Behavioural and Physiological Responses of Gammarus pulex Exposed to Cadmium and Arsenate at Three Temperatures: Individual and Combined Effects

This study aimed at investigating both the individual and combined effects of cadmium (Cd) and arsenate (AsV) on the physiology and behaviour of the Crustacean Gammarus pulex at three temperatures (5, 10 and15°C). G. pulex was exposed during 96 h to (i) two [Cd] alone, (ii) two [AsV] alone, and (iii) four combinations of [Cd] and [AsV] to obtain a complete factorial plane. After exposure, survival, [AsV] or [Cd] in body tissues, behavioural (ventilatory and locomotor activities) and physiological responses (iono-regulation of [Na+] and [Cl−] in haemolymph) were examined. The interactive effects (antagonistic, additive or synergistic) of binary mixtures were evaluated for each tested temperature using a predictive model for the theoretically expected interactive effect of chemicals. In single metal exposure, both the internal metal concentration in body tissues and the mortality rate increased along metallic gradient concentration. Cd alone significantly impaired both [Na+] and [Cl−] while AsV alone had a weak impact only on [Cl−]. The behavioural responses of G. pulex declined with increasing metal concentration suggesting a reallocation of energy from behavioural responses to maintenance functions. The interaction between AsV and Cd was considered as ‘additive’ for all the tested binary mixtures and temperatures (except for the lowest combination at 10°C considered as “antagonistic”). In binary mixtures, the decrease in both ventilatory and locomotor activities and the decline in haemolymphatic [Cl−] were amplified when respectively compared to those observed with the same concentrations of AsV or Cd alone. However, the presence of AsV decreased the haemolymphatic [Na+] loss when G. pulex was exposed to the lowest Cd concentration. Finally, the observed physiological and behavioural effects (except ventilation) in G. pulex exposed to AsV and/or Cd were exacerbated under the highest temperature. The discussion encompasses both the toxicity mechanisms of these metals and their interaction with rising temperature

Cent scientifiques répliquent à SEA (Suppression des Expériences sur l’Animal vivant) et dénoncent sa désinformation

La lutte contre la maltraitance animale est sans conteste une cause moralement juste. Mais elle ne justifie en rien la désinformation à laquelle certaines associations qui s’en réclament ont recours pour remettre en question l’usage de l’expérimentation animale en recherche

Bergson

2002 Soulez, Philippe (1943-1994)Bergson : biographie / Philippe Soulez et Frédéric WormsPresses universitaires de FranceInternational audienceLire : https://www.puf.com/content/Bergso

Bergson

1997 Soulez, Philippe (1943-1994)Bergson : biographie / par Philippe Soulez ; complétée par Frédéric WormsFlammarionInternational audienc

Record events attribution in climate studies

Within the statistical climatology literature, inferring the contributions of potential causes with regard to climate change has become a recurrent research theme during this last decade. In particular, disentangling human induced (anthropogenic) forcings from natural causes represents a non-trivial statistical task, especially when the focal point moves away from mean behaviors and goes towards extreme events with high societal impacts. Most studies found in the field of Extreme Event Attributions (EEA) rely on Extreme Value Theory (EVT). Under this theoretical umbrella, it is often assumed that, for a given location, temporal changes in extremes can be detected in both location and scale parameters of an extreme value distribution, while its shape parameter remains unchanged over time. This assumption of constant tail shape parameters between a so-called factual world (all forcings) and a counterfactual one (without anthropogenic forcing) can be challenged due to the fact that important forcing changes could impact large scale atmospheric and oceanic circulation patterns, and consequently, the later can reshape the full distribution, including its shape parameter that drives extremal behavior.In this paper, we study how allowing different extremal tail indices between the factual and counterfactual worlds can affect the analysis of records. In particular, we extend the work of Naveau, Rides, Zwiers, Hannart, Tuel, You (Journal of Climate, 2018) in which this case was not treated. We also add properties and theoretical inferential results about records in EEA and propose a procedure for model validation. A simulation study of our approach is detailed. Our method is applied on records of yearly maxima of daily maxima of near-surface air temperature issued from the numerical climate model CNRM-CM5 of Météo-France

Record events attribution in climate studies

International audienc

Record events attribution in climate studies

Within the statistical climatology literature, inferring the contributions of potential causes with regard to climate change has become a recurrent research theme during this last decade. In particular, disentangling human induced (anthropogenic) forcings from natural causes represents a non-trivial statistical task, especially when the focal point moves away from mean behaviors and goes towards extreme events with high societal impacts. Most studies found in the field of Extreme Event Attributions (EEA) rely on Extreme Value Theory (EVT). Under this theoretical umbrella, it is often assumed that, for a given location, temporal changes in extremes can be detected in both location and scale parameters of an extreme value distribution, while its shape parameter remains unchanged over time. This assumption of constant tail shape parameters between a so-called factual world (all forcings) and a counterfactual one (without anthropogenic forcing) can be challenged due to the fact that important forcing changes could impact large scale atmospheric and oceanic circulation patterns, and consequently, the later can reshape the full distribution, including its shape parameter that drives extremal behavior.In this paper, we study how allowing different extremal tail indices between the factual and counterfactual worlds can affect the analysis of records. In particular, we extend the work of Naveau, Rides, Zwiers, Hannart, Tuel, You (Journal of Climate, 2018) in which this case was not treated. We also add properties and theoretical inferential results about records in EEA and propose a procedure for model validation. A simulation study of our approach is detailed. Our method is applied on records of yearly maxima of daily maxima of near-surface air temperature issued from the numerical climate model CNRM-CM5 of Météo-France

Plongée cinématographique dans une bibliothèque

Entretien avec Alain Guillon et Philippe Worms, à l\u27occasion de la sortie de leur film Chut ! Sortie nationale le 8 janvier 202

A statistical method to model non-stationarity in precipitation records changes

International audienceIn the context of climate change, assessing how likely a particular change or event has been caused by human influence is important for mitigation and adaptation policies. In this work we propose an extreme event attribution (EEA) methodology to analyze yearly maxima records, key indicators of climate change that spark media attention and research in the EEA community. Although they deserve a specific statistical treatment, algorithms tailored to record analysis are lacking. This is particularly true in a non-stationarity context. This work aims at filling this methodological gap by focusing on records in transient climate simulations. We apply our methodology to study records of yearly maxima of daily precipitation issued from numerical climate model IPSL-CM6A-LR. Illustrating our approach with decadal records, we detect in 2023 a clear human induced signal in half of the globe, with probability mostly increasing, but decreasing in the south and north Atlantic ocean

A statistical method to model non-stationarity in precipitation records changes

International audienceIn the context of climate change, assessing how likely a particular change or event has been caused by human influence is important for mitigation and adaptation policies. In this work we propose an extreme event attribution (EEA) methodology to analyze yearly maxima records, key indicators of climate change that spark media attention and research in the EEA community. Although they deserve a specific statistical treatment, algorithms tailored to record analysis are lacking. This is particularly true in a non-stationarity context. This work aims at filling this methodological gap by focusing on records in transient climate simulations. We apply our methodology to study records of yearly maxima of daily precipitation issued from numerical climate model IPSL-CM6A-LR. Illustrating our approach with decadal records, we detect in 2023 a clear human induced signal in half of the globe, with probability mostly increasing, but decreasing in the south and north Atlantic ocean