Evaluation of the analysis of distance sampling data: a simulation study

Abstract

Distance sampling is used to estimate number of individuals in an area of interest. The idea is that with known distances to the observed individuals, one can model the probability of detection in relation to distance and thereby account for individuals that were not detected. Distances can be recorded either exactly or in discrete categories. In this study I validated the method using simulated distance sampling data for two hypothetical bird species and compared the estimated density values to the known true densities. Generally the true densities and numbers of individuals were very similar to (and always within the 95% confidence interval of) the parameter estimates from the analysis of the simulated data. The analyses were also robust to modifications of the data such as truncation and grouping of the distances into discrete categories. The confidence intervals increased, however, when using only two distance groups. Given that critical assumptions of the model can be met in the field situation, distance data can thus be used in a wide range of bird studies to calculate reliable density estimates.