Vanessa Minden

Internal and external regulation of plant organ stoichiometry

Internal differences between plant organs are caused by the functional differentiation of plant tissue, whereas external supply rates of elements constrain nutrient uptake. Previous studies have concentrated on foliar or whole-plant stoichiometric response to the environment, whereas investigation of organ-specific comparisons is still pending. We explore C:N:P ratios of stems, leaves, diaspores and belowground organs in marsh plants, and evaluate the influence of environmental constraints using standardised major axis regression (SMA). For a pooled dataset, SMA resulted in distinct patterns of isometric and anisometric slopes between plant organs. Bivariate line-fitting for a split dataset of four ecological groups revealed that species of the frequently inundated marsh had higher N:C ratios than those of the infrequently inundated marsh. The influence of nutrient availability was detectable in decreased P:C and increased N:P ratios in P-poor sites. Across ecological groups, leaves and diaspores showed higher elemental homeostasis than stems and belowground organs. Any change in N:C ratios of belowground organs and diaspores in response to the environment was accompanied by an even stronger internal change in stem N:C ratios, indicating a pivotal role of stems of herbaceous plants in ecosystem processes. We found distinct patterns of C:N:P ratios in plant organs related to their internal function and external environmental constraints. Leaves and diaspores showed a higher degree of homeostasis than stems and belowground organs. We detected a clear external signal in element:element ratios of plant organs, with low soil P translating into lower tissue P:C ratio and stronger N retention in leaves as a response to salt stress.

The influence of balanced and imbalanced resource supply on biodiversity–functioning relationship across ecosystems

Numerous studies show that increasing species richness leads to higher ecosystem productivity. This effect is often attributed to more efficient portioning of multiple resources in communities with higher numbers of competing species, indicating the role of resource supply and stoichiometry for biodiversity–ecosystem functioning relationships. Here, we merged theory on ecological stoichiometry with a framework of biodiversity–ecosystem functioning to understand how resource use transfers into primary production. We applied a structural equation model to define patterns of diversity–productivity relationships with respect to available resources. Meta-analysis was used to summarize the findings across ecosystem types ranging from aquatic ecosystems to grasslands and forests. As hypothesized, resource supply increased realized productivity and richness, but we found significant differences between ecosystems and study types. Increased richness was associated with increased productivity, although this effect was not seen in experiments. More even communities had lower productivity, indicating that biomass production is often maintained by a few dominant species, and reduced dominance generally reduced ecosystem productivity. This synthesis, which integrates observational and experimental studies in a variety of ecosystems and geographical regions, exposes common patterns and differences in biodiversity–functioning relationships, and increases the mechanistic understanding of changes in ecosystems productivity.

Mapping local and global variability in plant trait distributions

Significance Currently, Earth system models (ESMs) represent variation in plant life through the presence of a small set of plant functional types (PFTs), each of which accounts for hundreds or thousands of species across thousands of vegetated grid cells on land. By expanding plant traits from a single mean value per PFT to a full distribution per PFT that varies among grid cells, the trait variation present in nature is restored and may be propagated to estimates of ecosystem processes. Indeed, critical ecosystem processes tend to depend on the full trait distribution, which therefore needs to be represented accurately. These maps reintroduce substantial local variation and will allow for a more accurate representation of the land surface in ESMs. Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration—specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50×50-km cells across the entire vegetated land surface. We do this in several ways—without defining the PFT of each grid cell and using 4 or 14 PFTs; each model’s predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.

Consistent drivers of plant biodiversity across managed ecosystems.

Ecosystems managed for production of biomass are often characterized by low biodiversity because management aims to optimize single ecosystem functions (i.e. yield) involving deliberate selection of species or cultivars. In consequence, considerable differences in observed plant species richness and productivity remain across systems, and the drivers of these differences have remained poorly resolved so far. In addition, it has remained unclear if species richness feeds back on ecosystem functions such as yield in real-world systems. Here, we establish N = 360 experimental plots across a broad range of managed ecosystems in several European countries, and use structural equation models to unravel potential drivers of plant species richness. We hypothesize that the relationships between productivity, total biomass and observed species richness are affected by management intensity, and that these effects differ between habitat types (dry grasslands, grasslands, and wetlands). We found that local management was an important driver of species richness across systems. Management caused system disturbance, resulting in reduced productivity yet enhanced total biomass. Plant species richness was directly and positively driven by management, with consistently negative effects of total biomass. Productivity effects on richness were positive, negative or neutral. Our study shows that management and total biomass drive plant species richness across real-world managed systems.

Interactions between ecosystem properties and land use clarify spatial strategies to optimize trade-offs between agriculture and species conservation

ABSTRACT Species conservation and forage production are both important, yet conflicting components of sustainable grassland management. We modeled forage production and conservation value as dependents in a chain of responses and effects, starting with abiotic environmental conditions that affect the spatial distribution of land uses and biotic ecosystem properties. We asked which relationships in this causal chain determine trade-offs between forage production and conservation value. Abiotic and biotic ecosystem properties were recorded on 46 plots in the coastal marshes of Northwest Germany. Plant and bird conservation values were calculated using Red Lists, and sales of forage-based agricultural products were assessed by interviewing farmers. We used a structural equation model to determine responses and effects. Groundwater depth and salinity represent the ultimate causes for the spatial variation in sales and conservation value. The water gradient translated into more proximate causes, such as land-use intensity affecting aboveground net primary productivity, forage quality, and species richness. Plant species conservation and forage production were segregated along the water gradient, and both bird conservation and forage production depended on grassland management, albeit at different fertilization levels. Our study points to segregation and integration as two spatial strategies to react to trade-offs between services. EDITED BY Christine Fürst

Antibiotics impact plant traits, even at small concentrations

Antibiotics used in livestock industry are released to agricultural fields via grazing animals and manure. From there, they may affect human health due to consumption of antibiotic-exposed crop plants or drinking water. Also they may affect performance of natural occurring non-target species. Our study shows that antibiotics, even in small concentrations, significantly affect plant traits. Cropland species showed delayed germination and lower biomass allocation, indicating possible yield-effects in farmland fertilized with manure containing antibiotics. Antibiotics may also alter the composition of plant species in natural field margins, due to different species-specific responses, with unknown consequences for higher trophic levels.

Social bees are fitter in more biodiverse environments

Bee population declines are often linked to human impacts, especially habitat and biodiversity loss, but empirical evidence is lacking. To clarify the link between biodiversity loss and bee decline, we examined how floral diversity affects (reproductive) fitness and population growth of a social stingless bee. For the first time, we related available resource diversity and abundance to resource (quality and quantity) intake and colony reproduction, over more than two years. Our results reveal plant diversity as key driver of bee fitness. Social bee colonies were fitter and their populations grew faster in more florally diverse environments due to a continuous supply of food resources. Colonies responded to high plant diversity with increased resource intake and colony food stores. Our findings thus point to biodiversity loss as main reason for the observed bee decline.

A methodology to derive global maps of leaf traits using remote sensing and climate data

This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then aggregated to Plant Functional Types (PFTs). Next, the spatial abundance of PFTs at MODIS resolution (500 m) is calculated using Landsat data (30 m). Based on these PFT abundances, representative trait values are calculated for MODIS pixels with nearby trait data. Finally, different regression algorithms are applied to globally predict trait estimates from these MODIS pixels using remote sensing and climate data. The methods were compared in terms of precision, robustness and efficiency. The best model (random forests regression) shows good precision (normalized RMSE≤ 20%) and goodness of fit (averaged Pearsons correlation R = 0.78) in any considered trait. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales. The new data products – the gap-filled trait matrix, a global map of PFT abundance per MODIS gridcells and the high-resolution global leaf trait maps – are complementary to existing large-scale observations of the land surface and we therefore anticipate substantial contributions to advances in quantifying, understanding and prediction of the Earth system.

Phylogenetic patterns and phenotypic profiles of the species of plants and mammals farmed for food

The origins of agriculture were key events in human history, during which people came to depend for their food on small numbers of animal and plant species. However, the biological traits determining which species were domesticated for food provision, and which were not, are unclear. Here, we investigate the phylogenetic distribution of livestock and crops, and compare their phenotypic traits with those of wild species. Our results indicate that phylogenetic clustering is modest for crop species but more intense for livestock. Domesticated species explore a reduced portion of the phenotypic space occupied by their wild counterparts and have particular traits in common. For example, herbaceous crops are globally characterized by traits including high leaf nitrogen concentration and tall canopies, which make them fast-growing species and proficient competitors. Livestock species are relatively large mammals with low basal metabolic rates, which indicate moderate to slow life histories. Our study therefore reveals ecological differences in domestication potential between plants and mammals. Domesticated plants belong to clades with traits that are advantageous in intensively managed high-resource habitats, whereas domesticated mammals are from clades adapted to moderately productive environments. Combining comparative phylogenetic methods with ecologically relevant traits has proven useful to unravel the causes and consequences of domestication.Phylogenetic distribution and phenotypic traits of livestock and crops reveal that domesticated species explore a reduced portion of the phenotypic space occupied by their wild counterparts and have particular traits in common.

Multiple facets of biodiversity drive the diversity–stability relationship

A substantial body of evidence has demonstrated that biodiversity stabilizes ecosystem functioning over time in grassland ecosystems. However, the relative importance of different facets of biodiversity underlying the diversity–stability relationship remains unclear. Here we use data from 39 grassland biodiversity experiments and structural equation modelling to investigate the roles of species richness, phylogenetic diversity and both the diversity and community-weighted mean of functional traits representing the ‘fast–slow’ leaf economics spectrum in driving the diversity–stability relationship. We found that high species richness and phylogenetic diversity stabilize biomass production via enhanced asynchrony in the performance of co-occurring species. Contrary to expectations, low phylogenetic diversity enhances ecosystem stability directly, albeit weakly. While the diversity of fast–slow functional traits has a weak effect on ecosystem stability, communities dominated by slow species enhance ecosystem stability by increasing mean biomass production relative to the standard deviation of biomass over time. Our in-depth, integrative assessment of factors influencing the diversity–stability relationship demonstrates a more multicausal relationship than has been previously acknowledged.Analysing data from 39 grassland biodiversity experiments, the authors uncover the direct and indirect contributions to ecosystem stability of taxonomic, phylogenetic and functional trait diversity.