Biology subjects

Gupte, P. R.

Publications and source records attributed to Gupte, P. R..

3 recordsLinked to original sources

bioRxiv2022

Novel pathogen introduction rapidly alters evolved movement strategies, restructuring animal societies

Animal sociality emerges from individual decisions on how to balance the costs and benefits of being sociable. Movement strategies incorporating social information -- the presence and status of neighbours -- can modulate spatial associations, helping animals avoid infection while benefiting from indirect information about their environment. When a novel pathogen is introduced into a population, it should increase the costs of sociality, selecting against gregariousness. Yet current thinking about novel pathogen introductions into wildlife neglects hosts potential evolutionary responses. We built an individual-based model that captures essential features of the repeated introduction, and subsequent transmission of an infectious pathogen among social hosts. Examining movements in a foraging context, widely shared by many species, we show how introducing a novel pathogen to a population provokes a rapid evolutionary transition to a dynamic social distancing movement strategy. This evolutionary shift triggers a disease-dominated ecological cascade of increased individual movement, decreased resource harvesting, and fewer social encounters. Pathogen-risk adapted individuals form less clustered social networks than their pathogen-risk naive ancestors, which reduces the spread of disease. The mix of post-introduction social movement strategies is influenced by the usefulness of social information and disease cost. Our work demonstrates that evolutionary adaptation to pathogen introductions and re-introductions can be very rapid, comparable to ecological timescales. Our general modelling framework shows why evolutionary dynamics should be considered in movement-disease models, and offers initial predictions for the eco-evolutionary consequences of wildlife pathogen spillover scenarios.

ecology↗

bioRxiv2021

The joint evolution of movement and competition strategies

Competition typically takes place in a spatial context, but eco-evolutionary models rarely address the joint evolution of movement and competition strategies. Here we investigate a spatially explicit producer-scrounger model where consumers can either forage on a heterogeneous resource landscape or steal resource items from conspecifics (kleptoparasitism). We consider three scenarios: (1) a population of foragers in the absence of kleptoparasites; (2) a population of consumers that are either specialized on foraging or on kleptoparasitism; and (3) a population of individuals that can fine-tune their behavior by switching between foraging and kleptoparasitism depending on local conditions. By means of individual-based simulations, we study the joint evolution of movement and competition strategies, and we investigate the implications on the resource landscape and the distribution of consumers over this landscape. In all scenarios and for all parameters considered, movement and competition strategies evolved rapidly and consistently across replicate simulations. The evolved movement and resource exploitation patterns differ considerably across the three scenarios. For example, foragers are attracted by conspecifics in scenario (1), while they are repelled by conspecifics in scenario (2). Generally the movement strategies of kleptoparasites differ markedly from those of foragers, but even within each class of consumers polymorphisms emerge, corresponding to pronounced differences in movement patterns. In all scenarios, the distribution of consumers over resources differs substantially from ideal free predictions. We show that this is related to the intrinsic difficulty of moving effectively on a depleted landscape with few reliable cues for movement. Our study emphasises the advantages of a mechanistic approach when studying competition in a spatial context, and suggests how evolutionary modelling can be integrated with current work in animal movement ecology.

evolutionary biology↗

bioRxiv2020

A Guide to Pre-Processing High-Throughput Animal Tracking Data

O_LIModern, high-throughput animal tracking studies collect increasingly large volumes of data at very fine temporal scales. At these scales, location error can exceed the animals step size, leading to mis-estimation of key movement metrics such as speed. Cleaning the data to reduce location errors prior to analyses is one of the main ways movement ecologists deal with noisy data, and has the advantage of being more scalable to massive datasets than more complex methods. Though data cleaning is widely recommended, and ecologists routinely consider cleaned data to be the ground-truth, inclusive uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is still rather scarce. C_LIO_LIA pipeline for cleaning massive high-throughput datasets must balance ease of use and computationally efficient signal vs. noise screening, in which location errors are rejected without discarding 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, a robust pre-processing pipeline must be automated. C_LIO_LIIn this article we provide guidance on building pipelines for pre-processing high-throughput animal tracking data in order to prepare it for subsequent analysis. Our recommended pipeline, consisting of removing outliers, smoothing the filtered result, and thinning it to a uniform sampling interval, is applicable to many massive tracking datasets. We apply this pipeline to simulated movement data with location errors, and also show a case study of how large volumes of cleaned data can be transformed into biologically meaningful residence patches, for quick biological inference on animal space use. We use calibration data to illustrate how pre-processing improves its quality, and to verify that the residence patch synthesis accurately captures animal space use. Finally, turning to tracking data from Egyptian fruit bats (Rousettus aegyptiacus), we demonstrate the pre-processing pipeline and residence patch method in a fully worked out example. C_LIO_LITo 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. The atlastools function is easy to use for beginners, while providing a template for further development. The use of common pre-processing steps that are simple yet robust promotes standardised methods in the field of movement ecology and leads to better inferences from data. C_LI

ecology↗