Leaf spectroscopy reveals divergent inter- and intra-species foliar trait covariation and trait-environment relationships across NEON domains

Deciphering the variability of leaf phosphorus-allocation strategies using leaf economic traits and reflectance spectroscopy across diverse forest types

Allocation of leaf phosphorus (P) among different functional fractions represents a crucial adaptive strategy for optimizing P use. However, it remains challenging to monitor the variability in leaf P fractions and, ultimately, to understand P-use strategies across diverse plant communities. We explored relationships between five leaf P fractions (orthophosphate P, Pi; lipid P, PL; nucleic acid P, PN; metabolite P, PM; and residual P, PR) and 11 leaf economic traits of 58 woody species from three biomes in China, including temperate, subtropical and tropical forests. Then, we developed trait-based models and spectral models for leaf P fractions and compared their predictive abilities. We found that plants exhibiting conservative strategies increased the proportions of PN and PM, but decreased the proportions of Pi and PL, thus enhancing photosynthetic P-use efficiency, especially under P limitation. Spectral models outperformed trait-based models in predicting cross-site leaf P fractions, regardless of concentrations (R2 = 0.50-0.88 vs 0.34-0.74) or proportions (R2 = 0.43-0.70 vs 0.06-0.45). These findings enhance our understanding of leaf P-allocation strategies and highlight reflectance spectroscopy as a promising alternative for characterizing large-scale leaf P fractions and plant P-use strategies, which could ultimately improve the physiological representation of the plant P cycle in land surface models.

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.

Convergent Strategies for Leaf Traits in Tree Species From Divergent Habitats

Plant trait expressions and their trade-offs reflect the responses and long-term ecological adaptation to environmental gradients. However, how such expressions and trade-offs help plants to acclimate to a new environment remains poorly understood, which is a fundamental preset for plants' survival under a global change scenario. By comparing the trait-trait relationships of 4403 tree species from different climatic regions and the variation in trait trade-offs of 746 tree species that have been transplanted to a tropical botanical garden for several decades, our results reveal convergent but consistent alteration in trait-trait relationships of trees transplanted from different climatic regions to a common environment. The convergent trends enhance the capability of tree species in buffering the impacts of climate change through allocating more resources to growth and tolerance. We propose that altered trait-trait relationships may be the key mechanisms that underlie the long-term ecological stability and resilience of tree species.

Remotely sensed crown nutrient concentrations modulate forest reproduction across the contiguous United States

Global forests are increasingly lost to climate change, disturbance, and human management. Evaluating forests' capacities to regenerate and colonize new habitats has to start with the seed production of individual trees and how it depends on nutrient access. Studies on the linkage between reproduction and foliar nutrients are limited to a few locations and few species, due to the large investment needed for field measurements on both variables. We synthesized tree fecundity estimates from the Masting Inference and Forecasting (MASTIF) network with foliar nutrient concentrations from hyperspectral remote sensing at the National Ecological Observatory Network (NEON) across the contiguous United States. We evaluated the relationships between seed production and foliar nutrients for 56,544 tree-years from 26 species at individual and community scales. We found a prevalent association between high foliar phosphorous (P) concentration and low individual seed production (ISP) across the continent. Within-species coefficients to nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) are related to species differences in nutrient demand, with distinct biogeographic patterns. Community seed production (CSP) decreased four orders of magnitude from the lowest to the highest foliar P. This first continental-scale study sheds light on the relationship between seed production and foliar nutrients, highlighting the potential of using combined Light Detection And Ranging (LiDAR) and hyperspectral remote sensing to evaluate forest regeneration. The fact that both ISP and CSP decline in the presence of high foliar P levels has immediate application in improving forest demographic and regeneration models by providing more realistic nutrient effects at multiple scales.

Exploring the role of biotic factors in regulating the spatial variability in land surface phenology across four temperate forest sites

Land surface phenology (LSP), the characterization of plant phenology with satellite data, is essential for understanding the effects of climate change on ecosystem functions. Considerable LSP variation is observed within local landscapes, and the role of biotic factors in regulating such variation remains underexplored. In this study, we selected four National Ecological Observatory Network terrestrial sites with minor topographic relief to investigate how biotic factors regulate intra-site LSP variability. We utilized plant functional type (PFT) maps, functional traits, and LSP data to assess the explanatory power of biotic factors for the start and end of season (SOS and EOS) variability. Our results indicate that PFTs alone explain only 0.8-23.4% of intra-site SOS and EOS variation, whereas including functional traits significantly improves explanatory power, with cross-validation correlations ranging from 0.50 to 0.85. While functional traits exhibited diverse effects on SOS and EOS across different sites, traits related to competitive ability and productivity were important for explaining both SOS and EOS variation at these sites. These findings reveal that plants exhibit diverse phenological responses to comparable environmental conditions, and functional traits significantly contribute to intra-site LSP variability, highlighting the importance of intrinsic biotic properties in regulating plant phenology.

The drivers of intraspecific trait variation and their implications for future tree productivity and survival

Forests are facing unprecedented levels of stress from pest and disease outbreaks, disturbance, fragmentation, development, and a changing climate. These selective agents act to alter forest composition from regional to cellular levels. Thus, a central challenge for understanding how forests will be impacted by future change is how to integrate across scales of biology. Phenotype, or an observable trait, is the product of an individual's genes (G) and the environment in which an organism lives (E). To date, researchers have detailed how environment drives variation in tree phenotypes over long time periods (e.g., long-term ecological research sites [LTERs]) and across large spatial scales (e.g., flux network). In parallel, researchers have discovered the genes and pathways that govern phenotypes, finding high degrees of genetic control and signatures of local adaptation in many plant traits. However, the research in these two areas remain largely independent of each other, hindering our ability to generate accurate predictions of plant response to environment, an increasingly urgent need given threats to forest systems. I present the importance of both genes and environment in determining tree responses to climate stress. I highlight why the difference between G versus E in driving variation is critical for our understanding of climate responses, then propose means of accelerating research that examines G and E simultaneously by leveraging existing long-term, large-scale phenotypic data sets from ecological networks and adding newly affordable sequence (-omics) data to both drill down to find the genes and alleles influencing phenotypes and scale up to find how patterns of demography and local adaptation may influence future response to change.

Leaf Traits Explain the Growth Variation and Nitrogen Response of Eucalyptus urophylla × Eucalyptus grandis and Dalbergia odorifera in Mixed Culture

Mixed cultivation with legumes may alleviate the nitrogen (N) limitation of monoculture Eucalyptus. However, how leaf functional traits respond to N in mixed cultivation with legumes and how they affect tree growth are unclear. Thus, this study investigated the response of leaf functional traits of Eucalyptus urophylla × Eucalyptus grandis (E. urophylla × E. grandis) and Dalbergia odorifera (D. odorifera) to mixed culture and N application, as well as the regulatory pathways of key traits on seedling growth. In this study, a pot-controlled experiment was set up, and seedling growth indicators, leaf physiology, morphological parameters, and N content were collected and analyzed after 180 days of N application treatment. The results indicated that mixed culture improved the N absorption and photosynthetic rate of E. urophylla × E. grandis, further promoting seedling growth but inhibiting the photosynthetic process of D. odorifera, reducing its growth and biomass. Redundancy analysis and path analysis revealed that leaf nitrogen content, pigment content, and photosynthesis-related physiological indicators were the traits most directly related to seedling growth and biomass accumulation, with the net photosynthetic rate explaining 50.9% and 55.8% of the variation in growth indicators for E. urophylla × E. grandis and D. odorifera, respectively. Additionally, leaf morphological traits are related to the trade-off strategy exhibited by E. urophylla × E. grandis and D. odorifera based on N competition. This study demonstrated that physiological traits related to photosynthesis are reliable predictors of N nutrition and tree growth in mixed stands, while leaf morphological traits reflect the resource trade-off strategies of different tree species.

Exploring Intraspecific Trait Variation in a Xerophytic Moss Species Indusiella thianschanica (Ptychomitriaceae) across Environmental Gradients on the Tibetan Plateau

Investigating intraspecific trait variability is crucial for understanding plant adaptation to various environments, yet research on lithophytic mosses in extreme environments remains scarce. This study focuses on Indusiella thianschanica Broth. Hal., a unique lithophytic moss species in the extreme environments of the Tibetan Plateau, aiming to uncover its adaptation and response mechanisms to environmental changes. Specimens were collected from 26 sites across elevations ranging from 3642 m to 5528 m, and the relationships between 23 morphological traits and 15 environmental factors were analyzed. Results indicated that coefficients of variation (CV) ranged from 5.91% to 36.11%, with gametophyte height (GH) and basal cell transverse wall thickness (STW) showing the highest and lowest variations, respectively. Temperature, elevation, and potential evapo-transpiration (PET) emerged as primary environmental drivers. Leaf traits, especially those of the leaf sheath, exhibited a more pronounced response to the environment. The traits exhibited apparent covariation in response to environmental challenges and indicated flexible adaptive strategies. This study revealed the adaptation and response patterns of different morphological traits of I. thianschanica to environmental changes on the Tibetan Plateau, emphasizing the significant effect of temperature on trait variation. Our findings deepen the understanding of the ecology and adaptive strategies of lithophytic mosses.

Integrating field- and remote sensing data to perceive species heterogeneity across a climate gradient

Tropical forests exhibit significant diversity and heterogeneity in species distribution. Some tree species spread abundantly, impacting the functional aspects of communities. Understanding how these facets respond to climate change is crucial. Field data from four protected areas (PAs) were combined with high-resolution Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) datasets to extract large-scale plot data of abundant species and their functional traits. A supervised component generalized linear regression (SCGLR) model was used to correlate climate components with the distribution of abundant species across PAs. The recorded rainfall gradient influenced the proportion of PA-specific species in the observed species assemblages. Community weighted means (CWMs) of biochemical traits showed better correlation values (0.85-0.87) between observed and predicted values compared to biophysical traits (0.52-0.79). The model-based projection revealed distinct distribution responses of each abundant species to the climate gradient. Functional diversity and functional traits maps highlighted the interplay between species heterogeneity and climate. The appearance dynamics of abundant species in dark diversity across PAs demonstrated their assortment strategy in response to the climate gradient. These observations can significantly aid in the ecological management of PAs exposed to climate dynamics.

Functional trait divergence associated with heteromorphic leaves in a climbing fig

Introduction

Plants that display heteroblasty possess conspicuous variations in leaf morphology between their juvenile and adult phases, with certain species retaining juvenile-like leaves even in adulthood. Nevertheless, the ecological advantages of maintaining two or more distinct leaf types in heteroblastic plants at the adult stage remain unclear.

Method

The aim of this study is to examine the adaptive significance of heteroblastic leaves sampled from branches with divergent functions (sterile and fertile branches) of mature Ficus pumila individuals by comparing their morphological, anatomical, and physiological characteristics.

Result

Leaves on sterile branches (LSs) exhibited a significantly larger specific leaf area, thinner palisade and spongy tissues, lower chlorophyll contents, and lower light saturation points than leaves on fertile branches (LFs). These results demonstrate that LSs are better adapted to low light environments, while LFs are well equipped to take advantages of high light conditions. However, both LFs and LSs have a low light compensation point with no significant difference between them, indicating that they start to accumulate photosynthetic products under similar light conditions. Interestingly, significant higher net photosynthetic rate was detected in LFs, showing they have higher photosynthetic capacity. Furthermore, LFs produced significant more nutrients compared to LSs, which may associate to their ability of accumulating more photosynthetic products under full light conditions and higher photosynthetic capacity.

Discussion

Overall, we observed a pattern of divergence in morphological features of leaves on two functional branches. Anatomical and physiological features indicate that LFs have an advantage in varied light conditions, providing amounts of photosynthetic products to support the sexual reproduction, while LSs adapt to low light environments. Our findings provide evidence that heteroblasty facilitates F. pumila to utilize varying light environments, likely associated with its growth form as a climbing plant. This strategy allows the plant to allocate resources more effectively and optimize its overall fitness.

Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications

Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.

From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I-Harnessing theory

Solar-induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three-dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi-sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5-10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question-How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real-world applications under climate change? The analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales.

From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II-Harnessing data

Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in "data desert" regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.

Vein Network and Climatic Factors Predict the Leaf Economic Spectrum of Desert Plants in Xinjiang, China

The leaf economic spectrum (LES) has been repeatedly verified with regional and global datasets. However, the LES of desert plants and its drivers has not been fully explored at the species level. In this study, we sampled three desert perennial plant species (Alhagi sparsifolia, Karelinia caspia, and Apocynum venetum) at three different geographical areas of distribution in Xinjiang, China, and measured 10 leaf economic traits to determine their strategy of resource utilization. The scores of the first axis from the principal component analysis of 10 leaf economic traits as a continuous variable define the LES. This study showed that the LES did exist in desert plants in this region. The leaf economic spectrum shifted from a more conservative strategy to a more acquisitive strategy with increasing contents of soil potassium (K) and the ratio of K to phosphorus. Except for the vein density of A. venetum, which quadratically correlated with LES, the vein density, distance between veins, and vein loopiness significantly positively correlated with the LES (p < 0.05), indicating a covariation and tradeoff relationship. The annual mean temperature was significantly negatively correlated with LES, while the annual mean precipitation (MAP) and the aridity index (AI), which was calculated by the ratio of MAP to potential evapotranspiration, significantly positively correlated with the LES. Of these, vein loopiness and AI were more effective at predicting the change in LES from anatomical and climatic perspectives owing to their high regression coefficients (R²). The findings of this study will substantially improve the understanding of the strategies of desert plants to utilize resources and predict the structure and function of ecosystems.

Leaf traits divergence and correlations of woody plants among the three plant functional types on the eastern Qinghai-Tibetan Plateau, China

Leaf traits are important indicators of plant life history and may vary according to plant functional type (PFT) and environmental conditions. In this study, we sampled woody plants from three PFTs (e.g., needle-leaved evergreens, NE; broad-leaved evergreens, BE; broad-leaved deciduous, BD) on the eastern Qinghai-Tibetan Plateau, and 110 species were collected across 50 sites. Here, the divergence and correlations of leaf traits in three PFTs and relationships between leaf traits and environment were studied. The results showed significant differences in leaf traits among three PFTs, with NE plants showed higher values than BE plants and BD plants for leaf thickness (LT), leaf dry matter content (LDMC), leaf dry mass per area (LMA), carbon: nitrogen ratio (C/N), and nitrogen content per unit area (N_area), except for nitrogen content per unit mass (N_mass). Although the correlations between leaf traits were similar across three PFTs, NE plants differed from BE plants and BD plants in the relationship between C/N and N_area. Compared with the mean annual precipitation (MAP), the mean annual temperature (MAT) was the main environmental factor that caused the difference in leaf traits among three PFTs. NE plants had a more conservative approach to survival compared to BE plants and BD plants. This study shed light on the regional-scale variation in leaf traits and the relationships among leaf traits, PFT, and environment. These findings have important implications for the development of regional-scale dynamic vegetation models and for understanding how plants respond and adapt to environmental change.