Towards the fully automated monitoring of ecological communities

Besson, Marc; Alison, Jamie ORCID: https://orcid.org/0000-0002-6787-6192; Bjerge, Kim; Gorochowski, Thomas E.; Høye, Toke T.; Jucker, Tommaso; Mann, Hjalte M.R.; Clements, Christopher F.. 2022 Towards the fully automated monitoring of ecological communities. Ecology Letters, 25 (12). 2753-2775. https://doi.org/10.1111/ele.14123

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Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1111/ele.14123
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-)
ISSN:	1461-023X
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	community ecology, computer vision, deep learning, high-resolution monitoring, remote sensing
NORA Subject Terms:	Ecology and Environment Computer Science
Date made live:	29 Jan 2024 15:07 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/536805

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