Rapid estimation of photosynthetic leaf traits of tropical plants in diverse environmental conditions using reflectance spectroscopy

JA, Larson A, Townsend PA, Montgomery R, Cavender‐Bares J, Reich PB. All the light we cannot see: Climate manipulations leave short and long-term imprints in spectral reflectance of trees

Anthropogenic climate change, particularly changes in temperature and precipitation, affects plants in multiple ways. Because plants respond dynamically to stress and acclimate to changes in growing conditions, diagnosing quantitative plant-environment relationships is a major challenge. One approach to this problem is to quantify leaf responses using spectral reflectance, which provides rapid, inexpensive, and nondestructive measurements that capture a wealth of information about genotype as well as phenotypic responses to the environment. However, it is unclear how warming and drought affect spectra. To address this gap, we used an open-air field experiment that manipulates temperature and rainfall in 36 plots at two sites in the boreal-temperate ecotone of northern Minnesota, USA. We collected leaf spectral reflectance (400-2400 nm) at the peak of the growing season for three consecutive years on juveniles (two to six years old) of five tree species planted within the experiment. We hypothesized that these mid-season measurements of spectral reflectance capture a snapshot of the leaf phenotype encompassing a suite of physiological, structural, and biochemical responses to both long- and short-time scale environmental conditions. We show that the imprint of environmental conditions experienced by plants hours to weeks before spectral measurements is linked to regions in the spectrum associated with stress, namely the water absorption regions of the near-infrared and short-wave infrared. In contrast, the environmental conditions plants experience during leaf development leave lasting imprints on the spectral profiles of leaves, attributable to leaf structure and chemistry (e.g., pigment content and associated ratios). Our analyses show that after accounting for baseline species spectral differences, spectral responses to the environment do not differ among the species. This suggests that building a general framework for understanding forest responses to climate change through spectral metrics may be possible, likely having broader implications if the common responses among species detected here represent a widespread phenomenon. Consequently, these results demonstrate that examining the entire spectrum of leaf reflectance for environmental imprints in contrast to single features (e.g., indices and traits) improves inferences about plant-environment relationships, which is particularly important in times of unprecedented climate change.

Linking leaf dark respiration to leaf traits and reflectance spectroscopy across diverse forest types

Leaf dark respiration (Rdark), an important yet rarely quantified component of carbon cycling in forest ecosystems, is often simulated from leaf traits such as the maximum carboxylation capacity (Vcmax), leaf mass per area (LMA), nitrogen (N) and phosphorus (P) concentrations, in terrestrial biosphere models. However, the validity of these relationships across forest types remains to be thoroughly assessed. Here, we analyzed Rdark variability and its associations with Vcmax and other leaf traits across three temperate, subtropical and tropical forests in China, evaluating the effectiveness of leaf spectroscopy as a superior monitoring alternative. We found that leaf magnesium and calcium concentrations were more significant in explaining cross-site Rdark than commonly used traits like LMA, N and P concentrations, but univariate trait-Rdark relationships were always weak (r2 ≤ 0.15) and forest-specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait-Rdark relationships, accurately predicted cross-site Rdark (r2 = 0.65) and pinpointed the factors contributing to Rdark variability. Our findings reveal a few novel traits with greater cross-site scalability regarding Rdark, challenging the use of empirical trait-Rdark relationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimating Rdark, which could ultimately improve process modeling of terrestrial plant respiration.

The effect of the vertical gradients of photosynthetic parameters on the CO2 assimilation and transpiration of a Panamanian tropical forest

Terrestrial biosphere models (TBMs) include the representation of vertical gradients in leaf traits associated with modeling photosynthesis, respiration, and stomatal conductance. However, model assumptions associated with these gradients have not been tested in complex tropical forest canopies. We compared TBM representation of the vertical gradients of key leaf traits with measurements made in a tropical forest in Panama and then quantified the impact of the observed gradients on simulated canopy-scale CO₂ and water fluxes. Comparison between observed and TBM trait gradients showed divergence that impacted canopy-scale simulations of water vapor and CO₂ exchange. Notably, the ratio between the dark respiration rate and the maximum carboxylation rate was lower near the ground than at the top-of-canopy, leaf-level water-use efficiency was markedly higher at the top-of-canopy, and the decrease in maximum carboxylation rate from the top-of-canopy to the ground was less than TBM assumptions. The representation of the gradients of leaf traits in TBMs is typically derived from measurements made within-individual plants, or, for some traits, assumed constant due to a lack of experimental data. Our work shows that these assumptions are not representative of the trait gradients observed in species-rich, complex tropical forests.

Short-term variation in leaf-level water use efficiency in a tropical forest

The representation of stomatal regulation of transpiration and CO₂ assimilation is key to forecasting terrestrial ecosystem responses to global change. Given its importance in determining the relationship between forest productivity and climate, accurate and mechanistic model representation of the relationship between stomatal conductance (g_s ) and assimilation is crucial. We assess possible physiological and mechanistic controls on the estimation of the g₁ (stomatal slope, inversely proportional to water use efficiency) and g₀ (stomatal intercept) parameters, using diurnal gas exchange surveys and leaf-level response curves of six tropical broadleaf evergreen tree species. g₁ estimated from ex situ response curves averaged 50% less than g₁ estimated from survey data. While g₀ and g₁ varied between leaves of different phenological stages, the trend was not consistent among species. We identified a diurnal trend associated with g₁ and g₀ that significantly improved model projections of diurnal trends in transpiration. The accuracy of modeled g_s can be improved by accounting for variation in stomatal behavior across diurnal periods, and between measurement approaches, rather than focusing on phenological variation in stomatal behavior. Additional investigation into the primary mechanisms responsible for diurnal variation in g₁ will be required to account for this phenomenon in land-surface models.

Advances in field-based high-throughput photosynthetic phenotyping

Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis.