Application de modèles non paramétriques sous R pour l'analyse et le suivi de la qualité de l'eau

Application de modèles non paramétriques sous R pour l'analyse et le suivi de la qualité de l'ea

Water quality assessment by means of HFNI valvometry and high-frequency data modeling

International audienceThe high-frequency measurements of valve activity in bivalves (e.g., valvometry) over a long period of time and in various environmental conditions allow a very accurate study of their behaviors as well as a global analysis of possible perturbations due to the environment. Valvom- etry uses the bivalve's ability to close its shell when exposed to a contaminant or other abnormal environmental conditions as an alarm to indicate possible perturbations in the environment. The modeling of such high-frequency serial valvom- etry data is statistically challenging, and here, a nonparametric approach based on kernel estima- tion is proposed. This method has the advantage of summarizing complex data into a simple den- sity profile obtained from each animal at every 24-h period to ultimately make inference about time effect and external conditions on this profile. The statistical properties of the estimator are pre- sented. Through an application to a sample of 16 oysters living in the Bay of Arcachon (France), we demonstrate that this method can be used to first estimate the normal biological rhythms of permanently immersed oysters and second to de- tect perturbations of these rhythms due to changes in their environment. We anticipate that this ap- proach could have an important contribution to the survey of aquatic systems

In the darkness of the polar night, scallops keep on a steady rhythm

Published version. Source at http://doi.org/10.1038/srep32435. License CC BY 4.0.Although the prevailing paradigm has held that the polar night is a period of biological quiescence, recent studies have detected noticeable activity levels in marine organisms. In this study, we investigated the circadian rhythm of the scallop Chlamys islandica by continuously recording the animal’s behaviour over 3 years in the Arctic (Svalbard). Our results showed that a circadian rhythm persists throughout the polar night and lasts for at least 4 months. Based on observations across three polar nights, we showed that the robustness and synchronicity of the rhythm depends on the angle of the sun below the horizon. The weakest rhythm occurred at the onset of the polar night during the nautical twilight. Surprisingly, the circadian behaviour began to recover during the darkest part of the polar night. Because active rhythms optimize the fitness of an organism, our study brings out that the scallops C. islandica remain active even during the polar night

Monitoring biological rhythms through the dynamic model identification of an oyster population

International audienceThe measurements of valve activity in a population of bivalves under natural environmental conditions (16 oysters in the Bay of Arcachon, France) are used for a physiological model identification. A nonlinear auto-regressive exogenous (NARX) model is designed and tested. The method to design the model has two parts. 1) Structure of the model: The model takes into account the influence of environmental conditions using measurements of the sunlight intensity, the moonlight, tide levels, precipitation and water salinity levels. A possible influence of the internal circadian/circatidal clocks is also analyzed. 2) Least square calculation of the model parameters. Through this study, it is demonstrated that the developed dynamical model of the oyster valve movement can be used for estimating normal physiological rhythms of permanently immersed oysters and can be considered for detecting perturbations of these rhythms due to changes in the water quality, i.e. for ecological monitoring

A Fault Detection Method for Automatic Detection of Spawning in Oysters

Using measurements of valve activity (i.e., the distance between the two valves) in populations of bivalves under natural environmental conditions (16 oysters in the Bay of Arcachon, France, in 2007, 2013, and 2014), an algorithm for an automatic detection of the spawning period of oysters is proposed in this brief. Spawning observations are important in aquaculture and biological studies, and until now, such a detection is done through visual analysis by an expert. The algorithm is based on the fault detection approach and it works through the estimation of velocity of valve movement activity, which can be obtained by calculating the time derivative of the valve distance. A summarized description of the methods used for the derivative estimation is provided, followed by the associated signal processing and decision-making algorithm to determine spawning from the velocity signal. A protection from false spawning detection is also considered by analyzing the simultaneity in spawning. Through this study, it is shown that spawning in a population of oysters living in their natural habitat (i.e., in the sea) can be automatically detected without any human expertise, saving time and resources. The fault detection method presented in this brief can also be used to detect complex oscillatory behavior which is of interest to control engineering community.<br/

Model-based adaptive filtering of harmonic perturbations applied to high-frequency noninvasive valvometry

International audienceIn this paper, a model-based adaptive filter is used to suppress electrical noise in a high-frequency noninvasive valvometry device, which is part of an autonomous biosensor system using bivalve mollusks valve-activity measurements for ecological monitoring purposes. The proposed model-based adaptive filter uses the dynamic regressor extension and mixing method to allow a decoupled estimation of the parameters. Once the desired regression form of the output model is obtained, a fixed-time estimation approach is used to identify its parameters. By applying these two techniques, a flexible filter structure is obtained with the property of retaining the major relevant components of interest of the original valve-activity signals, even in the case when the unwanted signal frequency components are in the same frequency range as the useful variables

Deep behavioral impairment in the pearl oyster Pinctada radiata exposed to anthropogenic noise and light stress

The pearl oyster Pinctada radiata is an iconic species in the Arabian Gulf, which is one of the ecosystems most at risk in the world because of the multiple sources of pollution it faces. Alongside chemical pollution, the Gulf is ranked first with regard to noise and light pollution, and pearl oyster populations are at risk. The impact of these latter types of pollution on marine invertebrates is still poorly known. We used the difference in noise and brightness that can exist between a very quiet room without artificial lighting and a standard laboratory room equipped with a standard aquarium as a testbed to explore the possible impact of noise and light pollution on the behavioral and biological traits of Pinctada radiata without added chemical exposure. During an experiment that lasted 2.5 months, we analyzed their grouping behavior, valve activity, biological rhythm, growth rate and spawning activity. In the standard aquarium kept in the laboratory room, the oysters dispersed instead of regrouping as in their natural environment, regrouping which was observed in the quiet room. They stayed closed longer, the opening amplitude of their valves was systematically lower, and in the closed position, they squeezed their valves more tightly when subjected to noise and light pollution. Their daily opening rhythm was strongly structured by switching the electric light on and off, and females showed significantly less egg-laying behavior. In conclusion, seemingly innocuous human activities can lead to very significant alterations in pearl oyster behavior. We propose that it could have significant effects on populations and ecosystems

Evidence of separate influence of moon and sun on light synchronization of mussel's daily rhythm during the polar night

Marine organisms living at high latitudes are faced with a light climate that undergoes drastic annual changes, especially during the polar night (PN) when the sun remains below the horizon for months. This raises the question of a possible synchronization and entrainment of biological rhythms under the governance of light at very low intensities. We analyzed the rhythms of the mussel Mytilus sp. during PN. We show that (1) mussels expressed a rhythmic behavior during PN; (2) a monthly moonlight rhythm was expressed; (3) a daily rhythm was expressed and influenced by both sunlight and moonlight; and (4) depending on the different times of PN and moon cycle characteristics, we were able to discriminate whether the moon or the sun synchronize the daily rhythm. Our findings fuel the idea that the capability of moonlight to synchronize daily rhythms when sunlight is not sufficient would be a crucial advantage during PN.publishedVersio

Développement de modèles non paramétriques et robustes : application à l’analyse du comportement de bivalves et à l’analyse de liaison génétique

Le développement des approches robustes et non paramétriques pour l’analyse et le traitement statistique de gros volumes de données présentant une forte variabilité,comme dans les domaines de l’environnement et de la génétique, est fondamental.Nous modélisons ici des données complexes de biologie appliquées à l’étude du comportement de bivalves et à l’analyse de liaison génétique. L’application des mathématiques à l’analyse du comportement de mollusques bivalves nous a permis d’aller vers une quantification et une traduction mathématique de comportements d’animaux in-situ, en milieu proche ou lointain. Nous avons proposé un modèle de régression non paramétrique et comparé 3 estimateurs non paramétriques, récursifs ou non,de la fonction de régression pour optimiser le meilleur estimateur. Nous avons ensuite caractérisé des rythmes biologiques, formalisé l’évolution d’états d’ouvertures,proposé des méthodes de discrimination de comportements, utilisé la méthode des shot-noises pour caractériser différents états d’ouverture-fermetures transitoires et développé une méthode originale de mesure de croissance en ligne.En génétique, nous avons abordé un cadre plus général de statistiques robustes pour l’analyse de liaison génétique. Nous avons développé des estimateurs robustes aux hypothèses de normalités et à la présence de valeurs aberrantes, nous avons aussi utilisé une approche statistique, où nous avons abordé la dépendance entre variables aléatoires via la théorie des copules. Nos principaux résultats ont montré l’intérêt pratique de ces estimateurs sur des données réelles de QTL et eQTL.The development of robust and nonparametric approaches for the analysis and statistical treatment of high-dimensional data sets exhibiting high variability, as seen in the environmental and genetic fields, is instrumental. Here, we model complex biological data with application to the analysis of bivalves’ behavior and to linkage analysis. The application of mathematics to the analysis of mollusk bivalves’behavior gave us the possibility to quantify and translate mathematically the animals’behavior in situ, in close or far field. We proposed a nonparametric regression model and compared three nonparametric estimators (recursive or not) of the regressionfunction to optimize the best estimator. We then characterized the biological rhythms, formalized the states of opening, proposed methods able to discriminate the behaviors, used shot-noise analysis to characterize various opening/closing transitory states and developed an original approach for measuring online growth.In genetics, we proposed a more general framework of robust statistics for linkage analysis. We developed estimators robust to distribution assumptions and the presence of outlier observations. We also used a statistical approach where the dependence between random variables is specified through copula theory. Our main results showed the practical interest of these estimators on real data for QTL and eQTL analysis

Développement de modèles non paramétriques et robustes : application à l’analyse du comportement de bivalves et à l’analyse de liaison génétique

Le développement des approches robustes et non paramétriques pour l’analyse et le traitement statistique de gros volumes de données présentant une forte variabilité,comme dans les domaines de l’environnement et de la génétique, est fondamental.Nous modélisons ici des données complexes de biologie appliquées à l’étude du comportement de bivalves et à l’analyse de liaison génétique. L’application des mathématiques à l’analyse du comportement de mollusques bivalves nous a permis d’aller vers une quantification et une traduction mathématique de comportements d’animaux in-situ, en milieu proche ou lointain. Nous avons proposé un modèle de régression non paramétrique et comparé 3 estimateurs non paramétriques, récursifs ou non,de la fonction de régression pour optimiser le meilleur estimateur. Nous avons ensuite caractérisé des rythmes biologiques, formalisé l’évolution d’états d’ouvertures,proposé des méthodes de discrimination de comportements, utilisé la méthode des shot-noises pour caractériser différents états d’ouverture-fermetures transitoires et développé une méthode originale de mesure de croissance en ligne.En génétique, nous avons abordé un cadre plus général de statistiques robustes pour l’analyse de liaison génétique. Nous avons développé des estimateurs robustes aux hypothèses de normalités et à la présence de valeurs aberrantes, nous avons aussi utilisé une approche statistique, où nous avons abordé la dépendance entre variables aléatoires via la théorie des copules. Nos principaux résultats ont montré l’intérêt pratique de ces estimateurs sur des données réelles de QTL et eQTL.The development of robust and nonparametric approaches for the analysis and statistical treatment of high-dimensional data sets exhibiting high variability, as seen in the environmental and genetic fields, is instrumental. Here, we model complex biological data with application to the analysis of bivalves’ behavior and to linkage analysis. The application of mathematics to the analysis of mollusk bivalves’behavior gave us the possibility to quantify and translate mathematically the animals’behavior in situ, in close or far field. We proposed a nonparametric regression model and compared three nonparametric estimators (recursive or not) of the regressionfunction to optimize the best estimator. We then characterized the biological rhythms, formalized the states of opening, proposed methods able to discriminate the behaviors, used shot-noise analysis to characterize various opening/closing transitory states and developed an original approach for measuring online growth.In genetics, we proposed a more general framework of robust statistics for linkage analysis. We developed estimators robust to distribution assumptions and the presence of outlier observations. We also used a statistical approach where the dependence between random variables is specified through copula theory. Our main results showed the practical interest of these estimators on real data for QTL and eQTL analysis