| 1. |
- Thomas, H. J. D., et al.
(author)
-
Global plant trait relationships extend to the climatic extremes of the tundra biome
- 2020
-
In: Nature Communications. - : Nature Publishing Group. - 2041-1723. ; 11:1
-
Journal article (peer-reviewed)abstract
- The majority of variation in six traits critical to the growth, survival and reproduction of plant species is thought to be organised along just two dimensions, corresponding to strategies of plant size and resource acquisition. However, it is unknown whether global plant trait relationships extend to climatic extremes, and if these interspecific relationships are confounded by trait variation within species. We test whether trait relationships extend to the cold extremes of life on Earth using the largest database of tundra plant traits yet compiled. We show that tundra plants demonstrate remarkably similar resource economic traits, but not size traits, compared to global distributions, and exhibit the same two dimensions of trait variation. Three quarters of trait variation occurs among species, mirroring global estimates of interspecific trait variation. Plant trait relationships are thus generalizable to the edge of global trait-space, informing prediction of plant community change in a warming world.
|
|
| 2. |
- Maksimovic, M., et al.
(author)
-
First observations and performance of the RPW instrument on board the Solar Orbiter mission
- 2021
-
In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 656
-
Journal article (peer-reviewed)abstract
- The Radio and Plasma Waves (RPW) instrument on the ESA Solar Orbiter mission is designed to measure in situ magnetic and electric fields and waves from the continuum up to several hundred kHz. The RPW also observes solar and heliospheric radio emissions up to 16 MHz. It was switched on and its antennae were successfully deployed two days after the launch of Solar Orbiter on February 10, 2020. Since then, the instrument has acquired enough data to make it possible to assess its performance and the electromagnetic disturbances it experiences. In this article, we assess its scientific performance and present the first RPW observations. In particular, we focus on a statistical analysis of the first observations of interplanetary dust by the instrument's Thermal Noise Receiver. We also review the electro-magnetic disturbances that RPW suffers, especially those which potential users of the instrument data should be aware of before starting their research work.
|
|
| 3. |
|
|
| 4. |
- Maksimovic, M., et al.
(author)
-
The Solar Orbiter Radio and Plasma Waves (RPW) instrument
- 2020
-
In: Astronomy and Astrophysics. - : EDP SCIENCES S A. - 0004-6361 .- 1432-0746. ; 642
-
Journal article (peer-reviewed)abstract
- The Radio and Plasma Waves (RPW) instrument on the ESA Solar Orbiter mission is described in this paper. This instrument is designed to measure in-situ magnetic and electric fields and waves from the continuous to a few hundreds of kHz. RPW will also observe solar radio emissions up to 16 MHz. The RPW instrument is of primary importance to the Solar Orbiter mission and science requirements since it is essential to answer three of the four mission overarching science objectives. In addition RPW will exchange on-board data with the other in-situ instruments in order to process algorithms for interplanetary shocks and type III langmuir waves detections.
|
|
| 5. |
- Myers-Smith, Isla H., et al.
(author)
-
Complexity revealed in the greening of the Arctic
- 2020
-
In: Nature Climate Change. - : Springer Science and Business Media LLC. - 1758-678X .- 1758-6798. ; 10:2, s. 106-117
-
Journal article (peer-reviewed)abstract
- As the Arctic warms, vegetation is responding, and satellite measures indicate widespread greening at high latitudes. This ‘greening of the Arctic’ is among the world’s most important large-scale ecological responses to global climate change. However, a consensus is emerging that the underlying causes and future dynamics of so-called Arctic greening and browning trends are more complex, variable and inherently scale-dependent than previously thought. Here we summarize the complexities of observing and interpreting high-latitude greening to identify priorities for future research. Incorporating satellite and proximal remote sensing with in-situ data, while accounting for uncertainties and scale issues, will advance the study of past, present and future Arctic vegetation change.
|
|
| 6. |
|
|
| 7. |
|
|
| 8. |
- Berner, Logan T., et al.
(author)
-
The Arctic plant aboveground biomass synthesis dataset
- 2024
-
In: Scientific Data. - : Springer Nature. - 2052-4463. ; 11:1
-
Journal article (peer-reviewed)abstract
- Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m−2) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.
|
|
| 9. |
|
|
| 10. |
- Duncanson, Laura, et al.
(author)
-
Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
- 2022
-
In: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 270
-
Journal article (peer-reviewed)abstract
- NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
|
|
