Mon, Aug 02, 2021:On Demand
Background/Question/Methods
Functional traits and their relationships with demography have proved powerful for understanding forest structure. However, most studies have ignored belowground traits in this context, even though roots take up essential resources. Consequently, it remains poorly understood how above- and belowground traits interact and how they together determine tree demography. Here, we tested how leaf and root trait combinations explain tree growth across 13000 trees of ten common tree species in a temperate forest in Michigan, US.
Trait and soil data were collected in the field, and mycorrhizal associations of tree species were obtained from the literature. Covariation among traits was tested using a principal component analysis (PCA). Tree growth was calculated as the stem diameter increment over time using ForestGeo census data and modeled as a function of different combinations of traits, the trait principal components, mycorrhizal associations, and soil properties.
Results/Conclusions Traits were segregated into different multivariate axes, e.g., the RD–SRL axis which represented a tradeoff between root diameter (RD) and specific root length (SRL, root length / root dry mass). The best model explaining tree growth included leaf dry matter content (LDMC), the RD–SRL axis, mycorrhizal association, and soil nitrogen concentration. At a first glance, the overall effects of these traits on growth followed the general fast–slow continuum, e.g., trees with high LDMC and thick roots (i.e., conservative traits) grew slower than those with opposite trait expressions (i.e., acquisitive traits). However, the strong interactions between traits revealed that EcM- and AM-associating trees had divergent root strategies to enhance growth: EcM-associating trees grew faster by having thin, high-SRL roots, AM-associating trees grew faster when they had thick, low-SRL roots, possibly to allow higher AM-fungal colonization rates and thus resource uptake. Moreover, the ‘best’ root trait strategy (i.e., leading to the fastest growth) for EcM (thin, high-SRL roots) and AM trees (thick, low-SRL roots) modulated leaf trait effects on growth, so that trees improved their growth rates by either combining conservative leaves with the ‘best’ root strategy, or by combining acquisitive leaves with the ‘worst’ root strategy. Our work highlights how combinations of above- and belowground traits impact tree growth, rather than single traits: belowground traits modulated the effects of aboveground traits on tree growth. Accounting for these aboveground–belowground interactions enhances our knowledge of the drivers of tree growth, and ultimately forest structure.
Results/Conclusions Traits were segregated into different multivariate axes, e.g., the RD–SRL axis which represented a tradeoff between root diameter (RD) and specific root length (SRL, root length / root dry mass). The best model explaining tree growth included leaf dry matter content (LDMC), the RD–SRL axis, mycorrhizal association, and soil nitrogen concentration. At a first glance, the overall effects of these traits on growth followed the general fast–slow continuum, e.g., trees with high LDMC and thick roots (i.e., conservative traits) grew slower than those with opposite trait expressions (i.e., acquisitive traits). However, the strong interactions between traits revealed that EcM- and AM-associating trees had divergent root strategies to enhance growth: EcM-associating trees grew faster by having thin, high-SRL roots, AM-associating trees grew faster when they had thick, low-SRL roots, possibly to allow higher AM-fungal colonization rates and thus resource uptake. Moreover, the ‘best’ root trait strategy (i.e., leading to the fastest growth) for EcM (thin, high-SRL roots) and AM trees (thick, low-SRL roots) modulated leaf trait effects on growth, so that trees improved their growth rates by either combining conservative leaves with the ‘best’ root strategy, or by combining acquisitive leaves with the ‘worst’ root strategy. Our work highlights how combinations of above- and belowground traits impact tree growth, rather than single traits: belowground traits modulated the effects of aboveground traits on tree growth. Accounting for these aboveground–belowground interactions enhances our knowledge of the drivers of tree growth, and ultimately forest structure.