Multiple imputation step-selection analysis: Improving estimation accuracy of travel distance accounting for route uncertainty

O_LIUnderstanding animal movement behavior is essential for conservation and elucidating various ecological processes. In particular, assessing habitat suitability is a central theme in movement ecology, traditionally evaluated by estimating travel distances per unit time across diverse environmental conditions based on tracking data. Integrated step selection analysis (iSSA : Avgar et al., 2016) has been most widely applied in conservation studies to estimate animal movements that provide ecosystem services due to its ease of implementation and interpretability. C_LIO_LIBut despite its popularity, iSSA faces a critical issue--it can lead to underestimation of cumulative travel distances because it measures step lengths as conventional straight-line displacements between locations. This is primarily due to the application of linear interpolation between consecutive observed points, which fails to account for unobserved occurrences and non-linear trajectories taken by the individual. C_LIO_LIIn this paper, we propose a novel method to improve the estimation of travel distance in iSSA inspired by multiple imputation, a statistical method for missing data. We tested the extent to which our proposed method, Multiple Imputation Step Selection Analysis (MiSSA), improves the accuracy of step-length estimation (parameters of gamma distribution) compared to conventional iSSA using simulations across various scenarios. We compared the estimation bias and error under landscapes with different spatial autocorrelations (Simulation 1), as well as under various combinations of movement and habitat selection parameters (Simulation 2). In all simulations, MiSSA successfully reduced the estimation bias of the step length and substantially improved the estimation accuracy. In addition, although the coverage rates were comparable, we achieved a substantial reduction in the width of the confidence intervals. C_LIO_LIOur study demonstrates that incorporating missing data statistics into the iSSA framework improves the accuracy of travel distance estimations, which serve as the foundation for evaluating habitat selection. MiSSA maintains the core advantages of iSSA while enabling more accurate estimation of travel distances, even for low-resolution data where movement between sampling intervals is non-linear. We anticipate its broad application across various disciplines, with a primary focus on conservation. C_LI