Data from: A camera-based method for estimating absolute density in animals displaying home range behaviour
Cite this dataset
Campos-Candela, Andrea; Palmer, Miquel; Balle, Salvador; Alós, Josep (2018). Data from: A camera-based method for estimating absolute density in animals displaying home range behaviour [Dataset]. Dryad. https://doi.org/10.5061/dryad.m605m
1.The measurement of animal density may take advantage of recent technological achievements in wildlife video recording. Fostering the theoretical links between the patterns depicted by cameras and absolute density is required to exploit this potential. 2.We explore the applicability of the Hutchinson-Waser's postulate (i.e., when animal density is stationary at a given temporal and spatial scale, the absolute density is given by the average number of animals counted per frame), which is a counter-intuitive statement for most ecologists and managers who are concerned with counting the same individual more than once. We aimed to reconcile such skepticism for animals displaying home range behaviour. 3.The specific objectives of this paper are to generalize the Hutchinson-Waser's postulate for animals displaying home range behaviour and to propose a Bayesian implementation to estimate density from counts per frame using video cameras. 4.Accuracy and precision of the method was evaluated by means of computer simulation experiments. Specifically, six animal archetypes displaying well-contrasted movement features were considered. The simulation results demonstrate that density could be accurately estimated after an affordable sampling effort (i.e., number of cameras and deployment time) for a great number of animals across taxa. 5.The proposed method may complement other conventional methods for estimating animal density. The major advantages are that identifying an animal at the individual level and precise knowledge on how animals move are not needed, and that density can be estimated in a single survey. The method can accommodate conventional camera trapping data. The major limitations are related to some implicit assumptions of the underlying model: the home range centres should be homogeneously distributed, the detection probability within the area surveyed by the camera should be known, and animals should move independently to one another. Further improvements for circumventing these limitations are discussed.