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ATfiltR: A solution for managing and filtering detections from passive acoustic telemetry data.
Dhellemmes, Félicie; Aspillaga, Eneko; Monk, Christopher T.
Afiliação
  • Dhellemmes F; Department of Fish Biology, Fisheries and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany.
  • Aspillaga E; Instituto Mediterráneo de Estudios Avanzados, IMEDEA (CSIC-UIB), Spain.
  • Monk CT; GEOMAR Helmholtz-Centre for Ocean Research, Kiel, Germany.
MethodsX ; 10: 102222, 2023.
Article em En | MEDLINE | ID: mdl-37251651
Acoustic telemetry is a popular and cost-efficient method for tracking the movements of animals in the aquatic ecosystem. But data acquired via acoustic telemetry often contains spurious detections that must be identified and excluded by researchers to ensure valid results. Such data management is difficult as the amount of data collected often surpasses the capabilities of simple spreadsheet applications. ATfiltR is an open-source package programmed in R that allows users to integrate all telemetry data collected into a single file, to conditionally attribute animal data and location data to detections and to filter spurious detections based on customizable rules. Such tool will likely be useful to new researchers in acoustic telemetry and enhance results reproducibility.•ATfiltR compiles telemetry files and identifies and stores all data that was collected outside of your study period (e.g. when your receivers were on land for servicing) elsewhere.•As spurious detections are unlikely to appear sequentially in the data, ATfiltR finds all detections that occurred only once (per receiver or in the whole array) within a user-designated time period and stores them elsewhere.•ATfiltR identifies detections that are impossible given the animals' swimming speeds and the receivers detection range and stores them elsewhere.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article