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Whether animals can actively avoid food contaminated with harmful compounds through taste is key to understand their ecotoxicological risks. Here, we investigated the ability of honey bees to perceive and avoid food resources contaminated with common metal pollutants known to impair their cognition at low concentrations (lead, zinc and arsenic). In behavioural assays, bees did not discriminate food contaminated with field-realistic concentrations of these metals. Bees only reduced their food consumption and displayed aversive behaviours at very high, unrealistic concentrations of lead and zinc that they perceived through their antennae and proboscis. Electrophysiological analyses confirmed that high concentrations of the three metals in sucrose solution induced a reduced neural response to sucrose in their antennae. Our results thus show that honey bees can avoid metal pollutants in their food, but only at very high concentrations above regulatory levels. Their inability to detect lower, yet harmful, concentrations in a field-realistic range suggests that metal pollution is a major threat for pollinators.
Coline Monchanin; Maria Gabriela De Brito Sanchez; Lorelei Lecouvreur; Oceane Boidard; Gregoire Mery; Jerome Silvestre; Gael LE Roux; David Baque; Arnaud Elger; Andrew B. Barron; Mathieu Lihoreau; Jean-Marc Devaud. Honey bees cannot sense harmful concentrations of metal pollutants in food. 2021, 1 .
AMA StyleColine Monchanin, Maria Gabriela De Brito Sanchez, Lorelei Lecouvreur, Oceane Boidard, Gregoire Mery, Jerome Silvestre, Gael LE Roux, David Baque, Arnaud Elger, Andrew B. Barron, Mathieu Lihoreau, Jean-Marc Devaud. Honey bees cannot sense harmful concentrations of metal pollutants in food. . 2021; ():1.
Chicago/Turabian StyleColine Monchanin; Maria Gabriela De Brito Sanchez; Lorelei Lecouvreur; Oceane Boidard; Gregoire Mery; Jerome Silvestre; Gael LE Roux; David Baque; Arnaud Elger; Andrew B. Barron; Mathieu Lihoreau; Jean-Marc Devaud. 2021. "Honey bees cannot sense harmful concentrations of metal pollutants in food." , no. : 1.
Monitoring plant metal uptake is essential for assessing the ecological risks of contaminated sites. While traditional techniques used to achieve this are destructive, Visible Near-Infrared (VNIR) reflectance spectroscopy represents a good alternative to monitor pollution remotely. Based on previous work, this study proposes a methodology for mapping the content of several metals in leaves (Cr, Cu, Ni and Zn) under realistic field conditions and from airborne imaging. For this purpose, the reflectance of Rubus fruticosus L., a pioneer species of industrial brownfields, was linked to leaf metal contents using optimized normalized vegetation indices. High correlations were found between the vegetation indices exploiting pigment-related wavelengths and leaf metal contents (r ≤ − 0.76 for Cr, Cu and Ni, and r ≥ 0.87 for Zn). This allowed predicting the metal contents with good accuracy in the field and on the image, especially Cu and Zn (r ≥ 0.84 and RPD ≥ 2.06). The same indices were applied over the entire study site to map the metal contents at very high spatial resolution. This study demonstrates the potential of remote sensing for assessing metal uptake by plants, opening perspectives of application in risk assessment and phytoextraction monitoring in the context of trace metal pollution.
Guillaume Lassalle; Sophie Fabre; Anthony Credoz; Rémy Hédacq; Dominique Dubucq; Arnaud Elger. Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices. Scientific Reports 2021, 11, 1 -13.
AMA StyleGuillaume Lassalle, Sophie Fabre, Anthony Credoz, Rémy Hédacq, Dominique Dubucq, Arnaud Elger. Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices. Scientific Reports. 2021; 11 (1):1-13.
Chicago/Turabian StyleGuillaume Lassalle; Sophie Fabre; Anthony Credoz; Rémy Hédacq; Dominique Dubucq; Arnaud Elger. 2021. "Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices." Scientific Reports 11, no. 1: 1-13.
Ore processing is a source of soil heavy metal pollution. Vegetation traits (structural characteristics such as spatial cover and repartition; biochemical parameters—pigment and water contents, growth rate, phenological cycle…) and plant species identity are indirect and powerful indicators of residual contamination detection in soil. Multi-temporal multispectral satellite imagery, such as the Sentinel-2 time series, is an operational environment monitoring system widely used to access vegetation traits and ensure vegetation surveillance across large areas. For this purpose, methodology based on a multi-temporal fusion method at the feature level is applied to vegetation monitoring for several years from the closure and revegetation of an ore processing site. Features are defined by 26 spectral indices from the literature and seasonal and annual change detection maps are inferred. Three indices—CIred-edge (CIREDEDGE), IRECI (Inverted Red-Edge Chlorophyll Index) and PSRI (Plant Senescence Reflectance Index)—are particularly suitable for detecting changes spatially and temporally across the study area. The analysis is conducted separately for phyto-stabilized vegetation zones and natural vegetation zones. Global and specific changes are emphasized and explained by information provided by the site operator or meteorological conditions.
Sophie Fabre; Rollin Gimenez; Arnaud Elger; Thomas Rivière. Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing. Sensors 2020, 20, 4800 .
AMA StyleSophie Fabre, Rollin Gimenez, Arnaud Elger, Thomas Rivière. Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing. Sensors. 2020; 20 (17):4800.
Chicago/Turabian StyleSophie Fabre; Rollin Gimenez; Arnaud Elger; Thomas Rivière. 2020. "Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing." Sensors 20, no. 17: 4800.
Recent remote sensing studies have suggested exploiting vegetation optical properties for assessing oil contamination, especially total petroleum hydrocarbons (TPH) in vegetated areas. Methods based on the tracking of alterations in leaf biochemistry have been proposed for detecting and quantifying TPH under controlled and field conditions. In this study, we expand their use to airborne imagery, in order to monitor oil contamination at a larger scale. Airborne hyperspectral images with very high spatial and spectral resolutions were acquired over an industrial site with oil-contamination (mud pits) and control sites both colonized by Rubus fruticosus L. The method of oil detection exploiting 14 vegetation indices succeeded in classifying the sites in the case of high TPH contamination (overall accuracy ≥ 91.8%). Two methods, based on either the PROSAIL (PROSPECT + SAIL) radiative transfer model or elastic net multiple regression, were also developed for quantifying TPH. Both methods were tested on reflectance measurements in the field, at leaf and canopy scales, and on the image, and achieved accurate predictions of TPH concentrations (RMSE ≤ 3.28 g/kg−1 and RPD ≥ 1.90). The methods were validated on additional sites and open up promising perspectives of operational application for oil and gas companies, with the emergence of new hyperspectral satellite sensors.
Guillaume Lassalle; Arnaud Elger; Anthony Credoz; Rémy Hédacq; Georges Bertoni; Dominique Dubucq; Sophie Fabre. Toward Quantifying Oil Contamination in Vegetated Areas Using Very High Spatial and Spectral Resolution Imagery. Remote Sensing 2019, 11, 2241 .
AMA StyleGuillaume Lassalle, Arnaud Elger, Anthony Credoz, Rémy Hédacq, Georges Bertoni, Dominique Dubucq, Sophie Fabre. Toward Quantifying Oil Contamination in Vegetated Areas Using Very High Spatial and Spectral Resolution Imagery. Remote Sensing. 2019; 11 (19):2241.
Chicago/Turabian StyleGuillaume Lassalle; Arnaud Elger; Anthony Credoz; Rémy Hédacq; Georges Bertoni; Dominique Dubucq; Sophie Fabre. 2019. "Toward Quantifying Oil Contamination in Vegetated Areas Using Very High Spatial and Spectral Resolution Imagery." Remote Sensing 11, no. 19: 2241.
Noudéhouénou Lionel Jaderne Houssou; Juan Durango Cordero; Audren Bouadjio-Boulic; Lucie Morin; Nicolas Maestripieri; Sylvain Ferrant; Mahamadou Belem; Jose Ignacio Pelaez Sanchez; Melio Saenz; Emilie Lerigoleur; Arnaud Elger; Benoit Gaudou; Laurence Maurice; Mehdi Saqalli. Synchronizing Histories of Exposure and Demography: The Construction of an Agent-Based Model of the Ecuadorian Amazon Colonization and Exposure to Oil Pollution Hazards. Journal of Artificial Societies and Social Simulation 2019, 22, 1 .
AMA StyleNoudéhouénou Lionel Jaderne Houssou, Juan Durango Cordero, Audren Bouadjio-Boulic, Lucie Morin, Nicolas Maestripieri, Sylvain Ferrant, Mahamadou Belem, Jose Ignacio Pelaez Sanchez, Melio Saenz, Emilie Lerigoleur, Arnaud Elger, Benoit Gaudou, Laurence Maurice, Mehdi Saqalli. Synchronizing Histories of Exposure and Demography: The Construction of an Agent-Based Model of the Ecuadorian Amazon Colonization and Exposure to Oil Pollution Hazards. Journal of Artificial Societies and Social Simulation. 2019; 22 (2):1.
Chicago/Turabian StyleNoudéhouénou Lionel Jaderne Houssou; Juan Durango Cordero; Audren Bouadjio-Boulic; Lucie Morin; Nicolas Maestripieri; Sylvain Ferrant; Mahamadou Belem; Jose Ignacio Pelaez Sanchez; Melio Saenz; Emilie Lerigoleur; Arnaud Elger; Benoit Gaudou; Laurence Maurice; Mehdi Saqalli. 2019. "Synchronizing Histories of Exposure and Demography: The Construction of an Agent-Based Model of the Ecuadorian Amazon Colonization and Exposure to Oil Pollution Hazards." Journal of Artificial Societies and Social Simulation 22, no. 2: 1.
Accidental oil spills were assessed in the north-eastern Ecuadorian Amazon, a rich biodiversity and cultural heritage area. Institutional reports were used to estimate oil spill volumes over the period 2001–2011. However, we had to make with heterogeneous and incomplete data. After statistically discriminating well- and poorly-documented oil blocks, some spill factors were derived from the former to spatially allocate oil spills where fragmentary data were available. Spatial prediction accuracy was assessed using similarity metrics in a cross-validation approach. Results showed 464 spill events (42.2/year), accounting for 10,000.2 t of crude oil, equivalent to annual discharges of 909.1 (±SD = 1219.5) t. Total spill volumes increased by 54.8% when spill factors were used to perform allocation to poorly-documented blocks. Resulting maps displayed pollution ‘hotspots’ in Dayuma and Joya de Los Sachas, with the highest inputs averaging 13.8 t km−2 year−1. The accuracy of spatial prediction ranged from 32 to 97%, depending on the metric and the weight given to double-zeros. Simulated situations showed that estimation accuracy depends on variabilities in incident occurrences and in spill volumes per incident. Our method is suitable for mapping hazards and risks in sensitive ecosystems, particularly in areas where incomplete data hinder this process.
Juan Durango-Cordero; Mehdi Saqalli; Christophe Laplanche; Marine Locquet; Arnaud Elger. Spatial Analysis of Accidental Oil Spills Using Heterogeneous Data: A Case Study from the North-Eastern Ecuadorian Amazon. Sustainability 2018, 10, 4719 .
AMA StyleJuan Durango-Cordero, Mehdi Saqalli, Christophe Laplanche, Marine Locquet, Arnaud Elger. Spatial Analysis of Accidental Oil Spills Using Heterogeneous Data: A Case Study from the North-Eastern Ecuadorian Amazon. Sustainability. 2018; 10 (12):4719.
Chicago/Turabian StyleJuan Durango-Cordero; Mehdi Saqalli; Christophe Laplanche; Marine Locquet; Arnaud Elger. 2018. "Spatial Analysis of Accidental Oil Spills Using Heterogeneous Data: A Case Study from the North-Eastern Ecuadorian Amazon." Sustainability 10, no. 12: 4719.
Nanoparticles (NPs) and in particular TiO-NPs are increasingly included in commercial goods leading to their accumulation in sewage sludge which is spread on agricultural soils as fertilizers in many countries. Crop plants are thus a very likely point of entry for NPs in the food chain up to humans. So far, soil influence on NP fate has been under-investigated. In this article, we studied the partitioning of TiO-NPs between soil and soil leachate, their uptake and biotransformation in wheat seedlings and their impact on plant development after exposure on 4 different types of soil with different characteristics: soil texture (from sandy to clayey), soil pH, cationic exchange capacity, organic matter content. Results suggest that a NP contamination occurring on agricultural soils will mainly lead to NP accumulation in soil (increase of Ti concentration up to 302% in sand) but to low to negligible transfer to soil leachate and plant shoot. In our experimental conditions, no sign of acute phytotoxicity has been detected (growth, biomass, chlorophyll content). Clay content above 6% together with organic matter content above 1.5% lead to translocation factor from soil to plant leaves below 2.5% (i.e. below 13mgTi·kg dry leaves). Taken together, our results suggest low risk of crop contamination in an agro-ecosystem.
C. Larue; C. Baratange; D. Vantelon; H. Khodja; S. Surblé; A. Elger; M. Carrière. Influence of soil type on TiO2 nanoparticle fate in an agro-ecosystem. Science of The Total Environment 2018, 630, 609 -617.
AMA StyleC. Larue, C. Baratange, D. Vantelon, H. Khodja, S. Surblé, A. Elger, M. Carrière. Influence of soil type on TiO2 nanoparticle fate in an agro-ecosystem. Science of The Total Environment. 2018; 630 ():609-617.
Chicago/Turabian StyleC. Larue; C. Baratange; D. Vantelon; H. Khodja; S. Surblé; A. Elger; M. Carrière. 2018. "Influence of soil type on TiO2 nanoparticle fate in an agro-ecosystem." Science of The Total Environment 630, no. : 609-617.