Distance sampling from curving transects in dense tropical forests

Abstract

AbstractLine transect distance sampling is a popular method for estimating the density of animals across the tropics. The method relies on accurate measurements of the distance between an animal and a transect to model detectability. In dense tropical forests, surveys can become time‐consuming, expensive, and dangerous when randomly placed straight line transects cross landscape features such as rivers, swamps, and dense vegetation. As a result, transects often curve as an observer deviates from an ideal, straight line. Here, we examine the practical and statistical impact of these curves. We use distance sampling simulations to assess bias in density estimates based on scenarios corresponding with different methods reliant on distinct field measurements. Methods that measure the nearest distance between the animal and the curving transect produce the most reliable density estimates. Biased estimates were obtained when using methods that ignore curves, measure the distance to an ideal line (as opposed to the curve), or inaccurately calculate the area covered in the survey. Notably, these biased estimates were most severe when observation distances were short, highlighting the importance of accurate measurements in tropical forests characterized by low visibility. Given these results and the importance of reliable density estimates to conservation efforts across the tropics, we propose a method that addresses survey design, simplifies data collection, and alleviates some of the practical field challenges associated with straight line transects. We also developed an R package (CurveTransect) based on this method with functions that ensure accurate measurements and reliable density estimates in tropical forests.