dataesr/scientific-paragraphs-categorization huggingface dataset:
We finally computed FDis, FRic, and FEve using the function alpha. fd. multidim of the R package "mFD"  65. In addition, we computed the functional alpha diversity applied to the distance between species 66 (function "alpha. fd. hill" of the R package "mFD"), the Rao functional dispersion index  67  ; the functional dispersion index based on the framework of Laliberté & Legendre 68 (function "dbFD" of the R package "FD"), the "TOP" functional richness  69  ; the "TED" functional evenness  69  ; and "Fever" functional evenness  70. An example script for the calculations of these metrics is provided in a repository at https://gitlab. com/ urbionet/Trait_urban_syndromes. Since we specifically focus on functional diversity, we selected, for each aspect, the index showing the lowest correlation to species richness across all taxonomic groups (Supplementary Fig.
4 , Correlations of diversity metrics across taxonomic groups). We retained the functional dispersion (FDis), functional richness (FRic), and functional evenness (FEve) indices calculated using the alpha. fd. multidim function in the R package "mFD"  65. Functional dispersion (FDis) measures the mean distance of individual species to the centroid of all species in multidimensional trait space  68. A decrease in FDis shows a lower dispersion of species in trait space. FDis captures aspects of both functional richness and functional evenness. Functional richness is the amount of functional niche space occupied by species within a community  68  and was calculated using the revised FRic index  65. A decrease in FRic values suggests a decrease in the amount of functional trait space occupied by a community.
Effect of urbanisation on faunal community functional composition We analysed the global effect of urban land cover on the functional community composition of each taxon while controlling for the effects of forest land cover, climatic region and latitude (see Supplementary Fig. 5  for more information about the correlations among predictors). To do so, we built various models using the random forests algorithm  72. The random forest algorithm excels at extracting patterns from complex datasets and is becoming more common in ecological studies. This approach being nonparametric, the data need not come from a specific distribution (e. g. , Gaussian) and can contain collinear variables  73. Also, random forests can deal with model selection uncertainty because predictions are based on a consensus of many models and not just a single model selected with some measure of goodness of fit.
In stratified CV, the partition is stratified according to the response variable in order to balance the class distributions within the splits (function "createDataPartition" in the R package "caret"). In spatial CV, we created 30 spatial folds for cross validation (function "Create-SpacetimeFolds" in the R package "CAST") in order to maximise the spatial transferability of model results and avoid potential overfitting. Parameter tuning used 10 random values of the number of variables to be sampled at each split time. The best model was chosen based on RMSE, MAE, and R 2 measured on the trained dataset. The performances of the selected model were further evaluated on the test dataset using the same metrics which we reported in Table  1. Spatial autocorrelation in model residuals was examined using Mantel correlograms (function "correlog" in the R package "vegan").
To assess the changes in functional community composition metrics while limiting the influence of other descriptors, we used partial dependence plots (PDP)  75, 76. Partial dependence plots are especially useful for visualising the relationships discovered by complex machine learning algorithms such as random forests. PDPs help visualise the relationship between a subset of the features and the response while accounting for the average effect of the other predictors in the model (Fig. 2 ). All statistical analyses were performed in R version 4. 0. 3, while R version 3. 6. 2 was used to compile the individual datasets into a consolidated dataset for each taxon  77.
Acknowledgements This research was conducted as part of the  Urban Biodiversity Research Coordination Network (UrBioNet ) funded by the  National Science Foundation  ( NSF RCN :  DEB 1354676/1355151 ). We initiated this project as part of the  Workshop Group "Patterns, Drivers and Traits" of the Urban Biodiversity Research Coordination Network (UrBioNet , https://sites. rutgers. edu/urbionet/dsg/). We would like to thank  Madhusan Katti ,  Christopher Trisos , and  Julie Goodness  for helping to conceptualise the study at the New Jersey workshop;  Eliana Geretz  and  Carmela M. Buono  for assistance with initial data compilation;  Laurence Packer ,  Michael Batley ,  Stuart Roberts  for bee trait expertise; and  Béla Tóthmérész ,  Cecilia Tobar-Suárez ,  Etienne Normandin ,  Gary Luck ,  Lisa Smallbone ,  Maryna Kyrychenko-Babko ,  Rebecca Acosta ,  Yocoyani Meza-Parral  for providing data included in this study.
6µm 2 (FWHM) in the y and z directions), scanning the sample in the vertical direction, z, to resolve variation along the height of the crystal (as shown in Fig. 1a ). The observation plane for each image shown by the orange plane that slices through the cube showing the dislocations we observed in our single crystal. The direction of the scattering vector Q can be varied by the two tilts, φ and χ. The length of the scattering vector can be varied by a combined 2θφ scan. The figure is made using the MATLAB code in the link https:// github. com/ leora dm/ Dislo catio n3DAn alysis. git.
From a first glance, the dihedral angles of the triple junctions are far from 120 • , in contrast to what conventional growth models predict. Below, we zoom in on individual boundaries shown in Fig. 4  and analyze in detail to extract more information on the   4 ). The figure is made using the MATLAB code in the link https:// github. com/ leora dm/ Dislo catio n3DAn alysis. git. self-organization process. Inlays in Fig. 4  obtained from zooming in on each separate low-angle boundary plane demonstrate that we can resolve full structures. In particular, we can resolve the defect plane that separates subdomains of the crystal, and by projecting each boundary along different vectors we can identify the relevant in-plane and out-of-plane directions crystallographically. As described above, this allowed us to solve for the zone axes for the 5 primary boundaries in this structure, as labeled in Fig.
Discussion Upon closer examination, our 3D map generated from DFXM data resolves the dislocation lines that comprise the boundary planes. The boundary dislocations are relatively straight across the entire spatial extent of the boundary we resolve, suggesting a high level of stability across the 300-µ m length scale. This detailed view of dislocation packing suggests that the boundaries manifest the lowest energy structures that may form under the high-temperature processing conditions  34. The DFXM map reveals the self-organization of dislocations upon high-temperature annealing into welldefined planar boundaries. These boundaries are only identifiable due to the large field of view and high angular resolution afforded by DFXM. The high dislocation mobility in aluminum, especially at temperatures so near T m , the long mean free paths of motion owing to the low dislocation density, and the long duration of the temperature treatment implies that the patterns formed under these conditions are the most stable, preferred configurations for this sample.
92T m , there must be additional high-energy immobilizing defects inside the sample that immobilize some of the dislocations,   002 ) reflection. The color key shows the φ -2θ angular distribution in the scanned range around the local intensity maxima for the respective axes (c). These maps show the projections from a volume illuminated by box-shaped beam having a size of 400 × 400 µm 2 , rather than a line focused beam (See Supp. Mat. for more details). The measured strain is rather homogeneous having values below 10 -4 , in spite of the clear boundaries that are visible in the φ COM map. The figure is made using the MATLAB code in the link https:// github. com/ leora dm/ Dislo catio n3DAn alysis. git and https:// gitlab. esrf. fr/ julia. garri ga/ darfix. causing the energy for annihilation to be higher than 65 meV ( k B T at 590 • C).
Conclusions The present study demonstrates the dislocation structures that persist in a bulk single crystal of aluminum after high-temperature annealing. Using dimensional-reduction algorithms on a 4D DFXM dataset (2D scans), we measure the hierarchies of dislocations that span boundaries over hundreds of micrometers within a volume of 100 × 300 × 300 µm 3 , resolving the boundary planes and vectors of the component dislocations. Our results indicate the self-organization of dislocations into well-defined planar boundaries that separate sub-grains, whose mean dislocation spacings match the misorientations between subdomains that were measured independently with mosaicity maps, despite the heterogeneity. Based on the high-dislocation mobility afforded by the high annealing temperature that gave rise to this structure, we conclude that the few but long dislocation boundaries that persist indicate that forming single boundaries is energetically favored as compared to forming two boundaries with lower dislocation densities.
75 µm or 3. 75 µ m, respectively. This paper focuses on analysis from the highest-magnification 10× magnification images (total magnification of M t = 179×). Three types of scans were performed in this work: rocking scans, mosaicity scans, and axial strain scans. The rocking scans acquired images while scanning the tilt angle φ , see Fig. 1 , over a range of �φ = 0. 12 • in 30 steps (i. e. δφ = 0. 004 • per step). These 1D "scans of the rocking-curve" map components of the displacement gradient tensor field (i. e. strain and misorientation) to indicate the local variation in structure that is relevant to visualize dislocations 29. We collected this type of data for a total of 301 layers, with 1 µ m steps between the layers. The resulting information in four dimensions (x, y, z, φ) was imported into Matlab for subsequent processing and feature identification.
Data analysis methods. All dislocations were identified from the z ℓ -resolved stack ( {x ℓ y ℓ , z ℓ } in the laboratory coordinate system) of φ-resolved rocking scans using thresholding and a fiber-texturing methods for reconstruction. We first reduced the 4D ( x, y, z, φ ) dataset into an entirely spatial (x, y, z) 3D dataset by manually identifying the φ position characteristic of the weak-beam condition for each 2D layer (i. e. with the brightest and most sharply-defined spots for dislocations). The ideal φ orientation changed only slightly over the course of overnight scans -likely because of drift in the microscope or continuous rotation of the grain's orientation (i. e. bending). For the 3D image stack, intensities below a defined noise-floor were set to the threshold value of I min = 110 , and the edge 10 pixels were also set to I = 110 to avoid deleterious edge effects from the detector.
After defining the regions characteristic of each boundary, we indexed the dislocation boundary planes by defining all relevant points characteristic of each plane as a pointCloud object. For each boundary, we identified the local normal vectors that are characteristic of each voxel based on the ten nearest neighbors, using the pcNormals() function  49. The estimates from this pointCloud were then input to the pcfitplane() method in Matlab, which uses an M-estimator SAmple Consensus (MSAC) to find the plane (related to the RANdom SAmple Consensus, RANSAC approach), which excludes outlier points and outputs a mean uncertainty value 50. The resulting plane fits gave mathematical uncertainty quantification, based on the mean error in µ m that points lie beyond the boundary place, δz err. We input the Cartesian vectors estimated by MSAC to a trans- formation matrix to converts the vectors described in detector-frame into our coordinate system that takes into account the crystallographic basis vectors (i.
To identify the appropriate angle to convert the 2D boundary plane system into the 1D dislocation system, a sequence of 180 candidate transformations were assembled by scanning the rotation matrix through all unique 180 • angles, then compiling the rotated points into a histogram of counts vs x d i. The Fourier transform from each trace was plotted as a function of angle, and the "aligned" angle was identified as the point for which the number of spatial frequencies required to describe the 1D function was most sharply defined (as opposed to the scattered and diffuse frequency components required to describe the misaligned ones). All assignments were confirmed graphically, as demonstrated in the Supplemental Information. We include the Matlab scripts and functions used in this work in the Github folder available at https:// github.
Acknowledgements We thank the  ESRF  for provision of beamtime at ID06-HXM. GW and HFP acknowledge support from  ERC  Advanced Grant nr 885022 and from the  Danish ESS  lighthouse on hard materials in 3D, SOLID. LDM's initial contributions on the experiment were performed under the auspices of the  U. S. Department of Energy  by  Lawrence Livermore National Laboratory  under Contract  DE-AC52-07NA27344 , and the  Lawrence Fellowship.
In addition, the LULUCF sink of Eastern Europe is ten times larger than the overall net sink caused by land use change alone as simulated by the land use emission model BLUE (~0. 02 ± 0. 01 Gt C a -1 ). The gross carbon increase caused by land use change (agricultural abandonment) in BLUE, however, adds up to ~0. 11 ± 0. 01 Gt C a -1 , around half of the reported carbon sink from LULUCF in Eastern Europe (see Fig. 2 ). Greenhouse gas emissions from wood harvesting and agricultural land use change, which act as sources, partially offset this gross carbon sink from agricultural abandonment. Overall, the persistent deviations between the carbon sink estimates from different data streams can be explained by four reasons. First, the datasets differ in terms of methodology and incorporate different definitions (e.
41 Gt C a -1 ± 0. 09 Gt C a -1 , that is 78% of the entire carbon sink of the European continent. Considering only bottom-up, observation-based AGB change datasets with matching reference period (2010-2019), JPL and WRI gross and L-VOD-based estimates, we find that the AGB carbon sink of Eastern Europe is ~0. 38 Gt C a -1 ( ± 0. 07 Gt C a -1 ). From this, LULUCF contributes with ~53% according to UNFCCC and land use change with ~29% according to the BLUE model (when gross carbon sink estimates are considered). Decline of the Eastern European carbon sink. We find that the land carbon sink in Eastern Europe declined over the period 2010-2019. Mean annual carbon uptake from L-VOD is almost 20% lower in 2015-2019 (~0. 37 ± 0. 04 Gt C a -1 ), compared to 2010-2019 (~0. 45 ± 0. 10 Gt C a -1 ). This decline is also indicated from satellite inversions.
Methods In the following, the datasets and models used in this study are described. Carbon flux datasets CO2 inversions. We used the annual CO 2 flux data at a spatial resolution of 1. 875°l atitude × 3. 75°longitude from the atmospheric inversion of the Copernicus Atmosphere Monitoring Service inversion (CAMS). We compared the CO 2 flux data from CAMS constrained by surface air-sample measurement (referred to as SURF) and by CO 2 column retrievals from two satellites (referred to as OCO2 and GOSAT). SURF was released in 2019 and results from the assimilation of CO 2 surface airsample measurements in a global atmospheric transport model over the period from 1979-2018. We used version v18r3 of SURF inversion data  59. OCO-2 is a satellite from NASA that was launched in July 2014, providing spatially dense and fine-resolution CO 2 column retrievals.
68. The uncertainty of the AGB changes was estimated by the standard deviation (variability across time) of the estimates using the different reference maps. JPL: Xu et al. (  2021 ) from the Jet Propulsion Laboratory (JPL) provided annual estimates of the live biomass of the global terrestrial ecosystems between 2000 and 2019, which are based on a bottom-up framework using machine learning techniques to synthesize ground-based forest inventories with airborne and satellite data  13. We refer to the dataset here as JPL. We used the maps referred of global live woody vegetation carbon density, which are at ~10 km (0. 1 degree) spatial resolution, calculated the annual changes and derived the mean annual change for the period of 2010-2019. We resampled the map of biomass carbon density change to 0.
25°resolution using bilinear interpolation. Since the carbon density refers to both the above-ground and the below-ground biomass, we extracted the aboveground biomass with the help of the gridded above-/below-ground biomass ratio derived from Spawn et al. 62. WRI: The carbon flux model of Harris et al. 7  developed at the World Resources Institute (WRI) was modified to obtain the fluxes attributed to aboveground biomass (AGB) and carbon from 2010 to 2019. We refer to the dataset here as WRI. Net flux in this context is defined as the difference between the carbon emitted and removed by woody vegetation, set as areas with over 30% tree cover of the global forest change data by Hansen et al. 69. Note that in contrast to L-VOD and JPL, which consider AGB from all vegetation, WRI refers to forests only.
In particular, we modified the AGB map input, using the 2010 CCI Biomass map  67  adjusted for potential systematic differences when compared with plot-based reference data  70. We also changed all inputs and variables originally set for 2000 into our baseline year 2010 such as the primary forest and tree cover datasets. We further modified the original model to exclude fluxes from other gases aside from CO 2 and remove carbon components from below-ground and soil. The resulting carbon flux was divided by a factor of 0. 49 to derive the AGB. The tool Plot2Map (https://github. com/arnanaraza/PlotToMap) which implements the AGB map assessment framework  70  was used to adjust for the systematic differences in the 2010 CCI Biomass map. The framework implements a model-based approach that makes use of a worldwide database of reference AGB with uncertainty estimates as basis for modeling spatial uncertainties of the AGB map at aggregated levels, i.
UNFCCC inventories. The United Nations Framework Convention on Climate Change (UNFCCC) provides data from the national greenhouse gas (GHG) inventories submitted by countries that are Parties to the Convention. For comparing the land carbon uptake of the countries in Eastern Europe, we used GHG inventories referring to net CO 2 emissions/removals from land use, land use change, and forestry (LULUCF). We collected the data from the national inventories submitted to the UNFCCC. We acquired a complete time series from 1990 to 2019 (annual emissions) for Annex I countries Belarus, Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Ukraine from the UNFCCC website  71. For Moldova (Annex II country), inventory data was only available up to the year 2013.
In order to obtain the GHG inventory data for the region of European Russia, we acquired the national inventory reports (NIR) of Russia for all years from 2010 to 2019  72  , extracted and summed up the AGB carbon balance of managed forests from UNFCCC data for 54 Russian districts belonging to European Russia (Ural mountains as the eastern border). EFISCEN. The European Forest Information SCENario Model (EFISCEN) is a large-scale model for estimating forest resource development in Europe. The model uses National Forest Inventory data as a main input. By using biomass expansion factors, stem wood volume is converted into biomass and subsequently to carbon stocks of trees. It includes a detailed dynamic growth module, while natural mortality and harvesting are included as regimes, depending on the region.
For this study, EFISCEN data for 2010-2018 was acquired and used in the same version as from the European VERIFY project  5. CBM. The Carbon Budget Model developed by the Canadian Forest Service (CBM-CFS3) simulates the forest carbon dynamics under different scenarios of harvest and natural disturbances (fires, storms). The CBM has been validated by the Joint Research Centre of the European Commission (JRC) and adapted to forests in Europe. Forest stands are described by area, age and land use classes, and other parameters. Yield tables specify the merchantable volume production for each species, while allometric equations convert merchantable volume production into above-ground biomass at the stand level. The model provides annual data on net primary production (NPP), carbon stocks, and fluxes, as the annual C transfers between pools and to the forest product sector.
For this study, CBM data for 2010-2015 was acquired from and used in the same version as in the European VERIFY project  5. Estimating the land-based carbon sink. We estimated the average land carbon flux (in Gt C a -1 ) from inversions (SURF, GOSAT, OCO2), AGB sink estimates (L-VOD, JPL, WRI), forest ecosystem models and inventories (EFISCEN, CBM, UNFCCC) and land use bookkeeping model BLUE for different regions in Europe between 2010 and 2019. For this, we derived the annual land carbon flux estimates and calculated a multi-year average. The standard deviation was used as a measure of uncertainty and displayed as error bars in Fig. 2. For AGB biomass estimates available as annual maps, we first computed the annual AGB changes between two years. Then, the multi-year average AGB change was derived.
Again, the standard deviation was used as an uncertainty measure. This was performed for different regions of Europe, defines as: Mapping AGB carbon change. We calculated the agreement between AGB carbon changes in 2010-2019 between L-VOD, JPL, and WRI. TRENDY was excluded from the analysis due to its coarser spatial resolution, large inter-model deviations and relative distance to observational data streams (see Supplementary Fig. 4  for a comparison of TRENDY with L-VOD, JPL and WRI). The level of agreement was derived pixel-wise for either a carbon source or sink across Eastern Europe. We analyzed the statistical distribution of the AGB carbon change maps for each of the used datasets. Thresholding was applied by visual interpretation of the histograms. Accordingly, sources were defined by AGB below a negative threshold in Mg C ha -1 a -1 , sinks by AGB above a positive threshold in Mg C ha -1 a -1.
ΔAGB values that are between the negative and the positive threshold were treated as no change or only minor change. The number of agreeing datasets was derived for each pixel as levels of agreement. Only including levels of agreement from 2 to 3 (at least two datasets agree on either a carbon source, sink or no/minor change), the average AGB carbon change was derived from the agreeing datasets. This leads to a harmonized map of AGB change in 2010-2019 based on L-VOD, JPL, and WRI. We tested the usage of different AGB carbon change thresholds in Mg C ha -1 a -1 for defining carbon sinks and sources with a sensitivity analysis. The used thresholds were: 0, 0. 001, 0. 005, 0. 01, 0. 05, 0. 1, 0. 5. Supplementary Fig. 6  shows both the maps with the dataset agreement and the maps with the derived harmonized AGB carbon change for each of those thresholds.