Data for: Nonrandom foraging and resource distributions

Nonrandom foraging can cause animals to aggregate in resource-dense areas, increasing host density, contact rates, and pathogen transmission, but when should nonrandom foraging and resource distributions also have density-independent effects? Here, we used a factorial experiment with constant resource and host densities to quantify host contact rates across seven resource distributions. We also used an agent-based model to compare pathogen transmission when host movement was based on random foraging, optimal foraging, or something between those states. Nonrandom foraging strongly depressed contact rates and transmission relative to the classic random movement assumptions used in most epidemiological models. Given nonrandom foraging in the ABM and experiment, contact rates and transmission increased with resource aggregation and average distance to resource patches due to increased host movement in search of resources. Overall, we describe three density-independent mechanisms by which host behavior and resource distributions alter contact rate functions and pathogen transmission.

Methods
To understand how resource distributions influenced host contacts, we performed an 8x7 factorial microcosm experiment that varied snail density (2,4,6,8,10,12,14, or 16 snails) and periphyton resource distributions (ranging from uniformly distributed to completely clustered). Periphyton was grown on tiles and arranging these tiles along with empty tiles allowed for the creation of different resource distributions in the microcosm. In each of the microcosm experiments, snails were uniquely colored and visually observed for 45 minutes. During that 45 minutes, all snail contacts, durations of those contacts, number of patches visited, and durations of those visits were recorded. Each treatment was replicated twice and all treatments were videoed to be able to check observations. An agent-based model (ABM), described in the attached Metadata file, was built based on the microcosm experiments and used to examine how host foraging behavior and resource distribution influence host contact rates, and thus, disease dynamics. Using Bayesian methods, we fit Holling type II functions to the ABM and experimental contact data. All data files are described in the metadata file and R scripts used for analysis and the NetLogo files for the ABM used in this study can be found on GitHub and Zenodo.