Innovations in movement and behavioural ecology from camera traps: Day range as model parameter

Abstract

Camera‐trapping methods have been used to monitor movement and behavioural ecology parameters of wildlife. However, when considering movement behaviours to estimate DR is mandatory to include in the formulation the speed ratio, otherwise DR results will be biased. For instance, some wildlife populations present movement patterns characteristic of each behaviour (e.g. foraging or displacement between habitat patches), and further research is needed to integrate the behaviours in the estimation of movement parameters. In this respect, the day range (average daily distance travelled by an individual, DR) is a model parameter that relies on movement and behaviour. This study aims to provide a step forward concerning the use of camera‐trapping in movement and behavioural ecology. We describe a machine learning procedure to differentiate movement behaviours from camera‐trap data, and revisit the approach to consider different behaviours in the estimation of DR. Second, working within a simulated framework we tested the performance of three approaches to estimate DR: DROB (i.e. estimating DR without behavioural identification), DRTB (i.e. estimating DR by identifying behaviours manually and weighting each behaviour on the basis of the encounter rate obtained) and DRRB (i.e. estimating DR based on the classification of movement behaviours by a machine learning procedure and the ratio between speeds). Finally, we evaluated these approaches for 24 wild mammal species with different behavioural and ecological traits. The machine learning procedure to differentiate behaviours showed high accuracy (mean = 0.97). The DROB approach generated accurate results in scenarios with a speed‐ratio (fast relative to slow behaviours) lower than 10, and for scenarios in which the animals spend most of the activity period on the slow behaviour. However, when considering movement behaviours to estimate DR is mandatory to include in the formulation the speed ratio, otherwise the DR results will be biased. The new approach, DRRB, generated accurate results in all the scenarios. The results obtained from real populations were consistent with the simulations. In conclusion, the integration of behaviours and speed‐ratio in camera‐trap studies makes it possible to obtain unbiased DR. Speed‐ratio should be considered so that fast behaviour is not overrepresented. The procedures described in this work extend the applicability of camera‐trap‐based approaches in both movement and behavioural ecology.

Assessing the camera trap methodologies used to estimate density of unmarked populations

“…Our application of the three methods explored here was free‐standing, in that it derived all the necessary parameters exclusively from the camera trap survey, without references to auxiliary data that is a habitual practice in previous studies (Caravaggi et al., 2016; Cusack et al., 2015; Manzo et al., 2012). In this respect, it should be noted that more accurate day range values are estimated from camera trap data, because telemetry usually underestimates this parameter (Palencia et al., 2021; Rowcliffe et al., 2012). Derive all the parameters from the camera trap data required additional effort in the field to identify reference objects at known distances from camera traps (Figure 1), and these time costs were similar across methods.…”

“…Secondly, we estimated activity level. Finally, we estimated day range by following the procedure described by Palencia et al (2021). Briefly, using "trappingmotion" R package (Palencia, 2021), we identified different movement behaviours on the basis of the speeds measured for the sequences.…”

“…distance travelled by an individual during a day). However, approaches for estimating day range exclusively from camera trap data were described (Palencia et al., 2021; Rowcliffe et al., 2016), which has significantly increased its applicability. Nakashima et al.…”

“…distance travelled by an individual during a day). However, approaches for estimating day range exclusively from camera trap data were described (Palencia et al, 2021;Rowcliffe et al, 2016), which has significantly increased its applicability. Nakashima et al (2018) described the REST model as an extension of REM, but we would like to highlight the mathematical equivalence between REST and CT-DS (Appendix S1).…”

Behavioral “bycatch” from camera trap surveys yields insights on prey responses to human‐mediated predation risk

“…Sampling coverage by CT surveys has grown rapidly (Chen et al, 2022 ), and our simple index of behavior was easy to calculate from data collected to estimate species distributions and abundances. We note that our results were not always consistent across the three indicators we explored (Figures S14 and S15 in Appendix S1 ) and we encourage further inquiry into the strengths and weaknesses of different ways to measure behavior from CT data—for example, using video recordings to estimate movement speed (Rowcliffe et al, 2016 ) and machine learning to automate behavioral classifications (Palencia et al, 2021 ). We recommend more evaluation of the role of group size in affecting animal behavior (e.g., shared vigilance; Laundré et al, 2001 ; Olson et al, 2015 ), particularly for species that exhibit herding behaviors like caribou, as group sizes were low in our study and thus assumed to have limited effect.…”