dataesr/scientific-paragraphs-categorization huggingface dataset:
We next sought to identify the presence, number, and treatment-level overlap of genetic variants that significantly changed in frequency between larval samples. Specifically, Fisher's Exact Test (FET) and the CMH test were used to generate probabilities of observed allele frequency changes, using the package Popoolation 63 in R. P values for each SNP were converted to q values in the R package q value 64 , and significant SNPs were identified as those SNPs with a q value < 0. 01. As the CMH test computes probabilities for SNPs based on consistent changes among replicates, it is a powerful approach to identifying significantly changed SNPs when treatment replicates are available  63. Accordingly, this test was used identify significant allele frequency changes between the day 0 larval population and the day 26 ambient and low-pH treatment replicates (N = 3), the day 0 larval population and the settled individuals from ambient (N = 2) and low-pH treatment replicates (N = 3), and between the top 20% and bottom 80% of growers in each treatment (N = 2).
To provide a third, independent metric of genomic change in the larval population throughout the experiment, we computed the F ST statistic for a series of comparisons. Specifically, we implemented a methods-of-moments estimator of F ST from Pool-seq data in an analysis of variance framework, as described in Hivert et al. 65  (poolFstat package in R). A global (exome-wide) F ST statistic was computed pairwise between the day 0 larval population and the day 6 ambient and low pH larvae replicate buckets, day 26 ambient and low-pH larvae replicate buckets, and settled individuals from all replicate buckets in ambient and low pH. F ST was also computed to compare differentiation between phenotypic groups (top 20% and bottom 80% of growers) on day 6. As poolFstat necessitates an approximation of population size, we parameterized the model using larval counts obtained on days 0 and 26, and estimates of larval population size for days 6 and 43.
The population size in each replicate bucket on day 0 was 100,000 individuals, as larval counts were conducted on the starting larval culture and an equal volume (containing 100,000 individuals) was added to each replicate bucket. To calculate population sizes on day 26, 200 mL of seawater was extracted from each replicate bucket, from which larvae were concentrated using a 70 μm mesh filter and subsequently photographed using brightfield microscopy. This mesh size was selected based on data obtained from the size selection of larvae (Supplementary Fig. 1 ), and chosen to filter out Dveligers that had failed to grow, and therefore survive, beyond day 6. The number of photographed larvae on day 26 thus provided a proxy for the population size at this time point: population size was estimated to be 3685, 2090, and 1183 in the ambient replicates and 2503, 1733, and 2888 in the low-pH replicates.
This allowed for the observations that there were: (1) no treatment-specific patterns in the total number of settled larvae and (2) the number of settled larvae was greater than 100 but far less than 400 individuals at this time point. F ST was thus computed using input pool sizes between 100 and 400 individuals for day 43 samples. Results demonstrating how computed values of F ST changed according to these differences in input pool size are reported in Supplementary Table  3. Gene identification/Ontologies. We next sought to explore the biological pathways that were associated with survivorship in each pH treatment and/or size group during the experiment. To accomplish this, we indexed our list of outlier loci using the annotated transcriptome provided in Moreira et al. 23. Their annotation utilizes NCBI's nucleotide and nonredundant, Swissprot, KEGG, and COG databases, thus providing a thorough survey of potential genes and pathways associated with our candidate SNPs.
Acknowledgements We thank  Samir Alliouane  for extensive technical assistance during the completion of the experiment. We would also like to thank  Angelica Miglioli  for experimental assistance,  Régis Lasbleiz  for microalgal supply for larval feeding,  Jacob Enk at Arbor Biosciences for guidance during exome capture array design and generation of associated methods section , and the  University of Chicago Genomics Core Facility  for sequencing assistance. We thank  D. Rice  and  T. Price  for insightful comments on the paper. This research was supported by the  National Science Foundation Graduate Research Fellowship Program  under Grant No. 1746045  to M. C. B. and  NSF   OCE-1521597  to L. K. M. C. B. was supported by  Department of Education  Grant No. P200A150101. L. K. was also supported by the  European Commission Horizon 2020 Marie Sklodowska-Curie Action  (No.
Computed tomography Computed tomography (CT) and micro-computed tomography (Micro-CT) are powerful, nondestructive techniques to evaluate biomineralised structures. Micro-CT allows 3D visualisation of X-ray image series generated by scanning with axial rotation in small steps. This method enables examination of internal structural features at fine spatial resolution  (Li et al. 2016). A micro-CT data set allows a variety of quantifiable measurements, including thickness in terms of pixel distance, volume in terms of voxel counts and density in terms of brightness of each pixel at higher resolutions compared to CT  (Fantazzini et al. 2015 , Tambutté et al. 2015 , Chatzinikolaou et al. 2017). With these 3D geometric morphometrics and measurements, the growth rate, density and morphological changes due to OA can be investigated.
SDS-PAGE and MS Shell protein extraction is achieved by recovering the protein from decalcified shell. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), or 2-dimensional gel electrophoresis enables a comparison of protein profiles. In addition, mass spectrometry (MS) enables the identification of proteins from available databases. As marine organisms studied in OA research are usually not model-organisms, protein identification is challenging. Protein purification, protein sequencing and protein digestion may be required in the identification process  (Marxen et al. 2003 , Suzuki et al. 2004). When a large protein (more than 30 kDa) needs to be identified, cloning methods can be used to recover the nucleotide sequence for deduction of the protein sequence  (Miyamoto et al.
1996 , Samata et al. 1999 , Suzuki et al. 2009 , 2011). Many genome and transcriptome databases of calcifying invertebrates generated by next generation sequencing are also available as open resources  (Takeuchi et al. 2012 , Zhang et al. 2012). MS is also a promising technique to understand shell proteins (Table  4 ). The MS/MS spectra of peptides allow the determination of amino acid sequence without a need for a protein sequencer, and MS/MS has become a low-cost and high-throughput technique. The combination of MS/ MS and databases from next-generation sequencing is dramatically increasing the efficiency of protein identification  (Joubert et al. 2010 , Marie et al. 2010 , 2011). The potential disadvantage of generating big data from the high-throughput process is covered in more detail in the next section.
Omics Omics studies are generating 'big data'. However, these data cannot be simply used as proxies of fitness  (Feder & Walser 2005). As a consequence, little information can be extracted from purely exploratory studies (e. g. comparisons between two OA scenarios). A more powerful approach involves the analysis of the data to test a hypothesis based on physiological or ecological experiments. For example, De Wit et al. (  2016 ) filtered a large-scale transcriptomic database to select genes following the same pattern identified at the physiological level in copepods exposed to OA  (Thor & Dupont 2015 , De Wit et al. 2016). The future use of omics to investigate the effects of OA on biomineralisation is promising, especially using the integration of an omics technique with other physiological end points.
From these searches, we identified 62 relevant papers. Based on personal research expertize and a less-structured search of the literature, we identified another 38 relevant papers that also could be classified as facilitation cascade studies but were not found in the two systematic searches (see Table  1 ). For each publication in the resulting database, we extracted information about the geographic study location (i. e. the geocoordinates of field data collection sites) and year of publication; latitude, which we binned into traditional climate zones (Tropical = 0-23. 5°; Subtropical = 23. 5-35°; Temperate = 35-66. 5°; Polar = 66. 5-90°); overall ecosystem type where the study was conducted (e. g. in a salt-marsh or a mangrove forest); substrate type (rock or soft-sediment substrata); and tidal elevation (subtidal versus intertidal).
To address this knowledge gap, we conducted a systematic quantitative review of studies that used hydrodynamic models and Lagrangian particle tracking with at least some level of biological relevance to investigate larval dispersal in marine environments. The resulting database was then used to answer the following questions: • Is there any bias in the distribution of research effort based on geographical or taxonomic coverage? • Are hydrodynamic models resolving ocean circulation at spatial scales (resolution and extent) relevant to the dispersal process under study? • Where, when and how many particles are being tracked, and is this effort sufficient to capture the spatiotemporal variability in dispersal? A systematic quantitative review of marine larval dispersal studies using Lagrangian biophysical models.
Development of the database We performed a literature search on 31 October 2017 using the ISI Web of Science and the following Boolean search term: (larv* OR propagul* OR egg*) AND (transport* OR retention OR dispers* OR connectivity OR 'residence time*') AND (hydrodynamic* OR numerical OR biophysical OR Lagrangian) AND (model* OR simulation*). This resulted in an initial list of 1089 references. No additional references were identified through other sources. The titles and abstracts of all references were screened to eliminate irrelevant studies (n = 594). As the purpose of the review was to evaluate biophysical models of larval dispersal, we excluded references that only modelled ocean circulation, with no particle tracking (n = 25) and references that were not conducted on animals with pelagic larval development (n = 8).
In contrast,  Simons et al. (2013)  found the minimum number of particles per location in their model domain ranged from 2800 to 5900 for tracking times of 15 and 30 days, respectively. Relevant studies in our database (n = 299 with reported data) released between 72 and 4. 9 × 10 17 particles (median: 464,495 particles) in total, with particles being released over a period of 0. 15 to 2,523,744 days (median: 365 days). 38% of studies released particles in a single event/ location (Figure  6A ). The remainder implemented multiple releases, either within a fixed window (43%), a predicted spawning window (2%), or a known spawning window (11%); 6% of studies provided no information on timing of particle release. For the studies that used a single release (n = 110), the number released ranged from 1 to 1 × 10 7 particles (median = 1000), with 90% of studies releasing simultaneously.
Acknowledgements Many colleagues have shared their time and resources with us over the last 25 years of research. Since the beginning,  Ferdinando Boero  ( University of Naples ) has represented an outstanding scientific support of the studies. Esther Garcés ,  Nelson Jr Hairston ,  Nancy Helen Marcus  and  Marina Montresor  (in alphabetical order), above all, are acknowledged for their contribution to this work through interesting considerations and discussions. Two colleagues and dear friends ( Sebastiano Geraci  and  O. Daniela Saracino ) unfortunately passed prematurely, and we would like to dedicate this review to them, our only regret being that we were unable to work with them for longer. Finally, we want to warmly thank the valuable referee who allowed us to add contents to the review, other than assistance in the text amelioration.
Acknowledgements The authors thank  Andrew Mount ,  P. S. Murphy ,  Howard Browman  and  Kaimin Shih  for their initial thoughts and input to this review at the  2nd Interdisciplinary Symposium on Ocean Acidification and Climate Change (ISOACC)  meeting in Hong Kong in December 2016. We also appreciate  Sylvie Tambutté  and  Alexander Venn  for their comments and suggestions for this review. This research was supported by the  University of Glasgow and University of Hong Kong Principal 's  Early Career Mobility Scheme  awarded to SCF. SCF was supported by a  NERC   Independent Research Fellowship  ( NE/N01409X/1 ).
Acknowledgements This article represents a joint effort and collective views from an architect, ecologists, engineers, a social scientist and a governmental officer who participated in the  2nd International Workshop  on Eco-shoreline Designs for Sustainable Coastal Development, held at the  University of Hong Kong, Hong Kong  in May 2018. This work was a joint effort from the invited participants plus other colleagues. The authors would like to thank the  Civil Engineering and Development Department of the Government of the Hong Kong Special Administrative Region of China , the  HKU School of Biological Sciences  and the  World Harbour Project  for supporting the organisation of the workshop. This research is partially supported by the  National Research Foundation, Prime Minister's Office, Singapore , under its  Marine Science   Research and Development Programme  (Award No.
Acknowledgements This contribution was supported by the  National Centre for Coasts and Climate at the University of Melbourne. Many of our ideas were developed and refined with support from the  Victorian Government 's  Seagrass and Reefs Program  (SS & ET) and the  New Zealand Marsden Fund  (JS & SS; Awards:  2002  Awards:  -04 ,  2007  Awards:  -10 ,  2013  Awards:  -16  and  2016-19 ) -19 ). We are grateful to  D. Chamberlain  for assistance in assembling the database and to  S. Hawkins ,  S. Jenkins  and an anonymous reviewer for editorial suggestions. F Fabricius , K. E. , 94,96,97,99, 107, 114, 242, 249, 301 Fabro, E. , 60 Fabry, V. J. , 90, 94 Fagherazzi, S. , 178 Fahnenstiel, G. , 36 Fairhead, A. , 285 Fairhead, V. A. , 273 Fairweather, P. G. ,29, 174 Falconer, I. R. , 37 Falcon, J.
This will challenge efforts to meet the target of the 2015 UN Paris Agreement on Climate Change, to limit climate warming to 2 °C. Figure  1. Atmospheric methane at Earth's surface in the remote marine troposphere (see  Dlugokencky et al. , 1994, for methods ). Upper panel shows globally averaged surface atmospheric CH 4 at weekly resolution (red and blue) and deseasonalized trend (blue), 2000-2017. Lower panel as above, but for globally averaged surface atmospheric δ 13 C CH4. Data from NOAA (see section 2). The x axis tick marks denote the 1st of January of the year indicated. 10. 1029/2018GB006009 Global Biogeochemical Cycles NISBET ET AL.
Methods This paper focuses on direct in situ measurements of air. Air samples from remote sites reported here come from two groups of sources. Weekly air samples are collected in flask pairs at ~60 sampling sites in the Cooperative Global Air Sampling Network of the National Oceanic and Atmospheric Administration (NOAA), United States, a component of their Global Greenhouse Gas Reference Network. Air sampling, analysis, and data smoothing methods for methane are described in  Dlugokencky et al. (1994). All methane measurements are reported on the WMO X2004A scale  (Dlugokencky et al. , 2005; www. esrl. noaa. gov/gmd/ccl/ch4_scale. html) in mole fraction units, nanomole per mole of dry air, or parts per billion, abbreviated as "ppb. " Uncertainties range from approximately ±3 ppb in the early 1980s to approximately ±1 ppb in 2017 as measurement precisions have improved.
A subset of NOAA samples is analyzed for δ 13 C CH4 ; analysis methods are described in J. B. Miller et al. (2002) ; see also  White et al. (2017). The second data set is from the flask sampling program of the Greenhouse Gas Laboratory at Royal Holloway, University of London (RHUL) and the Norwegian Institute for Air Research (NILU) as well as from RHUL's cavity-ring-down absorption spectrometer operating on Ascension Island, United Kingdom, and NILU's measurement at Zeppelin, Svalbard (Spitsbergen). For methods, see Nisbet et al. 10. 1029/2018GB006009 (2016) and  Fisher et al. (2006 and 2017). The RHUL time series were collected under a variety of projects supported by the European Union and the U. K. Natural Environment Research Council, and most recently the Natural Environment Research Council "MOYA" Global Methane Budget project, with help from Environment Canada and the University of Heidelberg (UHEI; samples from Alert), NILU (Zeppelin), the U.
To summarize, in terms of radiative forcing, the methane growth in the past decade has added about 0. 025 or 0. 05 W/m 2 relative to the RCP2. 6 expectation (Figure  6 ). At this growth rate, if sustained to 2100, methane would add more than 0. 5 W/m 2 of unexpected forcing compared to the Paris target. Thus, even if anthropogenic CO 2 emissions are successfully constrained to a RCP2. 6-like pathway, the unexpected and sustained current rise in methane may so greatly overwhelm all progress from other reduction efforts that the Paris Agreement will fail. In the context of the UNFCCC it should be noted that the global methane emissions budget remains unconstrained  (Kirschke et al. , 2013; Saunois, Bousquet, et al. , 2016; Saunois, Jackson, et al. , 2016)  and there is concern about so-called "bottom-up" inventory assessments, made by aggregating estimates of emissions from local, regional, and national statistics and databases, total 600-900 Tg/year.
ceda. ac. uk/uuid/ dd2b03d085c5494a8cbfc6b4b99ca702). Global Biogeochemical Cycles 10. 1029/2018GB006009 This work was supported by the UK Natural Environment Research Council's MOYA (Global Methane Budget) project; as well as by a sequence of prior projects supported by NERC (MAMM, Arctic methane, Southern methane) and the European Union (InGOS, Geomon, IMECC, Meth-MonitEUr, etc. ) over the past 20 years; and also by the MOCA (Methane emissions from the Arctic Ocean) and SOCA (Signals from the Arctic OCean to the Atmosphere) pro- jects, funded by the Research Council of Norway.
Additional databases and statistical treatment Additional data (PM 10 and SO 2 ) from two air quality stations belonging to the regional air quality network (http://www. medioambiente. jcyl. es/) were used: the urban traffic station LE01 is located in an urban residential area in San Ignacio de Loyola Avenue (05° 35'14"W 42° 36'14"N) and the suburban/background station LE04, located in Coto escolar (05° 33'59"W 42° 34'31"N) (Fig. 1 ), which is not directly influenced by vehicular, industrial or residential emissions. The data were recorded from 0000 UTC of the sampling day to 0000 UTC of the next day. Furthermore, meteorological parameters such as temperature, relative humidity, precipitation, wind speed and direction were recorded by an automatic weather station located in the sampling site.
The origin of air masses during the study period was interpreted by the determination of daily 3-day back trajectories (at 500, 1500 and 3000 m agl) using the HYSPLIT model  (Draxler and Rolph, 2012; Rolph et al. , 2017; Stein et al. , 2015). The Spanish Meteorological Agency (AEMET) provided visibility and diffuse and direct radiation data for the studied period. The statistical treatment has been carried out using the SPSS software (IBM Statistics Software V. 24). The correlation between pollutant concentrations was made using the nonparametric Pearson correlation method. The Kruskal-Wallis non-parametric test  (Kruskal and Wallis, 1952) , followed by the Dunn 1964 test, was applied to the data in order to find statistically significant differences.
Aerosol size distribution The evolution of the aerosol size distributions, total particle number concentration (N t ) and particles concentration belonging to each of the three modes (nucleation, Aitken and accumulation) was also investigated through the data obtained from the PCASP and SMPS. The corrections corresponding to the data from the PCASP were carried out using a refractive index and a density for the days without intrusions (1. 645 -0. 01016i and 2. 086 g cm -3 , respectively) and for the days with intrusion (1. 601 -0. 00755i and 2. 295 g cm -3 , respectively), which were calculated from aerosol composition described by  Alves et al. (2014) , following the methodology described by  Levin et al. (2010). Figure  8a  shows a first peak in the particle number concentrations of the three modes (nucleation, Aitken and accumulation) at 1900 UTC on 20 February 2016 (37 × 10 3 particles cm -3 ), with maxima in the aerodynamic diameter interval 30-100 nm, suggesting emissions from anthropogenic sources  (Charron and Harrison, 2003; Wehner and Wiedensohler, 2002)  and confirming the contribution from domestic heating devices and traffic observed in Figure  4a.
(2017) , who studied the same event from historical datasets of meteorological variables, aerosol properties and ozone concentration at El Arenosillo observatory (southwestern Spain). In this work, a historical dataset  (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015)  of February meteorological scenarios over North Africa evidenced the existence of airflows from the Sahel to Algeria and consequently temperature increases from the surface to 700 hPa by up to 7-9 ºC relative to the last decade. This phenomenon transports particles to the Iberian Peninsula at ground level and higher altitudes as  Titos et al. ,(2017)  and  Cazorla et al. (2017)  showed in their studies carried out in different cities of Spain, between 20 and 24 February 2016. Similar episodes over the peninsula with AOD higher than 1 have been detected before, as the one in the summer of 2004, reaching an AOD of 2.
Acknowledgments This study was partially supported by the  Spanish Ministry of Economy and Competitiveness  (Grant  TEC2014-57821-R) , the  University of León  ( Programa Propio 2015/00054/001  and  2018/00203/001 ) and the  AERORAIN  project  (Ministry of Economy and Competitiveness , Grant  CGL2014-52556-R , co-financed with European FEDER funds). F. Oduber acknowledges the grant  BES-2015-074473  from the  Spanish Ministry of Economy and Competitiveness. C. Blanco-Alegre  acknowledges the grant  FPU16-05764  from the  Spanish Ministry of Education, Culture and Sport. The authors gratefully acknowledge the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model and/or READY website (http://www. ready. noaa. gov)used in this study. The authors would also like to express their gratitude to the  Naval Research Laboratory  for providing the NAAP aerosol map and  NASA  for the satellite image used in the graphical abstract.
The depth-dependent anisotropic structure of the lithosphere under the Borborema Province in northeast Brazil has been investigated via harmonic stripping of receiver functions developed at 39 stations in the region. This method retrieves the first (k = 1) and second (k = 2) degree harmonics of a receiver function dataset, which characterize seismic anisotropy beneath a seismic station. Anisotropic fabrics are in turn directly related to the deformation of the lithosphere from past and current tectonic processes. Our results reveal the presence of anisotropy within the crust and the lithospheric mantle throughout the entire province. Most stations in the continental interior report consistent anisotropic orientations in the crust and lithospheric mantle, suggesting a dominant northeast-southwest pervasive deformation along lithospheric-scale shear zones developed during the Brasiliano-Pan-African orogeny.