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Digital twins: dynamic model-data fusion for ecology

de Koning, Koen ; Broekhuijsen, Jeroen ; Kühn, Ingolf ; Ovaskainen, Otso ; Taubert, Franziska ; Endresen, Dag ; Schigel, Dmitry ; Grimm, Volker

Trends in ecology & evolution (Amsterdam), 2023-10, Vol.38 (10), p.916-926 [Periódico revisado por pares]

England: Elsevier Ltd

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  • Título:
    Digital twins: dynamic model-data fusion for ecology
  • Autor: de Koning, Koen ; Broekhuijsen, Jeroen ; Kühn, Ingolf ; Ovaskainen, Otso ; Taubert, Franziska ; Endresen, Dag ; Schigel, Dmitry ; Grimm, Volker
  • Assuntos: biodiversity conservation ; digital conservation ; digital twins ; evidence-based conservation ; model-data integration ; real-time monitoring
  • É parte de: Trends in ecology & evolution (Amsterdam), 2023-10, Vol.38 (10), p.916-926
  • Notas: ObjectType-Article-2
    SourceType-Scholarly Journals-1
    ObjectType-Feature-3
    content type line 23
    ObjectType-Review-1
    EC/HEU/https://doi.org/10.3030/101060954
  • Descrição: Digital twins (DTs) are rapidly gaining popularity across industries as a digital tool for continuous monitoring of physical phenomena, and the first DTs have now been developed in various environmental science disciplines.DTs are becoming part of the political sustainability agenda (e.g., in the ‘Destination Earth’ programme of the European Commission), with the vision of developing DTs for the climate, the ocean, and biodiversity.Digital transitions are happening across domains (including ecology) and have advanced the use of high-tech sensors for automated data collection and processing.Technological developments in digital infrastructure have made data storage, automation, large-scale models, and interactive applications cheaper by many orders of magnitude.These developments clear the way for DT adoption in ecology, but proper guidance is required. Digital twins (DTs) are an emerging phenomenon in the public and private sectors as a new tool to monitor and understand systems and processes. DTs have the potential to change the status quo in ecology as part of its digital transformation. However, it is important to avoid misguided developments by managing expectations about DTs. We stress that DTs are not just big models of everything, containing big data and machine learning. Rather, the strength of DTs is in combining data, models, and domain knowledge, and their continuous alignment with the real world. We suggest that researchers and stakeholders exercise caution in DT development, keeping in mind that many of the strengths and challenges of computational modelling in ecology also apply to DTs.
  • Editor: England: Elsevier Ltd
  • Idioma: Inglês;Norueguês
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