Abstract
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.
Original language | English |
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Pages (from-to) | 2753-2775 |
Number of pages | 23 |
Journal | Ecology Letters |
Volume | 25 |
Issue number | 12 |
Early online date | 20 Oct 2022 |
DOIs | |
Publication status | Published - 24 Nov 2022 |
Bibliographical note
Funding Information:MB is supported by NE/T003502/1 Natural Environment Research Council (NERC) grant. CC is supported by grants NE/T006579/1 and NE/T003502/1 from NERC, and RGS\R2\192033 from The Royal Society. T.E.G. is supported by a Royal Society University Research Fellowship grant UF160357, a Turing Fellowship from The Alan Turing Institute under the EPSRC grant EP/N510129/1 and BrisEngBio, a UKRI-funded Engineering Biology Research Centre under grant BB/W013959/1. T.T.H. acknowledges funding from Independent Research Fund Denmark Grant 8021-00423B. T.J. was supported by grant NE/S01537X/1 from UK NERC Independent Research Fellowship.
Funding Information:
MB is supported by NE/T003502/1 Natural Environment Research Council (NERC) grant. CC is supported by grants NE/T006579/1 and NE/T003502/1 from NERC, and RGS\R2\192033 from The Royal Society. T.E.G. is supported by a Royal Society University Research Fellowship grant UF160357, a Turing Fellowship from The Alan Turing Institute under the EPSRC grant EP/N510129/1 and BrisEngBio, a UKRI‐funded Engineering Biology Research Centre under grant BB/W013959/1. T.T.H. acknowledges funding from Independent Research Fund Denmark Grant 8021‐00423B. T.J. was supported by grant NE/S01537X/1 from UK NERC Independent Research Fellowship.
Publisher Copyright:
© 2022 The Authors. Ecology Letters published by John Wiley & Sons Ltd.
Research Groups and Themes
- Bristol BioDesign Institute
Keywords
- synthetic biology
- community ecology
- computer vision
- high-resolution monitoring
- deep learning
- remote sensing