Plantes rares de la Dombes (Ain, France)

Rare plants from Dombes (Ain, France) During 1995, a prospection of Dombes ponds tried to define the status of their rare vascular plants. Some precocious species are commonly overlooked by botanical investigations, in Dombes tradionnally taking place during late August and September. Other species are unrecognized whereas the status of some others have recently changed. At last, results are given for some rare species not typical of ponds.En 1995, une prospection des étangs dombistes a tenté de cerner le statut des plantes vasculaires rares des étangs de la Dombes. Certaines espèces précoces sont communément occultées par les prospections habituellement tardives des botanistes en Dombes (août et septembre). D'autres sont méconnues. D'autres encore ont un statut qui a beaucoup changé récemment. Quelques résultats sont enfin présentés pour des espèces rares non inféodées aux étangs.Curtet Laurence, Guignard Gaëtan, Philippe Marc. Plantes rares de la Dombes (Ain, France). In: Bulletin mensuel de la Société linnéenne de Lyon, 66ᵉ année, n°4, avril 1997. pp. 93-104

Le cuivré des marais Thersamolycaena dispar Haworth, 1803 (Lepidoptera, Lycaenidae) en Dombes (Ain). Habitats fréquentés, conditions nécessaires à sa survie.

Thersamolycaena dispar (Lepidoptera, Lycaenidae), the large Copper, in the Dombes (Ain). Habitat use and conservation. Thersamolycaena dispar was found in the Dombes predominantly in meadow habitat but also in herbaceous waste and fallow lands. In hay-meadows where the hay-making heavily threatens the survival of eggs and larvae, the proportion of females was lower than in other habitats. We hypothesize that hay-meadows act in Dombes as demographic sinks whereas other habitats that are not involved in the intensified agricultural system might be demographic sources which it would be important to preserve in the future.Le cuivré des marais Thersamolycaena dispar a été trouvé en Dombes majoritairement en habitat prairial, mais également dans des friches et des jachères. Dans les prés de fauche où la fenaison menace fortement la survie des pontes et des larves, la proportion des femelles était plus faible que dans les autres habitats. Nous faisons l'hypothèse que les prés de fauche fonctionnent en Dombes comme des «puits démographiques », tandis que les habitats non agricoles ou marginalement utilisés par l'agriculture peuvent être des «sources » qu'il conviendrait de préserver.Broyer Joël, Frégat Christian, Blanc Jérôme, Curtet Laurence. Le cuivré des marais Thersamolycaena dispar Haworth, 1803 (Lepidoptera, Lycaenidae) en Dombes (Ain). Habitats fréquentés, conditions nécessaires à sa survie.. In: Bulletin mensuel de la Société linnéenne de Lyon, 77ᵉ année, n°9-10, Novembre-décembre 2008. pp. 159-164

L'habitat de Leucorrhinia pectoralis Charpentier, 1825 (Odonata, Libellulidae) dans les étangs piscicoles de la Dombes (Ain)

Habitat of Leucorrhinia pectoralis (Odonata, Libellulidae), the large white-faced Darter, in fishponds of the Dombes (Ain) Leucorrhinia pectoralis habitat in the Dombes was described in samples of 50 fishponds in 2000 and 47 in 2008. This species was found in ponds characterized by areas of medium size (about 0,60 m) helophytes in more than 60% of the perimeter, with patches free of emergent vegetation in about one third of their total surface area and connected over a distance of more than 100 m to surrounding littoral woods which may be present in 30 to 80% of pond's periphery. Habitat units with both helophytes and littoral woods seem to secure adequate shelters which enable to tolerate the presence of high fish stock density in water bodies.Broyer Joël, Curtet Laurence, Bouniol Julien, Vieille Julien. L'habitat de Leucorrhinia pectoralis Charpentier, 1825 (Odonata, Libellulidae) dans les étangs piscicoles de la Dombes (Ain). In: Bulletin mensuel de la Société linnéenne de Lyon, 78ᵉ année, n°3-4, Mars-avril 2009. pp. 77-84

Nationwide operational mapping of grassland mowing events combining machine learning and Sentinel-2 time series

Grasslands cover approximately 40% of the Earth's land area, encompassing nearly 70% of the global agricultural land area, and are distributed on all continents and across all latitudes (Suttie et al., 2005; White et al., 2000). Grassland dynamics influence global ecosystem functioning, and their impact is widely modulated by management practices intensity on these landscapes (Zhao et al., 2020). Management practices are primarily driven by grassland landscape maintenance, as well as by ecosystem service of provisioning offered by the grasslands. Grasslands are subject to management practices such as mowing or grazing or a combination of both. Therefore, monitoring grassland management practices is essential for assessing management intensity level, which in turn plays a critical role in studies related to biodiversity (XXXX), water (XXXXX) and carbon (XXXXX) cycling and others topics (XXXX). In France, the National Observatory of Mowed Grassland Ecosystems conducts birdlife monitoring in mowed grasslands, with a particular focus on the rise in breeding failures attributed to increasingly early mowing. Early mowing intercepts birds' reproductive period and interrupts their breeding process (Broyer et al., 2012). Usually, responsible agencies conduct occasional observation campaigns to support ecosystem-related public policies, but ground observations are not spatially exhaustive and are time-consuming. As an alternative source, synoptic remote sensing data enables regular and global-scale monitoring, enabling tracking of vegetation dynamics. Currently, Sentinel-2 mission provides cost-free high resolution data at 10m spatial resolution with a 5-day temporal frequency (10 days before 2017), allowing intra-plot level observations. Grassland mowing events timing and intensity have already been mapped using remote sensing-based time series, mainly from features sensitive to vegetation status, such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and more. There have been several methods used to detect mowing events from satellite time series. These methods were mainly based on temporal changes in time series using threshold-based methods and anomalies detection approach. More recently, deep learning-based architectures were also used to detect mowing events timing. Estel et al. (2018) assessed grassland use intensity spatial patterns across Europe. To extract annual mowing frequency, a temporal change analysis based on spline-adjusted MODIS NDVI time series was used. Their approach involved identifying mowing events as instances where a local minima exhibited a change, relative to its preceding peak, exceeding 10% of growing season amplitude. The results showed an overall accuracy of 80%, which decreases as the frequency of events increases. In northern Switzerland, Kolecka et al. (2018) also estimated mowing frequency employing similar temporal change analysis, but based on raw Sentinel-2 NDVI time series. Here, a drop in NDVI greater than 0.2, between two consecutive cloud-free acquisition dates, was counted as a mowing event. Their method accurately identified 77% of observed events and highlighted that false detection can occur due to residual cloud presence, while sparse time series led to the omission of mowing events. Regarding Griffiths et al. (2020), mowing events frequency and timing were mapped in Germany using 10-day composite Harmonized Landsat-Sentinel NDVI time series. Discrepancies between a hypothetical bell-shaped curve and the current polynomial-fitted curve were evaluated. An event was counted when the difference exceeded 0.2 NDVI. Findings revealed consistent spatial patterns in mowing frequency (indicating extensive and intensive management). However, estimated dates exhibited significant discrepancies compared to observed dates (MAE > 50 days), which could be due to lower temporal resolution of Sentinel-2 before 2017 and the absence of reliable ground data for calibration and validation. Stumpf et al. (2020) mapped grassland management (grazing or mowing) and its intensity based on biomass productivity and management frequency, respectively. The latter were extracted from n-day composite Landsat ETM + and Landsat OLI NDVI time series. As in previous cases, a management event was counted when NDVI loss is higher than a threshold, which was based on the probability density function of all NDVI changes across the time series and was specified for p = 0.01. Their approach yielded management patterns consistent with several management-related indicators (species richness, nutrient supply, slope, etc). Recently, Watzig et al. (2023) estimated mowing events in Austria, using Sentinel-2 NDVI time series and implementing discrepancy analysis between a idealized unmowed trajectory and actual NDVI values. An event was recorded if the difference exceeded-0.061. Commission errors due to residual clouds were reduced via a subsequent binary classification of each estimated event using a gradient boosting algorithm trained over cloudy plots. Findings indicated an overall accuracy of 80% in correct event detection, with estimated dates closely aligning with observed dates (MAE < 5 days). Vroey et al. (2022) developed a algorithm for detecting mowing events across Europe. Here, raw Sentinel-2 NDVI and Sentinel-1 VH-coherence time series were used separately

Nationwide operational mapping of grassland mowing events combining machine learning and Sentinel-2 time series

Grasslands cover approximately 40% of the Earth's land area, encompassing nearly 70% of the global agricultural land area, and are distributed on all continents and across all latitudes (Suttie et al., 2005; White et al., 2000). Grassland dynamics influence global ecosystem functioning, and their impact is widely modulated by management practices intensity on these landscapes (Zhao et al., 2020). Management practices are primarily driven by grassland landscape maintenance, as well as by ecosystem service of provisioning offered by the grasslands. Grasslands are subject to management practices such as mowing or grazing or a combination of both. Therefore, monitoring grassland management practices is essential for assessing management intensity level, which in turn plays a critical role in studies related to biodiversity (XXXX), water (XXXXX) and carbon (XXXXX) cycling and others topics (XXXX). In France, the National Observatory of Mowed Grassland Ecosystems conducts birdlife monitoring in mowed grasslands, with a particular focus on the rise in breeding failures attributed to increasingly early mowing. Early mowing intercepts birds' reproductive period and interrupts their breeding process (Broyer et al., 2012). Usually, responsible agencies conduct occasional observation campaigns to support ecosystem-related public policies, but ground observations are not spatially exhaustive and are time-consuming. As an alternative source, synoptic remote sensing data enables regular and global-scale monitoring, enabling tracking of vegetation dynamics. Currently, Sentinel-2 mission provides cost-free high resolution data at 10m spatial resolution with a 5-day temporal frequency (10 days before 2017), allowing intra-plot level observations. Grassland mowing events timing and intensity have already been mapped using remote sensing-based time series, mainly from features sensitive to vegetation status, such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and more. There have been several methods used to detect mowing events from satellite time series. These methods were mainly based on temporal changes in time series using threshold-based methods and anomalies detection approach. More recently, deep learning-based architectures were also used to detect mowing events timing. Estel et al. (2018) assessed grassland use intensity spatial patterns across Europe. To extract annual mowing frequency, a temporal change analysis based on spline-adjusted MODIS NDVI time series was used. Their approach involved identifying mowing events as instances where a local minima exhibited a change, relative to its preceding peak, exceeding 10% of growing season amplitude. The results showed an overall accuracy of 80%, which decreases as the frequency of events increases. In northern Switzerland, Kolecka et al. (2018) also estimated mowing frequency employing similar temporal change analysis, but based on raw Sentinel-2 NDVI time series. Here, a drop in NDVI greater than 0.2, between two consecutive cloud-free acquisition dates, was counted as a mowing event. Their method accurately identified 77% of observed events and highlighted that false detection can occur due to residual cloud presence, while sparse time series led to the omission of mowing events. Regarding Griffiths et al. (2020), mowing events frequency and timing were mapped in Germany using 10-day composite Harmonized Landsat-Sentinel NDVI time series. Discrepancies between a hypothetical bell-shaped curve and the current polynomial-fitted curve were evaluated. An event was counted when the difference exceeded 0.2 NDVI. Findings revealed consistent spatial patterns in mowing frequency (indicating extensive and intensive management). However, estimated dates exhibited significant discrepancies compared to observed dates (MAE > 50 days), which could be due to lower temporal resolution of Sentinel-2 before 2017 and the absence of reliable ground data for calibration and validation. Stumpf et al. (2020) mapped grassland management (grazing or mowing) and its intensity based on biomass productivity and management frequency, respectively. The latter were extracted from n-day composite Landsat ETM + and Landsat OLI NDVI time series. As in previous cases, a management event was counted when NDVI loss is higher than a threshold, which was based on the probability density function of all NDVI changes across the time series and was specified for p = 0.01. Their approach yielded management patterns consistent with several management-related indicators (species richness, nutrient supply, slope, etc). Recently, Watzig et al. (2023) estimated mowing events in Austria, using Sentinel-2 NDVI time series and implementing discrepancy analysis between a idealized unmowed trajectory and actual NDVI values. An event was recorded if the difference exceeded-0.061. Commission errors due to residual clouds were reduced via a subsequent binary classification of each estimated event using a gradient boosting algorithm trained over cloudy plots. Findings indicated an overall accuracy of 80% in correct event detection, with estimated dates closely aligning with observed dates (MAE < 5 days). Vroey et al. (2022) developed a algorithm for detecting mowing events across Europe. Here, raw Sentinel-2 NDVI and Sentinel-1 VH-coherence time series were used separately

French national map of 2022 mowing dates

National map of mowing dates for 2022. Produces using machine learning and sentinel-2 time series

Miniaturization of an extraction protocol for the monitoring of pesticides and polar transformation products in biotic matrices

The authors sincerely thank the fish farmers and owners for granting access to their ponds. They are also grateful to Alain Iurétig (sampling and pre-treatment, Univ. of Lorraine), Pamela Hartmeyer (pre-treatment, Univ. of Lorraine), Aisha Nunoo (extractions, Univ. of Lorraine/ISA), and Maud Dessein-Lepasteur (extractions and analysis, Univ. of Lorraine/ISA) for their work, as well as to Arnaud Chaumot and Laura Garnero from the Ecotoxicology Team of the UR RIVERLY (INRAE, Centre de Lyon-Villeurbanne) for providing chironomid larvae. They would also like to thank ABC Translation for proofreading the manuscript.International audienceMonitoring pesticides in the environment requires the use of sensitive analytical methods. However, existing methods are generally not suitable for analyzing small organisms, as they require large matrix masses. This study explores the development of a miniaturized extraction protocol for the monitoring of small organisms, based on only 30 mg of matrix. The miniaturized sample preparation was developed using fish and macroinvertebrate matrices. It allowed the characterization of 41 pesticides and transformation products (log P from −1.9 to 4.8) in small samples with LC-MS/MS, based on European guidelines (European Commission DG-SANTE, 2019). Quantification limits ranged from 3 to 460 ng g−1 dry weight (dw) for fish and from 0.1 to 356 ng g−1 dw for invertebrates, with most below 60 ng g−1 dw. Extraction rates ranged from 70% to 120% for 35 molecules in fish. Recoveries ranged from 70% to 120% for 37 molecules in macroinvertebrates. Inter-day precision was below 30% for 32 molecules at quantification limits. The method was successfully applied to 17 fish and 19 macroinvertebrates collected from two ponds of the French region of Dombes in November and May 2018, respectively. Both sample matrices were nearly always contaminated with benzamide, imidacloprid-desnitro, and prosulfocarb at respective concentrations of 42–237, 3, and 30–165 ng g−1 dw in fish, and 62–438, 2–6, and 15–29 ng g−1 dw in macroinvertebrates. Results show that this method is an effective tool for characterizing polar pesticides in small biotic samples