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Towards the fully automated monitoring of ecological communities

High‐resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real‐time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components—for example, individual behaviours and traits...

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Autores principales: Besson, Marc, Alison, Jamie, Bjerge, Kim, Gorochowski, Thomas E., Høye, Toke T., Jucker, Tommaso, Mann, Hjalte M. R., Clements, Christopher F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9828790/
https://www.ncbi.nlm.nih.gov/pubmed/36264848
http://dx.doi.org/10.1111/ele.14123
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author Besson, Marc
Alison, Jamie
Bjerge, Kim
Gorochowski, Thomas E.
Høye, Toke T.
Jucker, Tommaso
Mann, Hjalte M. R.
Clements, Christopher F.
author_facet Besson, Marc
Alison, Jamie
Bjerge, Kim
Gorochowski, Thomas E.
Høye, Toke T.
Jucker, Tommaso
Mann, Hjalte M. R.
Clements, Christopher F.
author_sort Besson, Marc
collection PubMed
description High‐resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real‐time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components—for example, individual behaviours and traits, and species abundance and distribution—is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high‐throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high‐resolution, multidimensional and standardised data across complex ecologies.
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spelling pubmed-98287902023-01-10 Towards the fully automated monitoring of ecological communities Besson, Marc Alison, Jamie Bjerge, Kim Gorochowski, Thomas E. Høye, Toke T. Jucker, Tommaso Mann, Hjalte M. R. Clements, Christopher F. Ecol Lett Synthesis High‐resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real‐time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components—for example, individual behaviours and traits, and species abundance and distribution—is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high‐throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high‐resolution, multidimensional and standardised data across complex ecologies. John Wiley and Sons Inc. 2022-10-20 2022-12 /pmc/articles/PMC9828790/ /pubmed/36264848 http://dx.doi.org/10.1111/ele.14123 Text en © 2022 The Authors. Ecology Letters published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Synthesis
Besson, Marc
Alison, Jamie
Bjerge, Kim
Gorochowski, Thomas E.
Høye, Toke T.
Jucker, Tommaso
Mann, Hjalte M. R.
Clements, Christopher F.
Towards the fully automated monitoring of ecological communities
title Towards the fully automated monitoring of ecological communities
title_full Towards the fully automated monitoring of ecological communities
title_fullStr Towards the fully automated monitoring of ecological communities
title_full_unstemmed Towards the fully automated monitoring of ecological communities
title_short Towards the fully automated monitoring of ecological communities
title_sort towards the fully automated monitoring of ecological communities
topic Synthesis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9828790/
https://www.ncbi.nlm.nih.gov/pubmed/36264848
http://dx.doi.org/10.1111/ele.14123
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