animal abundance, citizen science, deep learning, density estimation, home range, remote camera sampling, snapshot, wildlife monitoring
| INTRODUC TI ONEvaluating absolute population density is an extremely complex task that requires strengthening the links between theorists and empiricists. Both, Campos-Candela, Palmer, Balle, and Alós (2018) and Abolaffio, Forcadi, and Santini (2019), contributed to this challenge from the theoretical side; the first one by demonstrating a theoretical postulate to estimate absolute densities from camera counts; the second one by exploring the proposed method for a specific species (the moose, Alces alces). Note, however, that the undifferentiated use of model, method and simulation terms in Abolaffio et al. (2019), when addressing concerns exclusively about our simulation procedure or the applicability of the method for a particular case, may be misleading.Concerning the camera-based method, our key contribution is that, for the case of animals whose movement leads to a stationary spatial pattern, absolute animal density can be properly estimated from the average number of animals counted per frame whenever a number of assumptions are met. The underlying model to this method states that, for a given camera, the number of animals' counts per frame is given by a Poisson distribution with mean equals to the product of the true animal density, the detection area of the camera and the probability of detection. Essentially, our model is equivalent to determine the distribution of the space occupation in time, which is a question that has been largely discussed from the probabilistic perspective (Godrèche & Luck, 2001). The only strict condition for this model to apply is that animal density must be stationary within the scale of the sampled space and time. When focusing on moving animals, such a stationary property meets for animals displaying home range (HR) behaviour, which is a widespread movement type leading to the establishment of a bounded space-use area (Börger, Dalziel, & Fryxell, 2008;Burt, 1943). In these cases, the stationary condition should apply to the density of HR centres. The model was theoretically derived in Campos-Candela et al. (2018) but provided that it may be counter-intuitive, we also performed a number of simulations emulating a camera sampling program for demonstrating that animal density (i.e., number of HR centres per area unit) can be properly recovered by averaging the counts by frame.In our extensive simulation analysis, a number of simplifications were stated (we refer the readership to the original work in Campos-Candela et al. (2018) for further details) because the objective was to demonstrate the generality of the model performance. Accordingly, our reported simulation results should be interpreted as a general guidance and not as species-specific recommendations. Likewise, the work by Abolaffio et al. (2019) should be considered an improvedsimulation exercise that takes into account several species specificities but, even so, it may still leave ...