Estimating animal density without individual recognition using information derivable exclusively from camera traps

Abstract

Efficient and reliable methods for estimating animal density are essential to wildlife conservation and management. Camera trapping is an increasingly popular tool in this area of wildlife research, with further potential arising from technological improvements, such as video‐recording functions that allow for behavioural observation of animals. This information may be useful in the estimation of animal density, even without individual recognition. Although several models applicable to species lacking individual markings (i.e. unmarked populations) have been developed, a methodology incorporating behavioural information from videos has not yet been established. We developed a likelihood‐based model: the random encounter and staying time (REST) model. It is an extension of the random encounter model by Rowcliffe et al. (J Appl Ecol 45:1228, 2008). The REST model describes the relationship among staying time, trapping rate, and density, which is estimable using a frequentist or Bayesian approach. We tested the reliability and feasibility of the REST model using Monte Carlo simulations. We also applied the approach in the African rainforest and compared the results with those of a line‐transect survey. The simulations showed that the REST model provided unbiased estimates of animal density. Even when animal movement speeds varied among individuals, and when animals travelled in pairs, the model provided unbiased density estimates. However, the REST model was vulnerable to unsynchronized activity patterns among individuals. Moreover, it is necessary to use a camera model with a fast and reliable infrared sensor and to set the camera trap's parameters appropriately (i.e. video length, delay period). The field survey showed that the staying time of two ungulate species in the African rainforest exhibited good fit with a temporal parametric distribution, and the REST model provided density estimates consistent with those of a line‐transect survey. Synthesis and applications. The random encounter and staying time model provides better efficiency and higher feasibility than the random encounter model in estimating animal density without individual recognition. Careful application of the random encounter and staying time model provides the potential to estimate density of many ground‐dwelling vertebrates lacking individually recognizable markings, and thus should be an effective method for population monitoring.

Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras

“…For SCR models, distances between sampling stations greater than 2 σ may lead to bias in estimates (Sun, Fuller, & Royle, ). Random encounter or distance methods that do not rely on spatial autocorrelation in detections may be useful alternatives if sufficient camera spacing cannot be achieved (Howe et al., ; Nakashima et al., ). Including all data, the mean distance between sampling stations was 2,500 m, while the mean value of σ was 1,146 m as estimated by the most‐supported SC model (Table ).…”

Hierarchical mark‐recapture distance sampling to estimate moose abundance

“…Unmanned aerial systems (UAS, colloquially known as drones) are finding increasing use in wildlife survey work (Linchant et al ), and WDFW has used them to document calving status of radio‐marked cow moose (unpublished data), but current regulations in the United States restrict their use to short distances, limiting their utility for large‐scale surveys. Using ground‐based, remotely activated cameras (Burton et al ) may avoid many of the detection problems we faced, and theory allowing estimation of unmarked populations is an active area of research (Chauvenet et al , Howe et al , Moeller , Nakashima et al ). Similarly, non‐invasive, spatially explicit mark‐recapture approaches (Effords and Fewster ), using DNA from hair or scat to identify individuals (e.g., Morehouse and Boyce ) represents an attractive option.…”

Density‐dependent space use affects interpretation of camera trap detection rates

“…These results support previous calls for caution in the use of relative abundance indices from CT sampling (Burton et al, ; Harmsen et al, ; Jennelle et al, ; Sollmann, Mohamed, et al, ). While more statistically sophisticated alternatives are available for estimating density of unmarked populations from camera traps (e.g., Chandler & Royle, ; Howe, Buckland, Despres‐Einspenner, & Kuhl, ; Moeller, Lukacs, & Horne, ; Nakashima, Fukasawa, & Samejima, ; Rowcliffe, Field, Turvey, & Carbone, ), these require careful planning of study design and entail other assumptions that are largely untested (cf Johnson, ). Such methods may require direct measurement, or a priori knowledge, of species movement characteristics, or be similarly susceptible to density‐dependent movement behaviur (e.g., Efford, Dawson, Jhala, & Qureshi, ; Rowcliffe, Jansen, Kays, Kranstauber, & Carbone, ).…”