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

Understanding animal movement behavior is essential for conservation and elucidating various ecological processes. In particular, assessing habitat selection 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 and ecosystem service quantifications due to its ease of implementation and interpretability. Despite its popularity, however, iSSA faces a critical issue since it can lead to an underestimation of the travel distance per unit time, potentially biasing estimates of step length. This is primarily due to the assumption of linear interpolation between consecutive observed points, which fails to account for the unobserved locations and the actual, non-linear trajectories taken by the animal. In this paper, we proposed a novel method to improve the estimation of travel distance in iSSA, inspired by multiple imputation, which is a statistical method for missing data. We conducted a simulation study to evaluate 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. In simulation studies across various scenarios, MiSSA estimated the step length more accurately than iSSA. Our 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 with 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.