Towards the fully automated monitoring of ecological communities.

Marc Besson, Jamie Alison, Kim Bjerge, Thomas E Gorochowski, Toke T Høye, Tommaso Jucker, Hjalte M R Mann, Christopher F Clements
Author Information
  1. Marc Besson: School of Biological Sciences, University of Bristol, Bristol, UK. ORCID
  2. Jamie Alison: Department of Ecoscience, Aarhus University, Aarhus, Denmark. ORCID
  3. Kim Bjerge: Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark. ORCID
  4. Thomas E Gorochowski: School of Biological Sciences, University of Bristol, Bristol, UK. ORCID
  5. Toke T Høye: Department of Ecoscience, Aarhus University, Aarhus, Denmark. ORCID
  6. Tommaso Jucker: School of Biological Sciences, University of Bristol, Bristol, UK. ORCID
  7. Hjalte M R Mann: Department of Ecoscience, Aarhus University, Aarhus, Denmark. ORCID
  8. Christopher F Clements: School of Biological Sciences, University of Bristol, Bristol, UK. ORCID

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.

Keywords

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Grants

  1. 8021-00423B/Danmarks Frie Forskningsfond
  2. BB/W013959/1/Engineering and Physical Sciences Research Council
  3. EP/N510129/1/Engineering and Physical Sciences Research Council
  4. NE/T003502/1/Natural Environment Research Council
  5. NE/T006579/1/Natural Environment Research Council
  6. NE/S01537X/1/Natural Environment Research Council
  7. UF160357/Royal Society
  8. RGS\R2\192033/Royal Society

MeSH Term

Ecosystem
Artificial Intelligence
Biodiversity
Biota

Word Cloud

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