dataesr/scientific-paragraphs-categorization huggingface dataset:
We mapped the number of EPTO genera for the whole database and for each order individually in a similar way as for the number of occurrence records (Figure  1b ,f). The apparent lack of congruence between the number of observations and the number of genera per basin (Figure  1a ,c,e and b,d,f, respectively) highlights the fact that some of the richest areas in terms of taxonomic diversity are being overlooked regarding sampling and/or data digitalization/mobilization (e. g. , the Amazon basin, or basins in Africa and south-western Asia). Likewise, due to the spatial sampling bias, genera from under-sampled areas may be missing (Figure  1 , grey areas in the maps).
| Time frame All records are accompanied by information on the year of observation, with 95 and 94% also having information on the month and day of the observation, respectively (Table  2 ). The dated occurrences can serve in defining EPTO distributions at a given time using species distribution modelling and therefore serve in conservation planning. The increase in record numbers after the year 2000 follows the development of GIS technology and data digitization (Figure  2c ). The steeper increase in the number of records for Odonata is due to a strong increase in the number of observations submitted by citizen scientists, especially in North America and western Europe. Given the lack of a consistent sampling effort through time, the database is generally unsuitable for investigating temporal trends at the global scale.
| CON CLUS IONS By integrating heterogeneous data sources of EPTO occurrence records, we attempted to respond to the priority need for a comprehensive compilation of biological data, as recently suggested by  Maasri et al. (2022). As a result, the Global EPTO Database is easily accessible and developed according to the FAIR principles regarding Findability, Accessibility, Interoperability and Reusability of data  (Wilkinson, 2016)  In the light of the ongoing global freshwater biodiversity decline  (Reid et al. , 2019) , mobilizing and openly sharing existing data will be crucial for advancing biodiversity and conservation research. More detailed species level and trait information  (Sarremejane et al. , 2020; Schmidt-Kloiber & Hering, 2015)  is required for more in-depth analyses and informed decision-making  (Maasri et al.
FOR received research grants from  National Council of Development for Scientific and Technological Development -CNPq, Brazil. FVN was supported by grant # 2021/13299-0 ,  São Paulo Research Foundation (FAPESP). SCJ acknowledges funding through the  Leibniz Association  for the project  Freshwater Megafauna Futures. JDT is supported by a  Rutherford Discovery Fellowship  administered by the  Royal Society Te Apārangi  ( RDF-18-UOC-007 ), and  Bioprotection Aotearoa  and  Te Pūnaha Matatini , both  Centres of Research Excellence  funded by the  Tertiary Education Commission, New Zealand. AA-R acknowledges funding from the programme  ANID Programa Regional   R20F0002. MC is grateful for continued funding by  Programa Peixe Vivo of the Companhia Energética de Minas Gerais  ( P&D Aneel -Cemig GT 599 , and  GT 611 ),  Coordination for the Improvement of Higher Education Personnel (CAPES) ,  National Council for Scientific & Technological Development (CNPq)  research productivity grant  304060/2020-8 ,  Fundação de Amparo à Pesquisa do Estado de Minas Gerais  (grant  PPM 00104-18 ).
DATA AVA I L A B I L I T Y S TAT E M E N T The entire tab-separated value (. csv) database can be downloaded and visualized at https://glowa bio. org/proje ct/epto_datab ase/ and https://doi. org/10. 18728/ igb-fred-811. 0. The URLs to the individual dataset entries can be found in the column "FRED_link-DOI" of.
Every drainage basin and sub-catchment worldwide holds a separate, unique ID. By combining the Global EPTO Database with the 14668238, 2023, 5, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/geb. 13648 by Universidad De Granada, Wiley Online Library on [12/06/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 647 GRIGOROPOULOU et al. Hydrography90m dataset, we aimed to yield global statistics regarding the number of observation records and unique genera per drainage basin. Moreover, we acquired the elevation value of every record's location based on the Multi-Error-Removed Improved-Terrain DEM (MERIT) Hydro digital elevation model (DEM), which is the most accurate, globally seamless, 3 arc-second resolution (~90 m at the equator) DEM to date.
( 216 genera of Ephemeroptera out of 310 have at least 15 records, Plecoptera: 138/199, Trichoptera: 281/391, Odonata: 422/573 genera, Figure 2b). Although the number of presence observations TA B L E 1 Column names of the individual dataset tables available in the Global EPTO Database and their explanations. the dataset of a given data source, e. g. , 003 occurrence_ID Unique ID of the individual occurrence record, e. g. points of the coordinates that are published no_decimals_available Number of decimal points of the coordinates that are available upon communication with the authors (could include more detailed coordinates) location_river Locality or river of the observation record country Country of the observation record sampling_day Day of the observation month Month of the observation year Year of the observation source Data source (e.
g. , name or DOI of the original database) FRED_link-DOI Dataset link to FRED, where all metadata are available, including dataset authors' names notes Dataset authors' notes cen_fl Centroid flag, Boolean. If "1", then the coordinates represent the centroid of a country instead of the exact location of the observation cap_fl Capital flag, Boolean. If "1", then the coordinates belong to the capital of a country instead of the exact location of the observation coun_fl Country flag, Boolean. If "1", then the coordinates of the record do not match with the assigned country snapped Boolean. If "1", then the original coordinates of the record, that fell into the sea, have been snapped to the closest sub-catchment subcatchment_Idy9.
FEOW_ID Freshwater Ecoregions of the World ID (Abell et al. , 2008)  Note: DEM = digital elevation model; EPTO = Ephemeroptera, Plecoptera, Trichoptera and Odonata; FRED = IGB Freshwater Research and Environmental Database; MERIT = Multi-Error-Removed Improved-Terrain DEM; WD-OECM = World Database on Other Effective Area-based Conservation Measures; WDPA = World Database on Protected Areas; WGS84 = World Geodetic System 1984. The FRED link of each specific dataset is only included in the comprehensive table of all orders.
Collecting campaign of Aquatic Insects -LACIA/INPA dataset was supported in part by INCT ADAPTA II funded by CNPq (465540/2014-7), Amazonas State Research Foundation -FAPEAM (0621187/2017), CAPES -Coordination for the Improvement of Higher Education Personnel, FAPEAM-Program POSGRAD, BIODIVERSA/FAPEAM, and Universal/CNPq (403758/2021-1). RTM received a fellowship from BIODIVERSA/FAPEAM. F I G U R E 1 Number of observation records (a) and genera (b) for all orders, aggregated in a 10-km regular grid. Number of observation records (c) and genera (d) for all orders per drainage basin. Number of observation records (e) and genera (f) per drainage basin for each order individually. E = Ephemeroptera; P = Plecoptera; T = Trichoptera; O = Odonata. White and grey areas lack information. A more detailed interactive view of the maps (e) and (f) is available at https://glowa bio.
At the same time, it highlights the spatial patterns Summary statistics of the Global EPTO Database for the number of records, drainage basins and sub-catchments, the geographic location and precision as well as the temporal precision of the samplings. Information on the numbers of the described extant genera and families of Ephemeroptera, Plecoptera, Trichoptera, and Odonata (EPTO) was retrieved from Barber-James et al. (2013 ), DeWalt et al. (2021) , Morse (2022)  and Paulson et al. (2022) , respectively. Ephemeroptera Plecoptera Trichoptera Odonata EPTO Observation records 933,070 538,236 1,640,723 5,256,438 8,368,467 Genera 310 199 391 573 1,473 Described genera 442 313 632 694 2,081 Families 41 17 46 38 142 Described families 42 17 63 45 167 Occurrence locations 114,711 58,939 164,985 1,328,255 1,505,302 Sub-catchments covered 89,521 42,182 129,658 593,135 702,409 Drainage basins covered 5,220 3,318 8,343 36,623 39,584 % of records in protected areas 12 10 16 22 20 % of coordinates with ≥ 4 decimal digits 95 95 91 82 86 % of records including sampling year 99 99 99 100 99 % of records including sampling month 93 95 88 97 95 % of records including sampling day 92 94 86 96 94 Note: of sampling efforts and/or data digitization and mobilization world- wide, which are mostly concentrated in the Northern Hemisphere, in accordance with the findings of Meyer et al.
During each experiment, abiotic conditions (temperature, salinity, etc. ) were monitored in each cylinder to ensure that all cylinders displayed similar conditions. Speakers were connected to an amplifier (DENON/DN-300Z/ 16-bit/20-20000Hz/44. 1KHz, Cumberland, RI, USA), then to a matrix mixer with a signal processor (Yamaha 26x8 MTX3, Buena Park, CA, USA). Pile driving sound was played via an SD card, directly inserted into the amplifier set in repeat mode. Drilling and boat sounds were played independently with 2 computers connected to the amplifier using VLC media player software set in repeat mode, with both volumes adjusted to 100%. Sound treatments were recorded for 30s in each 5L experimental cylinders (10 cm above the bottom) with an underwater acoustic recorder (Loggerhead LS1, HTI 96-MIN/3V/LED/-170 dB/44.
1 KHz, Sarasota, FL, USA) and calibrated to obtain a similar level to that measured in the field. The sound pressure level (dB re 1µPa) peak to peak -was calculated using the following equation: SPL PP = 20 log½max (p(t) -min (p(t)) where t is the length of the sound and p the pressure units after correcting from volts to mPa. Fourier transformation was applied to each recording to analyze the power spectral density (PSD) using a custom Matlab script (The MathWorks Inc. ). Sound treatments were emitted 12 h each day with an alternating sound exposure period of 6 hours followed by 6 hours of silence. A 30 s sequence was looped during experiments. The boat sound used was from a 11 m long D & H Boatbuilding hull equipped with a diesel motor (Cummins 300 hp C series) and was the same originally recorded and used by  Jolivet et al.
Low intensity cool white lights (2. 5 ± 0. 4 mmol photon m -2 s -1 ) were aligned and adjusted above each tank with a natural light period of 14h day : 8h night. The temperature during all larvae and postlarvae rearing was maintained between 20 and 22°C. At 48 h intervals, larvae from each cylinderwere collected on a 35 mm nylon mesh screens and resuspended with 300 mL of 1 mm ultrafiltered and UVs treated seawater to sample 1 mL of larvae for survival and growth estimation. For the growth rates, 30 larvae were measured with the image analysis software Image-Pro Plus coupled to the Evolution VF camera (Media Cybernetics, Silver Spring, MD, USA) and an Olympus BX41 microscope. Survival rates were expressed as the total number of individuals minus the cumulative number of empty shells and based on the first sampling time point.
Fisheries and Oceans Canada and the Northern Institute for Research in Environment and Occupational Health and Safety (INREST), representing the Port de Sept-I ̂les and Ville de Sept-I ̂les. This work was also supported by complementary funding provided by Ressources Aquatiques Quebec (RAQ), a strategic research network funded by the Fonds de recherche du Quebec -Nature et technologies (FRQNT, #2014-RS-171172).
However, spectroscopically inferred (SI) data are subject to higher uncertainties than reference laboratory analysis. Many DSM practices integrated SI data with soil observations into spatial modelling while few studies addressed the key question that whether these non-errorless soil data improve map accuracy in DSM. In this study, French Soil Monitoring Network (RMQS) and Land Use and Coverage Area frame Survey Soil (LUCAS Soil) datasets were used to evaluate the potential of SI data from Vis-NIR and MIR in digital mapping of soil properties (i. e. soil organic carbon, clay, and pH) at a national scale. Cubist and quantile regression forests were used for spectral predictive modelling and DSM modelling, respectively. For both RMQS and LUCAS Soil dataset, different scenarios regarding varying proportions of SI data and laboratory observations were tested for spectral predictive models and DSM models.
, 2013)  datasets where both laboratory physico-chemical measurements and spectral data are available for more than 2000 sites in France, were used to assess the potential of SI data in mapping soil organic carbon (SOC), clay and pH in mainland France. Using 50 times repeated consistent external validation sets from two datasets, we assessed (1) whether the ratio share between SI data and laboratory observations will affect the map accuracy and uncertainty estimates? (2) whether the results are specific for different soil properties and datasets?.
Soil data and environmental covariates Considering the potential effect of sampling design, the RMQS (grid sampling) and LUCAS Soil (stratified sampling) datasets were tested in this study (Fig. 1 ). The sites of RMQS program are located following a systematic square grid of 16 km which resulted in around 2,200 sampling sites collected from 2001 and 2009 covering France under different pedo-climatic, relief, and land cover conditions  (Jolivet et al. , 2006). From a 20 m square located at the centre of each 16 km grid, topsoil (0-30 cm) and subsoil (30-50 cm) layers were collected by merging 25 sub-samples based on an unaligned sampling design  (Chen et al. , 2018a). After airdrying and sieving to <2 mm, SOC was measured by the dry combustion method using an automated C:N analyzer, pH was determined in a 1:5 soil:water mixture (AFNOR, 1994), and clay (0-2 μm) was measured with the pipette method  (AFNOR, 2003).
A total of 2036 topsoil samples  located in mainland France were used in this study. The LUCAS Soil data was sampled in 2009, and it contains a total of 19,967 samples collected in topsoil (0-20 cm) from 25 EU member states by stratified random sampling according to topography and land use  (Tóth et al. , 2013). All the soil samples were air-dried and sieved to a fraction of <2 mm, and then SOC, pH and clay (0-2 μm) were determined using ISO standard methods  (Stevens et al. , 2013). In this study, 2685 LUCAS Soil samples located in mainland France were used. Fig. 2  shows the statistics of SOC, clay and pH in the RMQS and LUCAS Soil. SOC had similar distributions (Mann-Whitney test, p = 0. 76) in the RMQS and LUCAS Soil with 1st quartile (Q1) of 13. 1 and 13. 2 g kg -1 , median of 19. 5 and 19.
Spectral measurement, pre-processing and modelling For the RMQS dataset, soil samples were further sieved to <0. 2 mm before measuring MIR absorbance spectra using a Nicolet 6700 Diffusive Reflectance Fourier Transform Spectrophotometer (Thermo Fisher Scientific Inc. , USA)  (Grinand et al. , 2012). The MIR spectra ranged from 4000 to 400 cm with a spectral resolution of 3. 86 cm. Thirty-two scans of spectra were recorded for each soil sample, and the average was taken as the representative spectra. The Vis-NIR absorbance spectra (350-2500 nm with a special resolution of 1 nm) for RMQS dataset were measured on the soil samples sieved to <1 mm using a NIRSystems 6500 spectrophotometer (Foss Analytical, Sweden)  (Gogé et al. , 2012; Gogé et al. , 2014). The spectral ranges of 350-399 nm and 2451-2500 nm were removed due to their low signal-to-noise ratio.
Based on previous studies on spectral modelling for RMQS dataset and after initial comparison, Savitzky-Golay algorithm (window size of 11 and 21 points for MIR and Vis-NIR, and a 2nd order polynomial) with the 1st derivative followed by Standard Normal Variate was chosen to smooth both MIR and Vis-NIR spectra and enhance the signal. The processed MIR and Vis-NIR spectra were resampled to a spectral resolution of 20 cm -1 and 10 nm, respectively, for speeding the computation efficiency while not losing the predictive performance  (Yang et al. , 2012). While in the LUCAS Soil database, the vis-NIR absorbance spectra were measured on the air-dried and sieved (<2 mm) soil samples using a FOSS XDS rapid content analyzer (FOSS NIRSystems Inc. , Denmark)  (Nocita et al. , 2014). The Vis-NIR spectra had a range of 400-2500 nm with a spectral resolution of 0.
(2022). Once the weight is assigned for each calibration sample, the "case. weights" in the "ranger" R package can be used to set the probability of each calibration sample to be used in the bootstrapping procedure. That means a calibration sample with a high measurement error will have a low probability to be sampled in the bootstrapping. In summary, a traditional QRF was used when all the calibration data were laboratory observations while MEF-QRF was applied when integrating SI data with laboratory observations.
General framework The general framework of this study is present in Fig. 3  (created with BioRender. com), which involves five steps as follows. (1) RMQS and LUCAS Soil were both randomly divided into D1 (75%) and D2 (25%). Here D2 was used as consistent external data to evaluate the accuracy of spectral predictive models and DSM models while D1 was used to create four scenarios (see details in step 2). This random split was repeated 50 times for a robust evaluation  (Xiao et al. , 2022) ; (2) Based on D1 dataset, four scenarios were created to evaluate the effect of ratio share between SI data and laboratory observations. Scenario 0 was a benchmark where no data was SI and all D1 was used for DSM modelling. The proportion of soil samples assumed with spectra only (to be predicted by the spectroscopic model) increased from 1/3 in scenario 1 (S1U) to 1/2 in scenario 2 (S2U) and 2/3 in scenario 3 (S3U) by random sampling (50 repeats as we did in step 1), while the remaining data in D1 was used to the calibrate the spectral models accordingly (S1C, S2C and S3C); (3) The spectral predictive models were calibrated using Cubist model, and the model performance was evaluated by the consistent external data D2; (4) The soil samples assumed with spectra only were predicted by relevant Cubist models (S1P, S2P, and S3P); (5) In scenarios 1, 2 and 3, two strategies were compared.
Performance evaluation for DSM models Table  4  presents the performance of the DSM model for SOC, clay, and pH under different scenarios. When using all the soil observations for DSM modelling (S0), the performance of the RMQS data (R 2 of 0. 41, 0. 36 and 0. 53 for SOC, clay and pH respectively) was generally better than the LUCAS Soil (R 2 of 0. 33, 0. 27 and 0. 46 for SOC, clay and pH, respectively). Table  4  also shows that, regardless of datasets (i. e. LUCAS Soil, RMQS) and soil properties (i. e. SOC, clay, pH), including additional SI data to laboratory observations (i. e. S1M, S2M, S3M) in DSM models slightly improved performance (gain of R 2 proportion at 3-19%) when compared to these models using only equal-sized laboratory observations (i. e. S1C, S2C, S3C). In addition, these improvements resulting from additional SI data were more evident for both RMQS and LUCAS Soil datasets when more SI data and less laboratory observations were used in modelling from S1M (2/3 observations and 1/3SI) to S2M (1/3 observations and 2/3SI).
It is also clear that these models using both laboratory observations and SI data (i. e. S1M, S2M, S3M) had close performance to those using all the laboratory observations (S0) while a slight decrease of performance can be seen when the proportion of SI data increased (e. g. S1M, S2M, S3M for RMQS Vis-NIR in clay prediction). When comparing the two spectroscopic techniques for RMQS dataset, we found that the DSM models including MIR inferred predictions always performed better than models including Vis-NIR inferred predictions (i. e. S1M, S2M, S3M).
Uncertainty qualification for DSM models As shown in Table  5 , all the median PICP indicators derived from QRF models were located between 86. 8% and 94. 4%, which were quite close to the pre-defined 90% PIs. The predictive models of LUCAS Soil using all the soil observations (S0) for DSM modelling had similar uncertainty quantifications to those of RMQS data for SOC and clay, and a slightly greater PICP (94. 3%) was found for RMQS than that of LUCAS Soil (92. 2%). The PICP for the DSM models using different proportions of soil observations (i. e. S1C, S2C, S3C) were close to the DSM models using all the soil observations (S0) for both RMQS and LUCAS Soil datasets, and a slightly decreasing PICP trend was found when less soil observations were used for DSM (S1C to S2C and then to S3C). When the proportion of RMQS MIR inferred data was low (S1M), the PICP for these DSM models was quite close to S0 (ΔPICP <0.
7%), while a greater decrease of PICP (ΔPICP of 0. 7-1. 7%) was found for the DSM models with higher proportion of RMQS MIR inferred data (S2M, S3M). In comparison to S0, RMQS Vis-NIR related DSM models showed a greater decrease of PICP (ΔPICP of 0. 5-4. 0%), and this decreasing trend was more evident for the DSM models with more Vis-NIR inferred data (S3M). LUCAS Soil dataset showed a similar pattern of PICP to RMQS Vis-NIR with a greater decreasing trend (ΔPICP of 0. 7-5. 9%). The most significant decrease of PICP for LUCAS Soil and RMQS (Vis-NIR and MIR) datasets was found for these DSM models in predicting clay. As shown in Table  5 , PIW gradually increased when the number of observations decreased from S0 (all observations) to S1C (2/3 observations) and then to S3C (1/3 observations), including an increasing model uncertainty.
Dataset Soil property As shown in Fig. 4a , high SOC was found in mountainous regions (eastern, southwestern and central France) while low SOC was mainly located in agricultural region (northern and southwestern France). The southwestern and central France had low clay content and highly clayey soil was sparsely distributed in western France and southern France near Mediterranean (Fig. 5a ). Acid soil was mainly located in mountainous regions (eastern, southwestern and central France) and alkaline soil could be found in the region near Mediterranean and northern France (Fig. 6a ). S0 S1M S1C S2M S2C S3M S3C R 2 RMSE R 2 RMSE R 2 RMSE R 2 RMSE R 2 RMSE R 2 RMSE R The result showed that the difference to S0 gradually increased from S1 to S3 for three soil properties using data from RMQS and MIR inferred data (Fig.
55 in this study) that the small prediction error of SI data will not impact that much on the DSM model. Therefore, it can be expected that a higher quality of SI data would be needed for DSM models with higher accuracy. We suggest that the opposite result (adding SI data did not improve DSM model) from  Somarathna et al. (2018)  might result from low spectral predictive model, and another reason may be linked to the high proportion (60-95%) of SI data (Table  1 ) in DSM models, especially for the subsoil. We found that integrating more SI data into DSM modelling lowers the PICP and PIW, which may result in underestimation of prediction uncertainty as quantified with a given PI. In this study this underestimation becomes apparent when the SI data make up a larger proportion of the model calibration data, with PICP values dropping below the 90% for some of the RMQS Vis-NIR and LUCAS Soil S3M models.