DOI: 10.5061/dryad.sqv9s4n29
title: Tree seedling trait optimization and growth in response to local-scale soil and light variability

description: 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 (i) theories that suggest that
 trait optimization and high growth occur in the most common resource level
 and (ii) 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. 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 contents. Our
 analyses consisted of comparing community trait means, trait kurtosis (a
 measurement of trait optimization), 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 high light 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.
description: <b>File: Dryad_rgr.csv</b> row.names = individual seedlings plot = seedling plot identity rgr = regalive growth rate, measured the change in log-transformed total seedling height (cm) from 2013 to 2014 <b>File: DRYAD_soil.light.csv</b> plot = seedling plot identity Mg.mg.kg = magnesium content in the soil (mg/kg) extracted using the Mehlich-III solution K.mg.kg = potasium content in the soil (mg/kg) extracted using the Mehlich-III solution N% = Total soil N concentration (%) obtained using the total combustion method canopy.open% = canopy openness (%) obtained by taken hemispherical photographs at 1m height in the center of each seedling plot at uniform light conditions at dawn with homogeneous light conditions <b>File: Dryad_trait.csv</b> row.names = individual seedlings plot = seedling plot identity leaf_area = leaf area (cm<sup>2</sup>) sla = specific leaf area (cm<sup>2</sup>/g) lar =  leaf area ratio calculated as leaf area/total plant dry mass cm<sup>2</sup>/g lmf = leaf mass fraction calculated as leaf dry mass/total plant dry mass (g/g) rmf = root mass fraction calculated as root dry mass/total plant dry mass (g/g)