Enhancing wildlife density estimation: a new generalized weighted exponential detection function for line transect sampling

Accurate estimation of wildlife density is vital for effective ecological monitoring, conservation, and management. Line transect sampling, a central technique in distance sampling, relies on selecting an appropriate detection function to model the probability of detecting individuals as a function of their distance from the transect line. In this study, we propose a new parametric detection function, known as Generalized Weighted Exponential (GWE) that extends the flexibility of traditional models such as the half-normal and exponential, while retaining interpretability and computational tractability. Notably, one of the parameters is assumed to take a known integer value, allowing us to explore a range of detection curve shapes by varying this parameter across different settings. This structure enables the model to capture a broader spectrum of detection patterns. Unlike multi-parameter models such as the weighted Weibull, the proposed model reduces reliance on numerical optimization and admits simple closed-form estimation under the method of moments, improving stability and computational efficiency. The proposed estimators are evaluated through extensive simulation studies and applied to real ecological survey data. The results show that the new model generally provides improved fit and more accurate estimates of animal density, offering ecologists a practical and robust alternative for use in diverse field conditions.