A guide to pre‐processing high‐throughput animal tracking data
| Autor: | Ran Nathan, Emmanuel Lourie, Allert I. Bijleveld, Christine E. Beardsworth, Orr Spiegel, Pratik Rajan Gupte, Sivan Toledo |
|---|---|
| Přispěvatelé: | Weissing group |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
business.industry
Computer science Movement Big data Volume (computing) Inference Reproducibility of Results Usability computer.software_genre Tracking (particle physics) Pipeline (software) Field (computer science) Pipeline transport Set (abstract data type) Face (geometry) Scalability Animals Animal Science and Zoology Data mining business Cluster analysis Throughput (business) computer Ecology Evolution Behavior and Systematics |
| Zdroj: | Journal of Animal Ecology, 91(2), 287-307. Wiley |
| ISSN: | 1365-2656 |
| Popis: | Modern, high-throughput animal tracking studies collect increasingly large volumes of data at very fine temporal scales. At these scales, location error can exceed the animal’s 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.A 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.In 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.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. 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. |
| Databáze: | OpenAIRE |
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