Cost surfaces are a crucial aspect of route optimization and least cost path (LCP) calculations and are used in awide range of disciplines including computer science, landscape ecology, and energy-infrastructure modeling. Linear features present akey weakness to traditional routing calculations along cost surfaces because they cannot identify whether moving from acell to its adjacent neighbors constitutes crossing alinear barrier (increased cost) or following acorridor (reduced cost). Following and avoiding linear features can drastically change predicted routes. We introduce an approach to address this adjacency issue using asearch kernel that identifies these critical barriers and corridors. We have built this approach into anew Java-based open-source software package– CostMAP (cost surface multi-layer aggregation program)– which calculates cost surfaces and cost networks using the search kernel. CostMAP allows users to input multiple GIS data layers and to set weights and rules for developing aweighted-cost network. We compare CostMAP performance with traditional cost surface approaches and show significant performance gains– both following corridors and avoiding barriers– by modeling the movement of alarge terrestrial animal– the Baird’s Tapir (Tapirus bairdii)– in amovement ecology framework and by modeling pipeline routing for carbon capture and storage (CCS).

Home range estimates have been a key geographic unit for understanding the link between animals and their habitat/resource choices since the term was first described by Burt (1943) and formally quantified by Mohr (1947)—who introduced minimum convex polygons (MCP) as a method to delineate individual home ranges. Numerous methods have subsequently been developed to estimate home ranges. However, depending on the method used, widely different estimations can be found with the same animal location dataset. With different home range delineations, inferences in a heterogenous landscape about animal resource and habitat preferences with different delineations can impact wildlife management. In this research, time-based home range methods that account for autocorrelation in animal movement were evaluated for accuracy in terms of area, shape, and location in response to sample size and common wildlife GPS-point patterns. These characteristics of home range estimation are important for inferring animal habitat and resource use. Despite the improved accuracy of time-based methods compared to traditional point-based methods like MCP, location was often inaccurate for all GPS-point patterns, as were shape and area for GPS-point patterns with perforations (common for areas with large physical barriers like mountains or lakes). These findings are important to wildlife managers using time-based home range methods for analysis.