A Novel Method to Identify the Global Sources and Sinks of Carbon Dioxide Based on Spatial Analysis

Today global warming has become one of the most important concerns of environmental science. The redundancy of greenhouse gases in the atmosphere is known as a major factor in this phenomenon. These gases contain water vapor, carbon dioxide, methane, nitrous oxide, and ozone. The CO2?gas is one of their most effective among these gases. According to scientific warnings, the amount of CO2?gases in the atmosphere has increased by 40% to 45% over the last 50 years. Reducing the abundant gas in the atmosphere requires a good knowledge of related factors involved, including sources that emit gases into the atmosphere and sinks that absorb the gas from the atmosphere. The amount of CO2?gas in the atmosphere has been accurately measured in previous years with great certainty. But the predicted values of emissions from sources and removals by sinks have large ambiguities. As studies show, even the computed residuals trends (which is obtained by subtracting the amounts of sinks from sources) strongly disagree with the trends of the existence of CO2?in the atmosphere. This study as a preliminary review, proposes a method to identify the locations of sources and sinks of carbon dioxide using global statistical information and adding spatial analysis approaches. By applying this method to the data observed from 2000 to 2011 and the extraction of likely sources and sinks, the region of the Black Sea, near Romania recognized as one of the strong points issued and Bukit Kototabang near Indonesia acknowledged as an Impressive CO2?absorption zone.

Data error propagation in stacked bioclimatic envelope models

Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models(BEMs).The approach can be used to investigate patterns and processes of species richness.If data limitations on individual species distributions are inevitable,but how do they affect inferences of patterns and processes of species richness?We investigate the influence of different data sources on estimated species richness gradients in China.We fitted BEMs using species distributions data for 334 bird species obtained from(1)global range maps,(2)regional checklists,(3)museum records and surveys,and(4)citizen science data using presence-only(Mahalanobis distance),presence-background(MAXENT),and presence–absence(GAM and BRT)BEMs.Individual species predictions were stacked to generate species richness gradients.Here,we show that different data sources and BEMs can generate spatially varying gradients of species richness.The environmental predictors that best explained species distributions also differed between data sources.Models using citizen-based data had the highest accuracy,whereas those using range data had the lowest accuracy.Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty.When multiple data sets exist for the same region and taxa,we advise that explicit treatments of uncertainty,such as sensitivity analyses of the input data,should be conducted during the process of modeling.