Quantifying climate–growth relationships at the stand level in a mature mixed‐species conifer forest

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 tre...

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Veröffentlicht in:Global change biology 2018-08, Vol.24 (8), p.3587-3602
Hauptverfasser: Teets, Aaron, Fraver, Shawn, Weiskittel, Aaron R., Hollinger, David Y.
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creator Teets, Aaron
Fraver, Shawn
Weiskittel, Aaron R.
Hollinger, David Y.
description 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. We tested the relative influence of seasonal climate variables on year‐to‐year variability of stand‐level, species‐level, and canopy‐position biomass increment. Our results indicate that stand‐level growth responded most strongly to previous summer and current spring climate variables,
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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. We tested the relative influence of seasonal climate variables on year‐to‐year variability of stand‐level, species‐level, and canopy‐position biomass increment. 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, and the magnitude of growth response varied less from to year‐to‐year for the stand than for individual species or canopy positions.</description><subject>Autocorrelation</subject><subject>Biomass</subject><subject>biomass increment</subject><subject>Canopies</subject><subject>Canopy</subject><subject>canopy position</subject><subject>Climate</subject><subject>Climate Change</subject><subject>Climate models</subject><subject>climatic factors</subject><subject>Coniferophyta - growth &amp; development</subject><subject>Coniferous forests</subject><subject>Core analysis</subject><subject>Core sampling</subject><subject>Coring</subject><subject>Data processing</subject><subject>Dendrochronology</subject><subject>Dynamics</subject><subject>Environmental factors</subject><subject>Environmental Monitoring</subject><subject>forest carbon cycle</subject><subject>Forest growth</subject><subject>Forest productivity</subject><subject>Forestry research</subject><subject>Forests</subject><subject>growth rings</subject><subject>Herbivores</subject><subject>Howland Forest</subject><subject>least squares</subject><subject>Maine</subject><subject>Plant species</subject><subject>Random sampling</subject><subject>Regression analysis</subject><subject>sampling</subject><subject>Seasons</subject><subject>Species</subject><subject>spring</subject><subject>Statistical sampling</subject><subject>summer</subject><subject>tree growth</subject><subject>tree growth response</subject><subject>trees</subject><subject>Trees - growth &amp; 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development</topic><topic>Variability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Teets, Aaron</creatorcontrib><creatorcontrib>Fraver, Shawn</creatorcontrib><creatorcontrib>Weiskittel, Aaron R.</creatorcontrib><creatorcontrib>Hollinger, David Y.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 3: Aquatic Pollution &amp; Environmental Quality</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>OSTI.GOV</collection><jtitle>Global change biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Teets, Aaron</au><au>Fraver, Shawn</au><au>Weiskittel, Aaron R.</au><au>Hollinger, David Y.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantifying climate–growth relationships at the stand level in a mature mixed‐species conifer forest</atitle><jtitle>Global change biology</jtitle><addtitle>Glob Chang Biol</addtitle><date>2018-08</date><risdate>2018</risdate><volume>24</volume><issue>8</issue><spage>3587</spage><epage>3602</epage><pages>3587-3602</pages><issn>1354-1013</issn><eissn>1365-2486</eissn><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. We tested the relative influence of seasonal climate variables on year‐to‐year variability of stand‐level, species‐level, and canopy‐position biomass increment. 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, and the magnitude of growth response varied less from to year‐to‐year for the stand than for individual species or canopy positions.</abstract><cop>England</cop><pub>Blackwell Publishing Ltd</pub><pmid>29520931</pmid><doi>10.1111/gcb.14120</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0003-1498-658X</orcidid><orcidid>https://orcid.org/000000031498658X</orcidid></addata></record>
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source MEDLINE; Wiley Online Library Journals Frontfile Complete
subjects Autocorrelation
Biomass
biomass increment
Canopies
Canopy
canopy position
Climate
Climate Change
Climate models
climatic factors
Coniferophyta - growth & development
Coniferous forests
Core analysis
Core sampling
Coring
Data processing
Dendrochronology
Dynamics
Environmental factors
Environmental Monitoring
forest carbon cycle
Forest growth
Forest productivity
Forestry research
Forests
growth rings
Herbivores
Howland Forest
least squares
Maine
Plant species
Random sampling
Regression analysis
sampling
Seasons
Species
spring
Statistical sampling
summer
tree growth
tree growth response
trees
Trees - growth & development
Variability
title Quantifying climate–growth relationships at the stand level in a mature mixed‐species conifer forest
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