Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments

Carbon-biodiversity relationships in a highly diverse subtropical forest

Carbon-focused climate mitigation strategies are becoming increasingly important in forests. However, with ongoing biodiversity declines we require better knowledge of how much such strategies account for biodiversity. We particularly lack information across multiple trophic levels and on established forests, where the interplay between carbon stocks, stand age, and tree diversity might influence carbon-biodiversity relationships. Using a large dataset (>4600 heterotrophic species of 23 taxonomic groups) from secondary, subtropical forests, we tested how multitrophic diversity and diversity within trophic groups relate to aboveground, belowground, and total carbon stocks at different levels of tree species richness and stand age. Our study revealed that aboveground carbon, the key component of climate-based management, was largely unrelated to multitrophic diversity. By contrast, total carbon stocks-that is, including belowground carbon-emerged as a significant predictor of multitrophic diversity. Relationships were nonlinear and strongest for lower trophic levels, but nonsignificant for higher trophic level diversity. Tree species richness and stand age moderated these relationships, suggesting long-term regeneration of forests may be particularly effective in reconciling carbon and biodiversity targets. Our findings highlight that biodiversity benefits of climate-oriented management need to be evaluated carefully, and only maximizing aboveground carbon may fail to account for biodiversity conservation requirements.

Evolutionary history explains foliar spectral differences between arbuscular and ectomycorrhizal plant species

Leaf spectra are integrated foliar phenotypes that capture a range of traits and can provide insight into ecological processes. Leaf traits, and therefore leaf spectra, may reflect belowground processes such as mycorrhizal associations. However, evidence for the relationship between leaf traits and mycorrhizal association is mixed, and few studies account for shared evolutionary history. We conduct partial least squares discriminant analysis to assess the ability of spectra to predict mycorrhizal type. We model the evolution of leaf spectra for 92 vascular plant species and use phylogenetic comparative methods to assess differences in spectral properties between arbuscular mycorrhizal and ectomycorrhizal plant species. Partial least squares discriminant analysis classified spectra by mycorrhizal type with 90% (arbuscular) and 85% (ectomycorrhizal) accuracy. Univariate models of principal components identified multiple spectral optima corresponding with mycorrhizal type due to the close relationship between mycorrhizal type and phylogeny. Importantly, we found that spectra of arbuscular mycorrhizal and ectomycorrhizal species do not statistically differ from each other after accounting for phylogeny. While mycorrhizal type can be predicted from spectra, enabling the use of spectra to identify belowground traits using remote sensing, this is due to evolutionary history and not because of fundamental differences in leaf spectra due to mycorrhizal type.

The grassland carbon cycle: Mechanisms, responses to global changes, and potential contribution to carbon neutrality

Grassland is one of the largest terrestrial biomes, providing critical ecosystem services such as food production, biodiversity conservation, and climate change mitigation. Global climate change and land-use intensification have been causing grassland degradation and desertification worldwide. As one of the primary medium for ecosystem energy flow and biogeochemical cycling, grassland carbon (C) cycling is the most fundamental process for maintaining ecosystem services. In this review, we first summarize recent advances in our understanding of the mechanisms underpinning spatial and temporal patterns of the grassland C cycle, discuss the importance of grasslands in regulating inter- and intra-annual variations in global C fluxes, and explore the previously unappreciated complexity in abiotic processes controlling the grassland C balance, including soil inorganic C accumulation, photochemical and thermal degradation, and wind erosion. We also discuss how climate and land-use changes could alter the grassland C balance by modifying the water budget, nutrient cycling and additional plant and soil processes. Further, we examine why and how increasing aridity and improper land use may induce significant losses in grassland C stocks. Finally, we identify several priorities for future grassland C research, including improving understanding of abiotic processes in the grassland C cycle, strengthening monitoring of grassland C dynamics by integrating ground inventory, flux monitoring, and modern remote sensing techniques, and selecting appropriate plant species combinations with suitable traits and strong resistance to climate fluctuations, which would help design sustainable grassland restoration strategies in a changing climate.

Is foliar spectrum predictive of belowground bacterial diversity? A case study in a peach orchard

Rhizosphere bacteria can have wide-ranging effects on their host plants, influencing plant biochemical and structural characteristics, and overall productivity. The implications of plant-microbe interactions provides an opportunity to interfere agriculture ecosystem with exogenous regulation of soil microbial community. Therefore, how to efficiently predict soil bacterial community at low cost is becoming a practical demand. Here, we hypothesize that foliar spectral traits can predict the diversity of bacterial community in orchard ecosystem. We tested this hypothesis by studying the ecological linkages between foliar spectral traits and soil bacterial community in a peach orchard in Yanqing, Beijing in 2020. Foliar spectral indexes were strongly correlated with alpha bacterial diversity and abundant genera that can promote soil nutrient conversion and utilization, such as Blastococcus, Solirubrobacter, and Sphingomonas at fruit mature stage. Certain unidentified or relative abundance <1% genera were also associated with foliar spectral traits. We selected specific indicators (photochemical reflectance index, normalized difference vegetable index, greenness index, and optimized soil-adjusted vegetation index) of foliar spectral indexes, alpha and beta diversities of bacterial community, and quantified the relations between foliar spectral traits and belowground bacterial community via SEM. The results of this study indicated that foliar spectral traits could powerfully predict belowground bacterial diversity. Characterizing plant attributes with easy-accessed foliar spectral indexes provides a new thinking in untangling the complex plant-microbe relationship, which could better cope with the decreased functional attributes (physiological, ecological, and productive traits) in orchard ecosystem.

Mechanistically-grounded pathways connect remotely sensed canopy structure to soil respiration

Variation in the soil-to-atmosphere C flux, or soil respiration (R_s), is influenced by a suite of biotic and abiotic factors, including soil temperature, soil moisture, and root biomass. However, whether light detection and ranging (lidar)-derived canopy structure is tied to soil respiration through its simultaneous influence over these drivers is not known. We assessed relationships between measures of above- and belowground vegetation density and complexity, and evaluated whether R_s is linked to remotely sensed canopy structure through pathways mediated by established biotic and abiotic mechanisms. Our results revealed that, at the stand-scale, canopy rugosity-a measure of complexity-and vegetation area index were coupled to soil respiration through their effects on light interception, soil microclimate, and fine root mass density, but this connection was stronger for complexity. Canopy and root complexity were not spatially coupled at the stand-scale, with canopy but not root complexity increasing through stand development. Our findings suggest that remotely sensed canopy complexity could be used to infer spatial variation in R_s, and that this relationship is grounded in known mechanistic pathways. The broad spatial inference of soil respiration via remotely sensed canopy complexity requires multi-site observations of canopy structure and R_s, which is possible given burgeoning open data from ecological networks and satellite remote sensing platforms.

A review on effective soil health bio-indicators for ecosystem restoration and sustainability

Preventing degradation, facilitating restoration, and maintaining soil health is fundamental for achieving ecosystem stability and resilience. A healthy soil ecosystem is supported by favorable components in the soil that promote biological productivity and provide ecosystem services. Bio-indicators of soil health are measurable properties that define the biotic components in soil and could potentially be used as a metric in determining soil functionality over a wide range of ecological conditions. However, it has been a challenge to determine effective bio-indicators of soil health due to its temporal and spatial resolutions at ecosystem levels. The objective of this review is to compile a set of effective bio-indicators for developing a better understanding of ecosystem restoration capabilities. It addresses a set of potential bio-indicators including microbial biomass, respiration, enzymatic activity, molecular gene markers, microbial metabolic substances, and microbial community analysis that have been responsive to a wide range of ecosystem functions in agricultural soils, mine deposited soil, heavy metal contaminated soil, desert soil, radioactive polluted soil, pesticide polluted soil, and wetland soils. The importance of ecosystem restoration in the United Nations Sustainable Development Goals was also discussed. This review identifies key management strategies that can help in ecosystem restoration and maintain ecosystem stability.

Integrating remote sensing with ecology and evolution to advance biodiversity conservation

Remote sensing has transformed the monitoring of life on Earth by revealing spatial and temporal dimensions of biological diversity through structural, compositional and functional measurements of ecosystems. Yet, many aspects of Earth's biodiversity are not directly quantified by reflected or emitted photons. Inclusive integration of remote sensing with field-based ecology and evolution is needed to fully understand and preserve Earth's biodiversity. In this Perspective, we argue that multiple data types are necessary for almost all draft targets set by the Convention on Biological Diversity. We examine five key topics in biodiversity science that can be advanced by integrating remote sensing with in situ data collection from field sampling, experiments and laboratory studies to benefit conservation. Lowering the barriers for bringing these approaches together will require global-scale collaboration.

Representing plant diversity in land models: An evolutionary approach to make "Functional Types" more functional

Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet, Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of "Plant Functional Types" (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, that is, to determine what new PFTs to add and with what data to constrain their parameters. We review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution-based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages ("Lineage Functional Types") will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next-generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.