Novel pathogen introduction triggers rapid evolution in animal social movement strategies.

Animal sociality emerges from individual decisions on how to balance the costs and benefits of being sociable. Novel pathogens introduced into wildlife populations should increase the costs of sociality, selecting against gregariousness. Using an individual-based model that captures essential features of pathogen transmission among social hosts, we show how novel pathogen introduction provokes the rapid evolutionary emergence and coexistence of distinct social movement strategies. These strategies differ in how they trade the benefits of social information against the risk of infection. Overall, pathogen-risk-adapted populations move more and have fewer associations with other individuals than their pathogen-risk-naive ancestors, reducing disease spread. Host evolution to be less social can be sufficient to cause a pathogen to be eliminated from a population, which is followed by a rapid recovery in social tendency. Our conceptual model is broadly applicable to a wide range of potential host-pathogen introductions and offers initial predictions for the eco-evolutionary consequences of wildlife pathogen spillover scenarios and a template for the development of theory in the ecology and evolution of animals' movement decisions.

A guide to pre-processing high-throughput animal tracking data.

Modern, high-throughput animal tracking increasingly yields 'big data' at very fine temporal scales. At these scales, location error can exceed the animal's step size, leading to mis-estimation of behaviours inferred from movement. 'Cleaning' the data to reduce location errors is one of the main ways to deal with position uncertainty. Although data cleaning is widely recommended, inclusive, uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is relatively scarce. A pipeline for cleaning massive high-throughput datasets must balance ease of use and computationally efficiency, in which location errors are rejected while preserving valid animal movements. Another useful feature of a pre-processing pipeline is efficiently segmenting and clustering location data for statistical methods while also being scalable to large datasets and robust to imperfect sampling. Manual methods being prohibitively time-consuming, and to boost reproducibility, pre-processing pipelines must be automated. We provide guidance on building pipelines for pre-processing high-throughput animal tracking data to prepare it for subsequent analyses. We apply our proposed pipeline to simulated movement data with location errors, and also show how large volumes of cleaned data can be transformed into biologically meaningful 'residence patches', for exploratory inference on animal space use. We use tracking data from the Wadden Sea ATLAS system (WATLAS) to show how pre-processing improves its quality, and to verify the usefulness of the residence patch method. Finally, with tracks from Egyptian fruit bats Rousettus aegyptiacus, we demonstrate the pre-processing pipeline and residence patch method in a fully worked out example. To help with fast implementation of standardised methods, we developed the R package atlastools, which we also introduce here. Our pre-processing pipeline and atlastools can be used with any high-throughput animal movement data in which the high data-volume combined with knowledge of the tracked individuals' movement capacity can be used to reduce location errors. atlastools is easy to use for beginners while providing a template for further development. The common use of simple yet robust pre-processing steps promotes standardised methods in the field of movement ecology and leads to better inferences from data.