Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1016/j.tree.2023.04.010
Licence creative commons licence
Title (Primary) Digital twins: dynamic model-data fusion for ecology
Author de Koning, K.; Broekhuijsen, J.; Kühn, I. ORCID logo ; Ovaskainen, O.; Taubert, F.; Endresen, D.; Schigel, D.; Grimm, V.
Journal Trends in Ecology & Evolution
Year 2023
Department BZF; OESA; iDiv
Volume 38
Issue 10
Page From 916
Page To 926
Language englisch
Topic T5 Future Landscapes
Keywords highlight; digital twins; biodiversity conservation; evidence-based conservation; model-data integration; real-time monitoring; digital conservation
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.
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=27031
de Koning, K., Broekhuijsen, J., Kühn, I., Ovaskainen, O., Taubert, F., Endresen, D., Schigel, D., Grimm, V. (2023):
Digital twins: dynamic model-data fusion for ecology
Trends Ecol. Evol. 38 (10), 916 - 926