Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.3389/fevo.2026.1894720 |
Lizenz  |
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| Titel (primär) | Paleo-grounded biodiversity foundation models for long-horizon species distribution forecasting |
| Autor | Herzschuh, U.; Schild, L.; Farkas, L.; Caus, D.; Xia, J.; Weigel, S.; Dammers, J.; Demir, B.; Golivets, M.
; Gupta, V.; Hagen, O.; Jansen, F.; Khan, T.
; Kramer, A.; Kühn, I.
; Mensio, M.; Nieto-Lugilde, D.; Persello, C.; Rasti, B.; Cabral, J.S.; Schröder, B.; Svenning, J.-C.; Tanneberger, F.; Taubert, F.; Zurell, D. |
| Quelle | Frontiers in Ecology and Evolution |
| Erscheinungsjahr | 2026 |
| Department | BZF; OESA; iDiv |
| Band/Volume | 14 |
| Seite von | art. 1894720 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Supplements | Supplement 1 |
| Keywords | biodiversity change; conservation planning; foundation models; paleoecology; species distribution models |
| Abstract | Global biodiversity change demands decision-ready forecasts that integrate heterogeneous biological and environmental data across space and time. Current species distribution models (SDMs) support conservation tasks, but most remain trained and evaluated as static snapshots, whereas restoration trajectories, range shifts, and ecological baselines are shaped by dispersal limitation, disturbance, demographic inertia, and historical legacies. We argue that the next step is a paleo-grounded biodiversity foundation model: a multimodal model that learns transferable species-environment representations from modern occurrences, remote sensing, land cover, climate, and topography, and then uses paleoecological archives to test and adapt these representations across centuries to millennia. We outline two complementary temporal-transfer strategies. A conservative route trains a modern species-environment encoder and applies it to paleoclimate and paleo-land-cover slices, using pollen and sedimentary ancient DNA either for independent validation or for lightweight proxy-aware adaptation. A more ambitious route learns latent ecological dynamics from irregular, uncertainty-dated paleo time series and rolls these dynamics out across gridded landscapes. This perspective on future SDMs emphasizes proxy observation models, disequilibrium-aware evaluation, transparent uncertainty, and decision-facing outputs. By linking AI-based representation learning with paleo archives, such models could move SDMs from static suitability maps toward time-stamped, testable trajectories for biodiversity synthesis, conservation planning, and restoration monitoring. |
| Herzschuh, U., Schild, L., Farkas, L., Caus, D., Xia, J., Weigel, S., Dammers, J., Demir, B., Golivets, M., Gupta, V., Hagen, O., Jansen, F., Khan, T., Kramer, A., Kühn, I., Mensio, M., Nieto-Lugilde, D., Persello, C., Rasti, B., Cabral, J.S., Schröder, B., Svenning, J.-C., Tanneberger, F., Taubert, F., Zurell, D. (2026): Paleo-grounded biodiversity foundation models for long-horizon species distribution forecasting Front. Ecol. Evol. 14 , art. 1894720 10.3389/fevo.2026.1894720 |
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