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.

Sample size calculation in metabolic phenotyping studies.

The number of samples needed to identify significant effects is a key question in biomedical studies, with consequences on experimental designs, costs and potential discoveries. In metabolic phenotyping studies, sample size determination remains a complex step. This is due particularly to the multiple hypothesis-testing framework and the top-down hypothesis-free approach, with no a priori known metabolic target. Until now, there was no standard procedure available to address this purpose. In this review, we discuss sample size estimation procedures for metabolic phenotyping studies. We release an automated implementation of the Data-driven Sample size Determination (DSD) algorithm for MATLAB and GNU Octave. Original research concerning DSD was published elsewhere. DSD allows the determination of an optimized sample size in metabolic phenotyping studies. The procedure uses analytical data only from a small pilot cohort to generate an expanded data set. The statistical recoupling of variables procedure is used to identify metabolic variables, and their intensity distributions are estimated by Kernel smoothing or log-normal density fitting. Statistically significant metabolic variations are evaluated using the Benjamini-Yekutieli correction and processed for data sets of various sizes. Optimal sample size determination is achieved in a context of biomarker discovery (at least one statistically significant variation) or metabolic exploration (a maximum of statistically significant variations). DSD toolbox is encoded in MATLAB R2008A (Mathworks, Natick, MA) for Kernel and log-normal estimates, and in GNU Octave for log-normal estimates (Kernel density estimates are not robust enough in GNU octave). It is available at http://www.prabi.fr/redmine/projects/dsd/repository, with a tutorial at http://www.prabi.fr/redmine/projects/dsd/wiki.

SRV: an open-source toolbox to accelerate the recovery of metabolic biomarkers and correlations from metabolic phenotyping datasets.

MOTIVATION: Supervised multivariate statistical analyses are often required to analyze the high-density spectral information in metabolic datasets acquired from complex mixtures in metabolic phenotyping studies. Here we present an implementation of the SRV-Statistical Recoupling of Variables-algorithm as an open-source Matlab and GNU Octave toolbox. SRV allows the identification of similarity between consecutive variables resulting from the high-resolution bucketing. Similar variables are gathered to restore the spectral dependency within the datasets and identify metabolic NMR signals. The correlation and significance of these new NMR variables for a given effect under study can then be measured and represented on a loading plot to allow a visual and efficient identification of candidate biomarkers. Further on, correlations between these candidate biomarkers can be visualized on a two-dimensional pseudospectrum, representing a correlation map, helping to understand the modifications of the underlying metabolic network. AVAILABILITY: SRV toolbox is encoded in MATLAB R2008A (Mathworks, Natick, MA) and in GNU Octave. It is available free of charge at http://www.prabi.fr/redmine/projects/srv/repository with a tutorial. CONTACT: benjamin.blaise@chu-lyon.fr or vincent.navratil@univ-lyon1.fr.

A 34-year survey under phosphorus decline and warming: Consequences on stoichiometry and functional trait composition of freshwater macroinvertebrate communities.

Worldwide, freshwater systems are subjected to increasing temperatures and nutrient changes. Under phosphorus and nitrogen enrichment consumer communities are often thought to shift towards fast-growing and P-rich taxa, supporting the well-known link between growth rate and body stoichiometry. While these traits are also favoured under warming, the temperature effect on stoichiometry is less clear. As recently shown, there is a general link between functional traits and body stoichiometry, which makes the integration of stoichiometric traits a promising tool to help understanding the mechanisms behind taxonomic and functional community responses to nutrient changes and/or warming. Yet, such approaches have been scarcely developed at community level and on a long-term perspective. In this study, we investigated long-term responses in stoichiometry and functional trait composition of macroinvertebrate communities to nutrient changes (decreasing water P; increasing water N:P) and warming over a 34-year period in the Middle Loire River (France), testing the potentially opposing responses to these drivers. Both drivers should cause shifts in species composition, which will alter the overall community stoichiometry and functional composition following assumptions from ecological stoichiometry theory. We found that the macroinvertebrate community shifted towards P-poor taxa, causing significant trends in overall community stoichiometry which indicates long-term changes in the nutrient pool provided by these consumers (i.e. decrease in %N and %P, increase in N:P). Further, while the former high-P conditions favoured traits associated to detritus feeding and fast development (i.e. small maximum body size, short life duration), recent conditions favoured predators and slow-developing taxa. These results suggest nutrients to be a more important driver than temperature over this period. By providing a pivotal link between environmental changes and functional trait composition of communities, approaches based on stoichiometric traits offer sound perspectives to investigate ecological relationships between multiple drivers operating at various scales and ecosystem functioning.

Effects of Chemical Compounds on the Activity of the N-acetyl-ß-D-Glucosaminidase of the Marine Prawn, Palaemon serratus: Screening In Vitro.

N-acetyl-ß-D-glucosaminidase (NAGase) is important for crustaceans because the enzyme activity is necessary for the molting process. The present study aimed to assess the sensitivity of Palaemon serratus NAGase activity to a set of compounds of diverse chemical families in the context of in vitro exposures. Compounds representing different chemical families were selected according to their abundance, impact in the environment, and relevance as disruptors of the molting process. In a first step, four solvents (dimethylsulfoxide [DMSO], methanol, acetone, and ethanol) were tested to determine their suitability to dissolve hydrophobic compounds without affecting NAGase activity. Exclusively, ethanol had no effect on enzyme activity and on the integrity of the proteins present in the enzyme extract. The 18 other compounds were tested and four of these compounds, pentoxifylline, fenoxycarb, dithiocarbamate, and RH5849, showed a specific alteration on the activity of NAGase, without affecting the protein content. However, cadmium, zinc, and glyphosate showed a nonspecific alteration, affecting both the enzyme activity and the proteins, whereas ibuprofen exclusively altered the protein content. Finally, 10 of the 22 tested compounds (including DMSO, acetone, and methanol) showed a direct alteration of NAGase activity. Environ Toxicol Chem 2023;42:846-858. © 2023 SETAC.

Population-level modeling to account for multigenerational effects of uranium in Daphnia magna.

As part of the ecological risk assessment associated with radionuclides in freshwater ecosystems, toxicity of waterborne uranium was recently investigated in the microcrustacean Daphnia magna over a three-generation exposure (F0, F1, and F2). Toxic effects on daphnid life history and physiology, increasing over generations, were demonstrated at the organism level under controlled laboratory conditions. These effects were modeled using an approach based on the dynamic energy budget (DEB). For each of the three successive generations, DEBtox (dynamic energy budget applied to toxicity data) models were fitted to experimental data. Lethal and sublethal DEBtox outcomes and their uncertainty were projected to the population level using population matrix techniques. To do so, we compared two modeling approaches in which experimental results from F0, F1, and F2 generations were either considered separately (F0-, F1-, and F2-based simulations) or together in the actual succession of F0, F1, and F2 generations (multi-F-based simulation). The first approach showed that considering results from F0 only (equivalent to a standard toxicity test) would lead to a severe underestimation of uranium toxicity at the population level. Results from the second approach showed that combining effects in successive generations cannot generally be simplified to the worst case among F0-, F1-, and F2-based population dynamics.

Comparison of bioassays with different exposure time patterns: the added value of dynamic modelling in predictive ecotoxicology.

The purpose of this study was to compare Daphnia magna responses to cadmium between two toxicity experiments performed in static and flow-through conditions. As a consequence of how water was renewed, the two experiments were characterised by two different exposure time patterns for daphnids, time-varying and constant, respectively. Basing on survival, growth and reproduction, we addressed the questions of organism development and sensitivity to cadmium. Classical analysis methods are not designed to deal with the time dimension and therefore not suitable to compare effects of different exposure time patterns. We used instead a dynamic modelling framework taking all timepoints and the time course of exposure into account, making comparable the results obtained from our two experiments. This modelling framework enabled us to detect an improvement of organism development in flow-through conditions compared to static ones and infer similar sensitivity to cadmium for both exposure time patterns.

Survival data analyses in ecotoxicology: critical effect concentrations, methods and models. What should we use?

In ecotoxicology, critical effect concentrations are the most common indicators to quantitatively assess risks for species exposed to contaminants. Three types of critical effect concentrations are classically used: lowest/ no observed effect concentration (LOEC/NOEC), LC( x) (x% lethal concentration) and NEC (no effect concentration). In this article, for each of these three types of critical effect concentration, we compared methods or models used for their estimation and proposed one as the most appropriate. We then compared these critical effect concentrations to each other. For that, we used nine survival data sets corresponding to D. magna exposition to nine different contaminants, for which the time-course of the response was monitored. Our results showed that: (i) LOEC/NOEC values at day 21 were method-dependent, and that the Cochran-Armitage test with a step-down procedure appeared to be the most protective for the environment; (ii) all tested concentration-response models we compared gave close values of LC50 at day 21, nevertheless the Weibull model had the lowest global mean deviance; (iii) a simple threshold NEC-model both concentration and time dependent more completely described whole data (i.e. all timepoints) and enabled a precise estimation of the NEC. We then compared the three critical effect concentrations and argued that the use of the NEC might be a good option for environmental risk assessment.

Lessons from linking bio- and ecological traits to stoichiometric traits in stream macroinvertebrates.

Ecologists rely on various functional traits when investigating the functioning of ecological systems and its responses to global changes. Changing nutrient levels, for example, can affect taxa expressing different trait combinations in various ways, e.g., favoring small, fast-growing species under high phosphorus conditions. Stoichiometric traits, describing the elemental composition of organism body tissues, can help in understanding the mechanisms behind such functional shifts. So far, mainly life-history traits have been related to body stoichiometry (e.g., the growth rate hypothesis) on a limited number of taxa, and there is little knowledge of the general link between stoichiometric and other functional traits on a taxonomically large scale. Here, we highlight this link in the freshwater macroinvertebrates, testing predictions from underlying trait-based and Ecological Stoichiometry Theory (EST) in >200 taxa belonging to eight larger taxonomic groups. We applied a series of multivariate analyses on six of their stoichiometric traits (%C, %N, %P, C:N, C:P, and N:P) and 23 biological and ecological traits. We found significant relationships between stoichiometric traits and other types of traits when analyzing single-trait and multi-trait profiles. Patterns found within traits related to organism development or nutrient cycling were in line with our assumptions based on EST, e.g., traits describing predators were associated with high %N; traits suggesting a fast development (small maximum body size and high molting frequency) with high %P. Associations between ecological traits and body stoichiometry could be explained by the longitudinal stream gradient: Taxa preferring headwater habitats (i.e., high altitude, coarse substrate, and cold temperature) exhibited high %N and %P. Demonstrating the link between stoichiometric and both bio- and ecological traits on a large diversity of taxa underlines the potential of integrating stoichiometric traits into ecological analyses to improve our understanding of taxonomic and functional responses of communities-and ecosystems-to changing environmental conditions worldwide.

Influence of microorganisms on uranium release from mining-impacted lake sediments under various oxygenation conditions.

Microbial processes can be involved in the remobilization of uranium (U) from reduced sediments under O2 reoxidation events such as water table fluctuations. Such reactions could be typically encountered after U-bearing sediment dredging operations. Solid U(IV) species may thus reoxidize into U(VI) that can be released in pore waters in the form of aqueous complexes with organic and inorganic ligands. Non-uraninite U(IV) species may be especially sensitive to reoxidation and remobilization processes. Nevertheless, little is known regarding the effect of microbially mediated processes on the behaviour of U under these conditions.

Temperature limits trail following behaviour through pheromone decay in ants.

In Mediterranean habitats, temperature affects both ant foraging behaviour and community structure. Many studies have shown that dominant species often forage at lower temperature than subordinates. Yet, the factors that constrain dominant species foraging activity in hot environments are still elusive. We used the dominant ant Tapinoma nigerrimum as a model species to test the hypothesis that high temperatures hinder trail following behaviour by accelerating pheromone degradation. First, field observations showed that high temperatures (> 30°C) reduce the foraging activity of T. nigerrimum independently of the daily and seasonal rhythms of this species. Second, we isolated the effect of high temperatures on pheromone trail efficacy from its effect on worker physiology. A marked substrate was heated during 10 min (five temperature treatments from 25°C to 60°C), cooled down to 25°C, and offered in a test choice to workers. At hot temperature treatments (>40°C), workers did not discriminate the previously marked substrate. High temperatures appeared therefore to accelerate pheromone degradation. Third, we assessed the pheromone decay dynamics by a mechanistic model fitted with Bayesian inference. The model predicted ant choice through the evolution of pheromone concentration on trails as a function of both temperature and time since pheromone deposition. Overall, our results highlighted that the effect of high temperatures on recruitment intensity was partly due to pheromone evaporation. In the Mediterranean ant communities, this might affect dominant species relying on chemical recruitment, more than subordinate ant species, less dependent on chemical communication and less sensitive to high temperatures.

Bayesian modelling of daphnid responses to time-varying cadmium exposure in laboratory aquatic microcosms.

Experiments were carried out to test the effects of cadmium on five aquatic species in 2-L indoor freshwater/sediment microcosms. Experimental data were collected over 21 days in static conditions, i.e. the microcosms evolved without water renewal. Because of speciation, the total cadmium concentration in water decreased with time. Here we present a focus on Daphnia magna responses. For the three life history traits we considered (survival, growth and reproduction), mathematical effect models were built based on threshold stress functions involving no effect concentrations (NECs). These models took the time-varying conditions of exposure into account through a time-recurrent formalism. Within a Bayesian framework, four kinds of data were fitted simultaneously (exposure, survival, growth and reproduction), using an appropriate error model for each endpoint. Hence, NECs were determined as well as their associated estimation uncertainty. Through this modelling approach, we demonstrate that thresholds for stress functions can be successfully inferred even in experimental setup more complex than standard bioassays.

Probabilistic modeling of the fate of Listeria monocytogenes in diced bacon during the manufacturing process.

To assess the impact of the manufacturing process on the fate of Listeria monocytogenes, we built a generic probabilistic model intended to simulate the successive steps in the process. Contamination evolution was modeled in the appropriate units (breasts, dice, and then packaging units through the successive steps in the process). To calibrate the model, parameter values were estimated from industrial data, from the literature, and based on expert opinion. By means of simulations, the model was explored using a baseline calibration and alternative scenarios, in order to assess the impact of changes in the process and of accidental events. The results are reported as contamination distributions and as the probability that the product will be acceptable with regards to the European regulatory safety criterion. Our results are consistent with data provided by industrial partners and highlight that tumbling is a key step for the distribution of the contamination at the end of the process. Process chain models could provide an important added value for risk assessment models that basically consider only the outputs of the process in their risk mitigation strategies. Moreover, a model calibrated to correspond to a specific plant could be used to optimize surveillance.

How diatom-, invertebrate- and fish-based diagnostic tools can support the ecological assessment of rivers in a multi-pressure context: Temporal trends over the past two decades in France.

The degradation of aquatic ecosystems, induced by worldwide intensification in the use of both land and aquatic resources, has highlighted the critical need for innovative methods allowing an objective quantification and ranking of anthropogenic pressure effects on aquatic organisms. Such diagnostic tools have a great potential for defining robust management responses to anthropogenic pressures. Our objective was to explore how the outputs of three diagnostic tools (based on benthic diatoms, macroinvertebrates and fishes) could be combined to (i) disentangle the temporal effects of multiple pressures over two decades and (ii) provide policy-relevant information for stream managers and decision makers. The diagnostic tools estimated, using taxonomy- and trait-based metrics, the impairment probabilities of biotic assemblages over time by different pressure categories, describing the alteration of water quality, hydromorphology and land use related to anthropogenic activities, in French streams (number of sites = 312). The main result shows that a large proportion of the time series exhibited no significant temporal patterns over the two decades (61.5% to 87.8%, depending on the used tests). Among time series exhibiting significant change, positive trends in impairment probabilities (i.e., degradation) were less frequent than negative ones, indicating a modest improvement in water quality at national scale over the study period. However, trends can be substantially different according to hydroecoregion and pressure category. The three biological compartments displayed convergent temporal responses according to the pressure category and regional context (e.g., lowland plains vs. mountains, pristine vs. agricultural regions). Altogether, this study proposes a unifying approach to integrate a vast amount of information in a single ecological diagnosis using an unparalleled database on natural and anthropized environments. Strengthening the synthesis of biological information provided by various biological compartments should be a priority before implementing evidence-based sustainable conservation and restoration actions.

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.

A multi-omics concentration-response framework uncovers novel understanding of triclosan effects in the chlorophyte Scenedesmus vacuolatus.

In aquatic ecosystems, the biocide triclosan represents a hazard for the non-target microalgae. So far, algal responses were mainly investigated at apical levels hampering the acquisition of a holistic view on primary, adaptive, and compensatory stress responses. We assessed responses of the chlorophyte Scenedesmus vacuolatus to triclosan at apical (growth, photosynthesis) and molecular (transcriptome, metabolome) levels for comparative pathway sensitivity analysis. For each responsive signal (contigs, metabolites), a concentration-response curve was modeled and effect concentrations were calculated leading to the setting of cumulative sensitivity distributions. Molecular responses showed higher sensitivity than apical observations. The functional annotation of contigs and metabolites revealed 118 metabolic pathways putatively impaired by triclosan, highlighting a wide repercussion on the algal metabolism. Metabolites involved in the lipid metabolism showed decreasing trends along the concentration gradient and a globally highest sensitivity, pointing to the primary target of triclosan. The pathways involved in xenobiotic degradation and membrane transporters were mainly regulated in the transcriptome with increasing response trends comprising compensatory responses. The suggested novel approach, combining apical and multi-omics analyses in a concentration-response framework improves mechanistic understanding and mode of action analysis on non-targeted organisms and is suggested to better implement high-throughput multi-omics data in environmental risk assessment.

Microplastics enhance Daphnia magna sensitivity to the pyrethroid insecticide deltamethrin: Effects on life history traits.

The aim of the study was to investigate the influence of microplastics (MPs) on the ecotoxicity of common contaminants of aquatic ecosystems. As a model contaminant, the hydrophobic pesticide deltamethrin (DM) was chosen, and its effects on life history traits of Daphnia magna were studied in the presence or absence of polyethylene MPs. Commercialized DM and MPs obtained as dry powder were used in the experiment. According the manufacturer (Cospheric, Santa Barbara, CA, USA) MPs were spherical (1-4 µm in diameter), had a density of 0.96 g/cm-3 and were without any solvent. Three concentrations of polyethylene MPs were tested (0, 1, 10 mg/L) with two realistic concentrations of DM (0 and 40 ng/L) and a solvent control (acetone). During the 21 d experiment, D. magna neonates were individually exposed to the treatments, and the effects of MPs and DM alone and together were evaluated by assessing survival, number of cumulative molts, days to first brood, number of broods, number of neonates per surviving adult, and body length. Significant detrimental effects on survival were only observed for the two mixture treatments. DM alone (40 ng/L) delayed the days to first brood and reduced the number of neonates per surviving adult, whereas MPs alone (10 mg/L) induced significant reduction in the number of juveniles by surviving adults. The combined exposure to DM and MPs clearly had a synergistic effect on survival, brood number, and number of neonates per surviving female. For example, compared to exposure to 40 ng/L of DM alone, the addition of 1 mg/L of MPs resulted in a 51.1% reduction in number of neonates per surviving female and a 46% reduction in brood number. These results suggest the potential drastic effects of this kind of mixed exposure on daphnid populations, which are key components of freshwater food webs.

DEBtox theory and matrix population models as helpful tools in understanding the interaction between toxic cyanobacteria and zooplankton.

Bioassays were performed to find out how field samples of the toxic cyanobacteria Microcystis aeruginosa affect Moina micrura, a cladoceran found in the tropical Jacarepagua Lagoon (Rio de Janeiro, Brazil). The DEBtox (Dynamic Energy Budget theory applied to toxicity data) approach has been proposed for use in analysing chronic toxicity tests as an alternative to calculating the usual safety parameters (NOEC, ECx). DEBtox theory deals with the energy balance between physiological processes (assimilation, maintenance, growth and reproduction), and it can be used to investigate and compare various hypotheses concerning the mechanism of action of a toxicant. Even though the DEBtox framework was designed for standard toxicity bioassays carried out with standard species (fish, daphnids), we applied the growth and reproduction models to M. micrura, by adapting the data available using a weight-length allometric relationship. Our modelling approach appeared to be very relevant at the individual level, and confirmed previous conclusions about the toxic mechanism. In our study we also wanted to assess the toxic effects at the population level, which is a more relevant endpoint in risk assessment. We therefore incorporated both lethal and sublethal toxic effects in a matrix population model used to calculate the finite rate of population change as a continuous function of the exposure concentration. Alongside this calculation, we constructed a confidence band to predict the critical exposure concentration for population health. Finally, we discuss our findings with regard to the prospects for further refining the analysis of ecotoxicological data.