Franziska Schrodt

Co‐limitation of photosynthetic capacity by nitrogen and phosphorus in West Africa woodlands

Photosynthetic leaf traits were determined for savanna and forest ecosystems in West Africa, spanning a large range in precipitation. Standardized major axis fits revealed important differences between our data and reported global relationships. Especially for sites in the drier areas, plants showed higher photosynthetic rates for a given N or P when compared with relationships from the global data set. The best multiple regression for the pooled data set estimated V(cmax) and J(max) from N(DW) and S. However, the best regression for different vegetation types varied, suggesting that the scaling of photosynthesis with leaf traits changed with vegetation types. A new model is presented representing independent constraints by N and P on photosynthesis, which can be evaluated with or without interactions with S. It assumes that limitation of photosynthesis will result from the least abundant nutrient, thereby being less sensitive to the allocation of the non-limiting nutrient to non-photosynthetic pools. The model predicts an optimum proportionality for N and P, which is distinct for V(cmax) and J(max) and inversely proportional to S. Initial tests showed the model to predict V(cmax) and J(max) successfully for other tropical forests characterized by a range of different foliar N and P concentrations.

Monitoring plant functional diversity from space

The world’s ecosystems are losing biodiversity fast. A satellite mission designed to track changes in plant functional diversity around the globe could deepen our understanding of the pace and consequences of this change and how to manage it.

On the delineation of tropical vegetation types with an emphasis on forest/savanna transitions

Background: There is no generally agreed classification scheme for the many different vegetation formation types occurring in the tropics. This hinders cross-continental comparisons and causes confusion as words such as ‘forest’ and ‘savanna’ have different meanings to different people. Tropical vegetation formations are therefore usually imprecisely and/or ambiguously defined in modelling, remote sensing and ecological studies. Aims: To integrate observed variations in tropical vegetation structure and floristic composition into a single classification scheme. Methods: Using structural and floristic measurements made on three continents, discrete tropical vegetation groupings were defined on the basis of overstorey and understorey structure and species compositions by using clustering techniques. Results: Twelve structural groupings were identified based on height and canopy cover of the dominant upper stratum and the extent of lower-strata woody shrub cover and grass cover. Structural classifications did not, however, always agree with those based on floristic composition, especially for plots located in the forest–savanna transition zone. This duality is incorporated into a new tropical vegetation classification scheme. Conclusions: Both floristics and stand structure are important criteria for the meaningful delineation of tropical vegetation formations, especially in the forest/savanna transition zone. A new tropical vegetation classification scheme incorporating this information has been developed.

Transgenic American elm shows reduced Dutch elm disease symptoms and normal mycorrhizal colonization

The American elm (Ulmus americana L.) was once one of the most common urban trees in eastern North America until Dutch-elm disease (DED), caused by the fungus Ophiostoma novo-ulmi, eliminated most of the mature trees. To enhance DED resistance, Agrobacterium was used to transform American elm with a transgene encoding the synthetic antimicrobial peptide ESF39A, driven by a vascular promoter from American chestnut. Four unique, single-copy transgenic lines were produced and regenerated into whole plants. These lines showed less wilting and significantly less sapwood staining than non-transformed controls after O. novo-ulmi inoculation. Preliminary observations indicated that mycorrhizal colonization was not significantly different between transgenic and wild-type trees. Although the trees tested were too young to ensure stable resistance was achieved, these results indicate that transgenes encoding antimicrobial peptides reduce DED symptoms and therefore hold promise for enhancing pathogen resistance in American elm.

Basin-wide variations in Amazon forest nitrogen-cycling characteristics as inferred from plant and soil 15N:14N measurements

Background: Patterns in tropical forest nitrogen cycling are poorly understood. In particular, the extent to which leguminous trees in these forests fix nitrogen is unclear. Aims: We aimed to determine factors that explain variation in foliar δ15N (δ15NF) for Amazon forest trees, and to evaluate the extent to which putatively N2-fixing Fabaceae acquire nitrogen from the atmosphere. Methods: Upper-canopy δ15NF values were determined for 1255 trees sampled across 65 Amazon forest plots. Along with plot inventory data, differences in δ15NF between nodule-forming Fabaceae and other trees were used to estimate the extent of N2 fixation. Results: δ15NF ranged from −12.1‰ to +9.3‰. Most of this variation was attributable to site-specific conditions, with extractable soil phosphorus and dry-season precipitation having strong influences, suggesting a restricted availability of nitrogen on both young and old soils and/or at low precipitation. Fabaceae constituted fewer than 10% of the sampled trees, and only 36% were expressed fixers. We estimated an average Amazon forest symbiotic fixation rate of 3 kg N ha−1 year−1. Conclusion: Plant δ15N indicate that low levels of nitrogen availability are only likely to influence Amazon forest function on immature or old weathered soils and/or where dry-season precipitation is low. Most Fabaceae species that are capable of nodulating do not fix nitrogen in Amazonia.

The imprint of plants on ecosystem functioning: A data-driven approach

Abstract Terrestrial ecosystems strongly determine the exchange of carbon, water and energy between the biosphere and atmosphere. These exchanges are influenced by environmental conditions (e.g., local meteorology, soils), but generally mediated by organisms. Often, mathematical descriptions of these processes are implemented in terrestrial biosphere models. Model implementations of this kind should be evaluated by empirical analyses of relationships between observed patterns of ecosystem functioning, vegetation structure, plant traits, and environmental conditions. However, the question of how to describe the imprint of plants on ecosystem functioning based on observations has not yet been systematically investigated. One approach might be to identify and quantify functional attributes or responsiveness of ecosystems (often very short-term in nature) that contribute to the long-term (i.e., annual but also seasonal or daily) metrics commonly in use. Here we define these patterns as “ecosystem functional properties”, or EFPs. Such as the ecosystem capacity of carbon assimilation or the maximum light use efficiency of an ecosystem. While EFPs should be directly derivable from flux measurements at the ecosystem level, we posit that these inherently include the influence of specific plant traits and their local heterogeneity. We present different options of upscaling in situ measured plant traits to the ecosystem level (ecosystem vegetation properties – EVPs) and provide examples of empirical analyses on plants’ imprint on ecosystem functioning by combining in situ measured plant traits and ecosystem flux measurements. Finally, we discuss how recent advances in remote sensing contribute to this framework.

Biome-specific effects of nitrogen and phosphorus on the photosynthetic characteristics of trees at a forest-savanna boundary in Cameroon

Photosynthesis/nutrient relationships of proximally growing forest and savanna trees were determined in an ecotonal region of Cameroon (Africa). Although area-based foliar N concentrations were typically lower for savanna trees, there was no difference in photosynthetic rates between the two vegetation formation types. Opposite to N, area-based P concentrations were—on average—slightly lower for forest trees; a dependency of photosynthetic characteristics on foliar P was only evident for savanna trees. Thus savanna trees use N more efficiently than their forest counterparts, but only in the presence of relatively high foliar P. Along with some other recent studies, these results suggest that both N and P are important modulators of woody tropical plant photosynthetic capacities, influencing photosynthetic metabolism in different ways that are also biome specific. Attempts to find simple unifying equations to describe woody tropical vegetation photosynthesis-nutrient relationships are likely to meet with failure, with ecophysiological distinctions between forest and savanna requiring acknowledgement.

American Elm ( Ulmus americana )

American elm (Ulmus americana) is a valuable and sentimental tree species that was decimated by Dutch elm disease in the mid-20th century. Therefore, any methods for modifying American elm or enhancing disease resistance are significant. This protocol describes transformation and tissue culture techniques used on American elm. Leaf pieces containing the midvein and petiole are used for explants. Agrobacterium tumefaciens strain EHA105 is used for transformation, with the binary vector pSE39, containing CaMV35S/nptII as a selectable marker, ACS2/ESF39A as a putative resistance enhancing gene, and CaMV35S/GUS as a reporter.

Uncertainty Quantified Matrix Completion Using Bayesian Hierarchical Matrix Factorization

Low-rank matrix completion methods have been successful in a variety of settings such as recommendation systems. However, most of the existing matrix completion methods only provide a point estimate of missing entries, and do not characterize uncertainties of the predictions. In this paper, we propose a Bayesian hierarchical probabilistic matrix factorization (BHPMF) model to (1) incorporate hierarchical side information, and (2) provide uncertainty quantified predictions. The former yields significant performance improvements in the problem of plant trait prediction, a key problem in ecology, by leveraging the taxonomic hierarchy in the plant kingdom. The latter is helpful in identifying predictions of low confidence which can in turn be used to guide field work for data collection efforts. A Gibbs sampler is designed for inference in the model. Further, we propose a multiple inheritance BHPMF (MI-BHPMF) which can work with a general directed acyclic graph (DAG) structured hierarchy, rather than a tree. We present comprehensive experimental results on the problem of plant trait prediction using the largest database of plant traits, where BHPMF shows strong empirical performance in uncertainty quantified trait prediction, outperforming the state-of-the-art based on point estimates. Further, we show that BHPMF is more accurate when it is confident, whereas the error is high when the uncertainty is high.