Towards the fully automated monitoring of ecological communities. (20th October 2022)

• Record Type: Journal Article
• Authors: Besson, Marc; Alison, Jamie; Bjerge, Kim; Gorochowski, Thomas E.; Høye, Toke T.; Jucker, Tommaso; Mann, Hjalte M. R.; Clements, Christopher F.
• Is Part Of: Ecology letters. Volume 25:Number 12(2022)
• Page Start: 2753
• Page End: 2775
• Publication Date: 2022-10-20
• Subjects: community ecology -- computer vision -- deep learning -- high‐resolution monitoring -- remote sensing; Ecology -- Periodicals; 577
• DOI: 10.1111/ele.14123
• Languages: English
• ISSNs: 1461-023X

Abstract

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. Abstract : Monitoring living organisms with high‐resolution and multidimensional is a complex and labour‐intensive task, yet it is fundamental to understand and predict the dynamics of ecological communities in an era of global change and biodiversity declines. Here, we review existing technologies for automated data recording and processing, and we present novel frameworks that combine these technologies into automated monitoring pipelines that detect, track, classify and count multiple species, and even record behavioural and morphological traits at resolutions which have previously been impossible to achieve. We illustrate a solution to one of the greatest challenges in ecology and conservation: the ability to rapidly generate high resolution, multidimensional and critically, standardised data across complex ecologies.