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Title: BoomBox: An Automated Behavioural Response (ABR) camera trap module for wildlife playback experiments
Camera traps (CTs) are a valuable tool in ecological research, amassing large quantities of information on the behaviour of diverse wildlife communities. CTs are predominantly used as passive data loggers to gather observational data for correlational analyses. Integrating CTs into experimental studies, however, can enable rigorous testing of key hypotheses in animal behaviour and conservation biology that are otherwise difficult or impossible to evaluate. We developed the 'BoomBox', an open-source Arduino-compatible board that attaches to commercially available CTs to form an Automated Behavioural Response (ABR) system. The modular unit connects directly to the CT’s passive infrared (PIR) motion sensor, playing audio files over external speakers when the sensor is triggered. This creates a remote playback system that captures animal responses to specific cues, combining the benefits of camera trapping (e.g. continuous monitoring in remote locations, lack of human observers, large data volume) with the power of experimental manipulations (e.g. controlled perturbations for strong mechanistic inference). Our system builds on previous ABR designs to provide a cheap (~100USD) and customizable field tool. We provide a practical guide detailing how to build and operate the BoomBox ABR system with suggestions for potential experimental designs that address a variety of questions in wildlife more » ecology. As proof-of-concept, we successfully field tested the BoomBox in two distinct field settings to study species interactions (predator–prey and predator–predator) and wildlife responses to conservation interventions. This new tool allows researchers to conduct a unique suite of manipulative experiments on free-living species in complex environments, enhancing the ability to identify mechanistic drivers of species' behaviours and interactions in natural systems. « less
Authors:
; ; ;
Editors:
Acvedo, Miguel
Award ID(s):
1656527 1810586
Publication Date:
NSF-PAR ID:
10314528
Journal Name:
Methods in Ecology and Evolution
ISSN:
2041-210X
Sponsoring Org:
National Science Foundation
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