Effects of experimental inbreeding on herbivore resistance and plant fitness: the role of history of inbreeding, herbivory and abiotic factors

Inbreeding is common in plant populations and can affect plant fitness and resistance against herbivores. These effects are likely to depend on population history. In a greenhouse experiment with plants from 17 populations of Lychnis flos-cuculi, we studied the effects of experimental inbreeding on resistance and plant fitness. Depending on the levels of past herbivory and abiotic factors at the site of plant origin, we found either inbreeding or outbreeding depression in herbivore resistance. Furthermore, when not damaged experimentally by snail herbivores, plants from populations with higher heterozygosity suffered from inbreeding depression and those from populations with lower heterozygosity suffered from outbreeding depression. These effects of inbreeding and outbreeding were not apparent under experimental snail herbivory. We conclude that inbreeding effects on resistance and plant fitness depend on population history. Moreover, herbivory can mask inbreeding effects on plant fitness. Thus, understanding inbreeding effects on plant fitness requires studying multiple populations and considering population history and biotic interactions.

Plant-microbe-herbivore interactions in invasive and non-invasive alien plant species

1. Plants interact with many organisms, such as microbes and herbivores, and these interactions are likely to affect the establishment and spread of plants. In the context of plant invasions, mycorrhizal fungi and constitutive and induced resistance of plants against herbivores have received attention independently of each other. However, plants are frequently involved in complex multi-trophic interactions, which might differ between invasive and non-invasive alien plants. 2. In a multi-species comparative experiment, we aimed to improve our understanding of plant traits associated with invasiveness. We tested whether eight invasive alien plant species use the mycorrhizal symbiosis in a more beneficial way, and have higher levels of constitutive or induced resistance against two generalist bioassay herbivores, than nine non-invasive alien species. We further assessed whether the presence of mycorrhizal fungi altered the resistance of the plant species, and whether this differed between invasive and non-invasive alien species. 3. While invasive species produced more biomass, they did not differ in their biomass response to mycorrhizal fungi from non-invasive alien species. Invasive species also did not have higher levels of constitutive or induced resistance against the two generalist herbivores. Mycorrhizal fungi greatly affected the resistance of our plant species, however, this was also unrelated to whether the alien species were invasive or not. 4. Our study confirms the previous findings that invasive species generally grow faster and produce more biomass than non-invasive alien species. We further show that alien plant species used a variety of defence strategies, and also varied in their interactions with mycorrhizal fungi. These multi-trophic interactions were not consistently related to invasiveness of the alien plant species. 5. We suggest that awareness of the fact that alien plant species are involved in multi-trophic interactions might lead to a more complete understanding of the factors contributing to a plant's success.

Optical properties and defect structure of Sr2+ co-doped LaBr3:5%Ce scintillation crystals

Sr2+ co-doped LaBr3:5%Ce scintillators show a record low energy resolution of 2% at 662 keV and a considerably better proportional response compared to standard LaBr3:5%Ce. This paper reports on the optical properties and time response of Sr co-doped LaBr3:5%Ce. Multiple excitation and emission bands were observed in X-ray and optically excited luminescence measurements. Those bands are ascribed to three different Ce3+ sites. The first is the unperturbed site with the same luminescence properties as those of standard LaBr3:Ce. The other two are perturbed sites with red-shifted 4f-5d1 Ce3+ excitation and emission bands, longer Ce3+ decay times, and smaller Stokes shifts. The lowering of the lowest 5d level of Ce3+ was ascribed to larger crystal field interactions at the perturbed sites. Two types of point defects in the LaBr3 matrix were proposed to explain the observed results. No Ce4+ ions were detected in Sr co-doped LaBr3:5%Ce by diffuse reflectance measurements.

Accounting for baseline differences and measurement error in the analysis of change over time

If change over time is compared in several groups, it is important to take into account baseline values so that the comparison is carried out under the same preconditions. As the observed baseline measurements are distorted by measurement error, it may not be sufficient to include them as covariate. By fitting a longitudinal mixed-effects model to all data including the baseline observations and subsequently calculating the expected change conditional on the underlying baseline value, a solution to this problem has been provided recently so that groups with the same baseline characteristics can be compared. In this article, we present an extended approach where a broader set of models can be used. Specifically, it is possible to include any desired set of interactions between the time variable and the other covariates, and also, time-dependent covariates can be included. Additionally, we extend the method to adjust for baseline measurement error of other time-varying covariates. We apply the methodology to data from the Swiss HIV Cohort Study to address the question if a joint infection with HIV-1 and hepatitis C virus leads to a slower increase of CD4 lymphocyte counts over time after the start of antiretroviral therapy.

Knowledge, beliefs, risk behaviors, and social skills for HIV/STD prevention in junior college students (grades 11--12) in Bombay, India

1230 year 11 and 12 college students, modal age 16 and 17, in three colleges in Bombay, India, were studied on sexual behaviors or risk of sexual behaviors, beliefs about sex, HIV/STD knowledge, perceived norms regarding sexual behaviors, and the relationships between social skills/anxieties in HIV/STD prevention and actual and anticipated sexual behaviors. A quantitative questionnaire examining HIV/STD risk behaviors, knowledge, attitudes and beliefs, and the AIDS Social Assertiveness Scale (ASAS) were administered to these 1230 college students. Data indicated that 8% of males and 1% of females had had sexual experience, but over one third were not sure at all of being able to abstain from sexual activity with either steady or casual partners. Perceived norms were slanted toward sexual abstinence for the majority of the sample. Knowledge of protective effects of condoms was high, although half of those who had had sex did not use condoms. Logistic regression showed knowledge was higher among males, those who believed it was OK to have sex with a steady partner and that they should not wait until they were older, those who believed that condoms should be used even if the partner is known, and those who believed it was acceptable to have multiple partners. Gender differences in sexual activity and beliefs about sexual activity showed males were less likely to believe in abstaining from sexual activity. The 5 scales of the ASAS were scored and compared on ANOVA on: those who had had sexual experience (HS), those who anticipated being unable to refuse sex (AS), and those who did not anticipate problems in refusing sex (DS). Those in the AS group had greater anxieties about refusing sexual or other risk behaviors than HS and DS groups. There were greater anxieties about dealing with condoms in the AS and DS groups compared with the HS group. Confiding sexual or HIV/STD-related problems to significant others was more anxiety-provoking for the AS group compared with the HS group, and the AS group were more anxious about interactions with people with HIV. Factor analysis produced the same 5 factors as those found in previous studies. Of these, condom interactions and confiding in significant others were most anxiety provoking, and condom interactions most variable based on demographic and attitudinal factors.^ This age group is appropriate for HIV/STD reduction education given the low rate of sexual activity but despite knowledge of the importance of condom use, social skills to apply this knowledge are lacking. Social skills training in sexual negotiations, condom negotiations, and confiding HIV/STD-related concerns to significant others should reduce the risks of Indian college students having unwanted or unprotected sex. ^

The importance of biotic interactions and local adaptation for plant response to environmental changes: field evidence along an elevational gradient

Predicting the response of species to environmental changes is a great and on-going challenge for ecologists, and this requires a more in-depth understanding of the importance of biotic interactions and the population structuration in the landscape. Using a reciprocal transplantation experiment, we tested the response of five species to an elevational gradient. This was combined to a neighbour removal treatment to test the importance of local adaptation and biotic interactions. The trait studied was performance measured as survival and biomass. Species response varied along the elevational gradient, but with no consistent pattern. Performance of species was influenced by environmental conditions occurring locally at each site, as well as by positive or negative effects of the surrounding vegetation. Indeed, we observed a shift from competition for biomass to facilitation for survival as a response to the increase in environmental stress occurring in the different sites. Unlike previous studies pointing out an increase of stress along the elevation gradient, our results supported a stress gradient related to water availability, which was not strictly parallel to the elevational gradient. For three of our species, we observed a greater biomass production for the population coming from the site where the species was dominant (central population) compared to population sampled at the limit of the distribution (marginal population). Nevertheless, we did not observe any pattern of local adaptation that could indicate adaptation of populations to a particular habitat. Altogether, our results highlighted the great ability of plant species to cope with environmental changes, with no local adaptation and great variability in response to local conditions. Our study confirms the importance of taking into account biotic interactions and population structure occurring at local scale in the prediction of communities’ responses to global environmental changes.

Thrombosis during pregnancy: Risks, prevention, and treatment for mother and fetus--harvesting the power of omic technology, biomarkers and in vitro or in vivo models to facilitate the treatment of thrombosis

Maternal thromboembolism and a spectrum of placenta-mediated complications including the pre-eclampsia syndromes, fetal growth restriction, fetal loss, and abruption manifest a shared etiopathogenesis and predisposing risk factors. Furthermore, these maternal and fetal complications are often linked to subsequent maternal health consequences that comprise the metabolic syndrome, namely, thromboembolism, chronic hypertension, and type II diabetes. Traditionally, several lines of evidence have linked vasoconstriction, excessive thrombosis and inflammation, and impaired trophoblast invasion at the uteroplacental interface as hallmark features of the placental complications. "Omic" technologies and biomarker development have been largely based upon advances in vascular biology, improved understanding of the molecular basis and biochemical pathways responsible for the clinically relevant diseases, and increasingly robust large cohort and/or registry based studies. Advances in understanding of innate and adaptive immunity appear to play an important role in several pregnancy complications. Strategies aimed at improving prediction of these pregnancy complications are often incorporating hemodynamic blood flow data using non-invasive imaging technologies of the utero-placental and maternal circulations early in pregnancy. Some evidence suggests that a multiple marker approach will yield the best performing prediction tools, which may then in turn offer the possibility of early intervention to prevent or ameliorate these pregnancy complications. Prediction of maternal cardiovascular and non-cardiovascular consequences following pregnancy represents an important area of future research, which may have significant public health consequences not only for cardiovascular disease, but also for a variety of other disorders, such as autoimmune and neurodegenerative diseases.

Diabetes genes and risk of prostate cancer in the Atherosclerosis Risk in Communities study

Prostate cancer (PrCa) is a leading cause of morbidity and mortality, yet the etiology remains uncertain. Meta-analyses show that PrCa risk is reduced by 16% in men with type 2 diabetes (T2D), but the mechanism is unknown. Recent genome-wide association studies and meta-analyses have found single nucleotide polymorphisms (SNPs) that consistently predict T2D risk. We evaluated associations of incident PrCa with 14 T2D SNPs in the Atherosclerosis Risk in Communities (ARIC) study. From 1987-2000, there were 397 incident PrCa cases ascertained from state or local cancer registries among 6,642 men (1,560 blacks and 5,082 whites) aged 45-64 years at baseline. Genotypes were determined by TaqMan assay. Cox proportional hazards models were used to assess the association between PrCa and increasing number of T2D risk-raising alleles for individual SNPs and for genetic risk scores (GRS) comprised of the number of T2D risk-raising alleles across SNPs. Two-way gene-gene interactions were evaluated with likelihood ratio tests. Using additive genetic models, the T2D risk-raising allele was associated with significantly reduced risk of PrCa for IGF2BP2 rs4402960 (hazard ratio [HR]=0.79; P=0.07 among blacks only), SLC2A2 rs5400 (race-adjusted HR=0.85; P=0.05) and UCP2 rs660339 (race-adjusted HR=0.84; P=0.02), but significantly increased risk of PrCa for CAPN10 rs3792267 (race-adjusted HR=1.20; P=0.05). No other SNPs were associated with PrCa using an additive genetic model. However, at least one copy of the T2D risk-raising allele for TCF7L2 rs7903146 was associated with reduced PrCa risk using a dominant genetic model (race-adjusted HR=0.79; P=0.03). These results imply that the T2D-PrCa association may be partly due to shared genetic variation, but these results should be verified since multiple tests were performed. When the combined, additive effects of these SNPs were tested using a GRS, there was nearly a 10% reduction in risk of PrCa per T2D risk-raising allele (race-adjusted HR=0.92; P=0.02). SNPs in IGF2BP2, KCNJ11 and SLC2A2 were also involved in multiple synergistic gene-gene interactions on a multiplicative scale. In conclusion, it appears that the T2D-PrCa association may be due, in part, to common genetic variation. Further knowledge of T2D gene-PrCa mechanisms may improve understanding of PrCa etiology and may inform PrCa prevention and treatment.^

The effect of genotype-by-environment interaction on longitudinal triglyceride profiles in the Bogalusa Heart Study

Triglyceride levels are a component of plasma lipids that are thought to be an important risk factor for coronary heart disease and are influenced by genetic and environmental factors, such as single nucleotide polymorphisms (SNPs), alcohol intake, and smoking. This study used longitudinal data from the Bogalusa Heart Study, a biracial community-based survey of cardiovascular disease risk factors. A sample of 1191 individuals, 4 to 38 years of age, was measured multiple times from 1973 to 2000. The study sample consisted of 730 white and 461 African American participants. Individual growth models were developed in order to assess gene-environment interactions affecting plasma triglycerides over time. After testing for inclusion of significant covariates and interactions, final models, each accounting for the effects of a different SNP, were assessed for fit and normality. After adjustment for all other covariates and interactions, LIPC -514C/T was found to interact with age3, age2, and age and a non-significant interaction of CETP -971G/A genotype with smoking status was found (p = 0.0812). Ever-smokers had higher triglyceride levels than never smokers, but persons heterozygous at this locus, about half of both races, had higher triglyceride levels after smoking cessation compared to current smokers. Since tobacco products increase free fatty acids circulating in the bloodstream, smoking cessation programs have the potential to ultimately reduce triglyceride levels for many persons. However, due to the effect of smoking cessation on the triglyceride levels of CETP -971G/A heterozygotes, the need for smoking prevention programs is also demonstrated. Both smoking cessation and prevention programs would have a great public health impact on minimizing triglyceride levels and ultimately reducing heart disease. ^

Detecting genetic and nutritional lung cancer risk factors related to folate metabolism using Bayesian generalized linear models

Complex diseases, such as cancer, are caused by various genetic and environmental factors, and their interactions. Joint analysis of these factors and their interactions would increase the power to detect risk factors but is statistically. Bayesian generalized linear models using student-t prior distributions on coefficients, is a novel method to simultaneously analyze genetic factors, environmental factors, and interactions. I performed simulation studies using three different disease models and demonstrated that the variable selection performance of Bayesian generalized linear models is comparable to that of Bayesian stochastic search variable selection, an improved method for variable selection when compared to standard methods. I further evaluated the variable selection performance of Bayesian generalized linear models using different numbers of candidate covariates and different sample sizes, and provided a guideline for required sample size to achieve a high power of variable selection using Bayesian generalize linear models, considering different scales of number of candidate covariates. ^ Polymorphisms in folate metabolism genes and nutritional factors have been previously associated with lung cancer risk. In this study, I simultaneously analyzed 115 tag SNPs in folate metabolism genes, 14 nutritional factors, and all possible genetic-nutritional interactions from 1239 lung cancer cases and 1692 controls using Bayesian generalized linear models stratified by never, former, and current smoking status. SNPs in MTRR were significantly associated with lung cancer risk across never, former, and current smokers. In never smokers, three SNPs in TYMS and three gene-nutrient interactions, including an interaction between SHMT1 and vitamin B12, an interaction between MTRR and total fat intake, and an interaction between MTR and alcohol use, were also identified as associated with lung cancer risk. These lung cancer risk factors are worthy of further investigation.^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Isotopic signature of specific biomarkers derived from anaerobic methanotrophic archaea (ANME groups) and sulphate-reducing bacteria (SRB) at different cold seep provinces

Anaerobic methane-oxidizing microbial communities in sediments at cold methane seeps are important factors in controlling methane emission to the ocean and atmosphere. Here, we investigated the distribution and carbon isotopic signature of specific biomarkers derived from anaerobic methanotrophic archaea (ANME groups) and sulphate-reducing bacteria (SRB) responsible for the anaerobic oxidation of methane (AOM) at different cold seep provinces of Hydrate Ridge, Cascadia margin. The special focus was on their relation to in situ cell abundances and methane turnover. In general, maxima in biomarker abundances and minima in carbon isotope signatures correlated with maxima in AOM and sulphate reduction as well as with consortium biomass. We found ANME-2a/DSS aggregates associated with high abundances of sn-2,3-di-O-isoprenoidal glycerol ethers (archaeol, sn-2-hydroxyarchaeol) and specific bacterial fatty acids (C16:1omega5c, cyC17:0omega5,6) as well as with high methane fluxes (Beggiatoa site). The low to medium flux site (Calyptogena field) was dominated by ANME-2c/DSS aggregates and contained less of both compound classes but more of AOM-related glycerol dialkyl glycerol tetraethers (GDGTs). ANME-1 archaea dominated deeper sediment horizons at the Calyptogena field where sn-1,2-di-O-alkyl glycerol ethers (DAGEs), archaeol, methyl-branched fatty acids (ai-C15:0, i-C16:0, ai-C17:0), and diagnostic GDGTs were prevailing. AOM-specific bacterial and archaeal biomarkers in these sediment strata generally revealed very similar d13C-values of around -100 per mill. In ANME-2-dominated sediment sections, archaeal biomarkers were even more 13C-depleted (down to -120 per mill), whereas bacterial biomarkers were found to be likewise 13C-depleted as in ANME-1-dominated sediment layers (d13C: -100 per mill). The zero flux site (Acharax field), containing only a few numbers of ANME-2/DSS aggregates, however, provided no specific biomarker pattern. Deeper sediment sections (below 20 cm sediment depth) from Beggiatoa covered areas which included solid layers of methane gas hydrates contained ANME-2/DSS typical biomarkers showing subsurface peaks combined with negative shifts in carbon isotopic compositions. The maxima were detected just above the hydrate layers, indicating that methane stored in the hydrates may be available for the microbial community. The observed variations in biomarker abundances and 13C-depletions are indicative of multiple environmental and physiological factors selecting for different AOM consortia (ANME-2a/DSS, ANME-2c/DSS, ANME-1) along horizontal and vertical gradients of cold seep settings.

Collection of data on soil carbon (particulate and dissolved) in the Jena Experiment (Main Experiment, time series since 2002)

This data set contains three time series of measurements of soil carbon (particular and dissolved) from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. 1. Particulate soil carbon: Stratified soil sampling was performed every two years since before sowing in April 2002 and was repeated in April 2004, 2006 and 2008 to a depth of 30 cm segmented to a depth resolution of 5 cm giving six depth subsamples per core. Total carbon concentration was analyzed on ball-milled subsamples by an elemental analyzer at 1150°C. Inorganic carbon concentration was measured by elemental analysis at 1150°C after removal of organic carbon for 16 h at 450°C in a muffle furnace. Organic carbon concentration was calculated as the difference between both measurements of total and inorganic carbon. 2. Particulate soil carbon (high intensity sampling): In one block of the Jena Experiment soil samples were taken to a depth of 1 m (segmented to a depth resolution of 5 cm giving 20 depth subsamples per core) with three replicates per block ever 5 years starting before sowing in April 2002. Samples were processed as for the more frequent sampling. 3. Dissolved organic carbon: Suction plates installed on the field site in 10, 20, 30 and 60 cm depth were used to sample soil pore water. Cumulative soil solution was sampled biweekly and analyzed for dissolved organic carbon concentration by a high TOC elemental analyzer. Annual mean values of DOC are provided.

Aboveground community and species-specific plant biomass from the Jena Experiment (Main Experiment, time series since 2002)

This data set comprises a time series of aboveground community plant biomass (Sown plant community, Weed plant community, Dead plant material, and Unidentified plant material; all measured in biomass as dry weight) and species-specific biomass from the sown species of the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. Aboveground community biomass was harvested twice a year just prior to mowing (during peak standing biomass twice a year, generally in May and August; in 2002 only once in September) on all experimental plots of the main experiment. This was done by clipping the vegetation at 3 cm above ground in up to four rectangles of 0.2 x 0.5 m per large plot. The location of these rectangles was assigned by random selection of new coordinates every year within the core area of the plots (i.e. the central 10 x 15 m). The positions of the rectangles within plots were identical for all plots. The harvested biomass was sorted into categories: individual species for the sown plant species, weed plant species (species not sown at the particular plot), detached dead plant material (i.e., dead plant material in the data file), and remaining plant material that could not be assigned to any category (i.e., unidentified plant material in the data file). All biomass was dried to constant weight (70°C, >= 48 h) and weighed. Sown plant community biomass was calculated as the sum of the biomass of the individual sown species. The data for individual samples and the mean over samples for the biomass measures on the community level are given. Overall, analyses of the community biomass data have identified species richness as well as functional group composition as important drivers of a positive biodiversity-productivity relationship.