Please use this identifier to cite or link to this item:
http://dx.doi.org/10.25673/115088
Title: | Digital twins : dynamic model-data fusion for ecology |
Author(s): | De Koning, Koen![]() Broekhuijsen, Jeroen Kühn, Ingolf ![]() Ovaskainen, Otso ![]() Taubert, Franziska ![]() Endresen, Dag Schigel, Dmitry Grimm, Volker ![]() |
Issue Date: | 2023 |
Type: | Article |
Language: | English |
Abstract: | 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. |
URI: | https://opendata.uni-halle.de//handle/1981185920/117044 http://dx.doi.org/10.25673/115088 |
Open Access: | ![]() |
License: | ![]() |
Journal Title: | Trends in ecology and evolution |
Publisher: | Elsevier |
Publisher Place: | Amsterdam [u.a.] |
Volume: | 38 |
Issue: | 10 |
Original Publication: | 10.1016/j.tree.2023.04.010 |
Page Start: | 916 |
Page End: | 926 |
Appears in Collections: | Open Access Publikationen der MLU |
Files in This Item:
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1-s2.0-S0169534723000903-main.pdf | 3.51 MB | Adobe PDF | ![]() View/Open |