| 1. |
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
- Jansen, Joachim, 1989-, et al.
(författare)
-
Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing
- 2023
-
Ingår i: Scientific Data. - : Springer Nature. - 2052-4463. ; 10:1
-
Tidskriftsartikel (refereegranskat)abstract
- Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002-2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983-2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.
|
|
| 5. |
- Shameer, S., et al.
(författare)
-
TrypanoCyc: a community-led biochemical pathways database for Trypanosoma brucei
- 2015
-
Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 43:D1, s. D637-D644
-
Tidskriftsartikel (refereegranskat)abstract
- The metabolic network of a cell represents thecatabolic and anabolic reactions that interconvertsmall molecules (metabolites) through the activity ofenzymes, transporters and non-catalyzed chemicalreactions. Our understanding of individual metabolicnetworks is increasing as we learn more aboutthe enzymes that are active in particular cells underparticular conditions and as technologies advanceto allow detailed measurements of the cellularmetabolome. Metabolic network databases areof increasing importance in allowing us to contextualisedata sets emerging from transcriptomic,proteomic and metabolomic experiments. Here wepresent a dynamic database, TrypanoCyc (http://www.metexplore.fr/trypanocyc/), which describesthe generic and condition-specific metabolic networkof Trypanosoma brucei, a parasitic protozoan responsiblefor human and animal African trypanosomiasis.In addition to enabling navigation through the BioCyc-based TrypanoCyc interface, we have alsoimplemented a network-based representation of theinformation through MetExplore, yielding a novel environmentin which to visualise the metabolism ofthis important parasite.
|
|
| 6. |
- Haeni, M., et al.
(författare)
-
Winter respiratory C losses provide explanatory power for net ecosystem productivity
- 2017
-
Ingår i: Journal of Geophysical Research - Biogeosciences. - 2169-8953. ; 122:1, s. 243-260
-
Tidskriftsartikel (refereegranskat)abstract
- Accurate predictions of net ecosystem productivity (NEPc) of forest ecosystems are essential for climate change decisions and requirements in the context of national forest growth and greenhouse gas inventories. However, drivers and underlying mechanisms determining NEPc (e.g., climate and nutrients) are not entirely understood yet, particularly when considering the influence of past periods. Here we explored the explanatory power of the compensation day (cDOY)-defined as the day of year when winter net carbon losses are compensated by spring assimilation-for NEPc in 26 forests in Europe, North America, and Australia, using different NEPc integration methods. We found cDOY to be a particularly powerful predictor for NEPc of temperate evergreen needleleaf forests (R2=0.58) and deciduous broadleaf forests (R2=0.68). In general, the latest cDOY correlated with the lowest NEPc. The explanatory power of cDOY depended on the integration method for NEPc, forest type, and whether the site had a distinct winter net respiratory carbon loss or not. The integration methods starting in autumn led to better predictions of NEPc from cDOY then the classical calendar method starting 1 January. Limited explanatory power of cDOY for NEPc was found for warmer sites with no distinct winter respiratory loss period. Our findings highlight the importance of the influence of winter processes and the delayed responses of previous seasons' climatic conditions on current year's NEPc. Such carry-over effects may contain information from climatic conditions, carbon storage levels, and hydraulic traits of several years back in time.
|
|
| 7. |
|
|
| 8. |
|
|
| 9. |
|
|
| 10. |
|
|
