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

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-9169

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DC Field	Value	Language
dc.contributor.author	Kuhl, Eileen	-
dc.contributor.author	Zang, Christian	-
dc.contributor.author	Esper, Jan	-
dc.contributor.author	Riechelmann, Dana F. C.	-
dc.contributor.author	Büntgen, Ulf	-
dc.contributor.author	Briesch, Martin	-
dc.contributor.author	Reinig, Frederick	-
dc.contributor.author	Römer, Philipp	-
dc.contributor.author	Konter, Oliver	-
dc.contributor.author	Schmidhalter, Martin	-
dc.contributor.author	Hartl, Claudia	-
dc.date.accessioned	2023-06-09T07:05:19Z	-
dc.date.available	2023-06-09T07:05:19Z	-
dc.date.issued	2023	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/9186	-
dc.description.abstract	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 (Larix decidua 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 (f1 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.	en_GB
dc.description.sponsorship	Deutsche Forschungsgemeinschaft (DFG)\|491381577\|Open-Access-Publikationskosten 2022–2024 Universität Mainz - Universitätsmedizin	-
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	550 Geowissenschaften	de_DE
dc.subject.ddc	550 Earth sciences	en_GB
dc.subject.ddc	910 Geografie	de_DE
dc.subject.ddc	910 Geography and travel	en_GB
dc.title	Using machine learning on tree-ring data to determine the geographical provenance of historical construction timbers	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-9169	-
jgu.type.contenttype	Scientific article	de
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 09 Chemie, Pharmazie u. Geowissensch.	de
jgu.organisation.number	7950	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Ecosphere	de
jgu.journal.volume	14	de
jgu.journal.issue	3	de
jgu.pages.alternative	e4453	de
jgu.publisher.year	2023	-
jgu.publisher.name	Wiley	de
jgu.publisher.place	Weinheim	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	550	de
jgu.subject.ddccode	910	de
jgu.publisher.doi	10.1002/ecs2.4453	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Naturwissenschaften	de
Appears in collections:	DFG-491381577-G

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