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 <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

Dynamic Patterns of the Vertical Distribution of Vegetation in Heihe River Basin since the 1980s

The vertical distribution of vegetation in Heihe River Basin has presented a significant dynamic change in the different elevation zones since the 1980s. To explore the dynamic patterns of vegetation types located in the different elevation zones of Heihe River Basin, this study collected 440 field sampling datapoints of vegetation types, remote sensing images, climatic observation data, and DEM and preprocessed them. On the basis of the vegetation distribution and the terrain characteristics of Heihe River Basin, this study classified the vertical distribution of vegetation in Heihe River Basin into six vegetation zones, namely, the oasis farmland and desert zone, desert-steppe zone, dry scrub-steppe zone, mountain forest-steppe zone, subalpine scrub-meadow zone, and alpine cold desert-meadow zone. Moreover, the mean annual biotemperature (MAB) and total annual average precipitation (TAP) were used to analyze the relationship between vegetation change and climate change in the different elevation zones. The results show that the change rate of vegetation was up to 25.75% in Heihe River Basin. The area of vegetation that changed in the oasis farmland and desert zone was the largest (7224 km2), and the rate of vegetation that changed in the mountain forest-steppe zone was up to 56.93%. The mean annual biotemperature (MAB) and total annual average precipitation (TAP) in the six elevation zones showed an increasing trend, in which the increased rate of TAP presented a downward trend with the increase of elevation, and that of MAB showed a continuous upward trend with the increase of elevation. The change rate of vegetation was generally higher than that of MAB and TAP in the low and middle vegetation zones. The influence intensity of human activities on vegetation change in the lower and middle elevation zones of Heihe River Basin was greater than that in the high elevation zone between the 1980s and the 2010s. MAB is the major impact factor to vegetation change in the alpine cold zone of Heihe River Basin

Shifts of the Mean Centers of Potential Vegetation Ecosystems under Future Climate Change in Eurasia

Climate change dominantly controls the spatial distributions of potential vegetation ecosystems; the shift trends in the mean centers of potential vegetation ecosystems could be used to explain their responses to climate change. In terms of the climate observation data of Eurasia for the period from 1981 to 2010 and the climate scenario data for the period from 2011 to 2100 under the three Representative Concentration Pathways (RCPs) scenarios of RCP2.6, RCP4.5 and, RCP8.5, which were released by the Coupled Model Intercomparison Project Phase 5 (CMIP5), the Holdridge Life Zone (HLZ) ecosystem model was improved to quantitatively classify the potential vegetation types, and the shift model of mean center was adopted to compute the trends in the spatiotemporal shifts of potential vegetation types in Eurasia. The results showed that the mean centers of the major potential vegetation ecosystems would be distributed in the central and southern parts of Eurasia. Under the RCP2.6, RCP4.5, and RCP8.5 scenarios, the potential shift distances of the mean centers of the vegetation types under the RCP8.5 scenario would be the largest, and those of the polar/nival area, subpolar/alpine moist tundra, warm temperate dry forest, subtropical moist forest, cool temperate moist forest, cool temperate wet forest, subtropical wet forest, subtropical thorn woodland, warm temperate moist forest and subtropical dry forest would be larger than those in the other potential vegetation types in Eurasia. Moreover, the shift directions of the mean centers of the major potential vegetation types would generally shift northward, and subtropical dry forest, warm temperate moist forest and subpolar/alpine moist tundra would be the most sensitive to change among all vegetation types under the three scenarios for the period from 2011 to 2100

Spatiotemporal Dynamics of Land Cover and Their Driving Forces in the Yellow River Basin since 1990

The national strategy for ecological protection and high-quality development is raising the ecological security protection to an unprecedented level in the Yellow River Basin (YRB) of China. Due to the explicitly analyzed land cover changes under climate change and rapid urbanization in the YRB area since 1990, land cover dynamic degree index, transfer matrix, and geo-detector method were used to explicate land cover changes and their key driving factors, based on the spatial data of land cover from 1990 to 2020. The results show that grasslands, croplands, and forests are the main land cover types, accounting for 48.37%, 25.05%, and 13.50%, respectively, of the total area in the YRB area. Grassland, cropland, and cropland are the major land cover type, accounting for 61.49%, 37.13%, and 66.33%, respectively, in the upstream, midstream, and downstream of the YRB area. Built-up land has showed a continual increasing trend, and its dynamic degree was up to 3.38% between 2010 and 2020. Population density was a key factor for land cover change, with an average contribution rate of 0.264; then, elevation and temperature also expressed an important role to drive the land cover change in the YRB area during the period from 1990 to 2020

Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR

Accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. Forest AGB estimation has been conducted with a variety of data sources and prediction methods, but many uncertainties still exist. In this study, six prediction methods, including Gaussian processes, stepwise linear regression, nonlinear regression using a logistic model, partial least squares regression, random forest, and support vector machines were used to estimate forest AGB in Jiangxi Province, China, by combining Geoscience Laser Altimeter System (GLAS) data, Moderate Resolution Imaging Spectroradiometer (MODIS) data, and field measurements. We compared the effect of three factors (prediction methods, sample sizes of field measurements, and cross-validation settings) on the predictive quality of the methods. The results showed that the prediction methods had the most considerable effect on the prediction quality. In most cases, random forest produced more accurate estimates than the other methods. The sample sizes had an obvious effect on accuracy, especially for the random forest model. The accuracy increased with increasing sample sizes. The random forest algorithm with a large number of field measurements, was the most precise (coefficient of determination (R2) = 0.73, root mean square error (RMSE) = 23.58 Mg/ha). Increasing the number of folds within the cross-validation settings improved the R2 values. However, no apparent change occurred in RMSE for different numbers of folds. Finally, the wall-to-wall forest AGB map over the study area was generated using the random forest model

Simulation of solar radiation on ground surfaces based on 1 km grid-cells

Multivariate nonlinear regression model was established to simulate the solar radiation on level surface of 1 km grid-cells in China. The validation shows the prediction accuracy is 96.63 percent. Based on digital elevation model (DEM), the direct radiation ratios of slope to level and the sky view factors of slope were calculated respectively to modify direct and diffuse solar radiation which aimed to get the actual solar radiation on 1 km ground surfaces. The results indicated that great difference of solar radiation between slope and level surfaces existed in hill and mountainous regions, and the maximum difference might reach 67 percent in China. The simulated radiation could be used in extensive fields of agricultural practices and researches, such as assessment of agricultural potential productivity, agricultural zoning, distribution of crops, and returning farmland into forest and grassland

Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data

An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. In this study, six methods, including partial least squares regression, regression kriging, k-nearest neighbour, support vector machines, random forest and high accuracy surface modelling (HASM), were used to simulate forest AGB. Forest AGB was mapped by combining Geoscience Laser Altimeter System data, optical imagery and field inventory data. The Normalized Difference Vegetation Index (NDVI) and Wide Dynamic Range Vegetation Index (WDRVI0.2) of September and October, which had a stronger correlation with forest AGB than that of the peak growing season, were selected as predictor variables, along with tree cover percentage and three GLAS-derived parameters. The results of the different methods were evaluated. The HASM model had the best modelling accuracy (small MAE, RMSE, NRMSE, RMSV and NMSE and large R2). A forest AGB map of the study area was generated using the optimal model