| 1. |
- Ichii, Kazuhito, et al.
(författare)
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New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression
- 2017
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Ingår i: Journal of Geophysical Research - Biogeosciences. - 2169-8953. ; 122:4, s. 767-795
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Tidskriftsartikel (refereegranskat)abstract
- The lack of a standardized database of eddy covariance observations has been an obstacle for data-driven estimation of terrestrial CO2 fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data-driven estimation of gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE). Data-driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site-level evaluation of the estimated CO2 fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8days are reproduced (e.g., r2=0.73 and 0.42 for 8day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor-based Sun-induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r2=1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere-land CO2 fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land CO2 fluxes from SVR-NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land CO2 fluxes. These data-driven estimates can provide a new opportunity to assess CO2 fluxes in Asia and evaluate and constrain terrestrial ecosystem models.
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| 2. |
- Kattge, Jens, et al.
(författare)
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TRY plant trait database - enhanced coverage and open access
- 2020
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Ingår i: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
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Tidskriftsartikel (refereegranskat)abstract
- Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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| 3. |
- Kim, Yong Suk, et al.
(författare)
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Greenhouse gas emissions after a prescribed fire in white birch-dwarf bamboo stands in northern Japan, focusing on the role of charcoal
- 2011
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Ingår i: European Journal of Forest Research. - : Springer Science and Business Media LLC. - 1612-4669 .- 1612-4677. ; 130:6, s. 1031-1044
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Tidskriftsartikel (refereegranskat)abstract
- Forest fires affect both carbon (C) and nitrogen (N) cycling in forest ecosystems, and thereby influence the soil-atmosphere exchange of major greenhouse gases (GHGs): carbon dioxide (CO(2)), methane (CH(4)), and nitrous oxide (N(2)O). To determine changes in the soil GHG fluxes following a forest fire, we arranged a low-intensity surface fire in a white birch forest in northern Japan. We established three treatments, having four replications each: a control plot (CON), a burned plot (BURN), and a plot burned with removal of the resulting charcoal (BURN-CHA). Soil GHG fluxes and various properties of the soil were determined on four or five occasions during a period that spanned two growing seasons. We observed increased concentrations of ammonium-N (NH(4)-N) in BURN and BURN-CHA after the fire, while nitrate-N (NO(3)-N) concentration was only increased in BURN-CHA after the fire. The soil CO(2) flux was significantly higher in CON than in BURN or BURN-CHA, but there was no difference in soil CH(4) uptake between the three treatments. Moreover, the N(2)O flux from BURN-CHA soil was slightly greater than in CON or BURN. In BURN-CHA, the soil N(2)O flux peaked in August, but there was no peak in BURN. We found temporal correlations between soil GHG fluxes and soil variables, e.g. soil temperature or NO(3)-N. Our results suggest that environmental changes following fire, including the increased availability of N and the disappearance of the litter layer, have the potential to change soil GHG fluxes. Fire-produced charcoal could be significant in reducing soil N(2)O flux in temperate forests.
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| 4. |
- Peltoniemi, Mikko, et al.
(författare)
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Does canopy mean nitrogen concentration explain variation in canopy light use efficiency across 14 contrasting forest sites?
- 2012
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Ingår i: Tree Physiology. - : Oxford University Press (OUP). - 1758-4469 .- 0829-318X. ; 32:2, s. 200-218
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Tidskriftsartikel (refereegranskat)abstract
- The maximum light use efficiency (LUE = gross primary production (GPP)/absorbed photosynthetic photon flux density (aPPFD)) of plant canopies has been reported to vary spatially and some of this variation has previously been attributed to plant species differences. The canopy nitrogen concentration [N] can potentially explain some of this spatial variation. However, the current paradigm of the N-effect on photosynthesis is largely based on the relationship between photosynthetic capacity (A(max)) and [N], i.e., the effects of [N] on photosynthesis rates appear under high PPFD. A maximum LUE-[N] relationship, if it existed, would influence photosynthesis in the whole range of PPFD. We estimated maximum LUE for 14 eddy-covariance forest sites, examined its [N] dependency and investigated how the [N]-maximum LUE dependency could be incorporated into a GPP model. In the model, maximum LUE corresponds to LUE under optimal environmental conditions before light saturation takes place (the slope of GPP vs. PPFD under low PPFD). Maximum LUE was higher in deciduous/mixed than in coniferous sites, and correlated significantly with canopy mean [N]. Correlations between maximum LUE and canopy [N] existed regardless of daily PPFD, although we expected the correlation to disappear under low PPFD when LUE was also highest. Despite these correlations, including [N] in the model of GPP only marginally decreased the root mean squared error. Our results suggest that maximum LUE correlates linearly with canopy [N], but that a larger body of data is required before we can include this relationship into a GPP model. Gross primary production will therefore positively correlate with [N] already at low PPFD, and not only at high PPFD as is suggested by the prevailing paradigm of leaf-level A(max)-[N] relationships. This finding has consequences for modelling GPP driven by temporal changes or spatial variation in canopy [N].
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