Jean Villerd

Filling the gap in functional trait databases: use of ecological hypotheses to replace missing data.

Functional trait databases are powerful tools in ecology, though most of them contain large amounts of missing values. The goal of this study was to test the effect of imputation methods on the evaluation of trait values at species level and on the subsequent calculation of functional diversity indices at community level using functional trait databases. Two simple imputation methods (average and median), two methods based on ecological hypotheses, and one multiple imputation method were tested using a large plant trait database, together with the influence of the percentage of missing data and differences between functional traits. At community level, the complete-case approach and three functional diversity indices calculated from grassland plant communities were included. At the species level, one of the methods based on ecological hypothesis was for all traits more accurate than imputation with average or median values, but the multiple imputation method was superior for most of the traits. The method based on functional proximity between species was the best method for traits with an unbalanced distribution, while the method based on the existence of relationships between traits was the best for traits with a balanced distribution. The ranking of the grassland communities for their functional diversity indices was not robust with the complete-case approach, even for low percentages of missing data. With the imputation methods based on ecological hypotheses, functional diversity indices could be computed with a maximum of 30% of missing data, without affecting the ranking between grassland communities. The multiple imputation method performed well, but not better than single imputation based on ecological hypothesis and adapted to the distribution of the trait values for the functional identity and range of the communities. Ecological studies using functional trait databases have to deal with missing data using imputation methods corresponding to their specific needs and making the most out of the information available in the databases. Within this framework, this study indicates the possibilities and limits of single imputation methods based on ecological hypothesis and concludes that they could be useful when studying the ranking of communities for their functional diversity indices.

Mapping and determinism of soil microbial community distribution across an agricultural landscape.

Despite the relevance of landscape, regarding the spatial patterning of microbial communities and the relative influence of environmental parameters versus human activities, few investigations have been conducted at this scale. Here, we used a systematic grid to characterize the distribution of soil microbial communities at 278 sites across a monitored agricultural landscape of 13 km². Molecular microbial biomass was estimated by soil DNA recovery and bacterial diversity by 16S rRNA gene pyrosequencing. Geostatistics provided the first maps of microbial community at this scale and revealed a heterogeneous but spatially structured distribution of microbial biomass and diversity with patches of several hundreds of meters. Variance partitioning revealed that both microbial abundance and bacterial diversity distribution were highly dependent of soil properties and land use (total variance explained ranged between 55% and 78%). Microbial biomass and bacterial richness distributions were mainly explained by soil pH and texture whereas bacterial evenness distribution was mainly related to land management. Bacterial diversity (richness, evenness, and Shannon index) was positively influenced by cropping intensity and especially by soil tillage, resulting in spots of low microbial diversity in soils under forest management. Spatial descriptors also explained a small but significant portion of the microbial distribution suggesting that landscape configuration also shapes microbial biomass and bacterial diversity.

Study of nitrogen and carbon transfer from soil organic matter to Tuber melanosporum mycorrhizas and ascocarps using 15N and 13C soil labelling and whole-genome oligoarrays

Background and aimsWe previously showed by 13CO2 host labelling that almost all of the constitutive carbon allocated to the truffles originated from the host. The objective of this present work was to determine the putative capacity of T. melanosporum ectomycorrhizas and ascocarps to use soil carbon and to uptake or assimilate soil nitrate.MethodsThe current investigation involved 13C and 15N soil labelling by incorporating labelled leaf litter and expression of genes involved in carbon and nitrogen metabolism in ascocarps and ectomycorrhizas.ResultsThe ascocarps harvested in the labelled plots were highly enriched in 15N but were almost never enriched in 13C. The main source of soil mineral nitrogen was nitrate. A nitrate transporter, one nitrate reductase and a nitrite reductase were well expressed in ectomycorrhizas. Several genes involved in aminoacid synthesis or in transamination processes were also well expressed in ectomycorrhizas. No nitrate transporter was expressed in ascocarps where the CAZyme genes upregulated were mainly Glycosyltransferases involved in saccharide transfer.ConclusionAscocarps did not exhibit saprotrophic capacity for C, supporting previous results from 13CO2 host labelling showing that C is provided by the host tree. The 15N present in the ascocarps after soil labelling is supplied as ammonium or aminoacids by the ectomycorrhizas, which are able to uptake, reduce and metabolize nitrate.

Predictive quality of 26 pesticide risk indicators and one flow model: A multisite assessment for water contamination

Stakeholders need operational tools to assess crop protection strategies in regard to environmental impact. The need to assess and report on the impacts of pesticide use on the environment has led to the development of numerous indicators. However, only a few studies have addressed the predictive quality of these indicators. This is mainly due to the limited number of datasets adapted to the comparison of indicator outputs with pesticide measurement. To our knowledge, evaluation of the predictive quality of pesticide indicators in comparison to the quality of water as presented in this article is unprecedented in terms of the number of tested indicators (26 indicators and the MACRO model) and in terms of the size of datasets used (data collected for 4 transfer pathways, 20 active ingredients (a.i.) for a total of 1040 comparison points). Results obtained on a.i. measurements were compared to the indicator outputs, measured by: (i) correlation tests to identify linear relationship, (ii) probability tests comparing measurements with indicator outputs, both classified in 5 classes, and assessing the probability i.e. the percentage of correct estimation and overestimation (iii) by ROC tests estimating the predictive ability against a given threshold. Results showed that the correlation between indicator outputs and the observed transfers are low (r<0.58). Overall, more complex indicators taking into account the soil, the climatic and the environmental aspects yielded comparatively better results. The numerical simulation model MACRO showed much better results than those for indicators. These results will be used to help stakeholders to appropriately select their indicators, and will provide them with advice for possible use and limits in the interpretation of indicator outputs.