scholarly journals Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks

2015 ◽  
Vol 12 (13) ◽  
pp. 3993-4004 ◽  
Author(s):  
U. Mishra ◽  
W. J. Riley
Keyword(s):  
Environmental Factors ◽  
Organic Carbon ◽  
Spatial Structure ◽  
Spatial Heterogeneity ◽  
Spatial Scale ◽  
Environmental Variables ◽  
Spatial Scales ◽  
Statistical Properties ◽  
Environmental Controls ◽  
Soc Stocks

Abstract. The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing the heterogeneity of terrestrial hydrological and biogeochemical processes in Earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a data set with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales (s = 100, 200, and 500 m and 1, 2, 5, and 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions (R2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 m to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98 % of variability in the variance of SOC stocks. We found moderately accurate linear relationships between mean and higher-order moments of predicted SOC stocks (R2 ∼ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks could improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.

scholarly journals Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks

2015 ◽  
Vol 12 (2) ◽  
pp. 1721-1751 ◽  
Author(s):  
U. Mishra ◽  
W. J. Riley
Keyword(s):  
Environmental Factors ◽  
Organic Carbon ◽  
Spatial Structure ◽  
Spatial Heterogeneity ◽  
Spatial Scale ◽  
Environmental Variables ◽  
Spatial Scales ◽  
Statistical Properties ◽  
Environmental Controls ◽  
Soc Stocks

Abstract. The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing heterogeneity of terrestrial hydrological and biogeochemical processes in earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a dataset with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales (s = 100, 200, 500 m, 1, 2, 5, 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions (R2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98% of variability in the variance of SOC stocks. We found moderately-accurate linear relationships between mean and higher-order moments of predicted SOC stocks (R2 ~ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks can improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.

scholarly journals Spatial heterogeneity and environmental predictors of permafrost region soil organic carbon stocks

2021 ◽  
Vol 7 (9) ◽  
pp. eaaz5236 ◽  
Author(s):  
Umakant Mishra ◽  
Gustaf Hugelius ◽  
Eitan Shelef ◽  
Yuanhe Yang ◽  
Jens Strauss ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Heterogeneity ◽  
Surface Air Temperature ◽  
Spatially Explicit ◽  
Soil Profiles ◽  
Permafrost Region ◽  
Environmental Predictors ◽  
Soil Organic Carbon Stocks ◽  
Soc Stocks

Large stocks of soil organic carbon (SOC) have accumulated in the Northern Hemisphere permafrost region, but their current amounts and future fate remain uncertain. By analyzing dataset combining >2700 soil profiles with environmental variables in a geospatial framework, we generated spatially explicit estimates of permafrost-region SOC stocks, quantified spatial heterogeneity, and identified key environmental predictors. We estimated that 1014−175+194 Pg C are stored in the top 3 m of permafrost region soils. The greatest uncertainties occurred in circumpolar toe-slope positions and in flat areas of the Tibetan region. We found that soil wetness index and elevation are the dominant topographic controllers and surface air temperature (circumpolar region) and precipitation (Tibetan region) are significant climatic controllers of SOC stocks. Our results provide first high-resolution geospatial assessment of permafrost region SOC stocks and their relationships with environmental factors, which are crucial for modeling the response of permafrost affected soils to changing climate.

scholarly journals Spatial scale-dependent land–atmospheric methane exchanges in the northern high latitudes from 1993 to 2004

2014 ◽  
Vol 11 (7) ◽  
pp. 1693-1704 ◽  
Author(s):  
X. Zhu ◽  
Q. Zhuang ◽  
X. Lu ◽  
L. Song
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Scale ◽  
Water Table ◽  
Spatial Scales ◽  
Grid Cell ◽  
High Latitudes ◽  
Atmospheric Methane ◽  
Ch4 Emissions ◽  
Biogeochemical Models ◽  
Macro Scale

Abstract. Effects of various spatial scales of water table dynamics on land–atmospheric methane (CH4) exchanges have not yet been assessed for large regions. Here we used a coupled hydrology–biogeochemistry model to quantify daily CH4 exchanges over the pan-Arctic from 1993 to 2004 at two spatial scales of 100 km and 5 km. The effects of sub-grid spatial variability of the water table depth (WTD) on CH4 emissions were examined with a TOPMODEL-based parameterization scheme for the northern high latitudes. We found that both WTD and CH4 emissions are better simulated at a 5 km spatial resolution. By considering the spatial heterogeneity of WTD, net regional CH4 emissions at a 5 km resolution are 38.1–55.4 Tg CH4 yr−1 from 1993 to 2004, which are on average 42% larger than those simulated at a 100 km resolution using a grid-cell-mean WTD scheme. The difference in annual CH4 emissions is attributed to the increased emitting area and enhanced flux density with finer resolution for WTD. Further, the inclusion of sub-grid WTD spatial heterogeneity also influences the inter-annual variability of CH4 emissions. Soil temperature plays an important role in the 100 km estimates, while the 5 km estimates are mainly influenced by WTD. This study suggests that previous macro-scale biogeochemical models using a grid-cell-mean WTD scheme might have underestimated the regional CH4 emissions. The spatial scale-dependent effects of WTD should be considered in future quantification of regional CH4 emissions.

Spatial modelling of soil organic carbon stocks with combined principal component analysis and geographically weighted regression

2018 ◽  
Vol 156 (6) ◽  
pp. 774-784 ◽  
Author(s):  
Long Guo ◽  
Mei Luo ◽  
Chengsi Zhangyang ◽  
Chen Zeng ◽  
Shanqin Wang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Prediction Models ◽  
Principal Component ◽  
Weighted Regression ◽  
Auxiliary Variables ◽  
Soc Stocks

AbstractWith the development of remote sensing and geostatistical technology, complex environmental variables are increasingly easily quantified and applied in modelling soil organic carbon (SOC). However, this emphasizes data redundancy and multicollinearity problems adding to the difficulty in selecting dominant influential auxiliary variables and uncertainty in estimating SOC stocks. The current paper considers the spatial characteristics of SOC density (SOCD) to construct prediction models of SOCD on the basis of reducing the data dimensionality and complexity using the principal component analysis (PCA) method. A total of 260 topsoil samples were collected from Chahe town, China. Eight environmental variables (elevation, aspect, slope, normalized difference vegetation index, normalized difference moisture index, nearest distance to construction area and road, and land use degree comprehensive index) were pre-analysed by PCA and then extracted as the main principal component variables to construct prediction models. Two geostatistical approaches (ordinary kriging and ordinary co-kriging) and two regression approaches (ordinary least squares and geographically weighted regression (GWR)) were used to estimate SOCD. Results showed that PCA played an important role in reducing the redundancy and multicollinearity of the auxiliary variables and GWR achieved the highest prediction accuracy in these four models. GWR considered not only the spatial characteristics of SOCD but also the related valuable information of the auxiliary attributes. In summary, PCA-GWR is a promising spatial method used here to predict SOC stocks.

scholarly journals Spatial scales of interannual recruitment variations of marine, anadromous, and freshwater fish

1997 ◽  
Vol 54 (6) ◽  
pp. 1400-1407 ◽  
Author(s):  
R A Myers ◽  
G Mertz ◽  
J Bridson
Keyword(s):  
Spatial Scale ◽  
Freshwater Fish ◽  
Marine Environment ◽  
Environmental Variables ◽  
Large Scale ◽  
Spatial Scales ◽  
Marine Species ◽  
Freshwater Species ◽  
Environmental Agents

We examine the spatial scale of variability in recruitment for 11 marine, three anadromous, and five freshwater species. Generally the spatial scale of recruitment correlations for marine species is approximately 500 km, compared with less than 50 km for freshwater; anadromous species fall between these two scales. The scale for marine species is comparable with (but less than) that of the largest-scale environmental variables (and is compatible with the idea that large-scale environmental agents influence recruitment). Our results are consistent with the hypothesis that predation is a more important factor in determining recruitment in freshwater than it is in the marine environment.

Spatial structuring of submerged aquatic vegetation in an estuarine habitat of the Gulf of Mexico

2012 ◽  
Vol 93 (4) ◽  
pp. 855-866 ◽  
Author(s):  
Aretha Moriana Burgos-León ◽  
David Valdés ◽  
Ma. Eugenia Vega ◽  
Omar Defeo
Keyword(s):  
Spatial Structure ◽  
Environmental Variables ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Spatial Scales ◽  
Ruppia Maritima ◽  
Spatial Partitioning ◽  
Estuarine Habitat ◽  
Relative Contribution ◽  
Geostatistical Techniques

Seasonal changes in spatial structure of biomass of submerged aquatic vegetation (SAV) and environmental variables were evaluated in Celestun Lagoon, an estuarine habitat in Mexico. Geostatistical techniques were used to evaluate spatial autocorrelation and to predict the spatial distribution by kriging. The relative contribution of 11 environmental variables in explaining the spatial structure of biomass of SAV was evaluated by canonical correspondence analysis. Spatial partitioning between species of SAV was evident: the seagrasses Halodule wrightii and Ruppia maritima dominated the seaward and central zones of the lagoon, respectively, whereas the green alga Chara fibrosa was constrained to the inner zone. The spatial structure and seasonal variability of SAV biomass were best explained by organic carbon in the sediments, salinity and total suspended solids in the water column. Analysis at different spatial scales allowed identifying the importance of spatial structure in biotic and abiotic variables of this estuarine habitat.

scholarly journals Spatial Scale Dependence of Ecological Factors That Regulate Functional and Phylogenetic Assembly in a Mediterranean High Mountain Grassland

2021 ◽  
Vol 9 ◽  
Author(s):  
David S. Pescador ◽  
Francesco de Bello ◽  
Jesús López-Angulo ◽  
Fernando Valladares ◽  
Adrián Escudero
Keyword(s):  
Global Warming ◽  
Organic Carbon ◽  
Spatial Scale ◽  
Community Assembly ◽  
Species Interactions ◽  
Species Turnover ◽  
Spatial Scales ◽  
Ecological Factors ◽  
Functional Heterogeneity ◽  
Multi Scale

Understanding how functional and phylogenetic patterns vary among scales and along ecological gradients within a given species pool is critical for inferring community assembly processes. However, we lack a clear understanding of these patterns in stressful habitats such as Mediterranean high mountains where ongoing global warming is expected to affect species fitness and species interactions, and subsequently species turnover. In this study, we investigated 39 grasslands with the same type of plant community and very little species turnover across an elevation gradient above the treeline at Sierra de Guadarrama National Park in central Spain. In particular, we assessed functional and phylogenetic patterns, including functional heterogeneity, using a multi-scale approach (cells, subplots, and plots) and determined the relevance of key ecological factors (i.e., elevation, potential solar radiation, pH, soil organic carbon, species richness, and functional heterogeneity) that affect functional and phylogenetic patterns at each spatial scale. Overall, at the plot scale, coexisting species tended to be more functionally and phylogenetically similar. By contrast, at the subplot and cell scales, species tended to be more functionally different but phylogenetically similar. Functional heterogeneity at the cell scale was comparable to the variation across plots along the gradient. The relevance of ecological factors that regulate diversity patterns varied among spatial scales. An increase in elevation resulted in functional clustering at larger scales and phylogenetic overdispersion at a smaller scale. The soil pH and organic carbon levels exhibited complex functional patterns, especially at small spatial scales, where an increase in pH led to clustering patterns for the traits related to the leaf economic spectrum (i.e., foliar thickness, specific leaf area, and leaf dry matter content). Our findings confirm the presence of primary environmental filters (coldness and summer drought at our study sites) that constrain the regional species pool, suggesting the presence of additional assembly mechanisms that act at the smallest scale (e.g., micro-environmental gradients and/or species interactions). Functional and phylogenetic relatedness should be determined using a multi-scale approach to help interpret community assembly processes and understand the initial community responses to environmental changes, including ongoing global warming.

scholarly journals Unraveling the effects of environmental drivers and spatial structure on benthic species distribution patterns in Eurasian-Arctic seas (Barents, Kara and Laptev Seas)

Polar Biology ◽  
2020 ◽  
Vol 43 (11) ◽  
pp. 1693-1705
Author(s):  
Miriam L. S. Hansen ◽  
Dieter Piepenburg ◽  
Dmitrii Pantiukhin ◽  
Casper Kraan
Keyword(s):  
Environmental Factors ◽  
Spatial Structure ◽  
Large Scale ◽  
Environmental Changes ◽  
Spatial Scales ◽  
Distribution Patterns ◽  
Environmental Drivers ◽  
Arctic Seas ◽  
Faunal Distribution ◽  
The Impact

Abstract In times of accelerating climate change, species are challenged to respond to rapidly shifting environmental settings. Yet, faunal distribution and composition are still scarcely known for remote and little explored seas, where observations are limited in number and mostly refer to local scales. Here, we present the first comprehensive study on Eurasian-Arctic macrobenthos that aims to unravel the relative influence of distinct spatial scales and environmental factors in determining their large-scale distribution and composition patterns. To consider the spatial structure of benthic distribution patterns in response to environmental forcing, we applied Moran’s eigenvector mapping (MEM) on a large dataset of 341 samples from the Barents, Kara and Laptev Seas taken between 1991 and 2014, with a total of 403 macrobenthic taxa (species or genera) that were present in ≥ 10 samples. MEM analysis revealed three spatial scales describing patterns within or beyond single seas (broad: ≥ 400 km, meso: 100–400 km, and small: ≤ 100 km). Each scale is associated with a characteristic benthic fauna and environmental drivers (broad: apparent oxygen utilization and phosphate, meso: distance-to-shoreline and temperature, small: organic carbon flux and distance-to-shoreline). Our results suggest that different environmental factors determine the variation of Eurasian-Arctic benthic community composition within the spatial scales considered and highlight the importance of considering the diverse spatial structure of species communities in marine ecosystems. This multiple-scale approach facilitates an enhanced understanding of the impact of climate-driven environmental changes that is necessary for developing appropriate management strategies for the conservation and sustainable utilization of Arctic marine systems.

Relative importance of environmental variables for the distribution of the invasive marsh species Spartina alterniflora across different spatial scales

2018 ◽  
Vol 69 (5) ◽  
pp. 790 ◽  
Author(s):  
Huiyu Liu ◽  
Haibo Gong ◽  
Xiangzhen Qi ◽  
Yufeng Li ◽  
Zhenshan Lin
Keyword(s):  
Organic Carbon ◽  
Spartina Alterniflora ◽  
Environmental Variables ◽  
Spatial Scales ◽  
Regional Scale ◽  
Species Distribution Modelling ◽  
Relative Importance ◽  
Soil Drainage ◽  
Variable Type ◽  
Chinese Coast

The relative importance of environmental variables for Spartina alterniflora distribution was investigated across different spatial scales using maximum entropy modelling (MaxEnt), a species distribution modelling technique. The results showed that elevation was the most important predictor for species presence at each scale. Mean diurnal temperature range and isothermality were the second most important predictors at national and regional scales respectively. Soil drainage class, pH and organic carbon were important on the northern Chinese coast. The importance of climatic variable type was highest at global and national scales and declined as the scale decreased. The importance of soil variable type was lower at coarser scales, but varied greatly at finer scales. The relationships between environmental variables and species presence changed as the variables’ ranges changed across different scales. Climatic and soil variables were substantially affected by interactions among variables, which changed their relationships with species presence and relative importance. The modelled suitable area on the Chinese coast decreased from 54.16 to 12.64% limited by elevation from the global to national scale, and decreased to 8.04% limited by soil drainage, pH and organic carbon from the national to regional scale. The findings of the present study emphasise the importance of spatial scale for understanding relationships between environmental variables and the presence of S. alterniflora.

scholarly journals Spatial scale-dependent land-atmospheric methane exchange in the northern high latitudes from 1993 to 2004

2013 ◽  
Vol 10 (11) ◽  
pp. 18455-18478 ◽  
Author(s):  
X. Zhu ◽  
Q. Zhuang ◽  
X. Lu ◽  
L. Song
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Scale ◽  
Water Table ◽  
Spatial Scales ◽  
Grid Cell ◽  
High Latitudes ◽  
Atmospheric Methane ◽  
Ch4 Emissions ◽  
Biogeochemical Models ◽  
Macro Scale

Abstract. Effects of various spatial scales of water table dynamics on the land-atmospheric methane (CH4) exchange have not yet been assessed for large regions. Here we used a coupled hydrology-biogeochemistry model to quantify daily CH4 exchange over the pan-Arctic from 1993 to 2004 at two spatial scales (100 km and 5 km). The effects of sub-grid spatial variability of the water table depth (WTD) on CH4 emissions were examined with a TOPMODEL-based parameterization scheme for northern high latitudes regions. Our results indicate that 5 km CH4 emissions (38.1–55.4 Tg CH4 yr−1, considering the spatial heterogeneity of WTD) were 42% larger than 100 km CH4 emissions (using grid-cell-mean WTD) and the differences in annual CH4 emissions were due to increased emitting area and enhanced flux density after WTD redistribution. Further, the inclusion of sub-grid WTD spatial heterogeneity also influences the inter-annual variability of CH4 emissions. Soil temperature plays a more important role in the 100 km estimates, while the 5 km estimates are more influenced by WTD. This study suggests that previous macro-scale biogeochemical models using grid-cell-mean WTD might have underestimated the regional CH4 budget. The spatial scale-dependent effects of WTD should be considered in future quantifications of regional CH4 emissions.

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