Ghostbusting - Reducing bias due to identification errors in spatial capture-recapture histories

1. Identifying individuals is key to estimating population sizes by spatial capture-recapture, but identification errors are sometimes made. The most common identification error is the failure to recognise a previously detected individual, thus creating a “ghost” (Johansson et al., 2020). This results in positively biased abundance estimates.

2. Ghosts typically manifest as single detection individuals (“singletons”) in the capture history. To deal with ghosts, we develop a spatial capture-recapture method conditioned on at least K detections. The standard spatial capture-recapture (SCR) model is the special case of K = 1. Ghosts can mostly be excluded by fitting a model with K = 2 (SCR-2).

3. We investigated the effect of “singleton” ghosts on the estimation of the model parameters by simulation. The SCR method increasingly over-estimated abundance with increasing percentage of ghosts, with positive bias even when only 10% of the detected individuals were ghosts, and bias between 43% and 71% when 30% were ghosts. Estimates from the SCR-2 method showed lower bias in the presence of ghosts, at the cost of a loss of precision. The mean squared error of the estimated abundance from the SCR-2 method was lower in all scenarios with ghosts under high encounter rates and for scenarios with 30% or more ghosts with low encounter rates. We also applied our method to capture histories from camera trap surveys of snow leopards (Panthera uncia) at 2 sites from Mongolia and find that the SCR method produced higher abundance estimates at both sites.

4. Capture histories are susceptible to errors when generated from passive detectors such as camera traps and genetic samples. The SCR-2 method can remove bias from ghost capture histories, at the cost of some loss in precision. We recommend using the SCR-2 method in cases when there may be more than 10% ghosts or surveys with a large number of single detection capture histories, except perhaps when the sample size is very low.