Integrating nitrogen fixing structures into above- and belowground functional trait spectra in soy (Glycine max)

Abstract

Aims: Phenotypic trait variation across environmental gradients and through plant ontogeny is critical in driving ecological processes, especially in agroecosystems where single genotypes exist in high abundances. While variability in root traits plays a key role in belowground processes, few studies have identified the presence of an intraspecific "Root Economics Spectrum" (RES) within domesticated plants.Furthermore, little is known regarding if an intraspecific RES changes through plant ontogeny, and how tropic interactionsnamely root nodulationrelate to above-or belowground trait spectra.• Methods: We evaluated covariation among 12 root, nodule, leaf, and stem traits in 134 plants of a single genotype of soy (Glycine max). Variation in these traits was assessed across five managed environmental conditions, and three plant ontogenetic stages.• Results: Root traits covaried along an intraspecific RES that represents a trade-off between resource acquisition and resource conservation. Variation along the RES was closely coordinated with hydraulic traits, but was orthogonal to nodule and leaf economics traits. Trait relationships varied strongly across managed environmental conditions and plant developmental stages.• Conclusions: Our results indicate the presence of an intraspecific RES in soy that is independent of root nodule investment. Patterns of phenotypic variation in below and aboveground soy traits demonstrate multivariate trait syndromes vary across environmental gradients and are dynamic through plant ontogeny.

Accumulating crop functional trait data with citizen science

“…Consistent with existing literature on crop traits 9,14 , leaf chemical traits including leaf carbon (C) and nitrogen (N) were least variable across our dataset (CV = 4.3–17.3), in comparison to leaf morphological traits including leaf area and leaf dry mass which were most variable among samples (CV = 59.7–69.6); specific leaf area (SLA), and petiole length and diameter were intermediary in terms of overall trait variability (CV = 27.0–34.2; Table S1).…”

“…This was done by first assessing whether traits were normally or log-normally distributed, using a maximum-likelihood-based approach implemented with the ‘fitdistrplus’ R package 41 . Where traits were best described by a normal distribution (as per maximum likelihood scores), descriptive statistics were calculated means and standard deviations (SD); where traits were best described by log-normal distributions we calculated medians and median absolute deviation (MAD) values 14 . For all traits we also calculated coefficients of variation (CV) as an overall estimate of ITV.…”

Nutrient acquisition strategies in agroforestry systems

“…However, despite the key role root trait variation plays in resource acquisition potential (Cahill et al 2010;Bardgett et al 2014), and other ecosystem functions such as soil stability (Rillig et al 2015;Le Bissonnais et al 2018), there are very few analyses and applications of functional traits at the root scale in agroforestry systems (Martin and Isaac 2015). Evidence from community ecology (Larson and Funk 2016;Roumet et al 2016;Weemstra et al 2016) and agroforestry systems (Isaac et al 2017;Borden and Isaac 2019;Martin et al 2019) supports the hypothesis that certain root functional traits covarying along a dominant axis of resource acquiring to resource conserving traits (Fig. 1).…”

“…While predictable patterns in root trait variation are emerging from ecology (Weemstra et al 2016;Freschet and Roumet 2017), more work is needed within agroforestry systems to link tree and crop root form to function. This is especially true since intraspecific variation is of particular importance in cultivated systems (Martin and Isaac 2015;Damour et al 2018), and the integration of trees is an important determinant in crop root functional trait expression (Isaac et al 2017;Borden and Isaac 2019;Borden et al 2019;Martin et al 2019).…”

“…In some cases, these trait trade-offs have been linked to established metrics of plant performance in agroforestry systems [e.g. yield (Gagliardi et al 2015) and N 2 fixation rates (Martin et al 2019)].…”