Does the leaf economic spectrum hold within plant functional types? A Bayesian multivariate trait meta-analysis

Responses to Airborne Ozone and Soilborne Metal Pollution in Afforestation Plants with Different Life Forms

With the current increases in environmental stress, understanding species-specific responses to multiple stress agents is needed. This science is especially important for managing ecosystems that are already confronted with considerable pollution. In this study, responses to ozone (O3, ambient daily course values + 20 ppb) and mixed metal contamination in soils (MC, cadmium/copper/lead/zinc = 25/1100/2500/1600 mg kg-1), separately and in combination, were evaluated for three plant species (Picea abies, Acer pseudoplatanus, Tanacetum vulgare) with different life forms and ecological strategies. The two treatments elicited similar stress reactions, as shown by leaf functional traits, gas exchange, tannin, and nutrient markers, irrespective of the plant species and life form, whereas the reactions to the treatments differed in magnitude. Visible and microscopic injuries at the organ or cell level appeared along the penetration route of ozone and metal contamination. At the whole plant level, the MC treatment caused more severe injuries than the O3 treatment and few interactions were observed between the two stress factors. Picea trees, with a slow-return strategy, showed the highest stress tolerance in apparent relation to an enhancement of conservative traits and an exclusion of stress agents. The ruderal and more acquisitive Tanacetum forbs translocated large amounts of contaminants above ground, which may be of concern in a phytostabilisation context. The deciduous Acer trees-also with an acquisitive strategy-were most sensitive to both stress factors. Hence, species with slow-return strategies may be of particular interest for managing metal-polluted sites in the current context of multiple stressors and for safely confining soil contaminants below ground.

The Terrestrial Biosphere Model Farm

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901-2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

Evidence of Differences in Covariation Among Root Traits Across Plant Growth Forms, Mycorrhizal Types, and Biomes

Fine roots play an important role in plant ecological strategies, adaptation to environmental constraints, and ecosystem functions. Covariation among root traits influence the physiological and ecological processes of plants and ecosystems. Root trait covariation in multiple dimensions at the global scale has been broadly discussed. How fine-root traits covary at the regional scale and whether the covariation is generalizable across plant growth forms, mycorrhizal types, and biomes are largely unknown. Here, we collected six key traits - namely root diameter (RD), specific root length (SRL), root tissue density (RTD), root C content (RCC), root N content (RNC), and root C:N ratio (RCN) - of first- and second-order roots of 306 species from 94 sampling sites across China. We examined the covariation in root traits among different plant growth forms, mycorrhizal types, and biomes using the phylogenetic principal component analysis (pPCA). Three independent dimensions of the covariation in root traits were identified, accounting for 39.0, 26.1, and 20.2% of the total variation, respectively. The first dimension was represented by SRL, RNC, RTD, and RCN, which was in line with the root economics spectrum (RES). The second dimension described a negative relationship between RD and SRL, and the third dimension was represented by RCC. These three main principal components were mainly influenced by biome and mycorrhizal type. Herbaceous and ectomycorrhizal species showed a more consistent pattern with the RES, in which RD, RTD, and RCN were negatively correlated with SRL and RNC within the first axis compared with woody and arbuscular mycorrhizal species, respectively. Our results highlight the roles of plant growth form, mycorrhizal type, and biome in shaping root trait covariation, suggesting that root trait relationships in specific regions may not be generalized from global-scale analyses.

Maintenance of Intraspecific Diversity in Response to Species Competition and Nutrient Fluctuations

Intraspecific diversity is a substantial part of biodiversity, yet little is known about its maintenance. Understanding mechanisms of intraspecific diversity shifts provides realistic detail about how phytoplankton communities evolve to new environmental conditions, a process especially important in times of climate change. Here, we aimed to identify factors that maintain genotype diversity and link the observed diversity change to measured phytoplankton morpho-functional traits Vmax and cell size of the species and genotypes. In an experimental setup, the two phytoplankton species Emiliania huxleyi and Chaetoceros affinis, each consisting of nine genotypes, were cultivated separately and together under different fluctuation and nutrient regimes. Their genotype composition was assessed after 49 and 91 days, and Shannon's diversity index was calculated on the genotype level. We found that a higher intraspecific diversity can be maintained in the presence of a competitor, provided it has a substantial proportion to total biovolume. Both fluctuation and nutrient regime showed species-specific effects and especially structured genotype sorting of C. affinis. While we could relate species sorting with the measured traits, genotype diversity shifts could only be partly explained. The observed context dependency of genotype maintenance suggests that the evolutionary potential could be better understood, if studied in more natural settings including fluctuations and competition.

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (V_c,max ), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait-V_c,max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (V_c,max ), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type-specific relationships between V_c,max and the four morphological and biochemical traits (R²  ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait-V_c,max relationships across three forest biomes into a single robust model for V_c,max (R²  = 0.77), and also accurately estimated the four traits (R²  = 0.75-0.94). These findings challenge the traditional use of the empirical trait-V_c,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising V_c,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.

A hierarchical, multivariate meta-analysis approach to synthesising global change experiments

Meta-analyses enable synthesis of results from globally distributed experiments to draw general conclusions about the impacts of global change factors on ecosystem function. Traditional meta-analyses, however, are challenged by the complexity and diversity of experimental results. We illustrate how several key issues can be addressed by a multivariate, hierarchical Bayesian meta-analysis (MHBM) approach applied to information extracted from published studies. We applied an MHBM to log-response ratios for aboveground biomass (AB, n = 300), belowground biomass (BB, n = 205) and soil CO₂ exchange (SCE, n = 544), representing 100 studies. The MHBM accounted for study duration, climate effects and covariation among the AB, BB and SCE responses to elevated CO₂ (eCO₂ ) and/or warming. The MHBM revealed significant among-study covariation in the AB and BB responses to experimental treatments. The MHBM imputed missing duration (4.2%) and climate (6%) data, and revealed that climate context governs how eCO₂ and warming impact ecosystem function. Predictions identified biomes that may be particularly sensitive to eCO₂ or warming, but that are under-represented in global change experiments. The MHBM approach offers a flexible and powerful tool for synthesising disparate experimental results reported across multiple studies, sites and response variables.

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data-model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is a key to meeting the pressing needs of science and society in the 21st century.