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POLLENOMICS: Decoding the Farming History of Europe Using a Bayesian Approach Combining Compositional Data with a Point Process

Pirzamanbein, Behnaz LU orcid ; Elhaik, Eran LU orcid ; Poska, Anneli LU and Lindström, Johan LU orcid (2024) Bayes@Lund 2024
Abstract
This study uniquely combines advanced continental-scale data from
two distinct sources: pollen-based land cover (PbLC) and ancient DNA
(aDNA), developing a novel statistical model for spatiotemporal reconstructions
of past land use across Europe.
The aDNA data serves as a proxy for human habitation, differentiating
anthropogenic and natural land cover from PbLC reconstruction. This
will be accomplished using a Bayesian hierarchical model that combines
compositional data, Gaussian Markov random fields and point process
models.
This groundbreaking approach gives insights into the environmental
impacts of Holocene human migration and subsistence practices, and
marks a major advancement in... (More)
This study uniquely combines advanced continental-scale data from
two distinct sources: pollen-based land cover (PbLC) and ancient DNA
(aDNA), developing a novel statistical model for spatiotemporal reconstructions
of past land use across Europe.
The aDNA data serves as a proxy for human habitation, differentiating
anthropogenic and natural land cover from PbLC reconstruction. This
will be accomplished using a Bayesian hierarchical model that combines
compositional data, Gaussian Markov random fields and point process
models.
This groundbreaking approach gives insights into the environmental
impacts of Holocene human migration and subsistence practices, and
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
conference name
Bayes@Lund 2024
conference location
Lund, Sweden
conference dates
2024-03-06 - 2024-03-07
language
English
LU publication?
yes
id
de014e62-de90-46a1-84dc-d5487df8eb4a
date added to LUP
2025-02-15 15:13:12
date last changed
2025-02-17 10:56:50
@misc{de014e62-de90-46a1-84dc-d5487df8eb4a,
  abstract     = {{This study uniquely combines advanced continental-scale data from<br/>two distinct sources: pollen-based land cover (PbLC) and ancient DNA<br/>(aDNA), developing a novel statistical model for spatiotemporal reconstructions<br/>of past land use across Europe.<br/>The aDNA data serves as a proxy for human habitation, differentiating<br/>anthropogenic and natural land cover from PbLC reconstruction. This<br/>will be accomplished using a Bayesian hierarchical model that combines<br/>compositional data, Gaussian Markov random fields and point process<br/>models.<br/>This groundbreaking approach gives insights into the environmental<br/>impacts of Holocene human migration and subsistence practices, and<br/>marks a major advancement in understanding human-environmental dynamics<br/>over millennia.}},
  author       = {{Pirzamanbein, Behnaz and Elhaik, Eran and Poska, Anneli and Lindström, Johan}},
  language     = {{eng}},
  title        = {{POLLENOMICS: Decoding the Farming History of Europe Using a Bayesian Approach Combining Compositional Data with a Point Process}},
  year         = {{2024}},
}