Showing papers by "Christian Wirth published in 2015"

Global variability in leaf respiration in relation to climate, plant functional types and leaf traits

Abstract: Leaf dark respiration (R-dark) is an important yet poorly quantified component of the global carbon cycle. Given this, we analyzed a new global database of R-dark and associated leaf traits. Data for 899 species were compiled from 100 sites (from the Arctic to the tropics). Several woody and nonwoody plant functional types (PFTs) were represented. Mixed-effects models were used to disentangle sources of variation in R-dark. Area-based R-dark at the prevailing average daily growth temperature (T) of each siteincreased only twofold from the Arctic to the tropics, despite a 20 degrees C increase in growing T (8-28 degrees C). By contrast, R-dark at a standard T (25 degrees C, R-dark(25)) was threefold higher in the Arctic than in the tropics, and twofold higher at arid than at mesic sites. Species and PFTs at cold sites exhibited higher R-dark(25) at a given photosynthetic capacity (V-cmax(25)) or leaf nitrogen concentration ([N]) than species at warmer sites. R-dark(25) values at any given V-cmax(25) or [N] were higher in herbs than in woody plants. The results highlight variation in R-dark among species and across global gradients in T and aridity. In addition to their ecological significance, the results provide a framework for improving representation of R-dark in terrestrial biosphere models (TBMs) and associated land-surface components of Earth system models (ESMs).

BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography

Abstract: Aim Functional traits of organisms are key to understanding and predicting biodiversity and ecological change, which motivates continuous collection of traits and their integration into global databases. Such trait matrices are inherently sparse, severely limiting their usefulness for further analyses. On the other hand, traits are characterized by the phylogenetic trait signal, trait–trait correlations and environmental constraints, all of which provide information that could be used to statistically fill gaps. We propose the application of probabilistic models which, for the first time, utilize all three characteristics to fill gaps in trait databases and predict trait values at larger spatial scales. Innovation For this purpose we introduce BHPMF, a hierarchical Bayesian extension of probabilistic matrix factorization (PMF). PMF is a machine learning technique which exploits the correlation structure of sparse matrices to impute missing entries. BHPMF additionally utilizes the taxonomic hierarchy for trait prediction and provides uncertainty estimates for each imputation. In combination with multiple regression against environmental information, BHPMF allows for extrapolation from point measurements to larger spatial scales. We demonstrate the applicability of BHPMF in ecological contexts, using different plant functional trait datasets, also comparing results to taking the species mean and PMF. Main conclusions Sensitivity analyses validate the robustness and accuracy of BHPMF: our method captures the correlation structure of the trait matrix as well as the phylogenetic trait signal – also for extremely sparse trait matrices – and provides a robust measure of confidence in prediction accuracy for each missing entry. The combination of BHPMF with environmental constraints provides a promising concept to extrapolate traits beyond sampled regions, accounting for intraspecific trait variability. We conclude that BHPMF and its derivatives have a high potential to support future trait-based research in macroecology and functional biogeography.

Simple measures of climate, soil properties and plant traits predict national‐scale grassland soil carbon stocks

Abstract: 1. Soil carbon (C) storage is a key ecosystem service. Soil C stocks play a vital role in soil fertility and climate regulation, but the factors that control these stocks at regional and national scales are unknown, particularly when their composition and stability are considered. As a result, their mapping relies on either unreliable proxy measures or laborious direct measurements. 2. Using data from an extensive national survey of English grasslands we show that surface soil (0-7cm) C stocks in size fractions of varying stability can be predicted at both regional and national scales from plant traits and simple measures of soil and climatic conditions. 3. Soil C stocks in the largest pool, of intermediate particle size (50-250 µm), were best explained by mean annual temperature (MAT), soil pH and soil moisture content. The second largest C pool, highly stable physically and biochemically protected particles (0.45-50 µm), was explained by soil pH and the community abundance weighted mean (CWM) leaf nitrogen (N) content, with the highest soil C stocks under N rich vegetation. The C stock in the small active fraction (250-4000 µm) was explained by a wide range of variables: MAT, mean annual precipitation, mean growing season length, soil pH and CWM specific leaf area; stocks were higher under vegetation with thick and/or dense leaves. 4. Testing the models describing these fractions against data from an independent English region indicated moderately strong correlation between predicted and actual values and no systematic bias, with the exception of the active fraction, for which predictions were inaccurate. 5. Synthesis and Applications: Validation indicates that readily available climate, soils and plant survey data can be effective in making local- to landscape-scale (1-100,000 km2) soil C stock predictions. Such predictions are a crucial component of effective management strategies to protect C stocks and enhance soil C sequestration.

Early subtropical forest growth is driven by community mean trait values and functional diversity rather than the abiotic environment

Abstract: While functional diversity (FD) has been shown to be positively related to a number of ecosystem functions including biomass production, it may have a much less pronounced effect than that of environmental factors or species-specific properties. Leaf and wood traits can be considered particularly relevant to tree growth, as they reflect a trade-off between resources invested into growth and persistence. Our study focussed on the degree to which early forest growth was driven by FD, the environment (11 variables characterizing abiotic habitat conditions), and community-weighted mean (CWM) values of species traits in the context of a large-scale tree diversity experiment (BEF-China). Growth rates of trees with respect to crown diameter were aggregated across 231 plots (hosting between one and 23 tree species) and related to environmental variables, FD, and CWM, the latter two of which were based on 41 plant functional traits. The effects of each of the three predictor groups were analyzed separately by mixed model optimization and jointly by variance partitioning. Numerous single traits predicted plot-level tree growth, both in the models based on CWMs and FD, but none of the environmental variables was able to predict tree growth. In the best models, environment and FD explained only 4 and 31% of variation in crown growth rates, respectively, while CWM trait values explained 42%. In total, the best models accounted for 51% of crown growth. The marginal role of the selected environmental variables was unexpected, given the high topographic heterogeneity and large size of the experiment, as was the significant impact of FD, demonstrating that positive diversity effects already occur during the early stages in tree plantations.

From pots to plots: Hierarchical trait-based prediction of plant

Abstract: Traits are powerful predictors of ecosystem functions pointing to underlying physiological and ecological processes. Plant individual performance results from the coordinated operation of many processes, ranging from nutrient uptake over organ turnover to photosynthesis, thus requiring a large set of traits for its prediction. For plant performance on higher hierarchical levels, e.g. populations, additional traits important for plant-plant and trophic interactions may be required which should even enlarge the spectrum of relevant predictor traits.(2)The goal of this study was to assess the importance of plant functional traits to predict individual and population performance of grassland species with particular focus on the significance of root traits. We tested this for 59 grassland species using 35 traits divided into three trait clusters: leaf traits (16), stature traits (8) and root traits (11), using individual biomass of mesocosm plants as a measure of individual performance and population biomass of monocultures as a measure of population performance. We applied structural equation models to disentangle direct effects of single traits on population biomass and indirect effects via individual plant biomass or shoot density.We tested multivariate trait effects on individual and population biomass to analyze whether the importance of different trait clusters shifts with increasing hierarchical integration from individuals to populations.(3)Traits of all three clusters significantly correlated with individual and population biomass. However, in spite of a number of significant correlations, above-below-ground linkages were generally week, with few exceptions like N content.(4)Stature traits exclusively affected population biomass indirectly via their effect on individual biomass, whereas root and leaf traits showed also direct effects and partly indirect effects via density.(5)The inclusion of root traits in multiple regression models improved the prediction of individual biomass compared to models with only above-ground information only slightly (95% vs. 93% of variance prediction with and without root traits, respectively) but was crucial for the prediction of population biomass (77% and 49%, respectively). Root traits were more important for plant performance than leaf traits and were even the most important predictors at the population level(6)Synthesis: Upscaling from the individual to the population level reflects an increasing number of processes requiring traits from different trait clusters for their prediction. Our results emphasize the importance of root traits for trait-based studies especially at higher organizational levels. Our approach provides a comprehensive framework acknowledging the hierarchical nature of trait influences. This is one step towards a more process-oriented assessment of trait-based approaches

Multitrophic diversity in a biodiverse forest is highly nonlinear across spatial scales

Abstract: Date of Acceptance: 10/11/2015 Acknowledgements We thank the administration of the Gutianshan National Nature Reserve and members of the BEF-China consortium for support, the many people involved in the plant and arthropod censuses, and T. Fang, S. Chen, T. Li, M. Ohl and C.-D. Zhu for help with species identification. G. Seidler kindly calculated forest cover and T. Scholten and P. Kuhn provided soil data. The study was funded by the German Research Foundation (DFG FOR 891/1, 891/2), the Sino-German Centre for Research Promotion (GZ 524, 592, 698, 699, 785 and 1020) and the National Science Foundation of China (NSFC 30710103907 and 30930005).

Drivers of CO2 Emission Rates from Dead Wood Logs of 13 Tree Species in the Initial Decomposition Phase

Abstract: Large dead wood is an important structural component of forest ecosystems and a main component of forest carbon cycles. CO2 emissions from dead wood can be used as a proxy for actual decomposition rates. The main drivers of CO2 emission rates for dead wood of temperate European tree species are largely unknown. We applied a novel, closed chamber measurement technique to 360 dead wood logs of 13 important tree species in three regions in Germany. We found that tree species identity was with 71% independent contribution to the model (R2 = 0.62) the most important driver of volume-based CO2 emission rates, with angiosperms having on average higher rates than conifers. Wood temperature and fungal species richness had a positive effect on CO2 emission rates, whereas wood density had a negative effect. This is the first time that positive fungal species richness—wood decomposition relationship in temperate forests was shown. Certain fungal species were associated with high or low CO2 emission rates. In addition, as indicated by separate models for each tree species, forest management intensity, study region, and the water content as well as C and N concentration of dead wood influenced CO2 emission rates.

Influence of wood density in tree-ring-based annual productivity assessments and its errors in Norway spruce

Abstract: . Estimations of tree annual biomass increments are used by a variety of studies related to forest productivity or carbon fluxes. Biomass increment estimations can be easily obtained from diameter surveys or historical diameter reconstructions based on tree rings' records. However, the biomass models rely on the assumption that wood density is constant. Converting volume increment into biomass also requires assumptions about the wood density. Wood density has been largely reported to vary both in time and between trees. In Norway spruce, wood density is known to increase with decreasing ring width. This could lead to underestimating the biomass or carbon deposition in bad years. The variations between trees of wood density have never been discussed but could also contribute to deviations. A modelling approach could attenuate these effects but will also generate errors. Here a model of wood density variations in Norway spruce, and an allometric model of volume growth were developed. We accounted for variations in wood density both between years and between trees, based on specific measurements. We compared the effects of neglecting each variation source on the estimations of annual biomass increment. We also assessed the errors of the biomass increment predictions at tree level, and of the annual productivity at plot level. Our results showed a partial compensation of the decrease in ring width in bad years by the increase in wood density. The underestimation of the biomass increment in those years reached 15 %. The errors related to the use of an allometric model of volume growth were modest, around ±15 %. The errors related to variations in wood density were much larger, the biggest component being the inter-tree variability. The errors in plot-level annual biomass productivity reached up to 40 %, with a full account of all the error sources.

Taking a closer look: disentangling effects of functional diversity on ecosystem functions with a trait-based model across hierarchy and time.

Abstract: Biodiversity and ecosystem functioning (BEF) research has progressed from the detection of relationships to elucidating their drivers and underlying mechanisms. In this context, replacing taxonomic predictors by trait-based measures of functional composition (FC)—bridging functions of species and of ecosystems—is a widely used approach. The inherent challenge of trait-based approaches is the multi-faceted, dynamic and hierarchical nature of trait influence: (i) traits may act via different facets of their distribution in a community, (ii) their influence may change over time and (iii) traits may influence processes at different levels of the natural hierarchy of organization. Here, we made use of the forest ecosystem model ‘LPJ-GUESS’ parametrized with empirical trait data, which creates output of individual performance, community assembly, stand-level states and processes. To address the three challenges, we resolved the dynamics of the top-level ecosystem function ‘annual biomass change’ hierarchically into its various component processes (growth, leaf and root turnover, recruitment and mortality) and states (stand structures, water stress) and traced the influence of different facets of FC along this hierarchy in a path analysis. We found an independent influence of functional richness, dissimilarity and identity on ecosystem states and processes and hence biomass change. Biodiversity effects were only positive during early succession and later turned negative. Unexpectedly, resource acquisition (growth, recruitment) and conservation (mortality, turnover) played an equally important role throughout the succession. These results add to a mechanistic understanding of biodiversity effects and place a caveat on simplistic approaches omitting hierarchical levels when analysing BEF relationships. They support the view that BEF relationships experience dramatic shifts over successional time that should be acknowledged in mechanistic theories.

rBEFdata: documenting data exchange and analysis for a collaborative data management platform.

Abstract: We are witnessing a growing gap separating primary research data from derived data products presented as knowledge in publications. Although journals today more often require the underlying data products used to derive the results as a prerequisite for a publication, the important link to the primary data is lost. However, documenting the postprocessing steps of data linking, the primary data with derived data products has the potential to increase the accuracy and the reproducibility of scientific findings significantly. Here, we introduce the rBEFdata R package as companion to the collaborative data management platform BEFdata. The R package provides programmatic access to features of the platform. It allows to search for data and integrates the search with external thesauri to improve the data discovery. It allows to download and import data and metadata into R for analysis. A batched download is available as well which works along a paper proposal mechanism implemented by BEFdata. This feature of BEFdata allows to group primary data and metadata and streamlines discussions and collaborations revolving around a certain research idea. The upload functionality of the R package in combination with the paper proposal mechanism of the portal allows to attach derived data products and scripts directly from R, thus addressing major aspects of documenting data postprocessing. We present the core features of the rBEFdata R package along an ecological analysis example and further discuss the potential of postprocessing documentation for data, linking primary data with derived data products and knowledge.