Dataset Information

Spatial and temporal variations in global soil respiration and their relationships with climate and land cover.

ABSTRACT: Soil respiration (R s) represents the largest flux of CO2 from terrestrial ecosystems to the atmosphere, but its spatial and temporal changes as well as the driving forces are not well understood. We derived a product of annual global R s from 2000 to 2014 at 1 km by 1 km spatial resolution using remote sensing data and biome-specific statistical models. Different from the existing view that climate change dominated changes in R s, we showed that land-cover change played a more important role in regulating R s changes in temperate and boreal regions during 2000-2014. Significant changes in R s occurred more frequently in areas with significant changes in short vegetation cover (i.e., all vegetation shorter than 5 m in height) than in areas with significant climate change. These results contribute to our understanding of global R s patterns and highlight the importance of land-cover change in driving global and regional R s changes.

SUBMITTER: Huang N

PROVIDER: S-EPMC7541079 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Spatial and temporal variations in global soil respiration and their relationships with climate and land cover.

Huang Ni N Wang Li L Song Xiao-Peng XP Black T Andrew TA Jassal Rachhpal S RS Myneni Ranga B RB Wu Chaoyang C Wang Lei L Song Wanjuan W Ji Dabin D Yu Shanshan S Niu Zheng Z

Science advances 20201007 41

Soil respiration (R s) represents the largest flux of CO2 from terrestrial ecosystems to the atmosphere, but its spatial and temporal changes as well as the driving forces are not well understood. We derived a product of annual global R s from 2000 to 2014 at 1 km by 1 km spatial resolution using remote sensing data and biome-specific statistical models. Different from the existing view that climate change dominated changes in R s, ...[more]

PMID: 33028522

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Project description:The patterns of change in bioclimatic conditions determine the vegetation cover and soil properties along the altitudinal gradient. Together, these factors control the spatial variability of soil respiration (RS) in mountainous areas. The underlying mechanisms, which are poorly understood, shape the resulting surface CO2 flux in these ecosystems. We aimed to investigate the spatial variability of RS and its drivers on the northeastern slope of the Northwest Caucasus Mountains, Russia (1,260-2,480 m a.s.l.), in mixed, fir, and deciduous forests, as well as subalpine and alpine meadows. RS was measured simultaneously in each ecosystem at 12 randomly distributed points using the closed static chamber technique. After the measurements, topsoil samples (0-10 cm) were collected under each chamber (n = 60). Several soil physicochemical, microbial, and vegetation indices were assessed as potential drivers of RS. We tested two hypotheses: (i) the spatial variability of RS is higher in forests than in grasslands; and (ii) the spatial variability of RS in forests is mainly due to soil microbial activity, whereas in grasslands, it is mainly due to vegetation characteristics. Unexpectedly, RS variability was lower in forests than in grasslands, ranging from 1.3-6.5 versus 3.4-12.7 μmol CO2 m-1 s-1, respectively. Spatial variability of RS in forests was related to microbial functioning through chitinase activity (50% explained variance), whereas in grasslands it was related to vegetation structure, namely graminoid abundance (27% explained variance). Apparently, the chitinase dependence of RS variability in forests may be related to soil N limitation. This was confirmed by low N content and high C:N ratio compared to grassland soils. The greater sensitivity of grassland RS to vegetation structure may be related to the essential root C allocation for some grasses. Thus, the first hypothesis concerning the higher spatial variability of RS in forests than in grasslands was not confirmed, whereas the second hypothesis concerning the crucial role of soil microorganisms in forests and vegetation in grasslands as drivers of RS spatial variability was confirmed.

Project description:Very few principles have been unraveled that explain the relationship between soil properties and soil biota across large spatial scales and different land-use types. Here, we seek these general relationships using data from 52 differently managed grassland and forest soils in three study regions spanning a latitudinal gradient in Germany. We hypothesize that, after extraction of variation that is explained by location and land-use type, soil properties still explain significant proportions of variation in the abundance and diversity of soil biota. If the relationships between predictors and soil organisms were analyzed individually for each predictor group, soil properties explained the highest amount of variation in soil biota abundance and diversity, followed by land-use type and sampling location. After extraction of variation that originated from location or land-use, abiotic soil properties explained significant amounts of variation in fungal, meso- and macrofauna, but not in yeast or bacterial biomass or diversity. Nitrate or nitrogen concentration and fungal biomass were positively related, but nitrate concentration was negatively related to the abundances of Collembola and mites and to the myriapod species richness across a range of forest and grassland soils. The species richness of earthworms was positively correlated with clay content of soils independent of sample location and land-use type. Our study indicates that after accounting for heterogeneity resulting from large scale differences among sampling locations and land-use types, soil properties still explain significant proportions of variation in fungal and soil fauna abundance or diversity. However, soil biota was also related to processes that act at larger spatial scales and bacteria or soil yeasts only showed weak relationships to soil properties. We therefore argue that more general relationships between soil properties and soil biota can only be derived from future studies that consider larger spatial scales and different land-use types.

Project description:Research on spatial non-stationarity of land-cover classification accuracy has been ongoing for over two decades with most of the work focusing on single date maps. We extend the understanding of thematic map accuracy spatial patterns by: (1) quantifying spatial patterns of map-reference agreement for class-specific land-cover change rather than class-specific land cover for both omission and commission expressions of map error; (2) reporting goodness-of-fit estimates for the empirical models, which have been lacking in previous assessments, and; (3) using the empirical model results to map the locations of the relative likelihoods of map-reference agreement for specific land-cover change classes. We evaluated 10 map-based explanatory variables in single and multivariable logistic regression models to predict the likelihood of agreement between map and reference land-cover change (2001-2011) labels using the National Land Cover Database (NLCD) 2011 land cover and accuracy data. Logistic models for omission error had better goodness-of-fit estimates than models for commission error. For the omission error models, the explanatory variable, density of the mapped class-specific change in the immediate neighbourhood surrounding the sample pixel, produced the best model fit results (Tjur coefficient of discrimination, D, ranged from 0.59 to 0.98) compared to multivariable models and all other single explanatory variable models. Maps of the predicted likelihood of map-reference agreement produced from the best fitting omission error models provide a spatially explicit description of spatial variation of classification uncertainty at both local and regional scales. Application of the models indicated higher likelihoods of agreement (>50%) comprised a greater proportion of the land-cover change class area than the proportion of the land-cover change class with lower likelihoods of agreement. NLCD users can apply reported equations to map land-cover change uncertainty.

Dataset Information

Spatial and temporal variations in global soil respiration and their relationships with climate and land cover.

Publications

Spatial and temporal variations in global soil respiration and their relationships with climate and land cover.

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