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Using ancient sedimentary DNA to forecast ecosystem trajectories under climate change.
Alsos, Inger Greve; Boussange, Victor; Rijal, Dilli Prasad; Beaulieu, Marieke; Brown, Antony Gavin; Herzschuh, Ulrike; Svenning, Jens-Christian; Pellissier, Loïc.
Afiliación
  • Alsos IG; The Arctic University Museum of Norway, UiT The Arctic University of Norway, 9037 Tromsø, Norway.
  • Boussange V; Department of Environmental System Science, ETH Zürich, Universitätstrasse 16, 8092 Zürich, Switzerland.
  • Rijal DP; Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland.
  • Beaulieu M; The Arctic University Museum of Norway, UiT The Arctic University of Norway, 9037 Tromsø, Norway.
  • Brown AG; The Arctic University Museum of Norway, UiT The Arctic University of Norway, 9037 Tromsø, Norway.
  • Herzschuh U; The Arctic University Museum of Norway, UiT The Arctic University of Norway, 9037 Tromsø, Norway.
  • Svenning JC; Alfred Wegener Institute for Polar and Marine Research, Telegraphenberg A43, 14473 Potsdam, Germany.
  • Pellissier L; Institute of Environmental Sciences and Geography, Potsdam University, 14479 Potsdam, Germany.
Philos Trans R Soc Lond B Biol Sci ; 379(1902): 20230017, 2024 May 27.
Article en En | MEDLINE | ID: mdl-38583481
ABSTRACT
Ecosystem response to climate change is complex. In order to forecast ecosystem dynamics, we need high-quality data on changes in past species abundance that can inform process-based models. Sedimentary ancient DNA (sedaDNA) has revolutionised our ability to document past ecosystems' dynamics. It provides time series of increased taxonomic resolution compared to microfossils (pollen, spores), and can often give species-level information, especially for past vascular plant and mammal abundances. Time series are much richer in information than contemporary spatial distribution information, which have been traditionally used to train models for predicting biodiversity and ecosystem responses to climate change. Here, we outline the potential contribution of sedaDNA to forecast ecosystem changes. We showcase how species-level time series may allow quantification of the effect of biotic interactions in ecosystem dynamics, and be used to estimate dispersal rates when a dense network of sites is available. By combining palaeo-time series, process-based models, and inverse modelling, we can recover the biotic and abiotic processes underlying ecosystem dynamics, which are traditionally very challenging to characterise. Dynamic models informed by sedaDNA can further be used to extrapolate beyond current dynamics and provide robust forecasts of ecosystem responses to future climate change. This article is part of the theme issue 'Ecological novelty and planetary stewardship biodiversity dynamics in a transforming biosphere'.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Ecosistema / ADN Antiguo Límite: Animals Idioma: En Revista: Philos Trans R Soc Lond B Biol Sci Año: 2024 Tipo del documento: Article País de afiliación: Noruega Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Ecosistema / ADN Antiguo Límite: Animals Idioma: En Revista: Philos Trans R Soc Lond B Biol Sci Año: 2024 Tipo del documento: Article País de afiliación: Noruega Pais de publicación: Reino Unido