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Near-infrared spectroscopy-based models correctly classify Abies alba seed origin and predict germination properties

hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorPOUGHON, Jeanne
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorLEPOITTEVIN, Camille
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorVICENTE, Eduardo
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorCARME, Marion
hal.structure.identifierNATIONAL INSTITUTE FOR RESEARCH AND DEVELOPMENT IN FORESTRY VOLUNTARI ROM
dc.contributor.authorMIHAI, Georgeta
hal.structure.identifierTRAGSA
dc.contributor.authorLARIO LEZA, Francisco
hal.structure.identifierNational Research Council (CNR), Institute of Biosciences and BioResources
dc.contributor.authorPIOTTI, Andrea
hal.structure.identifierNational Research Council (CNR), Institute of Biosciences and BioResources
dc.contributor.authorAVANZI, Camilla
hal.structure.identifierNational Research Council (CNR), Institute of Biosciences and BioResources
dc.contributor.authorMARCHI, Maurizio
hal.structure.identifierNational Research Council (CNR), Institute of Biosciences and BioResources
dc.contributor.authorVENDRAMIN, Giovanni Giuseppe
hal.structure.identifierEcologie des Forêts Méditerranéennes [URFM]
dc.contributor.authorSCOTTI-SAINTAGNE, Caroline
hal.structure.identifierEcologie des Forêts Méditerranéennes [URFM]
dc.contributor.authorFADY, Bruno
hal.structure.identifierBiologie intégrée pour la valorisation de la diversité des Arbres et de la Forêt [BioForA]
dc.contributor.authorTEYSSIER, Caroline
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorBENITO GARZÓN, Marta
dc.date.issued2025-12
dc.identifier.issn0378-1127
dc.description.abstractEn<div><p>Forestry industry requires high-quantity and quality seeds for afforestation and assisted migration programs. Finding reliable non-destructive methods to characterize seeds would significantly enhance efforts to identify climate-adapted populations. This study presents near-infrared (NIR) spectroscopy models to classify seed origin and predict germination characteristics at different temperatures non-destructively. We focus on Abies alba Mill., a key European forest tree with genetic variation along climatic gradients and seeds with shallow physiological dormancy. Seeds from six populations were analyzed using NIR spectroscopy, and germination was tested at 15 • C, 20 • C, and 25 • C after stratification treatments at 4 • C (0 or 3 weeks). Population classification accuracy using Partial Least Squares Discriminant Analysis was 69 %, with contributing NIR absorbance peaks at 1712, 1929, and 2111 nm, linked to moisture content and storage compounds. NIR spectra explained 51 % and 65 % of the variation in germination probability and timing using Partial Least Squares Regression, with contributing peaks at 1712, 1929, 2111, 1632, and 2073 nm. General Linear Mixed-Effects Models showed that NIR absorbances (processed using a Principal Component Analysis to reduce dimensionality) contributed to 39 % of the germination probability variance explained by fixed-effects, and the stratification treatment was the most important driver explaining germination time. Our results proved the utility of NIR-based tools to effectively classify bulked seeds and predict germination, opening new perspectives to nursery and forestry sectors and populations' adaptation and adjustments to warming climate. This study will facilitate further investigations on the physiological processes that occur during dormancy, a critical process for forest regeneration given the expected impact of shorter and warmer winters on seed behavior.</p></div>
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enNear-infrared spectroscopy
dc.subject.enClimate change
dc.subject.enPhysiological dormancy
dc.subject.enTranslocation germination experiment
dc.subject.enForest Genetic Resources
dc.subject.enSilver fir
dc.title.enNear-infrared spectroscopy-based models correctly classify Abies alba seed origin and predict germination properties
dc.typeArticle de revue
dc.identifier.doi10.1016/j.foreco.2025.123068
dc.subject.halSciences du Vivant [q-bio]/Biodiversité/Evolution [q-bio.PE]
dc.subject.halSciences du Vivant [q-bio]/Biochimie, Biologie Moléculaire
dc.subject.halSciences du Vivant [q-bio]/Biologie végétale
dc.description.sponsorshipEuropeHarnessing forest genetic resources for increasing options in the face of environmental and societal challenges
bordeaux.journalForest Ecology and Management
bordeaux.page123068
bordeaux.volume597
bordeaux.peerReviewedoui
hal.identifierhal-05244701
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-05244701v1
bordeaux.COinSctx_ver=Z39.88-2004&amp;rft_val_fmt=info:ofi/fmt:kev:mtx:journal&amp;rft.jtitle=Forest%20Ecology%20and%20Management&amp;rft.date=2025-12&amp;rft.volume=597&amp;rft.spage=123068&amp;rft.epage=123068&amp;rft.eissn=0378-1127&amp;rft.issn=0378-1127&amp;rft.au=POUGHON,%20Jeanne&amp;LEPOITTEVIN,%20Camille&amp;VICENTE,%20Eduardo&amp;CARME,%20Marion&amp;MIHAI,%20Georgeta&amp;rft.genre=article


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