Literature DB >> 29520931

Quantifying climate-growth relationships at the stand level in a mature mixed-species conifer forest.

Aaron Teets1, Shawn Fraver1, Aaron R Weiskittel1, David Y Hollinger2.   

Abstract

A range of environmental factors regulate tree growth; however, climate is generally thought to most strongly influence year-to-year variability in growth. Numerous dendrochronological (tree-ring) studies have identified climate factors that influence year-to-year variability in growth for given tree species and location. However, traditional dendrochronology methods have limitations that prevent them from adequately assessing stand-level (as opposed to species-level) growth. We argue that stand-level growth analyses provide a more meaningful assessment of forest response to climate fluctuations, as well as the management options that may be employed to sustain forest productivity. Working in a mature, mixed-species stand at the Howland Research Forest of central Maine, USA, we used two alternatives to traditional dendrochronological analyses by (1) selecting trees for coring using a stratified (by size and species), random sampling method that ensures a representative sample of the stand, and (2) converting ring widths to biomass increments, which once summed, produced a representation of stand-level growth, while maintaining species identities or canopy position if needed. We then tested the relative influence of seasonal climate variables on year-to-year variability in the biomass increment using generalized least squares regression, while accounting for temporal autocorrelation. Our results indicate that stand-level growth responded most strongly to previous summer and current spring climate variables, resulting from a combination of individualistic climate responses occurring at the species- and canopy-position level. Our climate models were better fit to stand-level biomass increment than to species-level or canopy-position summaries. The relative growth responses (i.e., percent change) predicted from the most influential climate variables indicate stand-level growth varies less from to year-to-year than species-level or canopy-position growth responses. By assessing stand-level growth response to climate, we provide an alternative perspective on climate-growth relationships of forests, improving our understanding of forest growth dynamics under a fluctuating climate.
© 2018 John Wiley & Sons Ltd.

Keywords:  Howland Forest; biomass increment; canopy position; climate change; dendrochronology; forest carbon cycle; tree growth response

Mesh:

Year:  2018        PMID: 29520931     DOI: 10.1111/gcb.14120

Source DB:  PubMed          Journal:  Glob Chang Biol        ISSN: 1354-1013            Impact factor:   10.863


  5 in total

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Authors:  Andrew D Richardson; David Y Hollinger; Julie K Shoemaker; Holly Hughes; Kathleen Savage; Eric A Davidson
Journal:  Sci Data       Date:  2019-07-05       Impact factor: 6.444

3.  Drought timing and species growth phenology determine intra-annual recovery of tree height and diameter growth.

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Journal:  AoB Plants       Date:  2022-03-18       Impact factor: 3.138

4.  Joint effects of climate, tree size, and year on annual tree growth derived from tree-ring records of ten globally distributed forests.

Authors:  Kristina J Anderson-Teixeira; Valentine Herrmann; Christine R Rollinson; Bianca Gonzalez; Erika B Gonzalez-Akre; Neil Pederson; M Ross Alexander; Craig D Allen; Raquel Alfaro-Sánchez; Tala Awada; Jennifer L Baltzer; Patrick J Baker; Joseph D Birch; Sarayudh Bunyavejchewin; Paolo Cherubini; Stuart J Davies; Cameron Dow; Ryan Helcoski; Jakub Kašpar; James A Lutz; Ellis Q Margolis; Justin T Maxwell; Sean M McMahon; Camille Piponiot; Sabrina E Russo; Pavel Šamonil; Anastasia E Sniderhan; Alan J Tepley; Ivana Vašíčková; Mart Vlam; Pieter A Zuidema
Journal:  Glob Chang Biol       Date:  2021-10-30       Impact factor: 13.211

5.  Forest disturbances and climate constrain carbon allocation dynamics in trees.

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Journal:  Glob Chang Biol       Date:  2022-04-13       Impact factor: 13.211

  5 in total

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