traps: Day range as model parameter
importance 3/10
ai assisted
Wildlife Ecology and Conservation wildlife ecology stage: research
Problem
Estimating animal day range from camera-trap data is biased when different movement behaviours are not accounted for.
Why it matters
Biased day range estimates can distort wildlife movement and behavioural ecology studies.
What the AI did
Describes a machine learning procedure to differentiate movement behaviours from camera-trap data and incorporates behaviours into day range estimation.
Results
not stated in source
Impact
Abstract claims a methodological step forward for camera-trap movement studies; no measured impact stated.
Record
| Venue | Methods in Ecology and Evolution |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Pablo Palencia, Javier Fernández López, Joaquín Vicente, Pelayo Acevedo |
| Citations | 43 (OpenAlex, at import time) |
| References | 56 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1111/2041-210x.13609 |
| Open access | closed |
| Keywords | camera traps, machine learning, day range, movement behaviour, behavioural ecology |
Importance rating
3
incremental / niche
Niche methodological advance: correcting bias in camera-trap day range estimates for wildlife movement and behavioural ecology studies.