Nitrogen to phosphorus ratio shapes the bacterial communities involved in cellulose decomposition and copper contamination alters their stoichiometric demands.

All living organisms theoretically have an optimal stoichiometric nitrogen: phosphorus (N: P) ratio, below and beyond which their growth is affected, but data remain scarce for microbial decomposers. Here, we evaluated optimal N: P ratios of microbial communities involved in cellulose decomposition and assessed their stability when exposed to copper Cu(II). We hypothesized that (1) cellulose decomposition is maximized for an optimal N: P ratio; (2) copper exposure reduces cellulose decomposition and (3) increases microbial optimal N: P ratio; and (4) N: P ratio and copper modify the structure of microbial decomposer communities. We measured cellulose disc decomposition by a natural inoculum in microcosms exposed to a gradient of N: P ratios at three copper concentrations (0, 1 and 15 µM). Bacteria were most probably the main decomposers. Without copper, cellulose decomposition was maximized at an N: P molar ratio of 4.7. Contrary to expectations, at high copper concentration, the optimal N: P ratio (2.8) and the range of N: P ratios allowing decomposition were significantly reduced and accompanied by a reduction of bacterial diversity. Copper contamination led to the development of tolerant taxa probably less efficient in decomposing cellulose. Our results shed new light on the understanding of multiple stressor effects on microbial decomposition in an increasingly stoichiometrically imbalanced world.

How does interspecific competition modify the response of grass plants against herbicide treatment? A hierarchical concentration-response approach.

Ecological interactions are rarely taken into account in environmental risk assessment. The objective of this work was to assess how interspecific competition affects the way plant species react to herbicides and more specifically how it modifies the concentration-response curves that can be built using ecotoxicological bioassays. To do this, we relied on the results of ecotoxicological bioassays on six herbaceous species exposed to isoproturon under two conditions: in presence and in absence of a competitor. At the end of the experiments, eleven endpoints were measured. We modelled these data using a hierarchical modelling framework designed to assess the effects of competition on each of the four parameters of the concentration response curves (e.g. the level of response at the control or the concentration at the inflection point of the curve) simultaneously for the six species. The modelled effects could be of three types, 1) competition had no effect on the parameter, 2) competition had the same effect on the parameter for all species and 3) competition had a different effect on the parameter for each species. Our main hypothesis was that different species would react differently to competition. Results showed that about a half of the estimated parameters showed a modification under competition pressure among which only a fourth showed a species-specific effect, the three other fourth showing the same effect between the different species. Our initial hypothesis was thus not supported as species tended to react in the same way to competition. The competition effect on plants was mainly negative, thus showing that they were more affected by isoproturon under competition pressure. This study therefore establishes how competition modifies plant responses to chemical stress and how this interaction varies from one species to the other.

Effect of interspecific competition on species sensitivity distribution models: Analysis of plant responses to chemical stress.

Species Sensitivity Distributions (SSD) are widely used in environmental risk assessment to predict the concentration of a contaminant that is hazardous for 5% of species (HC5). They are based on monospecific bioassays conducted in the laboratory and thus do not directly take into account ecological interactions. This point, among others, is accounted for in environmental risk assessment through an assessment factor (AF) that is applied to compensate for the lack of environmental representativity. In this study, we aimed to assess the effects of interspecific competition on the responses towards isoproturon of plant species representative of a vegetated filter strip community, and to assess its impact on the derived SSD and HC5 values. To do so, we realized bioassays confronting six herbaceous species to a gradient of isoproturon exposure in presence and absence of a competitor. Several modelling approaches were applied to see how they affected the results, using different critical effect concentrations and investigating different ways to handle multiple endpoints in SSD. At the species level, there was a strong trend toward organisms being more sensitive to isoproturon in presence of a competitor than in its absence. At the community level, this trend was also observed in the SSDs and HC5 values were always lower in presence of a competitor (1.12-11.13 times lower, depending on the modelling approach). Our discussion questions the relevance of SSD and AF as currently applied in environmental risk assessment.

DRomics: A Turnkey Tool to Support the Use of the Dose-Response Framework for Omics Data in Ecological Risk Assessment.

Omics approaches (e.g., transcriptomics, metabolomics) are promising for ecological risk assessment (ERA) since they provide mechanistic information and early warning signals. A crucial step in the analysis of omics data is the modeling of concentration-dependency which may have different trends including monotonic (e.g., linear, exponential) or biphasic (e.g., U shape, bell shape) forms. The diversity of responses raises challenges concerning detection and modeling of significant responses and effect concentration (EC) derivation. Furthermore, handling high-throughput data sets is time-consuming and requires effective and automated processing routines. Thus, we developed an open source tool (DRomics, available as an R-package and as a web-based service) which, after elimination of molecular responses (e.g., gene expressions from microarrays) with no concentration-dependency and/or high variability, identifies the best model for concentration-response curve description. Subsequently, an EC (e.g., a benchmark dose) is estimated from each curve, and curves are classified based on their model parameters. This tool is especially dedicated to manage data obtained from an experimental design favoring a great number of tested doses rather than a great number of replicates and also to handle properly monotonic and biphasic trends. The tool finally provides restitution for a table of results that can be directly used to perform ERA approaches.