Enhancing Wildlife Density Estimation: A New Two-Parameter 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 novel two-parameter detection function 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 in our computational analysis. This structure enables the model to capture a broader spectrum of detection patterns, especially in cases where classical models fall short. The proposed method is evaluated through extensive simulation studies and applied to real ecological survey data. The results show that the new model consistently yields improved fit and more accurate estimates of animal density, offering ecologists a practical and robust alternative for use in diverse field conditions