Assessing repeatability of spatial trajectories

Abstract

Abstract Repeatability involves the assessment of the agreement among repeated measurements from the same cluster of subjects, and this concept has been widely used in different scientific fields when data is structured in clusters. In the context of spatial trajectories, a degree of repeatability implies that individual trips can be distinguished from those of other individuals. Repeatability is usually assessed by the intraclass correlation coefficient (ICC), which is defined as the proportion of the total variance accounted for by among‐subject variability. However, where data are spatial trajectories the common approach to estimate the ICC does not apply because data involves sets of ordered spatial locations rather than single values. In this work, a novel approach based on spatial distances is proposed to estimate the ICC to assess the repeatability of spatial trajectories. The methodology is illustrated with a real case example of the flight trajectories of 36 Audouin's gulls (Ichthyaetus audouinii) moving through a heterogeneous landscape over a period of 18 days. Additionally, simulations were used to evaluate the performance of the approach under various scenarios. I demonstrate that ICC can be estimated on complex, spatially ordered data such as spatial trajectories, when using the appropriate spatial distance.

Keywords

• animal movement
• cluster bootstrap
• Hausdorff distance
• intraclass correlation coefficient
• repeatability
• spatial trajectories