dataesr/scientific-paragraphs-categorization huggingface dataset:
EFISCEN The European Forest Information SCENario Model (EFIS-CEN) is a large-scale forest model that projects forest resource development on the regional to European scale. The model uses national forest inventory data as a main source of input to describe the current structure and composition of European forest resources. The model projects the development of forest resources, based on scenarios for policy, management strategies and climate change impacts. With the help of biomass expansion factors, stem wood volume is converted into whole-tree biomass and subsequently to wholetree carbon stocks. Information on litter fall rates, felling residues and natural mortality is used as input into the soil module YASSO  (Liski et al. , 2005) , which is dynamically linked to EFISCEN and delivers information on forest soil carbon stocks.
Carbon densities at a given point in time are only influenced by the land use history but not by the preceding changes in the environmental state. Carbon densities are taken from observations in the literature and thus reflect environmental conditions of the last decades. Uncertainties are not explicitly quantified in BLUE so far. A large contribution of uncertainty can be expected from various input datasets. Apparent uncertainties arise from the land use forcing data, the equilibrium carbon densities of soil and vegetation, and the response curves built to reflect carbon pool decay and regrow after land use transitions. Furthermore,  Hansis et al. (2015)  have shown that different accounting schemes and initialization settings lead to different emission estimates even decades after the model start.
FAOSTAT The Statistics Division of the Food and Agricultural Organization of the United Nations provides LULUCF CO 2 emissions for the period 1990-2017, available at http://www. fao. org/faostat/en/#data/GL (last access: January 2021) and its sub-domains. The FAOSTAT emissions land use database (metadata: http://fenixservices. fao. org/faostat/ static/documents/GL/GL_e_2019. pdf, last access: January 2021) is computed following Tier 1 of the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (http://www. ipcc-nggip. iges. or. jp/public/2006gl/index. html, last access: December 2019). Geospatial data are the source of AD for the estimates of emissions from cultivation of organic soils, biomass and peat fires. GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period 1990-present (with annual updates).
Land use total contains all GHG emissions and removals produced in the different land use sub-domains, representing three IPCC land use categories: forest land, cropland and grassland including biomass burning. LULUCF emissions consist of CO 2 associated with land use and change, including management activities. CO 2 emissions/removals are computed at Tier 3 using carbon stock change. To this end, FAOSTAT uses forest area and carbon stock data from FRA, gap-filled and interpolated to generate annual time series. As a result CO 2 emissions/removals are computed for forest land and net forest conversion, representing respectively the IPCC categories "forest land" and "forest land converted to other land uses". CO 2 emissions are provided by country, regions and special groups, with global coverage, relative to the period from 1990 to the most recent available year (with annual updates), expressed as net emissions/removals in gigagrams of CO 2 , by underlying land use emission sub-domain and by aggregate (land use total).
For TRENDY, pixel land cover/land use fractions were based on the land use map LUH2  (Hurtt et al. , 2020)  and the HYDE land_use change dataset  (Klein Goldewijk et al. , 2017a, b). Both of these maps rely on FAO statistics on agricultural land area and national harvest data. Uncertainties in TRENDY v7 are model-specific and described by  Le Quéré et al. (2018). The spread of the 14 TRENDY models used by this study (Fig. 9 ) gives an idea of the uncertainty due to model structure in dynamic global https://doi. org/10. 5194/essd-13-2363-2021 Earth Syst. Sci. Data, 13, 2363 Data, 13, -2406 Data, 13, , 2021  vegetation models, as the forcing data were harmonized for all models. A2. 2 Top-down CO 2 emission estimates CarboScopeReg, GCP 2019 (CTE, CAMS, CarboScopeReg) and EUROCOM Top-down estimates of land biosphere fluxes are provided by a number of different inverse modeling systems that use atmospheric concentration data as input, as well as prior information on fossil emissions, ocean fluxes and land biosphere fluxes.
25 • × 0. 25 • resolution) for the period 2006-2015 are taken from the six models used within EUROCOM  (Monteil et al. , 2020). These inversions make use of more than 30 atmospheric observing stations within Europe, including flask data and continuous observations. The CarboScopeReg (CSR) inversion system (also included within the EUROCOM ensemble) was also run for the extended period 2006-2018 using four different settings: three network configurations using 15, 40, or 46 sites and one using all 46 sites but a prior error correlation length scale that is a factor of 2 larger (200 instead of 100 km). B2 Source-specific methodologies: AD, EFs and uncertainties Tier 3 top-down regional inversions of CO and NO x emissions from the EMEP CEIP as prior knowledge of the emissions and CO 2 / CO and CO 2 / NO x emission ratios associated with the combustion of fossil from EDGARv4.
3. 2. Bayesian analysis in the CO and NO x inversions along with propagation of uncertainties in f CO 2 / CO and f CO 2 / NO x emission ratios. Detailed gridded data can be obtained by contacting the data providers: Gregoire Broquet (gregoire. broquet@lsce. ipsl. fr) and Igor Konovalov (konov@ipfran. ru). Transient change in C stocks following a given transition (timedependent EFs after an land use transition). N/A Detailed gridded data can be obtained by contacting the data provider: Richard A. Houghton (rhoughton@woodwellclimate. org).
ECOSSE The model is a point model, which provides spatial results by using spatial distributed input data (lateral fluxes are not considered). The model is a Tier 3 approach that is applied on grid map data, polygon organized input data or study sites. IPCC 2006: Tier 3 The simulation results will be allocated due to the available information (size of spatial unit, representation of considered land use, etc. ). N/A Detailed gridded data can be obtained by contacting the data providers: Matthias Kuhnert (matthias. kuhnert@abdn. ac. uk) and Pete Smith (pete. smith@abdn. ac. uk). EPIC-IIASA Cropland: static 1 × 1 km cropland mask from CORINE-PELCOM. Initial SOC stock from the map of organic carbon content in the topsoil  (Lugato et al. , 2014). Static crop management and input intensity by NUTS2 calibrated for 1995-2010  (Balkovič et al.
ORCHIDEE For the land cover/land use input maps: data on wood harvest from the FAO. Tier 3 model, process based. Any emission factors enter in the form of generic parameters for a given ecosystem type fit against observational data (both site level and remotely sensed). None, though some information on uncertainty due to model structure is given by looking at the spread from the TRENDY suite of models, of which OR-CHIDEE is a member. Detailed gridded data can be obtained by contacting the data providers: Matthew Mcgrath (matthew. mcgrath@lsce. ipsl. fr) and Philippe Peylin (peylin@lsce. ipsl. fr).
, 2014) , lake pCO 2 database from  Sobek et al. (2005) ; river channel slope and width calculated from GLOBE-DEM (GLOBE-Task-Team, 2020) and runoff data from  Fekete et al. (2002). Geodata for predictors of pCO 2 and gas transfer coefficient include air temperature, precipitation and wind speed  (Hijmans et al. , 2005) , population density  (CIESIN and CIAT, 2005) , catchment slope gradient (HydroSHEDS 15s), and terrestrial NPP  (Zhao et al. , 2005)  N/A Monte Carlo runs (uncertainty on pCO 2 and gas transfer velocity). Detailed gridded data can be obtained by contacting the data providers: Ronny Lauerwald (ronny. lauerwald@ulb. ac. be) and Pierre Regnier (pierre. regnier@ulb. ac. be).
Acknowledgements. FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. Philippe Ciais acknowledges the support of the  European Research Council  Synergy project SyG-2013-610028  IMBALANCE-P  and from the  ANR CLAND Convergence Institute. We acknowledge the work of the entire EDGAR group ( Marilena Muntean ,  Diego Guizzardi ,  Edwin Schaaf  and  Jos Olivier ). We acknowledge  Stephen Sitch  and the authors of the DGVMs TRENDY v7 ensemble models for providing us with the data. Financial support. This research has been supported by the  H2020 European Research Council  (grant no. 776810 ). Review statement. This paper was edited by  Nellie Elguindi  and reviewed by two anonymous referees.
The Toarcian sensu stricto was recognized in the Humilly-2, Chaleyriat-1 and La Chandelière wells based on paleontological data. In the Faucigny-1 and Chapeiry-1 wells, no paleontological data allowed us to identify the Toarcian with confidence. Thus, both Upper Lias and sometimes Lower Lias were sampled (based on their lithology) and analyzed in order to confirm the presence of organic-rich material and its maturity. A total of 22 samples was collected for the Lias and 10 for the Carboniferous in these 6 wells. The Toarcian in Humilly-2 is made of 8 m (2232-2240 m) of bituminous dark-brown to black marls and shales. In the Chapeiry-1 and Faucigny-1 wells, the Upper Lias is marked by black marls intercalated with silty to shaly limestones. In the La Chandelière and Chaleyriat-1 wells, located in the Jura region, the Toarcian consists of dark-grey silty to shaly limestone (Fig.
Acknowledgements This paper is a contribution to the  UNCONGAS  project supported by swisstopo, the  Swiss Federal Office of Energy , the  Canton of Geneva  and the  Canton of Vaud  and the  SCCER   SoE national research program  funded by the  SNF and Innosuisse. The authors would like to thank  Gunter Siddiqi  (SFOE) and  Roland Baumberger  (swisstopo) who initiated and encouraged this project. NAGRA  ( Nationale Genossenschaft für die Lagerung radioaktiver Abfälle ) is also thanked for sharing its technical reports, SEAG and  Werner Leu  for sharing their data and knowledge. We are thankful to  Alexei Milkov ,  Werner Leu  and the editor  Wilfried Winkler  for their constructive review that helped improve the manuscript.
Biomass Field Data and Equations The first step of the methodology aimed to obtain reliable biomass estimates for trees whose crown could be delineated on a LiDAR CHM. These estimates were subsequently used to calibrate a model based on LiDAR variables (see Section 2. 3). Nineteen plots were visited in 2016. Each plot was about 0. 3 ha in size and located in the immediate vicinity of a river (Figure  1 ). While in the field, tree crowns were delineated on an orthophoto (year 2016, 25 cm in resolution) and the LiDAR CHM. Each delineated crown corresponded to the smallest unit that could be distinguished on images and linked to stems measured on the ground. A crown usually covered several stems (trees were often multi-stemmed). Species and diameter at breast height were described for all stems (DBH > 12 cm) under each crown.
To estimate the total wood volume of other species, we used regional forest trunk and stump volume equations  [49]  and the volume expansion factors of Longuetaud et al. [50]  to estimate the total wood volume, which includes the volume of the stump, trunk and branches, following Equation (  2 ): Vtot = (Vd7 × VEF + Vstump) ( 2 ) where Vd7 is the volume of the trunk until 7 cm in diameter  [49] , Vstump the volume of the stump  [49] , and VEF the volume expansion factor  [50]. We systematically used doubleentry equations (diameter and height), because the use of single-entry forest equations significantly overestimates the volume of riparian trees, which have a lower height than upland forest trees. The conversion of total volume to biomass was carried out using the specific infra-density values presented in the database of Zanne et al.
Biomass Prediction from LiDAR Data at Tree Level A tree-level allometric relationship was then adjusted between the biomass calculated in the field (in Section 2. 2) and the LiDAR variables. A total of 39 trees were removed from the dataset at this stage because their crowns could not be accurately delineated. A further 23 trees were removed for other reasons, mainly because they had lost a significant part of their crown between the LiDAR and the field survey, or because they were highly distorted, and the validity of volume equations was doubtful. In the end, 293 (out of 355) trees remained. The LiDAR variables presented in Table  1  were extracted at the scale of the digitized tree crowns from the CHM and from the point cloud using the std. metrics function of the lidR R package  [53]. Two parametric models were adjusted using least squares regression method.
Individual Tree Segmentation Tree crowns were segmented using the algorithm of  [41] , implemented in the lidR package  [53]  and working only with a CHM. The segmentation parameters were adjusted by trial and error on a representative area. Segmentation accuracy was assessed on the 19 inventory plots by analyzing the overlaps between reference and segmented tree crowns. Two crowns corresponded when more than 50% of a reference crown was included in a segmented crown and the reference crown included more than 50% of the segmented crown  [54]. The overall accuracy of the segmentation was calculated using the equation Global accuracy = 2 × N accurate N re f erence + N segmented , where N accurate is the number of corresponding tree crowns, N reference the number of manually digitized tree crowns and N segmented the number of segmented tree crowns.
g. , point density, flight altitude or scan angle) or ground conditions (e. g. , phenology) on CHM properties or height estimates has been well studied  [53, [72] [73] [74]. Therefore, when working simultaneously with several CHMs (e. g. , multi-temporal or compiled from several locations), these effects can be accounted for or assumed to be negligible  [75]. In contrast, when using point clouds, it may be more difficult to do so with variables related to lower height percentiles, canopy closure or LAI (leaf area index)  [76]. A third advantage of CHMs beyond ease of use and robustness is that it can be updated with photogrammetric point clouds once a first LiDAR survey has been done  [77]. The m1 model could thus be better suited to the use of time series or to the comparison of sites covered by different datasets.
several CHMs (e. g. , multi-temporal or compiled from several locations), these effects can be accounted for or assumed to be negligible  [75]. In contrast, when using point clouds, it may be more difficult to do so with variables related to lower height percentiles, canopy closure or LAI (leaf area index)  [76]. A third advantage of CHMs beyond ease of use and robustness is that it can be updated with photogrammetric point clouds once a first Li-DAR survey has been done  [77]. The m1 model could thus be better suited to the use of time series or to the comparison of sites covered by different datasets. ).
Perspectives for Generalization of the Approach Our method essentially exploits LiDAR data, which are increasingly available at regional scales  [22]. Most of the auxiliary data used are available in many countries (land use, river network) or can be derived from a LiDAR dataset (vertical distance to the river, slope, floodplain boundaries). Most countries do not have soil maps as precise as those available in Belgium, where the map is based on about two survey plots per hectare. However, soil variables seemed to be accounted for by topographical variables, and were not kept in the final MLR model. The age and type of regeneration can nevertheless be costly or tedious to obtain, as they rely on photo-interpretation of historical orthophotos, whose accessibility is uneven from one country to another.
Butterfly monitoring data and community temperature index We used data from established schemes of butterfly monitoring in Finland (101 sites from 1999 to 2016) and the Netherlands (1074 sites from 1992 to 2016) to describe long-term changes in species composition (Appendix S1, Methods S1. 1). Both monitoring schemes belong to a standardised, pan-European programme of butterfly monitoring (reviewed in van Swaay et al. 2008) inspired from the national butterfly monitoring scheme initiated in the UK as early as 1976  (Pollard, 1977). As such, they allow for a comparable estimation of butterfly presence and abundance across sites and years, making them suitable for joint analysis of the long-term dynamics of European butterfly communities. During the period of monitoring, 99% of sites have experienced an increase in their mean annual temperature, with an average of + 0.
Landscape variables and fragmentation indices Our evaluation of habitat fragmentation was based on land cover maps obtained from the CORINE land cover database (European Environment Agency), reclassified into semi-natural habitat (SNH) and non-habitat. Our classification of SNH essentially excluded high-intensity land use such as urban and industrial areas, or croplands that have been repeatedly shown to affect populations negatively (e. g. Maes & Van Dyck, 2001)  and to limit the adaptive reorganisation of butterflies under climate warming  (Oliver et al. 2017). Because all butterfly species do not necessarily use the same habitats  (van Swaay et al. 2006) , we produced three different maps of SNH: (1) generalist, defined as all land covers that could potentially provide high-quality habitat for butterflies, (2) open, excluding forest land cover types from the previous classification, and (3) forest, including only forest land cover categories (Appendix S1, Methods S1.
Species traits We related the species-specific responses to habitat fragmentation and climate change to the traits of each species (Appendix S1, Methods S1. 2). First, we used a database of European butterflies' life-history traits that consists of 16 species traits reduced into four principal components  (Wal-lisDeVries, 2014). We used here the first, third and fourth principal components that represent, respectively: species' spatial use of the landscape, including individuals' mobility but also population area and reproduction productivity, thus representing both dispersal and propagule pressure; generation time (i. e. the inverse of developmental rate) and phenology; and resource specialisation. Instead of the climatically based second principal component of WallisDeVries (  2014 ), we introduce the concept of a relative species temperature index (rSTI), which we define as the difference between the species temperature index  (Schweiger et al.
Colonisations and extinctions To clarify the effect of fragmentation on climate-driven community dynamics, we aimed to disentangle how the processes of colonisation and extinction contribute to long-term changes in community composition. More precisely, we assessed the role habitat fragmentation plays in the colonisations of 'warm-adapted' climate-tracking species and in local extinctions of 'cold-adapted' species extirpated by climate change, as a way to gain insights into the mechanisms behind the observed CTI trends. Our approach consisted first in identifying events of colonisations and extinctions, taking into account the risk of imperfect detection (Appendix S2, Methods S1. 5). Since we based our inferences on the same data as for the analyses of CTI trends (i. e. the identity of species observed in each site and each year), we ensured that both analytical steps were directly comparable.
Data analysis All statistical models described below were computed in the R environment (R Development Core Team, 2018) using the 'lme4' R package  (Bates et al. 2015). Explanatory variables were centred and scaled to ease computation and to provide comparable estimates within and across models. Models were evaluated using several complementary approaches, including visual inspection of residuals, computation of goodness-of-fit metrics (R 2 ) and cross-validation of models' predictive accuracy (Appendix S1, Methods S1. 6). Confidence intervals (95%) around model coefficients were estimated by a bootstrap procedure. Partial regression plots were produced using the 'visreg' R package  (Breheny & Burchett, 2017 ) by computing the variation of the response across levels of a focal variable while keeping all other variables at their median value.
Community dynamics In a first step, we wanted to see how changes in community temperature index over time were affected by the proportion and aggregation of semi-natural habitat around monitoring sites. We used the classification of SNH based on the location of monitoring sites: sites dominated by a forest land cover category according to the CORINE data (broad-leaved forest, coniferous forest, mixed forest) were assigned fragmentation variables corresponding to forest habitats, all the others to open habitats (Appendix S1, Methods S1. 3). Then, we built linear mixed models with CTI as the response variable and the three-way interaction between year, proportion of SNH and aggregation of SNH as fixed explanatory variables. Moreover, we included as an additional predictor the two-way interaction between year and habitat type (forest vs.
In total, we fitted 84 different models (3 datasets [2 countries + both] 9 2 types of CTI [presence-only + abundance-weighted] 9 2 classification of SNH [generalist + site-specific] 9 7 spatial scales). For illustration purposes, estimates of CTI temporal trends were computed for a few discrete values of proportion and aggregation of SNH using the 'emmeans' R package  (Lenth, 2018) , and compared to the mean temperature trend across all monitoring sites during the period 1990-2016 estimated from the E-OBS temperature gridded data version 17. 0  (Haylock et al. 2008). Direct comparisons of estimates of CTI and temperature trends may be biased because they are based on different climate data. To make sure that they can be safely compared, we expressed them as a spatial shift in community composition or temperature  (Devictor et al.
By analysing the effects of habitat amount and distribution over a range of spatial scales, we were able to observe that the impact of fragmentation on community dynamics mainly occurred at a scale of 10-30 km, which is far larger than what is usually assumed a relevant scale to detect direct effects of landscape on butterfly communities (e. g. € Ockinger & Smith, 2006; Krauss et al. 2010). A possible explanation for the small effect sizes observed at small spatial scales could be the coarse resolution of the CORINE database. However, it also suggests that this pattern reflects the detected effect of habitat fragmentation acting on long-term processes such as range expansion and contraction. It is congruent with our hypothesis that, although higher aggregation of SNH may favour the local persistence of species, it could also be detrimental for range-expanding species that need stepping-stone habitats to colonise new sites at their leading edge.
ACKNOWLEDGEMENTS This work was funded by a grant from the  Swedish Research Council FORMAS  to YF, grant no. 2016-00667. E € O was supported by the  Swedish Research Council FORMAS  (grant no  942-2015-988 ). We thank  Arco van Strien  ( Statistics Netherlands ) for his useful advice on data analysis. Alistair Auffret provided valuable comments on the manuscript and edited the English language. The manuscript greatly benefitted from the careful comments of four anonymous reviewers.