dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS Thanks for field assistance to  U. Schmid  and  L. Leibold  at  Val Bercla ;  T. M. Mutia ,  L. Ársaelsdóttir  and  Á. Helgadóttir  at  Endalen ;  E. Liebig  and  Á. Helgadóttir  at  Auðkúluheiði ;  C. Flintrop ,  H. Thomas ,  A. Lowe  ( Team Shrub ) and  Territorial Park rangers on Qikiqtaruk -Herschel Island ;  B. Sabard  and  V. Gilg at Hochstetter ;  V. Olek ,  T. Brown  and L. L. Wright at Churchill;  D. Meliopoulos ,  J. Cosgrove ,  K. Tsujita ,  W. Egelhoff , and  T. Baerwald  at  Utqiaġvik ; students of the  Arctic Ecology course at Billefjorden ;  Lars H. Hansen  at Zackenberg;  B. Sittler  and  S. Büchner  at  Karupelv. Funding for two Herbivory Network workshops was provided by the  Terrestrial Working Group of IASC  to facilitate the participation of early career scientists.
CGB was funded by the  Estonian Research Council  (grant IUT  20-28 ), and the  European Regional Development Fund  ( Centre of Excellence EcolChange ). JDMS was supported by the  Research Council of Norway  ( 262064 ). OG and LB were supported by the  French Polar Institute  (program " 1036 Interactions ") and  PRC CNRS Russie 396  (program "ICCVAT"). DSH, NL, MAG, JB and JDR were supported by the  Natural Sciences and Engineering Research Council (Canada). NL, MAG, JB and JDR were supported by the  Polar Continental Shelf Program. NL was supported by the  Canada Research Chair program  and the  Canada Foundation for Innovation. NL and JB were supported by  Environment Canada and Polar Knowledge Canada. NL and MAG were supported by the  Government of Nunavut , the  Igloolik Community , and  Université de Moncton.
NL, MAG and JB were supported by the  Northern Scientific Training Program. JMA was funded by  Carl Tryggers stiftelse för vetenskaplig forskning and Qatar Petroleum  ( QUEX-CAS-QP-RD-18_19 ). IHM-S was funded by the  UK Natural Environmental Research Council Shrub Tundra  ( NE/M016323/1 ) grant. ISJ was funded by the  University of Iceland Research Fund. Fieldwork in Yamal peninsula ( Erkuta, Sabetta and Belyi ) for DE, NS and AS was supported by the  Russian Foundation for Basic Research  (No:  18-05-60261  and No:  18-54-15013 ),  Fram Centre  project  YaES  (No:  362259 ), the  Russian Center of Development of the Arctic, and the "Yamal-LNG " company. Fieldwork in Utqiaġvik was supported by the  U. S. Fish and Wildlife Service. Fieldwork in Svalbard was supported by the  Norwegian Research Council  (AFG No:  246080/E10 ), the  Norwegian Polar Institute, Climate-ecological Observatory for Arctic Tundra -COAT , the  Svalbard Environmental protection fund  (project number  15/20 ), and the  University Centre in Svalbard (UNIS)  and the AB-338/AB-838 students of 2018.
2. 2 Identify the focal vegetation or landscape strata. If habitat preferences of herbivores in the study area are not known, data from the literature or expert knowledge from other areas can be used. Alternatively, an initial survey could be performed to assess habitat preferences. In this aim, pellet © The Author(s) or their Institution(s) Arctic Science Downloaded from cdnsciencepub. com by 185. 37. 110. 40 on 04/18/21 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record. © The Author(s) or their Institution(s) Arctic Science Downloaded from cdnsciencepub. com by 185. 37. 110. 40 on 04/18/21 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition.
Acknowledgements We thank  Claire Soligot-Hognon ,  Paméla Hartmeyer  and  Marion Huguet  ( URAFPA, Nancy ) for their work and help with sample processing in this study. We also thank  PASM (Plateau d'Analyse Structurale et Métabolique) and Bio-DA  for participating to technical support. Finally, we would like to thank  Bárbara Souza Rodrigues  (M2 trainee) for her participation in the project. Ethical Approval: Not applicable Consent to Participate: All authors were agreed to participate to this study Consent to publish: All authors read and approved the final manuscript.
Forest-atmosphere carbon exchanges in a temperate oak forest remained insensitive to record drought. • Deep soil (150-300 cm) provisioned up to 60% of the water transpired by the forest during drought. • At the study site, soil databases underestimated soil water holding capacity by a factor of two. • Simulated forest productivity is strongly sensitive to soil water holding capacity. • Deep soil water content measurements are urgently needed to correctly estimate the soil water holding capacity.
Introduction The process of evapotranspiration plays a central role in the thermoregulation of leaves, the acquisition and transport of nutrients from the soil to plant organs and is an inescapable consequence of the stomatal opening for the acquisition of carbon from the atmosphere. In plant communities, the amount of water transpired per unit carbon gained through photosynthesis ranges from 190 g of water per unit gram of carbon as a global estimate for terrestrial vegetation  (Cramer et al. , 2009)  to 330 g(H 2 O)/g(C) in the FLUXNET database  (Baldocchi and Peñuelas, 2019). This means that considerable amounts of water are necessary for sustaining the basic functioning of plant communities, and particularly of forests which can grow high leaf areas  (Asner et al. , 2003)  leading to high evapotranspiration fluxes.
, 2018) , it is urgent to improve our understanding of the provisioning of water to forest ecosystems, and to evaluate the sensitivity of modelled projections of ecosystem functioning to the parameterization of soil properties. Here we used soil water content data measured over a 150-cm soil profile, combined with deep soil water content estimates (i. e. sensors down to -300 cm) to evaluate the role of surface vs. deep water in provisioning tree transpiration needs and modulating canopy gas exchange in a temperate oak forest equipped with a flux tower. We focused on the two contrasted, consecutive years of 2017 (a mild summer) and 2018 (characterized by a hot drought). Our objectives were: 1. to evaluate the response of a temperate oak forest to a hot drought in terms of carbon and water balances, 2.
to estimate the Soil Water Holding Capacity (SWHC, in millimeters of water) of the forest, 3. to estimate the contribution of individual soil horizons to the provision of water for canopy transpiration, contrasting both a dry and milder year, 4. to evaluate how the combinations of SWC and VPD modulate canopy conductance and transpiration, 5. to compare two estimates of SWHC: one provided by soil databases vs. one derived from our measurements of SWC, and quantify the sensitivity of the carbon balance simulated by a process-based model to the choice of one or the other SWHC estimate.
Eddy covariance and micrometeorological measurements We measured the fluxes of CO 2 and H 2 O between the forest and the atmosphere with the eddy covariance (EC) method, using both a Li7500 open-path and a Li7200 enclosed-path analyzer (LI-COR Inc. , Lincoln, NE, USA), associated with a R3-50 and a HS-50 sonic anemometer (Gill Instruments Ltd. , Lymington, UK), respectively. Both EC systems runned concurrently at 37 m a. g. l. , i. e. 9 m above the dominant height. EC data were acquired at 20 Hz with two data loggers time-synchronized with a PTP server: a CR3000 (Campbell Scientific Inc. , Loughborough, UK) for the Li7500/R3-50 and the Smartflux 2 for the Li7200/HS-50 (LI-COR Inc. , Lincoln, NE, USA). Half-hourly fluxes of CO 2 and H 2 O were calculated using the EddyPro software (v7. 0. 6, LI-COR Inc.
, Lincoln, NE, USA). Frequency response corrections were applied to raw fluxes, accounting for high-pass  (Moncrieff et al. , 2005)  and low-pass filtering  (Moncrieff et al. , 1997). The influence of air density fluctuations on the fluxes was accounted for  (Burba et al. , 2008). As usually done  (Baldocchi, 2008) , we did not correct eddy covariance fluxes despite a 18 ± 6% average energy imbalance (calculated as the Energy Balance Ratio,  Wilson et al. , 2002)  at the FR-Fon site. Half-hourly CO 2 and H 2 O fluxes have been acquired continuously from 2005 by the Li7500/R3-50 device and were considered the reference data series here. Fluxes computed from the Li7200/HS-50 device, running from 2012, were used to gap-fill the reference data through linear regressions established annually. Following this instrumental gap-filling, the calculated Net Ecosystem Productivity (NEP) and evapotranspiration (ETR) fluxes were quality-controlled, gap-filled, and NEP was partitioned into Gross Primary Productivity (GPP) and ecosystem respiration (Reco) according to standard procedures  (Reichstein et al.
Data acquisition Soil water content (SWC, in m water 3 m soil -3 ) was measured from 2012 with five EnviroSCAN probes (Sentek Sensor Technologies, Stepney, Australia) located in the vicinity of the tower and connected to a CR1000 datalogger (see Suppl. Mat. S2 for the location of the SWC sensors). Each probe had a 10-cm vertical distribution of 15 sensors from -5 cm to -145 cm, i. e. down to 150 cm depth. From 2016, a sixth probe reached the depth of -300 cm with the following vertical sensor distribution: -15, -55, -85, -135, -175, -215, -255, and -295 cm. The CR1000 datalogger achieved automatic data acquisition with half-hourly means calculation. In order to assess the spatial representativity of SWC measurement collected by the 6 probes, 24 access tubes of 160 cm length were installed in the vicinity of the tower in 2012 over a 3800-m 2 area (Suppl.
Consequently, we focused on this method, and calculated RWU at our site from SWC measurements with the python package rootwater  (Jackish et al. , 2020). This algorithm analyses daily fluctuations in SWC and takes into account possible hydraulic redistribution, a mechanism by which water is moved via the root system from moister to drier soil layers  (Caldwell et al. , 1998; Ishikawa and Bledsoe, 2000; Neumann and Cardon, 2012). We applied the rootwater algorithm on days when SWC profiles exhibited the specific transpirationinduced shape of RWU (i. e. daytime decline of SWC). We therefore rejected days displaying strong drainage along the soil profile, which could blur the effect of RWU. For selected days, a reference daily moisture profile was built, based on the SWC values at sunset/sunrise, and linear interpolations.
Soil database for parameterizing SWHC We parameterized CASTANEA with two different SWHC estimates at the FR-Fon forest. We used our own estimate of SWHC, based on SWC measurements (see Results). Beside this, and similar to what is usually done for parameterizing ecosystem models (e. g. Cheaib et al. , 2012) , we used a SWHC estimate obtained from a soil database  (Badeau et al. , 2010). Badeau et al. (2010)  built a 1-km 2 resolution database of SWHC over France, from a soil database of soil texture and rooting depth  (Jamagne et al. , 1995). Each 1 × 1 km 2 grid point encompasses different soil types, and the database gives for each grid point the average value of SWHC with respect to the area of each soil type over the 1 × 1 km 2 grid.
2b ). The daily fluctuations were neat from May at 120-130 cm depth and their amplitude increased in July, when they also became visible in the 290-300 cm layer (Fig. 2b ). As with the upper layers (not shown), the SWC signal in the 290-300 cm layer exhibited a strong daily pattern. In this layer, SWC decreased from noon to the late evening and remained stable during the rest of the daily cycle, evoking with some phase shift the day/ night cycle of tree transpiration, and water uptake by the roots (Fig. 2a  inset). We estimated the quantity of water extractable in each soil layer from SWC measurements (Suppl. Mat. S5). Field capacity (FC, the maximum moisture of unsaturated soil) was considered to be reached when SWC reached stable, high values (e. g. skyblue dashed line on Fig. 2a ), after checking the disappearance of a seasonal perched water table from piezometer data (not shown).
Sensitivity of an ecosystem model to SWHC parameterization The SWHC value of 390 mm determined from SWC measurements was 1. 9 times higher than the value of 207 mm estimated from the 1-km French soil database for the corresponding grid point  (Badeau et al. , 2010) , and in the high end of the SWHC distribution of soils appearing in the database over France (Fig. 6a ). The CASTANEA model fairly simulated the seasonality of GPP observed at FR-Fon when parameterized with SWHC = 390 mm (Fig. 6b ), as well as the interannual variability of GPP (Suppl. notes SN1). On the contrary, parameterizing CASTANEA with SWHC = 207 mm, as obtained from the soil database  (Badeau et al. , 2010)  leaded to higher soil water stress and reduced GPP in summer, as compared to GPP derived from the flux tower measurements (Fig.
, 2020, or 450 mm for a mixed Beech-Oak forest near Louvain-la-Neuve in Belgium, De Wergifosse et al. , 2020). The value of 390 mm for SWHC was 1. 9 times higher than the database estimate of 207 mm for the FR-Fon grid point  (Badeau et al. , 2010). It is noticeable that the latter estimate was obtained from the integration of pedo-transfer functions over a soil depth of 112 cm, far lower than the actual fine root exploration depth at FR-Fon (Suppl. Mat. S1). If considering a soil depth of 112 cm, we would estimate a SWHC of 191 mm based on our SWC measurements (integration of SWHC data per soil layers displayed in Suppl. Mat. S5). From this, we conclude (1) that our estimate of SWHC is conservative, meaning that deriving FC and PWP from SWC data (Suppl. Mat. S5) yielded similar (actually, slightly lower) values of SWHC than obtained from pedotransfer functions  (Badeau et al.
, 2021; Fan et al. , 2017; Carrière et al. , 2020). We showed that using a SWHC estimate of 390 mm, as opposed to 207 mm as predicted by a reference soil database, impacts strongly the simulation of GPP in the CASTANEA model (Fig. 6c ). CASTANEA is a "carbon-centric", still sensitive to the soil water balance through a modulation of the stomatal conductance vs. assimilation relation  (Dufrêne et al. , 2005). This model is not able to simulate the degree of xylem embolism, expected to increase as climate warms and dries   (RCP 4. 5 and RCP 8. 5). The red points at the beginning of the time series illustrate the average anomaly calculated over 2006-2019. (Cochard et al. , 2021). Running a hydraulic-enabled model such as SUREAU  (Cochard et al. , 2021)  or MAESPA  (Duursma and Medlyn, 2012)  at FR-Fon would quantify to what extent the deep-component of SWHC (i.
Conclusions and perspectives We evidenced that the FR-Fon oak forest can tap at least 390 mm of water from the soil. This is a large amount for a temperate forest, as compared to published estimates of SWHC based on measurements  (Bréda et al. , 1995; De La Motte et al. , 2020)  or the modelling  (Granier et al. , 2007)  of the soil water balance. This value is 1. 9 times higher than the estimate of SWHC (= 207 mm) retrieved for the FR-Fon grid point in the French consensus soil database  (Badeau et al. , 2010) , which is used in modelling studies predicting the future of French forests  (Cheaib et al. , 2012). Yet, several papers pointed recently the role of "deep" water in provisioning water to sustain ecosystem functioning, particularly during drought periods as the one encountered at FR-Fon in 2018.
Acknowledgements We thank  Augusto Zanella  and  Cécile Quantin  for their contributions in the description of soil cores,  Kamel Soudani  and  Eric Dufrêne  for insightful discussions and their critical readings of the manuscript,  Claude Doussan  for discussing with us questions relative to the role of capillarity, and students  Annaëlle Desgrippes  and  Jocelyn Ragu  for their contribution in the acquisition of root distribution data. We further thank three anonymous reviewers for their insightftul comments on the manuscript. IC acknowledges funding by the  ANR  under the " Investissements d'avenir " programme with the reference  ANR-16-CONV-0003  ( CLAND ). CRediT authorship contribution statement ND, JM and DB designed the research; DB, AM and GV collected the data; JM, ND and DB analyzed the field data; CF did the model simulations; CF and ND analyzed the model simulations; JM, ND and DB wrote a first working paper; ND wrote the final version of the manuscript with significant inputs from MJ, IC, GV, CF and SB.
BIOINFORMATIC A N A L Y S I S : The FROGS pipeline (Find Rapidly OTU with Galaxy Solution) implemented on a Galaxy platform (http://sigenae-workbench. toulouse. inra. fr/galaxy/) was used for data processing  (Escudié et al. 2015). In brief, paired reads were merged using FLASH  (Magoč & Salzberg 2011). After cleaning and removal of primer/adapters using cutadapt  (Martin 2011 ), de novo clustering was performed using SWARM  (Mahé et al. 2014). This uses a local clustering threshold with an aggregation distance (d) of 3. Chimeras were removed using VSEARCH  (Rognes et al. 2016). We filtered the dataset for singletons and performed affiliation using Blast+ against the Silva database (release 128, September 2016) for 16S amplicons. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under The copyright holder for this preprint (which was not this version posted August 23, 2018. ; https://doi. org/10. 1101/398602 doi: bioRxiv preprint 10 (  Altschul et al. 1990 ). For ITS2 metabarcoding, the Symbiodinium type was assessed using Blast+ against an in-house database of Symbiodinium reference sequences built from sequences publicly available. An OTU table in standard BIOM format with taxonomic affiliation was produced for subsequent analyses. For community composition analysis we used the phyloseq R package  (McMurdie & Holmes 2013)  to infer alpha diversity metrics at the OTU level, and beta diversity (between sample similarity) from the OTU table. Community similarity was assessed by Principal Coordinate Analysis (PCoA) using the Bray-Curtis distance matrices.
BIOINFORMATIC A N A L Y S E S Fastq read files were processed on the Galaxy instance of the IHPE (http://bioinfo. univ-perp. fr) (Giardine et al. 2005). Quality control and initial cleaning of the reads were performed using the filter by quality program (version 1. 0. 0) based on the FASTX-toolkit  (Blankenberg et al. 2010). Reads having fewer than 90% of bases having a Phred quality score ≤ 26 were discarded (probability of 2. 5 incorrect base call out of 1000, and a base call accuracy of 99. 75%). Adaptors used for sequencing were removed using the cutadapt program version 1. 6  (Martin 2011). All paired-end reads were aligned using RNAstar software under default parameters, with at least 66% of the bases being required to align to the reference, and a maximum of ten mismatches per read  (Dobin et al.
2013). The Pocillopora damicornis reference genome used in this study (manuscript in preparation) consisted of a draft assembly of 25,553 contigs (352 Mb total) and N50 = 171,375 bp. The resulting transcriptome served as the reference for reads mapping, and a GTF annotation file was constructed using cufflink/cuffmerge  (Trapnell et al. 2010). HTseq was used to produce count files for genes  (Anders et al. 2015). The DESeq2 package was used to estimate the normalized abundances of the transcripts, and to calculate differential gene expression for samples between the control temperature and the stress temperature for each locality  (Love et al. 2014) , considering the different genotypes (3 biological replicates for each genotype) and using default parameters. We next analyzed genes according to their expression patterns among localities and temperature treatments.
DISCRIMINANT A N A L Y S I S O F P R I N C I P A L C O M P O N E N T S (DAPC): Our aim was to quantify and compare the level of genome-wide transcriptome plasticity between colonies from Om and NC in response to heat stress. To achieve this we performed a discriminant analysis of principal components (DAPC) based on a log-transformed transcript abundance matrix (containing 26,600 genes) obtained from the 36 samples (i. e. 9 control and 9 stressed replicates per locality), as described previously  (Kenkel & Matz 2016). Specifically, we ran a DAPC analysis using the resulting log2 transformed dataset for the colonies from NC and Om reared in control conditions as predefined groups in the adegenet package implemented in R  (Jombart et al. 2010). Two principal components and a single discriminant function were retained.
It is made available under The copyright holder for this preprint (which was not this version posted August 23, 2018. ; https://doi. org/10. 1101/398602 doi: bioRxiv preprint (i. e. differences in DAPC scores between the control and the heat stress treatments) between Om and NC colonies we tested for significant effects of the interaction between the localities and the condition effects. GO E N R I C H M E N T O F D I F F E R E N T I A L L Y E X P R E S S E D G E N E S The transcriptome was annotated de novo using a translated nucleotide query (blastx  (Altschul et al. 1990 )) against the non-redundant protein sequence database (nr). The 25 best hits were then used to search for gene ontology terms using the Blast2Go program  (Conesa et al. 2005). Lists of significantly upregulated and down-regulated genes were subjected to GO enrichment analysis by comparison with all expressed gene using a Fischer exact test and a FDR value of 0.
, 2019; Gallego et al. , 2015). The climate conditions of the past two millennia have therefore always been more favourable than today, with the most humid period recorded in the pre-colonial time from ca. 1500 to 1800 C. E. , within the little ice age (LIA)  (Carré et al. , 2019). This paleoclimate reconstruction brings new key elements to understand the history of civilizations in western Sahel. In this study, changes in the frequency of radiocarbon dates in archaeological shell mounds are estimated and used to reconstruct the evolution of the intensity of shell fishing activities and human occupation in the Sine-Saloum mangrove Delta since the first occurrence recorded at 5849 yr BP. Radiocarbon datings from archaeology and paleoclimatology research projects together yield a database of 103 radiocarbon dates from 21 archaeological sites from across the Delta and an opportunity to estimate past human occupation through time.
Calibration Radiocarbon dates were all calibrated using the R package rcarbon  (Crema and Bevan, 2020)  with the calibration curves IntCal20  (Reimer et al. , 2020)  and Marine20  (Heaton et al. , 2020). For otolith and mollusk shell samples, it must be considered that the conditions in the estuary are not fully marine. Today, the Saloum Delta is an inverse estuary during the dry season: the sea water enters the Delta with tides and becomes hypersaline landward because of evaporation  (Pagès and Citeau, 1990; Savenije and Pagès, 1992; Mikhailov and Isupova, 2008; Carré et al. , 2019). The estuary recovers a normal salinity gradient during the rainy season (July-September) with salinities ranging approximately from 15 to 30 in the mangrove area where mollusc live  (Carré et al. , 2019). Oxygen isotopic values in archaeological shells showed that the salinity was lower in the past, especially from 1500 to 1800 C.
, 2006)  and a fractionation of 9‰ between dissolved CO2 and carbonate ions HCO3 -  (Zeebe and Wolf-Gladrow, 2001)  used in shell aragonite precipitation. The contribution of terrestrial carbon was generally less than 20%, with a maximum value of 54% (Supplementary Table  1 ). When a δ 13 C value was available, the calibration of mollusk shells was thus performed using a mix of IntCal20 and Marine20 (ΔR=0 ± 50) calibration curves in the R package rcarbon. Throughout the text, yr BP ages refer to calibrated years before present (using conventionally 1950 CE as the present). For the more recent period (the last 2000 years), the Current Era (CE) scale is used.