dataesr/scientific-paragraphs-categorization huggingface dataset:
| Effects of species turnover on the taxonomic and functional homogenization of island assemblages We investigated if anthropogenic species turnover (the combined effect of extinctions and introductions) is leading to homogenization or differentiation, by contrasting each island with each of the remaining 63 (global scale). We averaged the results to obtain, for each island, a value of change in taxonomic beta-diversity, and another one of change in functional beta-diversity, to measure taxonomic and functional homogenization, respectively. Then, we analysed the relationship between taxonomic and functional beta-diversity by building a generalized linear mixed-effects model with Gaussian distribution and identity link function, using the glmmTMB package  (Magnusson et al. , 2020). Two models were built: one using the individual change in dissimilarity of all island pairs (n = 64 2 = 4096), and another using the average change of each island (n = 64).
The change in functional beta-diversity of each island was the response variable and the change in taxonomic beta-diversity was the only fixed effect, while archipelago was used as a random effect (n = 11). Model assumptions were validated using simulated residuals and the DHARMa package  (Hartig, 2021). To check if the proportion of homogenized islands was greater than that of differentiated islands, we performed a chi-squared test, which is a two-sample test for equality of proportions with a continuity correction for small sample sizes (continuity of Yates).
Like for the global scale, we also analysed the relationship between taxonomic and functional beta-diversity both within and across archipelagos by performing a generalized linear mixed-effects model with Gaussian distribution and identity link function, using the glmmTMB package  (Magnusson et al. , 2020). The average change in functional beta-diversity of each island was the response variable and the average change in taxonomic betadiversity was the only fixed effect (n = 64), while archipelago was used as a random effect (n = 11). Model assumptions were validated using simulated residuals and the DHARMa package  (Hartig, 2021). To explore the relationship between the changes in taxonomic beta-diversity and original taxonomic betadiversity, as well as the relationship between the changes in functional beta-diversity and original functional beta-diversity, we performed four generalized linear mixed-effects models with Gaussian distribution and identity link function, using the glmmTMB package.
| RESULTS Our database included 2,512 populations of 677 species (Table  S1 ) distributed across 64 islands. Of these, 245 populations were extinct (178 species), 727 were introduced (162 species), and the remaining 1,540 populations (397 species) were extant natives. Some species were included in more than one group (e. g. , introduced to an island and native in another, or extinct from one island and extant in another).
, 2018). Several bird species have been intentionally introduced and established populations across islands worldwide, especially across those with the same history of colonization (e. g. European colonization), such as islands belonging to the same archipelago  (Blackburn et al. , 2009). In the Hawaiian archipelago, the introduction of several species (e. g. , common myna Acridotheres tristis, Eurasian skylark Alauda arvensis, rock partridge Alectoris graeca, black francolin Francolinus francolinus) across five islands caused strong taxonomic homogenization. Nowadays, some bird species are close to colonizing almost all of these archipelagos: for example, the common widespread rock dove Columba livia has been introduced to 62. 5% of the islands in our database. On the other hand, extinction was the main factor responsible for functional differentiation (17/19; e.
For example, the two Mascarene Islands lost different species of shelducks, night-herons and rails that were susceptible to human activities given their larger body size, low flight capacity (or flightlessness), and ground-foraging strategy  (Hume et al. , 2013). This pattern of high functional similarity among extinct birds has also been described for this island dataset  (Soares et al. , 2022) , corroborating that the extinction of functionally similar species promoted the trend for overall functional differentiation of island bird assemblages. Few islands became taxonomically differentiated but functionally homogenized, either within (13; Fig. 1c ) or across archipelagos (2; Fig. 1b ). Introductions were the main driver of taxonomic differentiation in most of these cases (within: 9/13 -Fig.
The salivary glands (SGs) were then extracted by pulling out the head capsule and removing all other tissues, such as fat body droplets. The SG tissues were pooled and transferred to a Qiazol lysis reagent for RNA extraction, according to the manufacturers' instructions (QIAGEN, Hilden, Germany; Cat No. : 79306), in pools of 20. Six pools were sent for total RNA sequencing on a HiSeq 4000 (Illumina, San Diego, CA, United States) by the Australian Genome Research Facility (AGRF, Melbourne), after DNase treatment and an RNA quality control check. Deep sequencing raw data were deposited in the National Centre for Biotechnology Information's (NCBI's) Gene Expression Omnibus (GEO) and are accessible through the GEO series accession number: GSE127658.
Transcriptome Data Analysis and Virus Discovery In this study, the CLC Genomics Workbench version 20. 0. 1 (Qiagen, Hilden, Germany) was used for bioinformatics analyses. All libraries were trimmed from any remaining vector or adapter sequences. The reads were 75 bp pair-ended, with an average fragment size of 350 bp and an insert size of 230 bp. Low-quality reads (i. e. , a quality score below 0. 05) and reads with more than two ambiguous nucleotides were discarded. As the I. flavus genome is not sequenced, all reads were mapped to the black soldier fly genome, Hermetia illucens (the closest available relative), which served as a proxy genome reference (GCF_905115235. 1) to remove insect-related reads. Unmapped reads were retained for de novo assembly and potential virus discovery. Contigs were constructed with a kmer size of 45, bubble size of 50, and a minimum length of 500 bp and were then corrected by mapping all the reads against the assembled sequences (min.
length fraction = 0. 9 and maximum mismatches = 2). The generated contigs were compared with the NCBI viral database, using local BLAST and BLASTx algorithms. The e-value was set to 1 × 10 -10 to maintain a high sensitivity and a low false-positive rate. To obtain the segment 2 sequence, the contigs were compared with a database comprising all available sequences from jingmenvirus segments with a glycoprotein (segment 4 for the following mosquito associated-jingmenviruses: the Guaico Culex virus and the Mole Culex virus, and segment 2 for all other jingmenviruses). Putative jingmenvirus sequences were re-mapped to the RNA-Seq data to inspect for sufficient coverage and possible mis-assembly. The CLC Genomic Workbench's RNA-Seq function (min. length fraction = 0. 9, maximum mismatches = 2, insertion cost = 3, and deletion cost = 3) on a non-strand-specific option was used.
Viral Derived Small RNA Analysis To analyze the host RNAi's response to IFJV1 and to determine whether the virus was replicating in its host, a small RNA (sRNA) library was generated from one of the positive pools of 20 SG from starved individuals, using the NEBNext ® Multiplex Small RNA Library Prep Kit for Illumina ® , at the Novogene Genomics Singapore Pte Ltd (Singapore). The purified cDNA libraries were sequenced on a Novaseq 6000 (SE50), and raw sequencing reads were obtained using Illumina's Sequencing Control Studio software v1. 7. Raw data were stripped of adapters, and reads with a quality score above 0. 05 and with less than two ambiguous nucleotides were retained. Reads without 3 adapters and reads with less than 16 nt were discarded. The clean reads were mapped to each IFJV1segment.
Segment 3 codes for the 805 aa-long second nonstructural protein (NSP2), which contains serine protease and helicase domains. Segment 4 is bicistronic and codes for two 254 and 471 aa-long structural proteins (VP2 and VP3). VP2 has a signal peptide with a predicted cleavage site between positions 16 and 17 and no predicted N-glycosylation sites, while VP3 contains at least six transmembrane domains. These proteins are the putative equivalent of the capsid and membrane proteins found in flaviviruses. The IFJV1 genomic sequences were deposited on Genbank and have been assigned the following accession numbers: OM869459-OM869462. found in flaviviruses. The IFJV1 genomic sequences were deposited on Genbank and have been assigned the following accession numbers: OM869459-OM869462.
Level-2 Processing Method The VENμS Level-2 processing performs atmospheric corrections which convert Level-1 top-of-atmosphere reflectances products into Level-2 surface reflectances. It produces surface reflectances for each band of VENμS, with masks of clouds, cloud shadows, snow, water and maps of retrieved aerosol optical depths and water vapor contents. VENμS Level-2 processing is performed with MAJA atmospheric correction software, which is based on the Multi-temporal Atmospheric Correction and Cloud Screening Software (MACCS)  [17]  and includes some modules of ATCOR software  [18] , which is also used to generate SENTINEL-2 Level-2 products within the French THEIA land data center. For clouds, cloud shadows masking and aerosol optical depths assessment, MAJA uses both multi-spectral and multi-temporal methods.
Performances of Level-2 VENµS Products 2. 4. 1. Level-2 Processing Method The VENµS Level-2 processing performs atmospheric corrections which convert Level-1 top-of-atmosphere reflectances products into Level-2 surface reflectances. It produces surface reflectances for each band of VENµS, with masks of clouds, cloud shadows, snow, water and maps of retrieved aerosol optical depths and water vapor contents. VENµS Level-2 processing is performed with MAJA atmospheric correction software, which is based on the Multi-temporal Atmospheric Correction and Cloud Screening Software (MACCS)  [17]  and includes some modules of ATCOR software  [18] , which is also used to generate SENTINEL-2 Level-2 products within the French THEIA land data center. For clouds, cloud shadows masking and aerosol optical depths assessment, MAJA uses both multi-spectral and multi-temporal methods.
THEIA THEIA is a data and services center specializing in land surfaces. Theia offers a portfolio of products and associated services for the scientific community and public bodies through its web portal  [29]. This data infrastructure pulls together a number of organizations, including CNES. For VENµS, THEIA is in charge of Level-2 and Level-3 processing, and Level-1 to Level-3 distribution (see Figure  43 ). For VENμS, THEIA is in charge of Level-2 and Level-3 processing, and Level-1 to Level-3 distribution (see Figure  43 ). The Level-2 processing chain takes Level-1 valid products coming from VIP (VENμS Image Processing center), using MAJA-3 software. Beyond 80% cloud cover, the Level-2 is considered as not valid and is cancelled. All sites are processed with the same configuration parameters, except sites located at high latitudes, which have a specific configuration for snow detection.
A complete reprocessing is planned with MAJA-4, taking into account meteorological The Level-3 processing chain generates two syntheses per month, on day 1 and 16, using WASP software. The configuration for VENμS is a 23-day window around the chosen date, corresponding to two 11-day half-windows. At least two Level-2 valid products must be available inside the window for the Level-3 product to be processed. For the whole VENμS mission, 78,040 products are available, including 30,631 Level-2, and 1330 Level-3. The processing of Level-3 product is currently in progress.
Image Quality Monitoring VENμS products' image quality is monitored through an expertise center, in CNES premises, named VIQ (VENμS Image Quality). The following paragraphs describe the VIQ itself, and the radiometric and geometric processes which allow us to maintain an optimal image quality for the Level-1 products delivered to THEIA land data center. Additionally, some statistics on the different levels of products (from Level-1 to Level-3) are introduced, to provide an idea of the VENμS data series completeness and quality.
Concerning the VENμS mission, these GIPP can be applied taking into account both a temporal and a spatial dimension. These spatial The Level-2 processing chain takes Level-1 valid products coming from VIP (VENµS Image Processing center), using MAJA-3 software. Beyond 80% cloud cover, the Level-2 is considered as not valid and is cancelled. All sites are processed with the same configuration parameters, except sites located at high latitudes, which have a specific configuration for snow detection. A complete reprocessing is planned with MAJA-4, taking into account meteorological data. The Level-3 processing chain generates two syntheses per month, on day 1 and 16, using WASP software. The configuration for VENµS is a 23-day window around the chosen date, corresponding to two 11-day half-windows.
Image Quality Monitoring VENµS products' image quality is monitored through an expertise center, in CNES premises, named VIQ (VENµS Image Quality). The following paragraphs describe the VIQ itself, and the radiometric and geometric processes which allow us to maintain an optimal image quality for the Level-1 products delivered to THEIA land data center. Additionally, some statistics on the different levels of products (from Level-1 to Level-3) are introduced, to provide an idea of the VENµS data series completeness and quality.
VIQ Breakdown As shown in Figure  31 , the VIQ is an expertise center, part of the VENµS French image ground The VIQ is a system (software and hardware) providing a database and a pool of tools to compute, monitor and maintain the radiometric and geometric performances of VENµS products. Combined with the VIP and other expertise systems (such as MUSCLE (Multi Sensor CaLibration Environment) dedicated to the radiometric calibration of different instruments), the VIQ drives an image quality loop, designed to monitor and maintain VENµS performances. The goal is to deliver to final users Level-1 products consistent with the scientific requirements  [30].
Radiometry Monitoring Activities If we consider image quality monitoring, the first thematic to be considered is the radiometric performances monitoring. The following paragraphs detail the main radiometric performances monitoring activities carried on thanks to the VIQ expertise center. Simultaneous Nadir Observation: Sentinel2 Inter-Calibration Expertise Every month, knowing the SENTINEL2 and VENµS orbits, the conjunction opportunities are computed in order to check the dates where a couple of acquisitions (VENµS, SEN-TINEL2A or B) exists. For all these dates, SENTINEL2 L1C and VENµS L1 are downloaded and ingested in VIQ database. After that, specific operations are processed to cross-calibrate VENµS and Sentinel2 data. The two following figures (see Figure  44 ) illustrate a typical couple of acquisitions Sentinel-2A and VENµS, on the SNOCHINA VENµS site.
VIQ Breakdown As shown in Figure  31 , the VIQ is an expertise center, part of the VENμS French image ground segment. The VIQ is a system (software and hardware) providing a database and a pool of tools to compute, monitor and maintain the radiometric and geometric performances of VENμS products. Combined with the VIP and other expertise systems (such as MUSCLE (Multi Sensor CaLibration Environment) dedicated to the radiometric calibration of different instruments), the VIQ drives an image quality loop, designed to monitor and maintain VENμS performances. The goal is to deliver to final users Level-1 products consistent with the scientific requirements  [30].
Radiometry Monitoring Activities If we consider image quality monitoring, the first thematic to be considered is the radiometric performances monitoring. The following paragraphs detail the main radiometric performances monitoring activities carried on thanks to the VIQ expertise center. Simultaneous Nadir Observation: Sentinel2 Inter-Calibration Expertise Every month, knowing the SENTINEL2 and VENμS orbits, the conjunction opportunities are computed in order to check the dates where a couple of acquisitions (VENμS, SENTINEL2A or B) exists. For all these dates, SENTINEL2 L1C and VENμS L1 are downloaded and ingested in VIQ database. After that, specific operations are processed to crosscalibrate VENμS and Sentinel2 data. The two following figures (see Figure  44.
These attitude changes and a poor quality of SRTM DTM over Everest area lead to significant errors in multitemporal registration. Therefore, it was decided to change the DTM on KHUMBU site by using the High Mountain Asia (HMA) digital elevation model, produced and distributed by the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC). The Figure  47  shows an illustration of a Level-1 DTM for JORDANA site. These data are delivered to the VIP to be taken into account in Level-1 processing chain, as part as reference data (in association with the reference image). Remote Sens. 2022, 14, 3281 37 of 46 The Figure  47  shows an illustration of a Level-1 DTM for JORDANA site. These data are delivered to the VIP to be taken into account in Level-1 processing chain, as part as reference data (in association with the reference image).
Reference Image Definition As a reminder, reference image is a key data for VENμS image processing. It is the basis of Level-1 data time series, i. e. , Level-1 products perfectly registered one to the others. For each site, a reference image is a specific mono-spectral image upon which every acquisition is registered on. This image is applicable over a period of time depending on the site landscape. For a specific acquisition (date and site), the VIP delivers to the users (through THEIA land data portal) a Level-1 product only if it is declared "valid", which means that its multitemporal and multispectral registration performances comply with the scientific requirements and that this product is correctly included in a time series of spatially registered Level-1 products. As this spatial registration is the result of the correlation success with the reference image, the validity of reference data is accurately monitored in order to maintain the image quality of the Level-1 products and the number of valid products delivered to the users.
• Reference Image Definition As a reminder, reference image is a key data for VENµS image processing. It is the basis of Level-1 data time series, i. e. , Level-1 products perfectly registered one to the others. For each site, a reference image is a specific mono-spectral image upon which every acquisition is registered on. This image is applicable over a period of time depending on the site landscape. For a specific acquisition (date and site), the VIP delivers to the users (through THEIA land data portal) a Level-1 product only if it is declared "valid", which means that its multitemporal and multispectral registration performances comply with the scientific requirements and that this product is correctly included in a time series of spatially registered Level-1 products. As this spatial registration is the result of the correlation success with the reference image, the validity of reference data is accurately monitored in order to maintain the image quality of the Level-1 products and the number of valid products delivered to the users.
providing Open Access to publications. Research data play a central role in Science in general. Annals of Forest Science strongly supports the Open Access to Data and authors are encouraged to deposit the data sets in support of their papers in an open data repository of their choice (usually, the one provided by their institutions). This has many advantages: the data sets are given a Digital Object Identifier (DOI) and considered a full, citable scientific production. Any reuse comes at the condition of proper citation of the data set. Moreover, to fully recognize the importance and value of documented and structured data sets, Annals of Forest Science publishes also data papers that describe important and useful data sets in order to facilitate access and reuse of such data. Annals of Forest Science is also aware of the importance of the review by peers of the submitted manuscripts, which is central to the scientific quality of a journal.
there has been increased interest in how global change drivers impact this critical ecosystem service. Because standardized data on crop pollination are rarely available, we are limited in our capacity to understand the variation in pollination benefits to crop yield, as well as to anticipate changes in this service, develop predictions, and inform management actions. Here, we present CropPol, a dynamic, open, and global database on crop pollination. It contains measurements recorded from 202 crop studies, covering 3,394 field observations, 2,552 yield measurements (i. e. , berry mass, number of fruits, and fruit density [kg/ha], among others), and 47,752 insect records from 48 commercial crops distributed around the globe. CropPol comprises 32 of the 87 leading global crops and commodities that are pollinator dependent.
Malus domestica is the most represented crop (32 studies), followed by Brassica napus (22 studies), Vaccinium corymbosum (13 studies), and Citrullus lanatus (12 studies). The most abundant pollinator guilds recorded are honey bees (34. 22% counts), bumblebees (19. 19%), flies other than Syrphidae and Bombyliidae (13. 18%), other wild bees (13. 13%), beetles (10. 97%), Syrphidae (4. 87%), and Bombyliidae (0. 05%). Locations comprise 34 countries distributed among Europe (76 studies), North America (60), Latin America and the Caribbean (29), Asia (20), Oceania (10), and Africa (7). Sampling spans three decades and is concentrated on  2001-2005 (21 studies), 2006-2010 (40), 2011-2015 (88), and 2016-2020 (50). This is the most comprehensive open global data set on measurements of crop flower visitors, crop pollinators and pollination to date, and we encourage researchers to add more datasets to this database in the future.
Data analysis The agreement between forward and reverse sequence chromatograms was checked by visualization with Chromas 2. 6 (Technelysium Pty Ltd, Brisbane, Australia) and the sequences were exported as FASTA files. The sequence alignment was done with Clustal Omega 1. 2. 4 with default parameters  (Madeira et al. 2019). EMBOSS-Transeq was used to translate DNA sequences to protein in silico  (Madeira et al. 2019 ). Among the 6 reading frames, we selected the one giving the conserved amino acid sequence IEQIP situated after the hypervariable region as described by  Lechner et al. (2014). Alignment visualization and edition were done with Jalview 2. 10. 5 using the default alignment parameters  (Waterhouse et al. 2009 ).
Trust in nodes was estimated by 1000 bootstrap repeats. The median joining network (MJN) analysis of the haplotypes based on the variable characters of the complete alignment was constructed to infer evolutionary relationships between the Algerian and European haplotypes of this study using the NETWORK 10 software  (Bandelt, Forster, and Rohl 1999). The principal component analysis (PCA) was carried out by RStudio v1. 4 (R Core Team 2018) using the following packages: the Adegenet 2. 1. 3 (Jombart and Ahmed 2011) , the Ape 5. 5  (Paradis and Schliep 2019) , and Ade4 1. 7-17  (Bougeard and Dray 2018). In this analysis, two outlier samples (Buckfast_ITSAP5 and Sahariensis_Bayadh26) were removed to improve the resolution of the figure.