Vargas Soto, J. S. | Search bioRxiv

bioRxiv2024

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

Despite decades of epidemiological theory making relatively simple assumptions about host movements, it is increasingly clear that non-random movements drastically affect disease transmission. To better predict transmission risk, theory needs to simultaneously account for how the environment affects host space use and how social dynamics affect correlation in space use. We develop new theory that decomposes the relative contributions of fine-scale space use and correlated movements to spatio-temporal transmission risk. Using analytical results, simulations, and empirical movement data, we show that even weak correlations can increase transmission risk by orders of magnitude compared to independent movement. Accounting for correlation is especially critical for pathogens with direct transmission or short environmental persistence. Our theory provides clear expectations for what has been observed empirically but largely ignored in disease models--movement correlation can reshape epidemiological landscapes, creating transmission hotspots whose magnitude and location are not necessarily predictable from spatial overlap alone.

ecology↗

bioRxiv2019

Estimating animal density in three dimensions using capture-frequency data from remote detectors

O_LIRemote detectors are being used increasingly often to study aquatic and aerial species, for which movement is significantly different from terrestrial species. While terrestrial camera-trapping studies have shown that capture frequency, along with the species movement speed and detector specifications can be used to estimate absolute densities, the approach has not yet been adapted to cases where movement occurs in three dimensions. Frameworks based on animal movement patterns allow estimating population density from camera-trapping data when animals are not individually distinguishable. C_LIO_LIHere we adapt one such framework to three-dimensional movement to characterize the relationship between population density, animal speed, characteristics of a remote sensors detection zone, and detection frequency. The derivation involves defining the detection zone mathematically and calculating the mean area of the profile it presents to approaching individuals. C_LIO_LIWe developed two variants of the model - one assuming random movement of all individuals, and one allowing for different probabilities for each approach direction (e.g. that animals more often swim/fly horizontally than vertically). We used computer simulations to evaluate model performance for a wide range of animal and detector densities. Simulations show that in ideal conditions the method approximates true density well, and that estimates become increasingly accurate using more detectors, or sampling for longer. Moreover, the method is robust to invalidation of assumptions, accuracy is decreased only in extreme cases where all detectors are facing the same way. C_LIO_LIWe provide equations for estimating population density from detection frequency and outline how to estimate the necessary parameters. We discuss how environmental variables and species-specific characteristics affect parameter estimates and how to account for these differences in density estimations. C_LIO_LIOur method can be applied to common remote detection methods (cameras and acoustic detectors), which are currently being used to study a diversity of species and environments. Therefore, our work may significantly expand the number and diversity of species for which density can be estimated. C_LI

ecology↗