dataesr/scientific-paragraphs-categorization huggingface dataset:
| RE SULTS The final confirmed dataset contained 186 island-by-species combinations, representing 41 species and 78 islands covering the entire Mediterranean (Figure  1 ). Fifty-three cases were occurrences of alien species (lizards and geckos: 45%; snakes: 34%; and turtles: 21%), which were particularly frequent in Western Mediterranean Islands. A total of 133 cases were occurrences of native species (lizards and geckos: 56%; snakes: 38%; turtles: 6%). The final putative dataset had 2045 island-by-species combinations, corresponding to 69 species and 598 islands (Figure  1b ). In total, 82 cases were putatively considered to be alien and 1963 to be native based on non-genetic information from the literature. Geckos and snakes were the groups with most (putative) alien cases.
| Assessing the putative status of insular populations The best model, obtained analysing the confirmed dataset (Table  2 , Model 1), was then projected on the island-by-species combinations of the putative dataset. When we projected the best model, 81% of occurrences in the putative dataset were predicted to be native and 19% to be alien. Model projections generally matched the status proposed in the literature (likelihood ratio test: χ 2 1 = 62. 9, p < 0. 0001). However, in some cases model projections suggested a different status. Of the 1942 species records taken as native in the literature, 328 (16. 9%) were projected as alien. Among lizards, geckos were the group with most cases projected to be alien, particularly with Hemidactylus turcicus (63 islands) and Tarentola mauritanica (69 islands), followed by skinks such as C ocellatus (56 islands) whereas, among lacertids, wall lizards of the genus Podarcis represented the majority of projected alien cases on islands (222 islands), especially Podarcis sicula (38 islands).
We also detected several projected alien occurrences for snakes, such as Hierophis viridiflavus (42 islands) and Natrix maura (7 islands). Of the 82 species records taken as alien in the literature, 54 (66%) were projected as alien, while 28 (34%) were projected as native. When we combined both putatively native species predicted to be alien, and putatively alien to be native, the frequency of alien records increased from 4% to 19%. Overall, if we used the predicted status instead of the status in the bibliography (putative dataset), the.
Thus, 28% of the confirmed dataset records were of alien species, while just 14% of them would have been considered alien if genetic information had been lacking. Moreover, when we analysed the same dataset ignoring the genetic information, we obtained a different picture of the factors related to the distribution of alien species and did not identify some of the most important variables (see Appendix S2 in Supporting Information). This sends a word of caution regarding conclusions extracted from meta-analyses not incorporating genetic evidence (e. g. ,  Capinha et al. , 2017). Obtaining exhaustive genetic data for all species-by-islands combinations is far from complete, yet the integration of genetic data in biogeographical analyses can be used to improve our understanding of present-day biodiversity.
When projecting the model built using genetic data to a dataset of records lacking genetic data, we identified many species-by-island combinations that might actually be alien records. Model extrapolation certainly incurs a degree of error and cannot be used as the sole source of information on the status of species on islands. Nevertheless, projecting the model to the putative dataset provides preliminary information on the frequency of cryptic invaders in this system and highlights species and islands that warrant targeted research. Geckos, for example, have been extensively studied, yet, their origin and the human role in their distributions remain widely discussed  (Delaugerre, Ouni, & Nouira, 2011; Rato, Carranza, & Harris, 2011 , 2012). Our predictions suggest that geckos are indeed alien on the islands where they are present.
for species not in the Red List (i. e. , Anguis fragilis, Chalcides ocellatus, Coronella austriaca, Dolichophis caspius, Eryx jaculus, Hemorrhois nummifer, Malpolon insignitus, Mauremys rivulata, Ophiomorus punctatissimus, Pseudopus apodus, Testudo graeca, Testudo hermanni, Testudo marginata and Xerotyphlops vermicularis). Species names follow the Reptile Database (Uetz, Freed, & Hosek, 2018).
TA B L E 1 Composition of the dataset with a brief description of variables and their importanceVariablesDescription and source: RationaleIslands featuresArea (km 2 ) Arnold (2008) Island biogeography theory predicts higher species richness on larger islands Link to mainland (yes/no) An island was considered linked if there was a land bridge connection with the mainland during the Last Glacial Maximum-LGM (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005).
Owing to various deadlines, MPI-ESM1. 2-HR had already begun running its DECK runs and it was therefore deemed too late to implement the bug fix into MPI-ESM1. 2-HR, but the LR version received this correction. The sea ice model consists of code both in MPIOM and in ECHAM. In ECHAM, a simplified thermodynamic sea ice model is incorporated to provide at each atmospheric time step a physically consistent surface temperature in ice-covered regions. This part of the sea ice model contains a melt-pond scheme, which divides the surface of the sea ice into snow, bare ice, and melt pond, with each their individual albedo  (Pedersen et al. , 2009). In MPI-ESM1. 2, these melt ponds are now fully activated, in contrast to an erroneous only partial activation in MPI-ESM (section 3. 5,  Roeckner et al.
Wet and dry deposition of all nitrogen species (NO y +NH x ) is directly used to update NO 3 , assuming instantaneous unlimited oxidation of NH x in sea water. The corresponding H+ change is accounted for as an alkalinity decrease. Gridded coupled chemistry model intercomparison N-deposition fields of version 1. 0 are used as provided via the CMIP6 input database (https://esgf-node. llnl. gov/projects/input4mips/). 10. 1029/2018MS001400.
Decomposition is simulated as a first-order decay process depending on air temperature, water availability, and litter size  (Goll et al. , 2015). In JSBACH3. 2 this pool structure is applied separately to the decomposition of organic matter originating from woody and nonwoody plant remains, and also separately for aboveground and belowground decomposition, resulting in a total of 18 carbon pools for dead organic matter. The soil carbon distribution in simulations with the predecessor MPI-ESM exhibited maxima in continental dry lands due to its parameterization of decomposition that depended on soil moisture. Total carbon storage was considerably overestimated in comparison with observations from the Harmonized World Soil Database (Figure  8b ). On the contrary, the soil carbon model YASSO applied in MPI-ESM1.
2 simulates plausible soil density patterns with relative maxima in tropical and boreal forests (Figure  8a ), and the distribution is comparable with observations (Figure  8b ). A detailed evaluation of the YASSO decomposition model in terms of simulating the spatial variability of soil carbon stocks using estimates from the Harmonized World Soil Database is provided by  Goll et al. (2015). JSBACH as applied in MPI-ESM1. 2 does not include permafrost carbon, and maximum of soil carbon storage in high northern latitudes, and hence, is not well reproduced by the model. These processes are applied to future implementations in ICON-ESM.
Interactions Between Fire, Vegetation Dynamics, and Nitrogen Cycle The updated processes implemented in JSBACH3. 2 lead to a number of interactions previously absent. In particular, an interesting interplay between the fire dynamics, land nitrogen cycling, and the dynamics of vegetation distribution was found. As a result of biomass burning, organic nitrogen is lost not only to the soils but also to the atmosphere. The resulting reduced nitrogen availability reduces plant productivity so that the dynamic vegetation module renders regions prone to wildfires less hospitable to vegetation and thus increases the bare land fraction. This loss of nitrogen, productivity, and vegetation cover is only incompletely compensated by the prescribed nitrogen deposition. Presumably, a regional redeposition of nitrogen emissions from combusted organic material  (Bauters et al.
Over land, where the observations are more reliable, the biases are much smaller, but again similar in all of the four model versions. In the tropics, a low bias over land goes often along with a high bias in the surrounding ocean areas (e. g. , northeastern Brazil, Indonesia, and Borneo). As analyzed by  Siongco et al. (2014) , all versions show the typical west Atlantic precipitation bias typical of low-resolution models. Related difference patterns with respect to CERES are also found for the top-of-atmosphere reflected shortwave radiation, (Figure  B6 ). The most pronounced feature is the low reflectance in the tropical West Pacific. This is attributed to an abundance of high-level clouds and lack of middle-and low-level clouds, relative to observed amounts seen in the CALIPSO-GOCCP data set (not shown), which causes outgoing shortwave radiation to be underestimated.
47  (Eggleston et al 2006, table 4. 3). We took speciesspecific wood densities from the database of Zanne et al  (2009). When there was no match at the species level, we used the average value at the genus level. When no value was available in the database, we used the default 0. 60g cm -3 recommended by Henry et al (2010, p. 1383) for tropical African woods. Although the calibration and validation procedures could be successfully completed, the simulations over the projection period systematically failed to converge when including CO 2. Forest dynamics reached unrealistic levels, with infinite values in stand characteristics and processes. This failure was due to the perfect correlation of the annual average atmospheric CO 2 concentration with time. Thus, CO 2 effect was confounded with linear temporal trend, a situation that prevents any valid projection.
Summarising MIMM outputs Because the number of species combined to the 10GCM versions led to an impractical number of information, results were summarised by re-aggregating MIMMʼs outputs according to the two following approaches. The first, exogenous to MIMM, is based on ecological guilds, the second, endogenous to MIMM, is based on the similarities of species groupings between GCM versions. For the latter, we used hierarchical clustering to define new meta-groups. The number of meta-groups was estimated using the gap statistics (Kaufman and Rousseeuw 2005). In comparison to guilds, meta-groups are more consistent with the information from the data as they follow the same logic of species grouping based solely on the similarity of their dynamic processes (Picard et al 2010). Further details are provided in the supplementary section S2.
Acknowledgments We thank the  Forestry Research Support  ( ARF, Appui à la recherche forestière ) and its seven partners: the  French Development Agency (AFD, Agence Française de Développement) , the  Centre for International Cooperation in Agricultural Research for Development (CIRAD, Centre de coopération internationale en recherche agronomique pour le développement) , the  CAR Institute of Agricultural Research (ICRA, Institut Centrafricain de Recherche Agronomique) , the  Ministry of Waters, Forests, Hunting and Fisheries (MEFCP, Ministère des Eaux, des Forêts, de la Chasse et de la Pêche ) of  Central African Republic , the  Service of Cooperation and Cultural Action (SCAC, Service de coopération et d'action culturelle)  of the  French Ministry of Foreign Affairs (MAE, Ministère des Affaires Étrangères) , the  University of Bangui  and the Central.
Europe. Deciding where public and private research spending will have the greatest impact is a complex process involving multiple interests. Often, ad hoc expert groups are created to provide decision makers with advice over specific topics. In addition, over the last decade several initiatives have emerged at European and global levels to foster international discussions and apply a structured approach to the identification of research gaps and priorities in the animal health domain, including livestock helminthology in Europe. DISCONTOOLS (www. discontools. eu) is a publicly funded, open-access database to assist public and private funders of animal health research and researchers in identifying research gaps and planning future research  [104]. The database contains research gaps as well as a gap scoring and prioritization model for more than 50 infectious diseases of animals.
The information is provided by disease-specific expert groups and updated on a 5-year cycle. The DISCONTOOLS database acts as a key resource for the STAR-IDAZ International Research Consortium on animal health (www. star-idaz. net), comprising research funders and programme owners from Europe, Asia, Australasia, the Americas, Africa and the Middle East, as well as international organisations, and includes representation from veterinary pharmaceutical companies. Members coordinate their research programmes to address agreed research needs, share results, and together seek new and improved animal health strategies for at least 30 priority diseases, infections or issues. These include candidate vaccines, diagnostics, therapeutics and other animal health products, procedures and key scientific information and tools to support risk analysis and disease control.
See Box 1. Bioactive forages -crops or feedstuffs that reduce the numbers of worms in, or available to, a host. The effect can be either direct (anthelmintic activity; reduced survivability of freeliving stages on pasture) or indirect (improved nutrition). Biological control -the control of infection using other organisms or their natural products, such as nematophagous fungi (Duddingtonia flagrans) or crystal (CRY) and cytolytic (CYT) proteins of the soil borne bacterium Bacillus thuringiensis. DISCONTOOLS -www. discontools. eu is a publicly funded, open-access database to assist public and private funders of animal health research and researchers in identifying research gaps and planning future research. FAMACHA -FAffa MAllan CHArt -a colour-guide chart used to assess the degree of anaemia in an animal via the colour of their ocular membranes to determine the need for anthelmintic administration.
Acknowledgements We thank the officers and members of the  Livestock Helminth Research Alliance (LiHRA)  for encouraging this initiative and giving space to it in their annual meetings, to the  World Association for the Advancement of Veterinary Parasitology  for permitting elicitation of questions as part of their 26th biennial conference in  Kuala Lumpur ,  Malaysia , and to the editors of Trends in Parasitology for commissioning this article. The authors credit the EU for funding leading to this work through  FP7 STREP GLOWORM. We gratefully acknowledge  Hassan Azaizeh ,  Sarah Beynon ,  Jacques Cabaret ,  Gerald Coles ,  Tina Alstrup Hansen ,  Alison Howell ,  Hamadi Karembe ,  Alvaro Martinez-Moreno ,  Francisco A. Rojo ,  Guillaume Sallé ,  Jože Starič ,  Eurion Thomas , and numerous anonymous participants for contributing generously to the exercise with questions and votes.
This article is based in part upon work from  COST Action  COMBAR  CA16230 , supported by  COST (European Cooperation in Science and Technology). MMV is funded by the  Spanish "Ramón y Cajal" Programme, Ministry of Economy and Competitiveness  ( RYC-2015-18368 ), and ERM and HRV by  UK BBSRC  grant  BB/M003949/1.
Besides outlet discharge, two types of additional data were used: surface soil moisture and water level in ephemeral streams. All these data had been produced by previous research projects and were reused opportunistically for the present study. This main general objective is specified into three sub-objectives that are: 1. To assess usability of additional data in a model evaluation process, and to assess strengths and weaknesses of corresponding hydrological signatures. These data cannot be compared directly to model outputs, either because their quality is not good enough (semiqualitative data) or because they do not have adequate spatial/ temporal resolution. So defining appropriate hydrological signatures from this data is a challenging task. 2. To evaluate all components of the used model (surface runoff, infiltration in the soil, groundwater flow, routing in the stream network), or specific catchment zones (specific sub-catchments, given soil type/land use).
Permanent data Consists of rainfall measurements at two gauges (one starting in 1997 and the other in 2005) and discharge measurement at the catchment outlet (since 1997). These data are available with a variable timestep. Reference evapotranspiration at a hourly timestep was calculated from the national SAFRAN database  (Quintana-Seguí et al. , 2008; Vidal et al. , 2010)  by  Braud et al. (2013) , using the Penman-Monteith equation  (FAO, 1998).
06 km 2 to 6. 6 km 2 and contrasted land uses. The main characteristics are presented in Table  1. Since sensor accuracy ( ± 1. 6 cm obtained by comparing the sensors and manual field measurements,  Sarrazin, 2012)  is of the same order of magnitude as the measured water levels, continuous series cannot be used directly for model evaluation. However, simple flow/no-flow patterns (for continuous periods), as well as water level variations and response during rainfall-runoff events can all be extracted  (Sarrazin, 2012). Surface soil moisture (0-5 cm depth layer) data were installed for a project monitoring surface runoff  (Dehotin et al. , 2015)  during one year. Data are available at 9 locations with a 2 min timestep from April 2010 to April 2011. This dataset is described in  Dehotin et al.
, 2010). Each of these objects is represented by a specific module: HEDGE for agricultural and forest areas, URBS for urban cadastral units, RIVER1D for the natural river network and artificial sewer network, TDSO for storm water overflow devices, SISTBA for lakes and retention basins. Additional modules allow the flow transfer between modelling units (WTI, WTRI, OLAF)  (Jankowfsky et al. , 2014)  (see Fig. 2 ). Model mesh consists of irregular modelling units that follow the land use patterns. The HEDGE runoff generation module simulates rural areas such as forests and agricultural fields, as well as hedges. The soil is represented using two interconnected compartments: drainable porosity where the water table is computed and micro-porosity where infiltration and evapotranspiration take place.
The latter is computed using three reservoirs representing respectively the surface, the non-saturated and saturated zones. Interception by trees is also taken into account. The water table level is computed as the average of the three components and is used to compute rain water drainage by sewer pipes. The Threshold Dependent Stormwater Overflow module (TDSO) simulates the storm water overflow between sewer network and natural streams. The overflow is calculated with a weir or orifice equation based on a water-control depth threshold value. Overland flow from the surface reservoirs of the HEDGE module and the natural part of the URBS module is calculated with the OLAF (OverLAnd Flow) interface. The flux between two units is computed based on the Manning Strickler approach. Finally, the SISTBA (SImulation of STorage BAsins) module simulates retention basins and lakes.
It represents retention basins or natural lakes with a simple linear reservoir. According to the principles of the LIQUID framework  (Branger et al. , 2010) , the way the PUMMA model handles timestep is as follows: each module manages its own variable timestep, according to inner numerical constraints (typically variation of state variables within a timestep). Moreover, each module has the ability to interrupt itself and to recalculate a new timestep if required (for example if a new value is received by another module). This is the way the modules exchange data. Timesteps for a model like PUMMA range typically from a few seconds to several hours, depending on the module and on the rainfall situation.
Model mesh The object-oriented model mesh of the PUMMA model is composed of a combination of Hydrological Response Units (HRUs)  (Flügel, 1995)  for rural land use units and urban hydrological elements (UHE)  (Rodriguez et al. , 2008)  for urban cadastral units, including roads. In the Mercier catchment, agricultural and road ditches were mapped using field survey and were incorporated to the natural river network. In the small urban area located in the catchment, rain water is collected in a separate sewer system, which was also incorporated to the natural river network. The river network presented in Fig. 3  incorporates all categories of reaches (natural river, road and agricultural ditches, sewers) and forms the basis for the RIVER1D module that simulates flow routing. Note also that water flow in some reaches is intermittent.
Furthermore, ephemeral streams were represented using the RIVER1D module. HRUs were obtained using GIS overlay of available land-use, geology, soil and sub-basin maps  (Jankowfsky, 2011). Using the 2 m Lidar DTM, rural sub-basins were calculated using the flow direction forcing technique  (Kenny and Matthews, 2005)  in order to determine one sub-basin for each river and artificial ditch branch  (Jankowfsky et al. , 2013). The UHEs encompass one cadastral unit (typically house plus surrounding garden) and half of the adjoining street. UHEs and HRUs, as well as lakes and retention basins (for the SISTBA module) were merged in order to get a non-overlapping model mesh. The object oriented model mesh consists of irregular vector objects: polygons for HRUs/UHEs and lakes/retention basins, and lines for the drainage network.
It is shown in Fig. 3  with the associated PUMMA process modules. Note that UHEs exclusively composed of roads represent 18% of the URBS modules surface. Each model elements is characterized by various geometric attributes (average altitude, slopes, etc…) which were computed using the DTM. The WTI/WTRI interfaces are line objects defined by intersection between polygons. Pre-processing methods were also developed to extract the interface geometries and their connections automatically  (Jankowfsky, 2011). The automatic preprocessing also provides the flow routing on the irregular model mesh for the OLAF module and determines the urban pipe connections. The model mesh considered in this study is based on the original land use map of  Jacqueminet et al. (2013).
Hydrological signatures derived from soil moisture data Soil moisture is quite commonly monitored in field experiments. The use of soil moisture data for model evaluation raises several issues, among which the issue of scaling (point measurements vs. integrated modelling units) and the issue of representativeness (how to compare model values using conceptual storage for soil compartment). In this study, soil moisture data were available only in natural areas, where hydrological processes were simulated using the HEDGE module. Although the soil is divided into layers in this module, soil moisture output of each layer was not available. Only total water storage (mm) of the whole modelling unit was available. The observed soil moisture, however, was a point surface measurement. Moreover, the model was run based on Thiessen polygons calculated from a mere 2 rain gaugeswhich means that very local variations of rainfall, to which surface soil moisture sensors are sensitiveare likely to be lost.