Data from: Refinement of a theoretical trait space for North American trees via environmental filtering

  • Kiona Ogle (Northern Arizona University) (Contributor)
  • Michael Fell (Contributor)

Dataset

Description

We refer to a theoretical trait space (TTS) as an n-dimensional hypervolume (“hypercube”) characterizing the range of values and covariations among multiple functional traits, in the absence of explicit filtering mechanisms. We previously constructed a 32-dimensional TTS for North American trees by fitting the Allometrically Constrained Growth and Carbon Allocation (ACGCA) model to USFS Forest Inventory and Analysis (FIA) data. Here, we sampled traits from this TTS, representing different individual “trees,” and subjected these trees to a series of gap dynamics simulations resulting in different annual light levels to explore the impact of environmental filtering (light stress) on the trait space. Variation in light limitation led to non-random mortality and a refinement of the TTS. We investigated potential mechanisms underlying such filtering processes by exploring how traits and the environment relate to mortality rates at the tree, phenotype (a specific set of trait values), and stand (a specific gap scenario) levels. The average light level at the forest floor explained 42% of the stand-level mortality, while phenotype- and tree-level mortality were best explained by six functional traits, especially radiation-use efficiency, maximum tree height, and xylem conducting area to sapwood area ratio (ΥX). These six “mortality” traits and six traits related to the leaf and wood economics spectra were used to construct trait hypercubes represented by trees that died or that survived each gap scenario. For trees that survived, the volume of their refined trait space decreased linearly with increasing stand-level mortality (up to ~50% mortality); the location also shifted, as indicated by non-zero distances between the hypercube centroids of surviving trees compared to dead trees and the original TTS. Overall, the patterns were consistent with empirical studies of functional traits, in terms of which traits predict mortality and the direction of the relationships. This work, however, also identified potentially important functional traits that are not commonly measured in empirical studies, such as ΥX and senescence rates of relatively long-lived tissues.,parameters_with_phenotype_mortThis file combines the trait data and phenotype level mortality. The vector of traits in columns 1 to 32 in each of the 33,000 rows can be thought of as a phenotype. The last two columns (PAR and m.thetap) represent the light level used when fitting the ACGCA model to FIA data in previous work and m.thetap is the phenotype level mortality (m theta p).stand_mortThis file contains the simulation or stand level mortality. It also has the average light level for each of the 62 gap simulations in this study (PARavg).tree_mortThis file contains tree level mortality (mgp). This file can be used to calculate the mortality data in the other two files.,
Date made availableAug 1 2018
PublisherDRYAD

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