PS 48-134 - Edge-focused network-based approach: an improved density estimator for home range

Background/Question/Methods

One of the central questions in ecology and animal behaviour is to generate animals’ home range estimations that provide critical information of their movement patterns and habitat preferences. Kernel density estimates for the home range has been one of the most widely used estimates since the last few decades, despite its limitations. More recently a network-based kernel density (NKDE) approach has been proposed, which uses Delaunay triangulation for generating the network. Nonetheless, NKDE too has limitations and generates a discontinuous kernel density. In this study, we develop a new network-based method that emphasizes entirely on the edges, instead of the nodes in the network. We call this Edge-Focused Network-based Kernel Density Estimation (EFNKDE). In EFNKDE, unit weight is distributed uniformly along each edge of the network and Euclidean distance is used to compute the contribution of each segment of the edge to the kernel density at a given point.

Results/Conclusions

This is a network-based method that leads to a continuous and differentiable (smooth) kernel density. An analytical expression for the same is obtained for the Gaussian kernel. Using a subset of data points of (100 random points) of the Caribou population from Northern British Columbia, as an example, we ran the different methods across a range of bandwidths. We generated Delaunay triangulation on this dataset that had 100 nodes, 290 edges and 191 triangles. We show that EFNKDE has several advantages over the other established home range estimators, because of its analysis-friendly application that uses Euclidean distances from the segments of the edges, instead of taking the distances along with the network. Hence, EFNKDE is easy to apply, and one can work with minimal data that provides a smooth kernel density that highlights the network and does not overemphasize the data. We hope that this technique would be an improvement over the currently existing methods available for spatial kernel density estimates that can be applied to varied fields ranging from ecology and animal behaviour to the fields of economics and geography.