Optimizing Sensor Placement with Greedy Algorithms: A Case Study in Wildlife Camera Trapping for Spatial Capture-Recapture Population Estimation

Abstract

Estimating wildlife populations is central to conservation planning, yet designing sensor deployments that produce reliable data for such estimates remains challenging. Spatial capture-recapture (SCR) models, widely used to estimate animal population sizes, are highly sensitive to sensor layout, where poor placement can substantially increase uncertainty in population estimates. We present a novel approach that formulates sensor placement for SCR as a scenario-based optimization problem under real-world resource constraints. Using collections of simulated animal capture histories spanning ecologically plausible parameter ranges, candidate sensor placements are evaluated via closed-form, SCR-derived design criteria linked to the precision of population estimates and optimized using both genetic algorithms and a greedy search strategy. We demonstrate our approach through an example American marten camera trapping study in British Columbia's South Chilcotin Mountains, achieving lower relative standard error and bias in population estimates than single-scenario and grid-based baselines. Our method was co-developed with conservation practitioners and is currently being used in real-world monitoring programs. This framework offers a general approach for designing wildlife surveys that support reliable population estimation across a range of realistic ecological scenarios.