Using machine learning on tree-ring data to determine the geographical provenance of historical construction timbers

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
  • Eileen Kuhl
  • Christian Zang
  • Jan Esper
  • Dana FC Riechelmann
  • Ulf Buentgen
  • Martin Briesch
  • Frederick Reinig
  • Philipp Roemer
  • Oliver Konter
  • Martin Schmidhalter
  • Claudia Hartl
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000960702200036&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=7c6cbd95cc08067bf5cec85ffdc20efd
DOI
10.1002/ecs2.4453
Externe Identifier
  • Clarivate Analytics Document Solution ID: C3BD5
ISSN
2150-8925
Ausgabe der Veröffentlichung
3
Zeitschrift
ECOSPHERE
Schlüsselwörter
  • artificial intelligence
  • dendrochronology
  • dendroprovenancing
  • European Alps
  • Extreme Gradient Boosting
  • Larix decidua
  • tree-ring density
  • tree-ring width
Artikelnummer
ARTN e4453
Datum der Veröffentlichung
2023
Status
Published
Titel
Using machine learning on tree-ring data to determine the geographical provenance of historical construction timbers
Sub types
  • Article
Ausgabe der Zeitschrift
14

Datenquelle: Web of Science (Lite)

Andere Metadatenquellen:
Autoren
  • Eileen Kuhl
  • Christian Zang
  • Jan Esper
  • Dana FC Riechelmann
  • Ulf Buentgen
  • Martin Briesch
  • Frederick Reinig
  • Philipp Roemer
  • Oliver Konter
  • Martin Schmidhalter
  • Claudia Hartl
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000945984300001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1002/ecs2.4453
Externe Identifier
  • Clarivate Analytics Document Solution ID: 9R9RC
ISSN
2150-8925
Ausgabe der Veröffentlichung
3
Zeitschrift
ECOSPHERE
Schlüsselwörter
  • artificial intelligence
  • dendrochronology
  • dendroprovenancing
  • European Alps
  • Extreme Gradient Boosting
  • Larix decidua
  • tree-ring density
  • tree-ring width
Artikelnummer
ARTN e4453
Datum der Veröffentlichung
2023
Status
Published
Titel
Using machine learning on tree-ring data to determine the geographical provenance of historical construction timbers
Sub types
  • Article
Ausgabe der Zeitschrift
14

Datenquelle: Web of Science (Lite)

Abstract
<jats:title>Abstract</jats:title><jats:p>Dendroclimatology offers the unique opportunity to reconstruct past climate at annual resolution and wood from historical buildings can be used to extend such information back in time up to several millennia. However, the varying and often unclear origin of timbers affects the climate sensitivity of individual tree‐ring samples. Here, we compare tree‐ring width and density of 143 living larch (<jats:italic>Larix decidua</jats:italic> Mill.) trees at seven sites along an elevational transect from 1400 to 2200 m asl and 99 historical tree‐ring series to parametrize state‐of‐the‐art classification models for the European Alps. To achieve geographical provenance of the historical series, nine different supervised machine learning algorithms are trained and tested in their capability to solve our classification problem. Based on this assessment, we consider a tree‐ring density‐based and a tree‐ring width‐based dataset for model building. For each of these datasets, a general not species‐related model and a larch‐specific model including the cyclic larch budmoth influence are built. From the nine tested machine learning algorithms, Extreme Gradient Boosting showed the best performance. The density‐based models outperform the ring‐width models with the larch‐specific density model reaching the highest skill (<jats:italic>f</jats:italic><jats:sub>1</jats:sub> score = 0.8). The performance metrics reveal that the larch‐specific density model also performs best within individual sites and particularly in sites above 2000 m asl, which show the highest temperature sensitivities. The application of the specific density model for larch allows the historical series to be assigned with high confidence to a particular elevation within the valley. The procedure can be applied to other provenance studies using multiple tree growth characteristics. The novel approach of building machine learning models based on tree‐ring density features allows to omit a common period between reference and historical data for finding the provenance of relict wood and will therefore help to improve millennium‐length climate reconstructions.</jats:p>
Autoren
  • Eileen Kuhl
  • Christian Zang
  • Jan Esper
  • Dana FC Riechelmann
  • Ulf Büntgen
  • Martin Briesch
  • Frederick Reinig
  • Philipp Römer
  • Oliver Konter
  • Martin Schmidhalter
  • Claudia Hartl
DOI
10.1002/ecs2.4453
eISSN
2150-8925
ISSN
2150-8925
Ausgabe der Veröffentlichung
3
Zeitschrift
Ecosphere
Sprache
en
Online publication date
2023
Datum der Veröffentlichung
2023
Status
Published
Herausgeber
Wiley
Herausgeber URL
http://dx.doi.org/10.1002/ecs2.4453
Datum der Datenerfassung
2023
Titel
Using machine learning on tree‐ring data to determine the geographical provenance of historical construction timbers
Ausgabe der Zeitschrift
14

Datenquelle: Crossref

Author's licence
CC-BY
Autoren
  • Eileen Kuhl
  • Christian Zang
  • Jan Esper
  • Dana FC Riechelmann
  • Ulf Büntgen
  • Martin Briesch
  • Frederick Reinig
  • Philipp Römer
  • Oliver Konter
  • Martin Schmidhalter
  • Claudia Hartl
Hosting institution
Universitätsbibliothek Mainz
Sammlungen
  • DFG-491381577-G
Resource version
Published version
DOI
10.1002/ecs2.4453
Funding acknowledgements
  • Deutsche Forschungsgemeinschaft (DFG)|491381577|Open-Access-Publikationskosten 2022–2024 Universität Mainz - Universitätsmedizin
File(s) embargoed
false
Open access
true
Ausgabe der Veröffentlichung
3
Zeitschrift
Ecosphere
Schlüsselwörter
  • 550 Geowissenschaften
  • 550 Earth sciences
  • 910 Geografie
  • 910 Geography and travel
Sprache
eng
Open access status
Open Access
Paginierung
e4453
Datum der Veröffentlichung
2023
Public URL
https://openscience.ub.uni-mainz.de/handle/20.500.12030/9186
Herausgeber
Wiley
Datum der Datenerfassung
2023
Datum, an dem der Datensatz öffentlich gemacht wurde
2023
Zugang
Public
Titel
Using machine learning on tree-ring data to determine the geographical provenance of historical construction timbers
Ausgabe der Zeitschrift
14

Files

using_machine_learning_on_tre-20230609085557154.pdf

Datenquelle: OPENSCIENCE.UB

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