Dataset Information

Understory vegetation mediates permafrost active layer dynamics and carbon dioxide fluxes in open-canopy larch forests of northeastern Siberia.

ABSTRACT: Arctic ecosystems are characterized by a broad range of plant functional types that are highly heterogeneous at small (~1-2 m) spatial scales. Climatic changes can impact vegetation distribution directly, and also indirectly via impacts on disturbance regimes. Consequent changes in vegetation structure and function have implications for surface energy dynamics that may alter permafrost thermal dynamics, and are therefore of interest in the context of permafrost related climate feedbacks. In this study we examine small-scale heterogeneity in soil thermal properties and ecosystem carbon and water fluxes associated with varying understory vegetation in open-canopy larch forests in northeastern Siberia. We found that lichen mats comprise 16% of understory vegetation cover on average in open canopy larch forests, and lichen abundance was inversely related to canopy cover. Relative to adjacent areas dominated by shrubs and moss, lichen mats had 2-3 times deeper permafrost thaw depths and surface soils warmer by 1-2°C in summer and less than 1°C in autumn. Despite deeper thaw depths, ecosystem respiration did not differ across vegetation types, indicating that autotrophic respiration likely dominates areas with shrubs and moss. Summertime net ecosystem exchange of CO2 was negative (i.e. net uptake) in areas with high shrub cover, while positive (i.e. net loss) in lichen mats and areas with less shrub cover. Our results highlight relationships between vegetation and soil thermal dynamics in permafrost ecosystems, and underscore the necessity of considering both vegetation and permafrost dynamics in shaping carbon cycling in permafrost ecosystems.

SUBMITTER: Loranty MM

PROVIDER: S-EPMC5863986 | biostudies-other | 2018

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Understory vegetation mediates permafrost active layer dynamics and carbon dioxide fluxes in open-canopy larch forests of northeastern Siberia.

Loranty Michael M MM Berner Logan T LT Taber Eric D ED Kropp Heather H Natali Susan M SM Alexander Heather D HD Davydov Sergey P SP Zimov Nikita S NS

PloS one 20180322 3

Arctic ecosystems are characterized by a broad range of plant functional types that are highly heterogeneous at small (~1-2 m) spatial scales. Climatic changes can impact vegetation distribution directly, and also indirectly via impacts on disturbance regimes. Consequent changes in vegetation structure and function have implications for surface energy dynamics that may alter permafrost thermal dynamics, and are therefore of interest in the context of permafrost related climate feedbacks. In this ...[more]

PMID: 29565980

Similar Datasets

Project description:IntroductionPlant species composition in forest ecosystems can alter soil greenhouse gas (GHG) budgets by affecting soil properties and microbial communities. However, little attention has been paid to the forest types characterized by understory vegetation, especially in boreal forests where understory species contribute significantly to carbon and nitrogen cycling.MethodIn the present study, soil GHG fluxes, soil properties and bacterial community, and soil environmental conditions were investigated among three types of larch forest [Rhododendron simsii-Larix gmelinii forest (RL), Ledum palustre-Larix gmelinii forest (LL), and Sphagnum-Bryum-Ledum palustre-Larix gmelinii forest (SLL)] in the typical boreal region of northeast China to explore whether the forest types characterized by different understory species can affect soil GHG fluxes.ResultsThe results showed that differences in understory species significantly affected soil GHG fluxes, properties, and bacterial composition among types of larch forest. Soil CO2 and N2O fluxes were significantly higher in LL (347.12 mg m-2 h-1 and 20.71 μg m-2 h-1) and RL (335.54 mg m-2 h-1 and 20.73 μg m-2 h-1) than that in SLL (295.58 mg m-2 h-1 and 17.65 μg m-2 h-1), while lower soil CH4 uptake (-21.07 μg m-2 h-1) were found in SLL than in RL (-35.21 μg m-2 h-1) and LL (-35.85 μg m-2 h-1). No significant differences between LL and RL were found in soil CO2, CH4, and N2O fluxes. Soil bacterial composition was mainly dominated by Proteobacteria, Actinobacteria, Acidobacteria, and Chloroflexi among the three types of larch forest, while their abundances differed significantly. Soil environmental variables, soil properties, bacterial composition, and their interactions significantly affected the variations in GHG fluxes with understory species. Specifically, structural equation modeling suggested that soil bacterial composition and temperature had direct close links with variations in soil GHG fluxes among types of larch forest. Moreover, soil NO3 --N and NH4 + - N content also affected soil CO2, CH4, and N2O fluxes indirectly, via their effects on soil bacterial composition.DiscussionOur study highlights the importance of understory species in regulating soil GHG fluxes in boreal forests, which furthers our understanding of the role of boreal forests in sustainable development and climate change mitigation.

Project description:Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.

Project description:Forest understory vegetation is an important characteristic of the forest. Predicting and mapping understory is a critical need for forest management and conservation planning, but it has proved difficult with available methods to date. LiDAR has the potential to generate remotely sensed forest understory structure data, but this potential has yet to be fully validated. Our objective was to examine the capacity of LiDAR point cloud data to predict forest understory cover. We modeled ground-based observations of understory structure in three vertical strata (0.5 m to < 1.5 m, 1.5 m to < 2.5 m, 2.5 m to < 3.5 m) as a function of a variety of LiDAR metrics using both mixed-effects and Random Forest models. We compared four understory LiDAR metrics designed to control for the spatial heterogeneity of sampling density. The four metrics were highly correlated and they all produced high values of variance explained in mixed-effects models. The top-ranked model used a voxel-based understory metric along with vertical stratum (Akaike weight = 1, explained variance = 87%, cross-validation error = 15.6%). We found evidence of occlusion of LiDAR pulses in the lowest stratum but no evidence that the occlusion influenced the predictability of understory structure. The Random Forest model results were consistent with those of the mixed-effects models, in that all four understory LiDAR metrics were identified as important, along with vertical stratum. The Random Forest model explained 74.4% of the variance, but had a lower cross-validation error of 12.9%. We conclude that the best approach to predict understory structure is using the mixed-effects model with the voxel-based understory LiDAR metric along with vertical stratum, because it yielded the highest explained variance with the fewest number of variables. However, results show that other understory LiDAR metrics (fractional cover, normalized cover and leaf area density) would still be effective in mixed-effects and Random Forest modelling approaches.

Project description:Carbon release from thawing permafrost soils could significantly exacerbate global warming as the active-layer deepens, exposing more carbon to decay. Plant community and soil properties provide a major control on this by influencing the maximum depth of thaw each summer (active-layer thickness; ALT), but a quantitative understanding of the relative importance of plant and soil characteristics, and their interactions in determine ALTs, is currently lacking. To address this, we undertook an extensive survey of multiple vegetation and edaphic characteristics and ALTs across multiple plots in four field sites within boreal forest in the discontinuous permafrost zone (NWT, Canada). Our sites included mature black spruce, burned black spruce and paper birch, allowing us to determine vegetation and edaphic drivers that emerge as the most important and broadly applicable across these key vegetation and disturbance gradients, as well as providing insight into site-specific differences. Across sites, the most important vegetation characteristics limiting thaw (shallower ALTs) were tree leaf area index (LAI), moss layer thickness and understory LAI in that order. Thicker soil organic layers also reduced ALTs, though were less influential than moss thickness. Surface moisture (0-6 cm) promoted increased ALTs, whereas deeper soil moisture (11-16 cm) acted to modify the impact of the vegetation, in particular increasing the importance of understory or tree canopy shading in reducing thaw. These direct and indirect effects of moisture indicate that future changes in precipitation and evapotranspiration may have large influences on ALTs. Our work also suggests that forest fires cause greater ALTs by simultaneously decreasing multiple ecosystem characteristics which otherwise protect permafrost. Given that vegetation and edaphic characteristics have such clear and large influences on ALTs, our data provide a key benchmark against which to evaluate process models used to predict future impacts of climate warming on permafrost degradation and subsequent feedback to climate.

Dataset Information

Understory vegetation mediates permafrost active layer dynamics and carbon dioxide fluxes in open-canopy larch forests of northeastern Siberia.

Publications

Understory vegetation mediates permafrost active layer dynamics and carbon dioxide fluxes in open-canopy larch forests of northeastern Siberia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets