DuchesneHouleOuimetEtAl2019

Référence

Duchesne, L., Houle, D., Ouimet, R., Caldwell, L., Gloor, M., Brienen, R. (2019) Large apparent growth increases in boreal forests inferred from tree-rings are an artefact of sampling biases. Scientific Reports, 9(1). (Scopus )

Résumé

Tree rings are thought to be a powerful tool to reconstruct historical growth changes and have been widely used to assess tree responses to global warming. Demographic inferences suggest, however, that typical sampling procedures induce spurious trends in growth reconstructions. Here we use the world’s largest single tree-ring dataset (283,536 trees from 136,621 sites) from Quebec, Canada, to assess to what extent growth reconstructions based on these - and thus any similar - data might be affected by this problem. Indeed, straightforward growth rate reconstructions based on these data suggest a six-fold increase in radial growth of black spruce (Picea mariana) from ~0.5 mm yr −1 in 1800 to ~2.5 mm yr −1 in 1990. While the strong correlation (R 2 = 0.98) between this increase and that of atmospheric CO 2 could suggest a causal relationship, we here unambiguously demonstrate that this growth trend is an artefact of sampling biases caused by the absence of old, fast-growing trees (cf. “slow-grower survivorship bias”) and of young, slow-growing trees (cf. “big-tree selection bias”) in the dataset. At the moment, we cannot envision how to remedy the issue of incomplete representation of cohorts in existing large-scale tree-ring datasets. Thus, innovation will be needed before such datasets can be used for growth rate reconstructions. © 2019, The Author(s).

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@ARTICLE { DuchesneHouleOuimetEtAl2019,
    AUTHOR = { Duchesne, L. and Houle, D. and Ouimet, R. and Caldwell, L. and Gloor, M. and Brienen, R. },
    TITLE = { Large apparent growth increases in boreal forests inferred from tree-rings are an artefact of sampling biases },
    JOURNAL = { Scientific Reports },
    YEAR = { 2019 },
    VOLUME = { 9 },
    NUMBER = { 1 },
    NOTE = { cited By 0 },
    ABSTRACT = { Tree rings are thought to be a powerful tool to reconstruct historical growth changes and have been widely used to assess tree responses to global warming. Demographic inferences suggest, however, that typical sampling procedures induce spurious trends in growth reconstructions. Here we use the world’s largest single tree-ring dataset (283,536 trees from 136,621 sites) from Quebec, Canada, to assess to what extent growth reconstructions based on these - and thus any similar - data might be affected by this problem. Indeed, straightforward growth rate reconstructions based on these data suggest a six-fold increase in radial growth of black spruce (Picea mariana) from ~0.5 mm yr −1 in 1800 to ~2.5 mm yr −1 in 1990. While the strong correlation (R 2 = 0.98) between this increase and that of atmospheric CO 2 could suggest a causal relationship, we here unambiguously demonstrate that this growth trend is an artefact of sampling biases caused by the absence of old, fast-growing trees (cf. “slow-grower survivorship bias”) and of young, slow-growing trees (cf. “big-tree selection bias”) in the dataset. At the moment, we cannot envision how to remedy the issue of incomplete representation of cohorts in existing large-scale tree-ring datasets. Thus, innovation will be needed before such datasets can be used for growth rate reconstructions. © 2019, The Author(s). },
    AFFILIATION = { Ministère des Forêts, de la Faune et des Parcs, Direction de la recherche forestière, 2700 Einstein Street, Quebec City, QC G1P 3W8, Canada; Consortium on Regional Climatology and Adaptation to Climate Change (Ouranos), 550 Sherbrooke Street West, Montreal, QC H3A 1B9, Canada; School of Geography, University of Leeds, Leeds, LS2 9JT, United Kingdom },
    ART_NUMBER = { 6832 },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1038/s41598-019-43243-1 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065191965&doi=10.1038%2fs41598-019-43243-1&partnerID=40&md5=4a5b373f7abb4ece3f8fc4140dc9f4fb },
}

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