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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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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. |
format | Online Article Text |
id | pubmed-9828790 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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