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Ancient sedimentary DNA to forecast trajectories of ecosystem under climate change

Ancient sedimentary DNA to forecast trajectories of ecosystem under climate change
Ancient sedimentary DNA to forecast trajectories of ecosystem under climate change
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. Ancient DNA 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 sedimentary ancient DNA (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.
Alsos, Inger Greve
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Boussange, Victor
8204835d-b380-40db-a551-aae0fefd1413
Rijal, Dilli Prasad
dbf1444f-07aa-4eda-8cc2-994109370fb8
Beaulieu, Marijke
5049cd73-e68f-4fce-b4bc-28d491656ce9
Brown, Antony Gavin
c51f9d3e-02b0-47da-a483-41c354e78fab
Herzschuh, Ulrike
46db0c83-b948-4e10-bdec-7fc66a42c334
Svenning, Jens-Christian
fcfdf7ec-dbf1-4e28-9357-753f6fc9ad8d
Pallissier, Loïc
514a647d-8df6-4fae-8a26-0f053f24bc60
Alsos, Inger Greve
f0b69aa1-30bc-4255-957b-7022a0f12d57
Boussange, Victor
8204835d-b380-40db-a551-aae0fefd1413
Rijal, Dilli Prasad
dbf1444f-07aa-4eda-8cc2-994109370fb8
Beaulieu, Marijke
5049cd73-e68f-4fce-b4bc-28d491656ce9
Brown, Antony Gavin
c51f9d3e-02b0-47da-a483-41c354e78fab
Herzschuh, Ulrike
46db0c83-b948-4e10-bdec-7fc66a42c334
Svenning, Jens-Christian
fcfdf7ec-dbf1-4e28-9357-753f6fc9ad8d
Pallissier, Loïc
514a647d-8df6-4fae-8a26-0f053f24bc60

[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

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. Ancient DNA 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 sedimentary ancient DNA (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.

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Published date: 7 November 2023

Identifiers

Local EPrints ID: 488059
URI: http://eprints.soton.ac.uk/id/eprint/488059
PURE UUID: c55c613c-10dc-4d29-a35d-c092a25ecbd6
ORCID for Antony Gavin Brown: ORCID iD orcid.org/0000-0002-1990-4654

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Date deposited: 14 Mar 2024 17:34
Last modified: 18 Mar 2024 03:05

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Contributors

Author: Inger Greve Alsos
Author: Victor Boussange
Author: Dilli Prasad Rijal
Author: Marijke Beaulieu
Author: Ulrike Herzschuh
Author: Jens-Christian Svenning
Author: Loïc Pallissier

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