Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1016/j.tree.2023.04.010 |
Licence  |
|
| Title (Primary) | Digital twins: dynamic model-data fusion for ecology |
| Author | de Koning, K.; Broekhuijsen, J.; Kühn, I.
; Ovaskainen, O.; Taubert, F.; Endresen, D.; Schigel, D.; Grimm, V. |
| Source Titel | 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 10.1016/j.tree.2023.04.010 |
|