J Appl Ecol. 2018;55:2055–2063. https://doi.org/10.1111/1365-2664.13117 First published: 06 February 2018
Abstract 1. Development of transportation corridors has accelerated globally, with infrastructure projects being implemented across remote ecosystems, particularly in the tropics. Such developments can have negative impacts on wildlife and their ecosystems. The importance of wildlife crossing structures to mitigate adverse effects of such features is widely recognized, but the siting of and investment in crossing structures is contentious. Data on animal movement provide valuable, highly specific information for such processes, but can present analytical challenges and remain underutilized in planning mitigation efforts. 2. We develop two algorithms based on Integer Linear Programming to prioritize crossing points based on frequency of use or breadth of coverage among tracked individuals. These scenarios represent metrics likely to guide the planning of crossing structures, where the former may relate to the objective of minimizing vehicle‐animal collisions and the latter on maintaining ecosystem connectivity. We exemplify the algorithms through application on a tracking dataset from over 150 African elephants living near the proposed Lamu Port‐South Sudan‐Ethiopia‐Transport corridor. We explore the influence of sampling bias on outcomes and discuss considerations to guide the application process. 3. Given the generally open, unfenced nature of this ecosystem, recorded movements occurred throughout the system and a third of the corridor length in the ecosystem was intersected by recorded elephant movements. The selection of crossing structure locations and their impacts on elephants varied whether we used a subsample of elephant representative of local population density or total sample of monitored individuals. The two algorithms also selected for different crossing structure locations. 4. Synthesis and applications. Our work shows some of the challenges of using Global Positioning System telemetry in deciding where to put crossing structures and demonstrates the need to identify the type of constraints in the system and desired crossing structure characteristics a priori. We recommend managers carefully evaluate the presence of potential biases in their data. High‐resolution data combined with objective prioritization methods allow reasoned planning actions, but are often lacking during critical infrastructure planning stages. Given the limited budget already allocated to mitigation measures in most proposed developments, the tools developed and applied here can facilitate effective spatial planning.