dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010; Geijzendorffer et al. , 2015). Hence, a first step towards improving global EBV datasets is to assess the underlying data sources and to identify existing biases. Only then will it be possible to develop strategies to cover data gaps and to optimize the use of available data. Here we demonstrate the links between data sources, EBVs and indicators. We classify the main sources of primary observations of terrestrial biodiversity as: (1) extensive monitoring schemes, (2) intensive monitoring schemes, (3) ecological field studies, and (4) satellite remote sensing. We define each class and its scope by its geographic, taxonomic, and temporal coverage. We then analyze the dataset informing the Living Planet Index indicator (LPI;  Collen et al. , 2009)  to illustrate the effects of primary data sources on EBV representativeness.
2015 ; Table  2 ). In recent years, data from ecological field studies and satellite remote sensing have been used increasingly as aggregated datasets have become more accessible  (Karl et al. , 2013; Hudson et al. , 2014; Pimm et al. , 2014). Ecological field studies, here defined as experimental or observational studies located outdoors  (Martin et al. , 2012) , are numerous but often conducted independently of each other. Despite the large spatial coverage achieved when independent studies are aggregated (e. g. ,  Hudson et al. , 2014) , data scalability is constrained by the fact that ecological field studies do not share a common design or data recording scheme. Also, compared with the other sources of biodiversity data, ecological field studies tend to deliver short-term data series  (Hudson et al.
, 2014 ). Yet, because ecological field studies explore different research questions and report many different types of data, they also cover multiple EBV classes. Similarly, citizen science generates numerous opportunistic data on species observations. Despite their large number and spatial coverage, the use of these data has been limited by quality issues, namely the lack of sampling protocols. Recent developments in data correction methods promise to allow researchers to use opportunistic citizen science data to monitor species distribution trends  (van Strien et al. , 2013; Isaac et al. , 2014). Satellite remote sensing can deliver long-term data series with a high sampling frequency and extensive geographic coverage. Satellite remote sensing can be distinguished from other types of remote sensing, such as aircraft or drones, by its global and continuous coverage.
, 2013). This is reflected in the paucity of map products, despite frequent data collection, and stresses the need for international and multidisciplinary approaches that harness the use of earth observation data  (Pasher et al. , 2013). In addition, the production of EBV datasets from satellite data will require coordinated action from data providers, biodiversity and remote sensing experts, and policy makers  (Pettorelli et al. , 2016). Biodiversity monitoring datasets may combine primary biodiversity observations from a single source, from different sources of the same type, or from different sources of different types (Fig. 1 ). The global map of 21st century forest cover change by  Hansen et al. (2013)  is an example of the first case. It combines time series of Landsat data to monitor forest cover change at the global scale.
An example of the second case is the global Wild Bird Index (WBI;  Gregory et al. , 2005; Gregory & van Strien 2010)  dataset, which currently combines the species abundance data delivered by the Pan-European Common Bird Monitoring Scheme and the North American Breeding Bird Survey (Table  2 ). The Living Planet Index (LPI;  Collen et al. , 2009)  dataset provides an example of the last case, as it combines data from extensive schemes, intensive schemes, and ecological field studies (see next section). Moreover, primary biodiversity observations may be compiled and made available through secondary sources, such as databases (e. g. , the PREDICTS database  (Hudson et al. , 2014) ), data repositories, or institutional reports.
The data sources behind the Living Planet Index The LPI is among the best established indicators of the state of global biodiversity  (Butchart et al. , 2010; Tittensor et al. , 2014). It monitors changes in population abundance relative to a 1970 baseline using time series of vertebrate populations across the globe  (Collen et al. , 2009; Fig. 2a ). The underlying dataset aggregates time series for N16,000 populations of over 3600 species of vertebrates (http://www. livingplanetindex. org/, accessed 14. 06. 2016) and is one of the most complete datasets on the population abundance EBV  (Collen et al. , 2009; Tittensor et al. , 2014). New data are added to the dataset as they become available. A candidate population is included in the dataset only if data on population size are available for at least two years and data were always collected using the same method on the same population throughout the time series  (Collen et al.
, 2009). Data generated by different types of primary sources, namely, extensive schemes, intensive schemes, and ecological field studies, are collected from a variety of available sources, including published scientific literature, on-line databases and gray literature  (Collen et al. , 2009). Therefore, the LPI dataset emerges from ongoing data survey and collection. Here we analyze two subsets of the LPI dataset, the subset of terrestrial birds (4406 time series of 1025 species, time interval: 1900-2013) and the subset of terrestrial mammals (2229 time series of 438 species, time interval: 1900-2014). We use information on the length of the time series (i. e. , the time interval between the start and end year), number of data points (i. e. , number of measurements made during the time interval) and the purpose of primary data collection (i.
e. , baseline monitoring, conservation or natural resource management, or population studies) to infer if data originate from long-term monitoring schemes (i. e. , extensive and intensive schemes) or from ecological field studies. Lindenmayer and Likens (2010)  proposed a minimum time series length of 10 years to qualify a study as long-term, while emphasizing that this is an operational criterion and that the adequate threshold depends on the taxa or ecosystem processes being monitored. For the purpose of our analysis, we assigned long-term monitoring schemes to long-term time series, here defined by a minimum of 10 data points if the series started after 1995, or a minimum of 15 data points if the series started in 1995 or before. We do not discriminate extensive from intensive schemes because the dataset does not provide precise information on the number of sampling sites and spatial coverage of the primary source.
For that reason, we do not discuss the relative contribution of these source types to the LPI. Ecological field studies were assigned to short-term time series of non-baseline monitoring studies. Moreover, recently started time series collected for baseline monitoring purposes could evolve in the future into long-term time series. Results show that for both birds and mammals a large share of the available data stems from temperate regions, in particular Europe and North America (Fig. 2b-c ); in the case of mammals, the equatorial region is also better represented than other world regions. In order to correct for geographic biases, a method of proportional weighting is currently applied to the data when calculating the global LPI  (McLellan et al. , 2014). Regarding the temporal coverage, long-term time series comprise a large share of bird population data but are largely confined to temperate regions (Figs.
3a, Appendix C ). Efforts are being pursued to implement similar schemes in gap regions, particularly in African countries  (BirdLife International, 2013). Once implemented, these schemes will contribute to reducing the data gap and consequently the geographic bias. Notwithstanding, the International Waterbird Census (Table  2 ) has global coverage, operating in 143 countries and building on the contribution of thousands of volunteers  (Wetlands International, 2016). Long-term time series on mammal population abundances also show a bias towards the northern hemisphere, with data, equally distributed across North America, Europe and Asia. For this species group, ecological field studies seem to be providing more long-term data than monitoring schemes (Fig. 3b, Appendix C ). Concurrently, short-term ecological field studies constitute an alternative, relatively abundant and globally distributed source of time series of population abundance.
Ecological field studies deliver a great part of the available time series of mammal population abundance and are essential to complement bird monitoring data (Fig. 3, Appendix  C ). However, differences in sampling protocols affect data scalability across space and time, hence limiting the full use of primary data  (Henry et al. , 2008). Furthermore, while the aggregation of data from different sources confers a broad taxonomic coverage (within the vertebrates) to the LPI dataset, many of the sources, particularly for mammals, target a single species or order  (Figs. 2c, 3b, Appendix B). This represents a limitation if community-level responses are relevant for monitoring the impacts of environmental change, such as the assessment of trophic chain effects or the identification of potential "loser" and "winner" species.
Improving the representativeness of EBV datasets The goal of global biodiversity monitoring is to measure biodiversity responses to environmental change. This goal implies the use of time series, in particular long-term data capable of capturing on-going changes through time  (Scholes et al. , 2012; Han et al. , 2014). Data must also be scalable, so that biodiversity change can be assessed across scales and compared between sites  (Pereira and Cooper, 2006; Han et al. , 2014; Latombe et al. , 2017-in this issue) , and taxonomically representative, so that a more complete understanding of biodiversity change, which includes community level changes, can be achieved. Here, we have examined what is arguably the most representative global dataset of population abundance, a dataset produced to inform the LPI indicator.
Therefore, taxon selection should follow pragmatic criteria, such as the feasibility of monitoring at the global scale and their functional role in ecosystem processes  (GEO BON, 2010). Generating new data through the establishment of new monitoring schemes is one of two main approaches to enhance the representativeness of EBV datasets. The second approach is through data integration, that is, by making the best use of data generated by ecological field studies and monitoring schemes, from local to regional scales, and by satellite remote sensing. The establishment of new monitoring programs will be particularly important in the case of gap regions and taxa for which data is scarce or virtually missing. For a more effective use of resources and coordinated action, new monitoring programs should be prepared within the efforts to build a global biodiversity monitoring network  (Pereira and Cooper, 2006).
In those cases, data integration will depend on post-collection data harmonization techniques  (Henry et al. , 2008; Schmeller et al. , 2015) , as happens with the LPI dataset. Both new monitoring schemes and better integration of data from different schemes are necessary to enhance global datasets. However, even with these efforts, the level of data completeness will inevitably be low at the global scale, requiring complementary approaches. Models can be used to estimate missing values and cover data gaps. Intensive monitoring schemes, in particular, collect a comprehensive set of variables at each site that can be used to support the development of process-based models of ecosystem and community functioning. Remote sensing data need to be linked to local observations to generate models on ecosystem processes, which can then be used to upscale local measures and estimate values at the regional scale based on proxies measured by remote sensing  (Pereira et al.
, 2014; Geijzendorffer et al. , 2015). Publication of biodiversity monitoring data is critical for a timely assessment of biodiversity state and change and should be encouraged  (Costello et al. , 2013). Required actions include the implementation of publishing mechanisms that reward data providers, ensure data quality standards and the sustainability of public databases  (Costello et al. 2013 (Costello et al. , 2014)). The demand for EBV datasets to support biodiversity change assessment at multiple spatial and temporal scales is far from being met. At present there are few globally representative EBV datasets that compile and integrate time series of primary observations  (Boakes et al. , 2010; Han et al. , 2014). For instance, data on species distribution/presence are available through GBIF (Global Biodiversity Information Facility), and GBIF's Integrated Publishing Toolkit (http://www.
gbif. org/ipt) is being adapted to support the recording of quantitative measures of species abundance  (Wieczorek et al. 2014 ), which will enhance the interoperability of existing datasets. Species traits data are available from the TRY database (https://www. try-db. org/), and data on species interactions are being mobilized into GloBI (http://www. globalbioticinteractions. org/). But in most cases, time series data, fundamental for the development of EBV datasets, are poorly represented in these databases. In order to develop representative EBV datasets, we must first assess the available primary data. Then, alongside efforts to improve data accessibility, we must integrate primary data through harmonization of methods and modeling, and establish new monitoring programs to cover critical data gaps and to ensure the robustness and stability of a global monitoring network.
The design of a global monitoring network should consider both extensive and intensive monitoring schemes. This will enable an inclusive coverage of EBV classes and promote data complementarity and their use in modeling efforts, in particular, in conjunction with satellite remote sensing data. Moreover, because resources are limited, a smart prioritization of investment will be required. Concurrently with a strategic selection of areas for the implementation of new schemes, it also is crucial to prioritize capacity building initiatives and to understand how to make the best use of modeling solutions.
Acknowledgements VP and PT were supported by  FCT -the Foundation for Science and Technology  ( SFRH/BPD/80726/2011  and  SFRH/BD/89543/2012 ), LJM was supported by the  National Science Foundation   Dissertation Improvement Grant  (Award No. 1329750 ),  DSS  and  NAB  were supported by the  EU BON  project of the 7th  Framework Programme  funded by the  European Union  -Contract No. 308454 , MB is supported by the  Rufford Foundation , JB was supported by  US Geological Survey Ecosystem program. FCT (PTDC/AAC-AMB/114522/2009, PEst-OE/BIA/UI0329/2011 ), and  GEO BON  supported the organization of a workshop of the  GEO BON Terrestrial Species Working Group.
S1 ). Moreover, she did not harbor the c. 315-48C FECH low-expressed allele  (4). Taken together, these data demonstrate that the proband has an unusual form of EPP (Table  1 ). Among the proband's family members, her father (II. 4) and uncle (II. 2) also presented with free and zinc-PPIX accumulation in erythrocytes and associated mild photosensitivity, but without the complete clinical symptoms of EPP (Fig. 1A  and Table  1 ). After linkage analysis and exclusion of candidate genes (SI Appendix, Table  S1 ), whole-exome sequencing revealed that the proband (III. 2) carried a single nucleotide substitution (g. 27511 G > A) at the exon 7 splice junction border in one of her two CLPX alleles (Fig. 1B ). This mutation was not present in 126,216 control reference exomes queried in the genome aggregation database (gnomad.
broadinstitute. org), suggesting that it is not a common human polymorphism. To establish the inheritance pattern of this mutation, we performed Sanger sequencing of gDNA obtained from whole blood of the proband's relatives and collected clinical data from these family members. Among nuclear family members, only the proband's father (II. 4) and uncle (II. 2) harbored the identical heterozygous CLPX mutation (Fig. 1A ). Thus, the G > A transition in CLPX cDNA was fully segregated with the PPIX accumulation and was inherited in a pattern consistent with Mendelian dominance. The pre-mRNA from the mutant allele was normally spliced (Fig. 1C ), and the mutation was predicted to encode the single amino acid p. Gly298Asp substitution in the mature CLPX protein (CLPX GD ). Thus, we propose that this CLPX mutation increases PPIX production with incomplete penetrance for full EPP; disease progression and severity are likely influenced by environmental conditions and/or genetic modifiers.
ACKNOWLEDGMENTS. We thank the individuals and family members for their participation in this study and  Jean-Charles Deybach  and  Said Lyoumi  for insightful suggestions during the preparation of the manuscript. We thank  Eva Buys  and her crew for zebrafish animal husbandry and  Thibaud Lefebvre ,  Steven Ghazal , and  Sylvie Simonin  for  ALA LC/MS/MS , porphyrin measurements, and biostatistics. In vitro HPLC analyses for porphyrins and ALAS activity were performed at the  University of Utah Center for Iron and Heme Disorders  (under  National Institutes of Health  Grant  U54 DK110858 ). Ex vivo HPLC and MS analyses for heme precursors and enzyme activity were performed at the  French Center for Porphyrias. This work was supported by grants from  The Netherlands Society for Biochemistry and Molecular Biology (Nora Baart Foundation) , the RadboudUMC Master thesis prize, the  Radboud University Master  thesis award, and the  Dutch Stomach Liver Bowel Foundation  (L.
N. v. d. V. ); the  Public Health and Consumer Protection Directorate Public Health Executive Agency of the European Commission, ANR-GIS Maladies Rares  Grant  ANR07-MRAR-008-01 ; and the  Laboratoire d'Excellence Gr-Ex , reference  ANR-11-LABX-0051  (to H. P. , L. G. , G. N. , Z. K. , and C. K. ). The labex GR-Ex is funded by the program " Investissements d'Avenir " of the  French National Research Agency , reference  ANR-11-IDEX-0005-02. Additional support was provided by the  Howard Hughes Medical Institute  (T. A. B. ) and  National Institutes of Health  Grants  F32 DK098866  (to Y. Y. Y. ),  K01 DK106156  (to Y. Y. Y. ),  F32 DK095726  (to J. R. K. ),  R01 GM049224  (to T. A. B. ),  R01 DK020503  (to J. D. P. ),  U54 DK083909  (to J. D. P. ),  R01 DK070838  (to B. H. P. ), and  P01 HL032262  (to B.
Agricultural landscapes often include a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes as simulated by land surface and distributed hydrological models. Sentinel-2 mission satellite remote sensing products allow for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy flux via the ISBA-SURFEX land surface model. The study area is a 24 km by 24 km agricultural zone in southwestern France.
An initial reference simulation was conducted from 2006-2010 using the ECOCLIMAP-II database. This global numerical land ecosystem database was created at a 1 km resolution and includes an ecosystem classification with a consistent set of land surface parameters required for the model, such as the Leaf Area Index (LAI) and albedo measures. The LAI of ECOCLIMAP is climatologic and derived from a 2000-2005 analysis of MODIS satellite products. This low resolution induces that several vegetation covers can be mixed in a model cell. The climatic construction of LAI dynamics also suggests that there is no interannual variability in the vegetation cycle. A second simulation was performed by forcing the same model with annual land cover maps and monthly LAI values derived from a series of 105 8 m-resolution Formosat-2 images for the same period.
It uses the ECOCLIMAP-II database to determine vegetation types and associated parameters (e. g. , LAI, fractional vegetation cover, and albedo) at a spatial resolution of 1 km  (Masson & al. , 2003; Faroux & al. , 2013). The ECOCLIMAP-II LAI is climatological with a temporal resolution of 10 days. It was derived from MODIS and SPOT-VEGETATION satellite observations collected between 1999 and 2005. Up to 12 vegetation types or plant functional types (PFTs) can be included in an ECOCLIMAP-II grid cell. The LAI of each PFT is determined by unmixing the grid cell MODIS LAI using neighboring, unmixed pixels. This study evaluates the impact of introducing high-resolution information on vegetation type and the LAI from Sentinel-2like observations rather than ECOCLIMAP-II in ISBA-SURFEX simulations.
The study area is a pilot site in southwestern France  (Dejoux & al. , 2012). This site is considered to be representative of the cultivated area in the upper Garonne River Basin. A fraction (estimated at 13%) of crop fields in the area is irrigated, but we chose not to focus on effects of irrigation due to a lack of spatially distributed data on irrigation quantity and timing. LAI and land cover values were determined from a 5-year time series of Formosat-2 satellite images, which has similar spatio-temporal characteristics as Sentinel-2. The model was applied at the "field" scale to identify a homogeneous vegetation type for each computation unit (one computation unit has only one PFT). This field-scale modeling approach allows one to determine the spatial variability of LAI values between fields while limiting the computation time in comparison to a pixel-based approach.
The SAFRAN data were spatially linearly interpolated to match parcel centroïds. Regarding soil parameters, by default, the ISBA uses the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2012), which gives the percentage of clay and sand at a 30 arc-second (~1 km) resolution. Soil parameters were then computed using empirical pedotransfer functions  (Masson & al. , 2013). Hydrol. Earth Syst. Sci. Discuss. , doi:10. 5194/hess-2016 -661, 2017  Manuscript under review for journal Hydrol. Earth Syst. Sci. Published: 10 January 2017 c Author(s) 2017. CC-BY 3. 0 License. The ECOCLIMAP-II database  (Faroux & al. , 2013)  is used to describe vegetation cover at a 1 km resolution, thus corresponding to the resolution of the SURFEX-ISBA simulation grid. Vegetation parameters were deduced from satellite products (MODIS, SPOT, FORMOSAT, etc.
) for 273 ecosystems. These ecosystems were based on vegetation types, climates and soil textures. Twelve different functional plant types (PFT, also referred to as patches) are considered in the ISBA (Fig. 2 ) to describe these ecosystems. Each pixel in the SURFEX-ISBA belongs to a unique ecosystem and thus is described as a combination of these patches. Vegetation parameters are thus determined for each ecosystem patch. Some of the parameters are fixed such as root depth and minimal stomatal resistance, and some are temporally variable such as albedo and the LAI. In particular, the LAI follows a cycle determined from MODIS LAI analysis data for 2000 to 2005 averaged for each vegetation type of each ecosystem with a temporal resolution of 10 days  (Faroux & al. , 2013). The ISBA then uses these parameters to simulate fluxes at a 1 km resolution.