Abstract:Biogeochemical processes driving the spatial variability of soil CO2 production and flux are well studied, but little is known about the variability in the spatial distribution of the stable carbon isotopes that make up soil CO2, particularly in complex terrain. Spatial differences in stable isotopes of soil CO2 could indicate fundamental differences in isotopic fractionation at the landscape level and may be useful to inform modeling of carbon cycling over large areas. We measured the spatial and seasonal var… Show more
“…In the Rocky Mountains, subalpine forests are a regionally important ecosystem for C sequestration and sensitive to future changes in snowpack amount and duration (Schimel et al, ; Sexstone et al, ; Winchell et al, ). Previous efforts within subalpine forests have focused on characterizing C pools and fluxes across individual sites (Bradford et al, ; Liang et al, ; Scott‐Denton et al, ). Larger landscape‐scale assessments are lacking, but they would improve our understanding of how C flux‐environmental relationships gleaned at individual sites drive variability in regional C cycling.…”
Landscape carbon (C) flux estimates help assess the ability of terrestrial ecosystems to buffer further increases in anthropogenic carbon dioxide (CO 2 ) emissions. Advances in remote sensing have led to coarse-scale estimates of gross primary productivity (GPP; e.g., MODIS 17), yet efforts to develop spatial respiration products are lacking. Here we demonstrate a method to predict growing season soil respiration at a regional scale in a mixed subalpine ecosystem. We related field measurements (n = 396) of growing season soil respiration mostly from subalpine forests in the Southern Rocky Mountains ecoregion to a suite of biophysical predictors using a Random Forest model (30-m pixel size). We found that Landsat Enhanced Vegetation Index, growing season aridity index, temperature, precipitation, elevation, and slope aspect explained spatiotemporal variability in soil respiration. Our model had a psuedo-r 2 of 0.45 and root-mean-square error of roughly one quarter of the mean value of respiration. Predicted growing season soil respiration across the region was remarkably consistent across 2004, 2005, and 2006 (150-day sums of 542.8, 544.3, and 536.5 g C/m 2 , respectively). Yet we observed substantial variability in spatial patterns of soil respiration predictions that varied among years, suggesting that our method is sensitive to changes in respiration drivers. Mean predicted growing season soil respiration was 73% of MODIS GPP, while predicted soil respiration was generally within 20% of nocturnal net ecosystem exchange from nearby eddy covariance towers. Thus, geospatial and remotely sensed data sets can be used to estimate soil respiration at landscape scales.
“…In the Rocky Mountains, subalpine forests are a regionally important ecosystem for C sequestration and sensitive to future changes in snowpack amount and duration (Schimel et al, ; Sexstone et al, ; Winchell et al, ). Previous efforts within subalpine forests have focused on characterizing C pools and fluxes across individual sites (Bradford et al, ; Liang et al, ; Scott‐Denton et al, ). Larger landscape‐scale assessments are lacking, but they would improve our understanding of how C flux‐environmental relationships gleaned at individual sites drive variability in regional C cycling.…”
Landscape carbon (C) flux estimates help assess the ability of terrestrial ecosystems to buffer further increases in anthropogenic carbon dioxide (CO 2 ) emissions. Advances in remote sensing have led to coarse-scale estimates of gross primary productivity (GPP; e.g., MODIS 17), yet efforts to develop spatial respiration products are lacking. Here we demonstrate a method to predict growing season soil respiration at a regional scale in a mixed subalpine ecosystem. We related field measurements (n = 396) of growing season soil respiration mostly from subalpine forests in the Southern Rocky Mountains ecoregion to a suite of biophysical predictors using a Random Forest model (30-m pixel size). We found that Landsat Enhanced Vegetation Index, growing season aridity index, temperature, precipitation, elevation, and slope aspect explained spatiotemporal variability in soil respiration. Our model had a psuedo-r 2 of 0.45 and root-mean-square error of roughly one quarter of the mean value of respiration. Predicted growing season soil respiration across the region was remarkably consistent across 2004, 2005, and 2006 (150-day sums of 542.8, 544.3, and 536.5 g C/m 2 , respectively). Yet we observed substantial variability in spatial patterns of soil respiration predictions that varied among years, suggesting that our method is sensitive to changes in respiration drivers. Mean predicted growing season soil respiration was 73% of MODIS GPP, while predicted soil respiration was generally within 20% of nocturnal net ecosystem exchange from nearby eddy covariance towers. Thus, geospatial and remotely sensed data sets can be used to estimate soil respiration at landscape scales.
“…Similarly, applications that depend on δ 13 C data often fail to consider the spatial grain at which different δ 13 C proxies integrate C (Auerswald et al., ). For example, the δ 13 C composition of soil surface layers is related to soil texture and organic matter over relatively small areas (~m 2 ; Bai et al., ; Liang, Riveros‐Iregui, & Risk, ; Wynn et al., ), while herbivore tissues correspond to vegetation composition over larger spatial extents (~10 s of km 2 ; Auerswald et al., ; Kohn & Fremd, ; Meagher, ; Widga, Walker, & Stockli, ). As a result, the spatial scale of C integration may impact how well δ 13 C proxies represent vegetation at the spatial extents and spatial grains that they are often used.…”
Aims: Understanding the functional response of ecosystems to past global change is crucial to predicting performance in future environments. One sensitive and functionally significant attribute of grassland ecosystems is the percentage of species that use the C 4 versus C 3 photosynthetic pathway. Grasses using C 3 and C 4 pathways are expected to have different responses to many aspects of anthropogenic environmental change that have followed the industrial revolution, including increases in temperature and atmospheric CO 2 , changes to land management and fire regimes, precipitation seasonality, and nitrogen deposition. In spite of dramatic environmental changes over the past 300 years, it is unknown if the C 4 grass percentage in grasslands has shifted.Location: Contiguous United States of America.Methods: Here, we used stable carbon isotope data (i.e. d 13 C) from 30 years of soil samples, as well as herbivore tissues that date to 1739 CE, to reconstruct coarsegrain C 3 and C 4 grass composition in North American grassland sites to compare with modern vegetation. We spatially resampled these three datasets to a shared 100-km grid, allowing comparison of d 13 C values at a resolution and extent common for climate model outputs and biogeographical studies.Results: At this spatial grain, the bison tissue proxy was superior to the soil proxy because the soils reflect integration of local carbon inputs, whereas bison sample vegetation across landscapes. Bison isotope values indicate that historical grassland photosynthetic-type composition was similar to modern vegetation.Main conclusions: Despite major environmental change, comparing modern plot vegetation data to three centuries of bison d 13 C data revealed that the biogeographical distribution of C 3 and C 4 grasses has not changed significantly since the 1700s. This is particularly surprising given the expected CO 2 fertilization of C 3 grasses. Our findings highlight the critical importance of capturing the full range of physiological, ecological and demographical processes in biosphere models predicting future climates and ecosystems.
“…In the model, isotopologues of CO 2 are treated as independent gases, with their own specific concentration gradients and diffusion rates (Cerling et al, 1991;Risk and Kellman, 2008;Nickerson and Risk, 2009). We assume total CO 2 to be 12 CO 2 because of its high abundance.…”
Abstract. Earth system scientists working with radiocarbon in
organic samples use a stable carbon isotope (δ13C) correction
to account for mass-dependent fractionation, but it has not been evaluated
for the soil gas environment, wherein both diffusive gas transport and
diffusive mixing are important. Using theory and an analytical soil gas
transport model, we demonstrate that the conventional correction is
inappropriate for interpreting the radioisotopic composition of CO2
from biological production because it does not account for important gas
transport mechanisms. Based on theory used to interpret δ13C of
soil production from soil CO2, we propose a new
solution for radiocarbon applications in the soil gas environment that fully
accounts for both mass-dependent diffusion and mass-independent diffusive
mixing.
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