POLLENOMICS: Decoding the Farming History of Europe Using a Bayesian Approach Combining Compositional Data with a Point Process
(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:
https://lup.lub.lu.se/record/de014e62-de90-46a1-84dc-d5487df8eb4a
- author
- Pirzamanbein, Behnaz
LU
; Elhaik, Eran LU
; Poska, Anneli LU and Lindström, Johan LU
- organization
-
- Department of Statistics
- MERGE: ModElling the Regional and Global Earth system
- eSSENCE: The e-Science Collaboration
- LTH Profile Area: Engineering Health
- Molecular Biosciences
- BECC: Biodiversity and Ecosystem services in a Changing Climate
- Mathematical Statistics
- Dept of Physical Geography and Ecosystem Science
- publishing date
- 2024-02
- 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}}, }