DuchesneDorangevilleOuimetEtAl2017

Référence

Duchesne, L., D'Orangeville, L., Ouimet, R., Houle, D. and Kneeshaw, D.D. (2017) Extracting coherent tree-ring climatic signals across spatial scales from extensive forest inventory data. PLoS ONE, 12(12):1-20. (URL )

Résumé

Increasing access to extensively replicated and broadly distributed tree-ring collections has led to a greater use of these large data sets to investigate climate forcing on tree growth. However, the number of chronologies added to large accessible databases is declining and few are updated, while chronologies are often sparsely distributed and are more representative of marginal growing environments. On the other hand, National Forest Inventories (NFI), although poorly replicated at the plot level as compared to classic dendrochronological sampling, contain a large amount of tree-ring data with high spatial density designed to be spatially representative of the forest cover. We propose an a posteriori approach to validating tree-ring measurements and dating, selecting individual tree-ring width time series, and building average chronologies at various spatial scales based on an extensive collection of ring width measurements of nearly 94,000 black spruce trees distributed over a wide area and collected as part of the NFI in the province of Quebec, Canada. Our results show that reliable signals may be derived at various spatial scales (from 37 to 583,000 km2) from NFI increment core samples. Signals from independently built chronologies are spatially coherent with each other and well-correlated with independent reference chronologies built at the stand level. We thus conclude that tree-ring data from NFIs provide an extraordinary opportunity to strengthen the spatial and temporal coverage of tree-ring data and to improve coordination with other contemporary measurements of forest growth to provide a better understanding of tree growth-climate relationships over broad spatial scales.

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@ARTICLE { DuchesneDorangevilleOuimetEtAl2017,
    TITLE = { Extracting coherent tree-ring climatic signals across spatial scales from extensive forest inventory data },
    AUTHOR = { Duchesne, L. AND D'Orangeville, L. AND Ouimet, R. AND Houle, D. AND Kneeshaw, D.D. },
    JOURNAL = { PLoS ONE },
    YEAR = { 2017 },
    MONTH = { 12 },
    NUMBER = { 12 },
    PAGES = { 1-20 },
    VOLUME = { 12 },
    ABSTRACT = { Increasing access to extensively replicated and broadly distributed tree-ring collections has led to a greater use of these large data sets to investigate climate forcing on tree growth. However, the number of chronologies added to large accessible databases is declining and few are updated, while chronologies are often sparsely distributed and are more representative of marginal growing environments. On the other hand, National Forest Inventories (NFI), although poorly replicated at the plot level as compared to classic dendrochronological sampling, contain a large amount of tree-ring data with high spatial density designed to be spatially representative of the forest cover. We propose an a posteriori approach to validating tree-ring measurements and dating, selecting individual tree-ring width time series, and building average chronologies at various spatial scales based on an extensive collection of ring width measurements of nearly 94,000 black spruce trees distributed over a wide area and collected as part of the NFI in the province of Quebec, Canada. Our results show that reliable signals may be derived at various spatial scales (from 37 to 583,000 km2) from NFI increment core samples. Signals from independently built chronologies are spatially coherent with each other and well-correlated with independent reference chronologies built at the stand level. We thus conclude that tree-ring data from NFIs provide an extraordinary opportunity to strengthen the spatial and temporal coverage of tree-ring data and to improve coordination with other contemporary measurements of forest growth to provide a better understanding of tree growth-climate relationships over broad spatial scales. },
    DOI = { 10.1371/journal.pone.0189444 },
    PUBLISHER = { Public Library of Science },
    URL = { https://doi.org/10.1371/journal.pone.0189444 },
}

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