Deriving spatially explicit direct and indirect interaction networks from animal movement data

Abstract

Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems ["GPS"]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous-time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily

Correlated host movements can reshape spatio-temporal disease dynamics: modeling the contributions of space use to transmission risk using movement data

“…As for the simulations, we used CTMM and AKDE to estimate individual UDs (Calabrese et al, 2016). We used the CTMMs to interpolate the positions to regular 10-minute intervals, to fill in missed positions, and to get the same timestamps across individuals (Yang et al, 2023b). Assuming a threshold contact distance of d = 10m, we divided the landscape in a grid of 10×10m cells.…”

“…Recent developments at the interface of movement and disease ecology leverage high-resolution animal tracking data to gain insight into contact among individuals and disease transmission (Richardson and Gorochowski, 2015; Wilber et al, 2022; Yang et al, 2023b). For example, movement-driven spatio-temporal infection risk (MoveSTIR) builds dynamic contact networks from movement data to estimate individual risk of infection across space and time (Wilber et al, 2022).…”

“…MoveSTIR provides a theoretical foundation to translate contacts into the epidemiological currency of FOI. These studies have highlighted the importance of individual heterogeneity and temporal scale for epidemiology, particularly how indirect contact—individuals at the same place at different times—can significantly reshape contact and transmission networks (Richardson and Gorochowski, 2015; Yang et al, 2023b). Current approaches are nonetheless based on occurrence, rather than range, distributions (in the terminology of Alston et al, 2022) – meaning they only consider where animals were and not where they potentially could be.…”

“…As some pathogens can persist in the environment for months or years (e.g. anthrax, chronic wasting disease–CWD), ignoring these processes could severely underestimate transmission risk (Wilber et al, 2022; Yang et al, 2023b; Richardson and Gorochowski, 2015).…”

Individual-level patterns of resource selection do not predict hotspots of contact

“…The location of where direct contacts occurred for each contact pair, i.e., the pair of animals that have contact, was determined from spatial overlay between GPS location data. Several methods have been developed to estimate spatial-explicit contacts based on telemetry data, including Noonan et al [ 28 ], Long et al [ 20 ], Yang et al [ 43 ]. Here we expand on these methods, implementing the continuous-time movement model (CTMM)-contact to estimate missing contacts [ 40 , 43 ].…”