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

Abstract

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 (γX). 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 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 γX and senescence rates of relatively long‐lived tissues.

Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data

“…However, as with any database, the plant trait values from such databases will represent the local trait-environment relationships for the site where they were collected, which may not be the area of interest. A key assumption in trait-based ecology is that the environment is filtering for an optimal set of trait characteristics so that the resulting communities are adapted to the environment where they are distributed (Fell & Ogle, 2018, Lebrija-Trejos et al, 2010, Lortie et al, 2004. Hence, we might expect an optimal set of trait characteristics for a given location, which when analysed over time could quantify the dynamics of community trait distributions or shifts in functional composition relating to environmental changes (Enquist et al 2015).…”