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POLLENOMICS: Decoding the Farming History of Europe Using Advanced Statistics to Combine Ancient DNA with Paleo-Pollen Data

Pirzamanbein, Behnaz LU orcid ; Elhaik, Eran LU orcid ; Poska, Anneli LU and Lindström, Johan LU orcid (2024) Swedish Climate Symposium 2024
Abstract
This study uniquely combines advanced continental-scale data from two distinct sources: pollen-based past land cover (paleoecology) and ancient DNA (aDNA), developing a novel statistical model for spatiotemporal reconstructions of past land use across Europe. This groundbreaking approach integrates paleo-pollen and aDNA data, providing unprecedented insights into the environmental impacts of Holocene human migration and subsistence practices.
Employing Supervised Machine Learning algorithms, the study identifies geographic-specific mutations in over 20,000 European Holocene aDNA samples to trace human migration patterns. Bayesian models are utilized for constructing probability maps of land-cover types from pollen data, to be compared... (More)
This study uniquely combines advanced continental-scale data from two distinct sources: pollen-based past land cover (paleoecology) and ancient DNA (aDNA), developing a novel statistical model for spatiotemporal reconstructions of past land use across Europe. This groundbreaking approach integrates paleo-pollen and aDNA data, providing unprecedented insights into the environmental impacts of Holocene human migration and subsistence practices.
Employing Supervised Machine Learning algorithms, the study identifies geographic-specific mutations in over 20,000 European Holocene aDNA samples to trace human migration patterns. Bayesian models are utilized for constructing probability maps of land-cover types from pollen data, to be compared with migration patterns from aDNA data. In addition, aDNA data serves as a proxy for human habitation, differentiating anthropogenic and natural land cover changes from paleo-pollen land cover reconstructions. This will be accomplished using a hierarchical statistical model that combines Gaussian Markov random fields and point process models. The study also integrates the LPJ-GUESS model to assess the impact of land use and land cover change (LULCC) on vegetation and carbon pools.
Key outcomes include combined pollen- and aDNA-based LULCC datasets, a consensus map of European agriculture spread, and insights into human-land interactions. The study marks a major advancement in understanding human-environmental dynamics over millennia. (Less)
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Contribution to conference
publication status
published
subject
keywords
Land use and land cover change, Paleo-Pollen REVEALS reconstruction, ancient DNA, Bayesian hierarchical modelling
conference name
Swedish Climate Symposium 2024
conference location
Norrköping, Sweden
conference dates
2024-05-15 - 2024-05-17
language
English
LU publication?
yes
id
6fa30fc0-0465-41ab-8727-c758c69c5cb2
date added to LUP
2025-02-15 15:06:13
date last changed
2025-04-04 14:21:03
@misc{6fa30fc0-0465-41ab-8727-c758c69c5cb2,
  abstract     = {{This study uniquely combines advanced continental-scale data from two distinct sources: pollen-based past land cover (paleoecology) and ancient DNA (aDNA), developing a novel statistical model for spatiotemporal reconstructions of past land use across Europe. This groundbreaking approach integrates paleo-pollen and aDNA data, providing unprecedented insights into the environmental impacts of Holocene human migration and subsistence practices.<br/>Employing Supervised Machine Learning algorithms, the study identifies geographic-specific mutations in over 20,000 European Holocene aDNA samples to trace human migration patterns. Bayesian models are utilized for constructing probability maps of land-cover types from pollen data, to be compared with migration patterns from aDNA data. In addition, aDNA data serves as a proxy for human habitation, differentiating anthropogenic and natural land cover changes from paleo-pollen land cover reconstructions. This will be accomplished using a hierarchical statistical model that combines Gaussian Markov random fields and point process models. The study also integrates the LPJ-GUESS model to assess the impact of land use and land cover change (LULCC) on vegetation and carbon pools.<br/>Key outcomes include combined pollen- and aDNA-based LULCC datasets, a consensus map of European agriculture spread, and insights into human-land interactions. The study marks a major advancement in understanding human-environmental dynamics over millennia.}},
  author       = {{Pirzamanbein, Behnaz and Elhaik, Eran and Poska, Anneli and Lindström, Johan}},
  keywords     = {{Land use and land cover change; Paleo-Pollen REVEALS reconstruction; ancient DNA; Bayesian hierarchical modelling}},
  language     = {{eng}},
  title        = {{POLLENOMICS: Decoding the Farming History of Europe Using Advanced Statistics to Combine Ancient DNA with Paleo-Pollen Data}},
  year         = {{2024}},
}