Tree seedling trait optimization and growth in response to local‐scale soil and light variability

Abstract

At local scales, it has been suggested that high levels of resources lead to increased tree growth via trait optimization (highly peaked trait distribution). However, this contrasts with (1) theories that suggest that trait optimization and high growth occur in the most common resource level and (2) empirical evidence showing that high trait optimization can be also found at low resource levels. This raises the question of how are traits and growth optimized in highly diverse plant communities. Here, we propose a series of hypotheses about how traits and growth are expected to be maximized under different resource levels (low, the most common, and high) in tree seedling communities from a subtropical forest in Puerto Rico, USA. We studied the variation in the distribution of biomass allocation and leaf traits and seedlings growth rate along four resource gradients: light availability (canopy openness) and soil K, Mg, and N content. Our analyses consisted of comparing trait kurtosis (a measurement of trait optimization), community trait means, and relative growth rates at three resource levels (low, common, and high). Trait optimization varied across the three resource levels depending on the type of resource and trait, with leaf traits being optimized under high N and in the most common K and Mg conditions, but not at any of the light levels. Also, seedling growth increased at highlight conditions and high N and K but was not related to trait kurtosis. Our results indicate that local-scale variability of soil fertility and understory light conditions result in shifts in species ecological strategies that increase growth despite a weak trait optimization, suggesting the existence of alternative phenotypes that achieve similar high performance. Uncovering the links between abiotic factors, functional trait diversity and performance is necessary to better predict tree responses to future changes in abiotic conditions.

Contrasting adaptation and optimization of stomatal traits across communities at continental scale

“…Selection for higher g (the benefit) involves a trade-off to minimize f (the cost) (de Boer et al, 2016), and such cost-benefit relationship is also involved in how stomatal traits regulate ecosystem productivity. Decreasing the skewness of g and increasing the skewness of f meant that species with high g and/or low f values were more dominant within communities; thus, the optimization of stomata on ecosystem productivity was economical through decreasing the skewness of g and increasing the skewness of f. A previous study also argued that high kurtosis in leaf traits indicated strong trait optimization (Umaña et al, 2021). Here, the high kurtosis of g meant that co-occurring species of g were convergent toward an optimal value.…”

“…The community trait variance, skewness, and kurtosis provide information beyond the community weighted mean, which can over-emphasize the role of dominant species (Enquist et al, 2015). Specifically, the community variance in a given traits represents the functional divergence, skewness the extent of asymmetric distribution of traits, and kurtosis the functional evenness, with a high kurtosis indicating strong trait optimization (Umaña et al, 2021). Skewness and kurtosis are mathematically related, according to skewness-kurtosis relationships (SKR):…”

Incorporating belowground traits: avenues towards a whole‐tree perspective on performance

“…belowground strategies across species and/or environments, but they do not provide absolute information about total belowground resource use and uptake, and therefore about performance, because these processes also depend on the size of the root system (Yang et al 2018). An aboveground example illustrates that combinations of SLA (an organ‐level trait) and leaf mass fraction (an organism‐level trait) better predicted seedling growth rates than SLA alone (Umaña et al 2021). Similarly, SRL values may be multiplied by measurements of the total root biomass of trees (resulting in total root length) to estimate the potential for soil resource uptake more accurately than either SRL or total root mass by themselves.…”

Relating leaf traits to seedling performance in a tropical forest: building a hierarchical functional framework

“…This species‐level variation along the acquisitive‐conservative spectrum for carbon processing is defined by organ‐level leaf economics traits (Reich et al 1999, Wright et al 2004) that tend to be more variable across species than within species (Messier et al 2010, 2017 b , Umaña et al 2018). This multilevel organization depicting the trait effects on performance not only represents a more realistic approach to understand the trait relationships in which a distinction in different organization levels (species and individuals) and trait types is explicitly considered, but also could explain the existence of the diverse range of phenotypes found in tropical regions and that seem to represent alternative ecological strategies (i.e., combinations of different traits such as biomass allocation and organ‐level traits, that lead to equivalent performance; Laughlin et al 2018, Umaña et al 2020 a , Worthy et al 2020).…”

“…Micro‐environmental variation in abiotic factors has shown to have significant effects on plant demography (Blonder et al 2018). In particular, for aboveground strategies, light availability is one of the most important resources determining plant strategies and functional diversity (Poorter and van der Werf 1998, Poorter and Rozendaal 2008, Umaña et al 2020 a ). Further, for tropical forests, light in the understory is highly limiting and key for determining the successful recruitment and establishment of seedlings (Chazdon and Fetcher 1984, Denslow 1987, Umaña et al 2020 b ).…”