dataesr/scientific-paragraphs-categorization huggingface dataset:
1 ). These questions mirror the main challenges faced when documenting and understanding broad-scale biodiversity patterns at the surface. The first challenge is assembling the data required to bring out the characteristic features of biodiversity patterns at such broad scales, while ensuring these patterns are not biased by sampling effort (Q28, Q31). Secondly, to combine multiple sampling techniques, species identification methods (e. g. morphological and DNA-based identification), and biodiversity metrics (e. g. alpha, beta, and gamma diversity) in a meaningful way to elucidate the many facets of biodiversity patterns (e. g. taxonomic, phylogenetic, and/or functional diversity; Jarzyna & Jetz, 2016) (Q27, Q26). Lastly, the relative contributions of different evolutionary processes (Q29) and diversification hypotheses (Q30) in shaping biodiversity patterns should be fully examined.
Q26 The publication of global subterranean diversity maps and databases is a recent phenomenon  (Culver & Pipan, 2019; Zagmajster et al. , 2019). While diversity maps are informative as they portray differences in species richness among regions or countries, we still lack global maps showing species richness for spatial units of equal area [but see  Zagmajster, Culver & Sket (2008) ,  Niemiller & Zigler (2013) , and  Eme et al. (2015)  for examples of regional-and continental-scale diversity maps]. Several approaches have been developed to minimize differences in species richness due to sampling bias (Q31). This issue is particularly germane to difficulties in sampling subterranean habitats. For example, sampling protocols were typically standardized among sites and completeness of species inventories were assessed using accumulation and rarefaction curves  (Zagmajster et al.
made this paper possible. Thanks to Alison Cooper, Simone Fattorini and two anonymous referees for useful suggestions through the review process. S. M. was supported by the CAWEB project "Testing macroecological theory using simplified systems", funded by the European Commission through Horizon 2020 Marie Skłodowska-Curie Actions (MSCA) individual XII. REFERENCES ANTWIS, R. E. , GRIFFITHS, S. M. , HARRISON, X. A. , ARANEGA-BOU, P. , ARCE, A. , BETTRIDGE, A. S. , BRAILSFORD, F. L. , DE MENEZES, A. , DEVAYNES, A. , FORBES, K. M. , FRY, E. L. , GOODHEAD, I. , HASKELL, E. , HEYS, C. , JAMES, C. , ET AL. (2017). Fifty important research questions in microbial ecology. FEMS Microbiology Ecology 93, 10. 1093/femsec/fix044. AUDRA, P. & PALMER, A. N. (2011). The pattern of caves: controls of the epigenic speleogensis.
fellowships (Grant no. 882221 ). I. R. A. was supported by  Portuguese  funds through  FCT -Fundação para a Ciência e a Tecnologia, I. P. , under the Norma Transitória - DL57/2016/CP1375/CT0003. D. E. was supported by  IFREMER  and by the  CERES "Climate change and European Aquatic Resources " project funded by  European Commission  through  Horizon 2020 research and innovation programme  (Grant no. 678193 ). C. F. , Z. F. , and M. Z. were supported by the  Slovenian Research Agency  (program  P1-0184 , project  N1-0069 ). J. E. K. was funded by  NSF  awards  DEB1754231  and  IOS1933428 , and EDGE award 1923372. F. M. was supported by the  French National Research Agency  projects  CONVERGENOMICS  ( ANR-15-CE32-0005 ) and  EUR H2O ' Lyon  ( ANR-17-EURE-0018 ). A. M. was supported by the  ANCAVE  project " Anchialine caves to understand evolutionary processes ", funded by the  European Commission through Horizon 2020 Marie Skłodowska-Curie Actions  (MSCA) individual fellowships (Grant no.
745530 ). P. A. V. B. was supported by the project  AZORESBIOPORTAL -PORBIOTA  ( ACORES-01-0145-FEDER-000072 ), financed by  FEDER  in 85% and by  Azorean Public  funds by  15% through Operational Program Azores 2020. A. S. P. S. R. was supported by  VILLUM FONDEN  (Grant no. 15471 ) and by a  Carlsbergfondet grant  ( CF19-0609 ). T. G. P. was supported by  Vale S. A. and Fundação de Amparo à Pesquisa de Minas Gerais (FAPEMIG)  for individual fellowship ( RDP 00092-18 ). T. P. was supported by the  Karst Research Programme   P6-0119 ,  LifeWatch ERIC, RI-SI LifeWatch , and  EU   H2020  project eLTER.
9 In the earlier study on NOFN, the irreversibility of the pressure-induced phase transition was confirmed via diffraction during decompression and, hence, a polymerization process was proposed. 9 In order to check for a possible chemical reaction in the other co-crystal, AOFN, and to investigate the nature of the high-pressure phase transition, we performed two additional decompression studies on AOFN. In one run (run #2), a single crystal of AOFN was compressed to 25. 6 GPa and wide scan images of broken single crystal parts of the highpressure phase (Figure  S3 ) were integrated as powder diffraction patterns and unit cell parameters were refined. Another crystal was compressed to 21 GPa (run #3) and single-crystal diffraction data were analyzed. However, the transition process was either not complete at 21 GPa or was slightly different due to the fact that the sample had been irradiated with a 40 mW 532 nm laser in an unsuccessful attempt to record Raman spectra.
e. , of pressure-induced polymerization (Figure  4 ). overshooting the reaction pressure, and transformed to the high-pressure phase on pressure equilibration overnight. In the third run, the pressure was increased to 21 GPa over a period of 9 days, during which the sample was irradiated with a laser (532 nm, ca. 40 mW) for several Raman measurements upon compression, and left at that pressure for 6 days before X-ray diffraction data were collected, which showed evidence of the unit cell deformation observed in the high-pressure phase. However, while the reaction may have been induced by the laser irradiation (i. e. , a 2-photon absorption induced photopolymerization), 1a, 4k, 29 it was not complete at that lower pressure, as no significant reduction of the unit cell volume was observed.
Crystal structure analysis at high pressure In order to understand the structural changes at the phase transition, it is crucial to have accurate structural data just before the transition. To the best of our knowledge, no crystal structures from single-crystal diffraction data have been reported on an aromatic compound up to a pressure close to the onset of polymerization. The crystal structures of NOFN (space group P21/c) and AOFN (space group P21/n) were determined at ambient pressure at room temperature and at 100 K in order to serve as a reference for the high-pressure structures. Our structural parameters agree well with those reported for NOFN at room temperature  30  and for AOFN at 120 K 16d (Tables  S1  and S2 ). In both NOFN and AOFN, the centroids of all molecules lie on inversion centers of the unit cell (Figure  5 ).
ACKNOWLEDGMENTS The authors thank the  ESRF  for synchrotron beamtime. Financial support from the  ESRF  and the  Julius-Maximilians-Universität Würzburg  is greatly appreciated. The research leading to these results has received funding from  LASERLAB-EUROPE  (grant agreement no. 654148 ,  European Union 's  Horizon 2020 research and innovation programme ). This work was supported by the  Spanish Ministry of Science, Innovation and Universities  under Grant  MAT2016-75586-C4-1-P  and by  Generalitat Valenciana  under Grant  Prometeo/2018/123  (EFIMAT). J. R. -F. thanks the  Spanish MICINN  for project  PGC2018-097520-A-100. Durham's  Hamilton HPC  service and the UK Archer supercomputer were used under grant  EPSRC EP/P022782/1.
visual inspection of the residuals (quantile-quantile plot, histogram of residuals, residuals vs. predictors plot, and observed vs. fitted values plot) to detect any violation of the assumptions (Supplementary Figure  S2 ). The analyses were performed using the MGCV package  (Wood, 2017)  in R version 3. 6. 2 (R Core Team, 2019). The resemblance of the coral communities (species composition and abundance) caught by the different métiers was evaluated using principal coordinates analysis (PCoA). Because the GAM analysis did not show any relationship between coral bycatch and sampling season, the data were pooled. Coral species data for each net was used to calculate a dissimilarity matrix using Hellinger distance  (Kindt and Coe, 2005). The resulting dissimilarity matrix was then used as input for the PCoA.
Spatial Patterns of Fishing Effort and Bycatch Consistent with the expectations, total coral bycatch was generally higher when the nets were deployed on or nearby areas where rocky substrate is known to occur (Figure  4 ). When examining the CPUEs for the pooled dataset (i. e. , irrespective of coral or target species), the mismatch between the amounts of coral and target species caught is evident, particularly in the summer-autumn for which the nets captured substantially more coral than fish or lobster (Figure  4A ). For instance, six of the nets deployed in the summer-autumn sampling season had a CPUE for coral specimens higher than 3. 40 (n/day. 100 m; Figure  4A ), which had an average net length of 1. 99 km, thus corresponding to more than 60 corals per net. In contrast, for the spring survey season there is a better correspondence between the amount of coral and fish caught, with areas where coral bycatch was high, generally matching those with high fish or lobster catches (Figure  4B ).
Coral Bycatch Community Structure and Biodiversity Hotspots The variation in coral community structure per gillnet set is illustrated in the PCoA analysis for the entire dataset, with the two axes capturing 43. 59% of the variation in the ecological distances. The analysis shows weak separation in species composition and abundance between the majority of the métiers documented, with the exception of the métier for P. americanus, which is clearly segregated from the remaining métiers (Figure  7 ). This separation is strongly correlated with the coral species C. verticillata for which a high number of colonies was caught during the spring sampling season (the only season in which the métier was used; Figure  7 ). As expected, the depth at which the nets were deployed was found to significantly affect coral bycatch species composition and abundance (df = 1, F = 11.
ACKNOWLEDGMENTS We would like to thank  DOCAPESCA Baleeira-Sagres  for providing facilities to process the coral specimens. We are grateful to the captain Casimiro and his fishing crew for the collaboration and help onboard of the fishing vessel. In addition, we would like to thank the students  Ana Carneiro ,  Sandra Costa , and  Candice Parkes  together with the volunteer  Ana Gheorghiu  for the help in measuring, tagging, and photographing the specimens collected.
FUNDING This research was funded by projects  HABMAR  (Grant No. MAR-01. 04. 02-FEAMP-0018 ) co-financed by the  European Maritime and Fisheries Fund  of the  Operational Program MAR 2020 for Portugal (Portugal 2020 ), TECPESCA (Grant No. MAR2020 16-01-04-FMP-0010 ), and  MARSW  (Grant No. POSEUR/ICNF/LPN ). Furthermore, this study received Portuguese national funds from  FCT-Foundation for Science and Technology  through project  UIDB/04326/2020  and fellowship  SFRH/BSAB/150485/2019. ES thanks a  Pew Marine Fellowship.
A redundancy analysis (RDA) as developed by  Van Den Wollenberg (1977)  was 188 carried out in place of the co-intertia analysis and have given very similar results. The 189 considered taxa were diatoms and dinoflagellates with percentage of occurrence ≥ 40%. The 190 abbreviated names of species are given in table 2. Only data related to high tide sampling were 191 considered for the environmental parameters, since phytoplankton was only taken at high tide 192 period. The abundances were transformed into log (X + 1) to minimize differences in numbers. The Oualidia Lagoon was characterized by relatively high nutrient concentrations, generally at 211 LT, with values increasing upstream (Fig. 3 ). Nitrate (NO3) showed the highest concentration 212 in August and October (up to 30 µM and 20 µM, respectively) at HT (Fig.
Effects of the environmental factors 259 The links between species composition and environmental variables was established using a 260 co-inertia analysis. The necessary preliminary step was to perform a centered PCA (Principal 261 Component Analysis) in order to evaluate the spatiotemporal distribution of taxa independently 262 of the environmental variables (Fig. 8 ). The analyzed matrix includes observations from all 263 stations as summarized in Table  2. The abundances were transformed into log (X + 1) to 264 account for the data distribution skewness and make them closer to a normal distribution. The 265 first two axes of the factorial plane F1 X F2 represented 41% of the total inertia for the PCA.
Characterizing and monitoring changes in biogenic 3-dimensional (3D) structures at multiple scales over time is challenging within the practical constraints of conventional ecological tools. Therefore, we developed a structure-from-motion (SfM)-based photogrammetry method, coupled with inspection and mesh processing software, to estimate important ecological parameters of underwater worm colonies (hummocks) constructed by the sabellariid polychaete Sabellaria alveolata, using non-destructive, 3D modeling and mesh analysis. High resolution digital images of bioconstructions (hummocks) were taken in situ under natural conditions to generate digital 3D models over different sampling periods to analyse the morphological evolution of four targeted hummocks. 3D models were analysed in GOM Inspect software, a powerful and freely available mesh processing software to follow growth as well as morphology changes over time of each hummock.
2017; Palma et al. 2017; Fukunaga et al. 2019). These approaches were limited to coral bioconstructions and developments to other biogenic structures such as sabellariid reefs are yet to be investigated (D'Urban  Jackson et al. 2020). Over the past decade, advances in treatments of remote sensing data have allowed further characterization of Sabellaria reefs morphology. In particular, aerial data acquired from both multispectral  (Collin et al. 2018; Collin et al. 2019 ) and three-band RGB cameras based on Structure from Motion (hereafter SfM) imagery  (Ventura et al. 2018) , LiDAR (Light Detection and Ranging) systems  (Noernberg et al. 2010 ) and high-frequency acoustic systems  (Harrison et al. 2011 ) have been used for mapping emerging and submerged biogenic Sabellaria reefs and to make finescale measurements of bathymetry.
These techniques have made possible to map large shallow areas efficiently at depth resolutions of decimetres, but generally produces data at spatial resolutions on the order of meters  (Storlazzi et al. 2016) , which are often incompatible with small-scale assessments aimed at understanding 3D habitat complexity and short-term changes of the bio-constructive role of this species. Considering these aspects, this study is the first attempt to determine whether close range SfM-based 3D photogrammetric modelling could reliably be used, in place of traditional methods, to measure important reef structural metrics such as volume, surface area and rugosity of S. alveolata colonies. Moreover we used 3D mesh processing software for a better understanding of morphological change over time of biogenic Sabellaria hummocks and to assess the effects of waves on this species that appears to play a considerable role for the ecology of the coastal systems.
Image processing and 3D model generation Prior to photogrammetry workflow, all images were manually enhanced. Minor adjustments regarding white balance, color contrast and saturation, exposure, shadows/highlights were performed in Adobe Lightroom v. 5. 1 (Adobe Systems, San Jose, CA, USA) to get more natural colors and defined edges. Subsequently chosen settings were applied to all images acquired the same day. Camera calibration, optimization, and 3D models of Sabellaria hummocks were computed using Agisoft PhotoScan Professional Edition V. 1. 3. 2 (Agisoft LLC, St. Petersburg, Russia). PhotoScan is a software for SfM photogrammetry which utilizes a sequence of overlapping digital images of a static subject taken from different spatial positions to produce a 3D point cloud by estimating 3D geometry of the scene and camera positions trough bundle adjustment algorithm.
The software can derive optical characteristics such as internal and external camera orientation parameters, including non-linear radial distortions directly from image's EXIF metadata, performing camera calibration using Brown's distortion model, eliminating the significant burden of pre-calibration  (Burns et al. 2015). In fact, using the camera calibration parameters obtained from the self-calibration during the alignment stage, PhotoScan removes the lens distortion from the images for further processing. To allow the reconstruction of three-dimensional scene geometry, SfM algorithms detect image feature points and subsequently monitor the movement of those points throughout the sequence of multiple images; subsequently the locations of those feature points can be estimated and rendered as a sparse three-dimensional point cloud  (Verhoeven 2011).
Before aligning images loaded into PhotoScan, the software's 'Image Quality' tool was used to filter photos. Only photos reaching a quality threshold (>0. 5, as measured by the software algorithms) were used to further processes. Underwater images of hummocks were rendered into 3D models at different sampling times (Fig. S1 ) following the standard procedures: (1) aligning photos, (2) marker-based optimization of camera position (3) building dense point cloud, (4) building 3D mesh, and (5) building texture. Digital markers were annotated onto the carpenter's squares, and the points were then used to create a scale bar set to the actual length of the square. The 3D point cloud of the Sabellaria hummocks was manually cropped from the rest of the scene to preserve only the living part of hummocks (with evident tube apertures).
3D model analysis and measurement of hummocks' metrics The scaled STL models were imported into GOM Inspect (GOM Inspect Version 10. 1 2018; a free software from Gesellschaft f ür Optische Messtechnik, Braunschweig, Germany) to extract key reef metrics capable of describing the morphological changes of the hummocks over time. This 3D inspection and mesh processing software usually used in Computer Aided Design (CAD) modelling end engineering applications provides several functions for 3D model analysis, including mesh editing, volume and surface comparison of 3D objects, manual and semi-automatic with best-fit alignment. Before performing measurements, refinement and cleaning of meshes were carried out with 'smooth' and 'refine' tools (available in mesh operation toolbox) to simplify the rough surfaces of hummocks and speed up the procedures.
SurfC 3D ½ ¼ SA 3D ½ A 2D ½ (2) In addition, we compared linear rugosity obtained from in situ traditional 'chain-and-tape' measurements (LR  [chain]  ) and as calculated in GOM Inspect software LR [3D]. To do so, we draped four virtual chains and tapes on Sabellaria hummocks and constructed along the Z-axis four fitting planes spaced at 45°intervals each other and perpendicular to the X-axis (i. e. the sea bottom) with the intersection point located in the center of each hummock (Fig. 5A ). These four reference planes were used to generate sections on the mesh (Fig. 5B ). Subsequently, the curve length (CL, i. e. the virtual 'chain') of each section was estimated and compared to the linear distance (D, i. e. the virtual 'tape') comprised between the starting and ending points of the section curve (Fig.
Statistical data analysis For graphical visualization of Sabellaria metrics during the year and to better capture the global trends, the estimated metrics' values at each sampling event were interpolated by using a cubic (or Hermite) spline interpolation with equally spaced knots and the location of the knots was fixed by the sampling occasions. Spline interpolation is a robust algorithm for interpolating between sets of points which ensure that the second derivative of the interpolated function are continuous and the first derivatives are smooth  (Press et al. 2007 ). The same interpolation technique was used to extract the average growth rate (considered as the variation of V [3D] over time which can be regarded as a good proxy for growth) between various nodes of the spline using discrete derivatives Δ f Δ t.
(A) four fitting planes (1-4) perpendicular to the X-axis (i. e. the sea bottom) intersecting the center of the hummock were created in GOM Inspect software. (B) Examples of two cross sections generated according the fitting planes 1-2. (C) Estimation of curve length (i. e. the virtual chain draped over the hummock, displayed as red and blue curves with small arrows) and linear distance (D, i. e. the virtual tape measure, displayed as green line) used for linear rugosity estimation. Q of 0. 01 to ensure smaller precisions for the joint distributions of β 1, β 2 ,β 3. A corner point choice was made setting the hummock labelled Sab-1, the minimum Douglas scale value 0, the maximum Douglas Scale value 1 and the late summerearly autumn categories to zero. β 01 ¼ β 10 ¼ β 21 ¼ β 33 ¼ 0 The model has been chosen among many others on the basis of the Deviance Information Criterion (DIC)  (Spiegelhalter et al.
10 to 2. 5 m, up to fourth grade according the Douglas scale) seemed to favor the growth phase of the colonies (as confirmed by the increase of V [3D] and SA  [3D]  values during the first six months). Under moderate hydrodynamic condition, only sand particles and small shell fragment are resuspended, which ultimately stimulate tube-building activity. In that case, wave agitation had positive role in reef enlargement as it ensured both food and sand particles to worms  (Gruet 1986). However, further studies should be carried out to better define the role of hydrodynamic conditions on biogenic Sabellaria reefs, especially if detailed and quantitative data on sea state and hydrodynamic conditions could be implemented in the analysis. During the growth phase of colonies, SurfC [3D] showed a clear drop which indicated that when the bio-constructions were in good condition (also confirmed by the high SandCD values for these periods) the individuals tended to fill in cracks and holes between tubes making the surface morphology more compact and continuous.
Acknowledgments This study was supported by small research projects funding program of the  Department of Environmental Biology and Ecology, University of Rome 'La Sapienza '. D. V. thanks  Veronica  for her love and support during field operations and sampling activities. The authors thank their colleagues  Dr. G. Mancini ,  Dr. E. Casoli  and  Dr. A. Belluscio  for their valuable support and suggestions. All the authors extend their appreciation toward the three anonymous Reviewers for taking the time and effort necessary to provide such insightful guidance.
Determining whether the Atlantic Meridional Overturning Circulation (AMOC)'s transport is in decline is challenging due to the short duration of continuous observations. To estimate how many years are needed to detect a decline, we conduct a simulation study using synthetic data that mimics an AMOC time series. The time series' characteristics are reproduced using the trend, variance, and autocorrelation coefficient of the AMOC strength at 26. 5 • N from 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) models under the RCP8. 5 future scenario, and from RAPID observations (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018). Our results suggest that the 14-year RAPID length has just entered the lower limits of the trend's "detection window" based on synthetic data generated using CMIP5 trends and variability (14-42 years; median = 24 years), but twice the length is required for detectability based on RAPID variability (29-67 years; median = 43 years).