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The benthic invertebrates fauna of most of the saline lakes of the Sud Lipez region (Bolivia, Altiplano) has been until now quite unstudied. Samples collected during an extensive survey of 12 lakes and two small inflow rivers allow a first list of the main macroinvertebrates living in the biotopes. The heterogeneous nature of these saline lakes with their freshwater springs and phreatic inflows offers a variety of habitats to macroinvertebrates. The benthic fauna in lakes with salinity > 10 g l-1 is not so low in density but includes few species and is dominated by Orthocladinae and Podonominae larvae. In contrast, the freshwater springs and inflows are colonized by a diverse fauna with a mixture of both freshwater and saline taxa, but dominated by Elmidae and Amphipoda. The lakes are quite isolated and, apart from some cosmopolitan organisms, their fauna can be quite distinctive. (Résumé d'auteur

Influence of freeze-thaw events on carbon dioxide emission from soils at different moisture and land use

BACKGROUND: The repeated freeze-thaw events during cold season, freezing of soils in autumn and thawing in spring are typical for the tundra, boreal, and temperate soils. The thawing of soils during winter-summer transitions induces the release of decomposable organic carbon and acceleration of soil respiration. The winter-spring fluxes of CO(2 )from permanently and seasonally frozen soils are essential part of annual carbon budget varying from 5 to 50%. The mechanisms of the freeze-thaw activation are not absolutely clear and need clarifying. We investigated the effect of repeated freezing-thawing events on CO(2 )emission from intact arable and forest soils (Luvisols, loamy silt; Central Germany) at different moisture (65% and 100% of WHC). RESULTS: Due to the measurement of the CO(2 )flux in two hours intervals, the dynamics of CO(2 )emission during freezing-thawing events was described in a detailed way. At +10°C (initial level) in soils investigated, carbon dioxide emission varied between 7.4 to 43.8 mg C m(-2)h(-1 )depending on land use and moisture. CO(2 )flux from the totally frozen soil never reached zero and amounted to 5 to 20% of the initial level, indicating that microbial community was still active at -5°C. Significant burst of CO(2 )emission (1.2–1.7-fold increase depending on moisture and land use) was observed during thawing. There was close linear correlation between CO(2 )emission and soil temperature (R(2 )= 0.86–0.97, P < 0.001). CONCLUSION: Our investigations showed that soil moisture and land use governed the initial rate of soil respiration, duration of freezing and thawing of soil, pattern of CO(2 )dynamics and extra CO(2 )fluxes. As a rule, the emissions of CO(2 )induced by freezing-thawing were more significant in dry soils and during the first freezing-thawing cycle (FTC). The acceleration of CO(2 )emission was caused by different processes: the liberation of nutrients upon the soil freezing, biological activity occurring in unfrozen water films, and respiration of cold-adapted microflora

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Forecasting sunflower grain yield using remote sensing data and statistical models

International audienceForecasting crop production a few weeks before harvest is of strategical interest for the cooperatives which collect, store and market grains. The recent development of Sentinel satellites opened new avenues for yield forecasting at field and farm level, thanks to their operational spatial resolution and revisiting time. In this study, we combined remote sensing data (in-season green area index, GAI) and statistical modeling to forecast sun- flower yield at field level for a range of cultivars and crop practices over different small production areas and years in southwestern France. From 2014–2016, 359 sunflower fields were monitored throughout the growing season in the ‘Haute-Garonne’ and ‘Gers’ administrative departments (SW France). From the satellite GAI esti- mates, two variables were calculated: GAImax (maximum GAI, between F1 stage and F1 + 10 days) and GAD (Green Area Duration). Different statistical modeling procedures were tested namely a linear regression (LR), a second degree polynomial regression (PR), a random forest regressor (RF) and a Gaussian process (GP). In each case, the models were tested using either GAD, GAImax or both variables, and each model was trained using in a first time GAD and GAImax obtained with linear interpolation, and in a second time, the same variables computed using the double sigmoid interpolation. In a perspective of yield prediction, GAD was calculated from anthesis to maturity but also from anthesis to 10/07, 20/07, 30/07 and 10/08 using remote sensing data. Sunflower grain yield at maturity was predicted with 10 models differing by their forms and the agronomic variables involved. At individual level, GY was slightly better predicted by models including GAD + GAImax or GAD, while models based only on GAImax were the less accurate. This was consistent with the major importance of post-anthesis radiation interception and senescence dynamics in the development of grain yield in sunflower. Better pre- dictions were achieved in 2014, then in 2015 and finally 2016. However, at the grain catchment area level, PR models including GAD were the most accurate ones with absolute errors ranging from 0.53 to 4.68 q.ha-1 as a function of years. Only the predictions obtained with 2014 data and over the 3 years were sufficiently accurate to be of operational value for a cooperative manager