dataesr/scientific-paragraphs-categorization huggingface dataset:
Community structure and diversity distribution among stream site types: We collected a total of 90 078 specimens belonging to 82 taxa and 15 macroinvertebrate orders from the 51 sampled sites (including rare taxa). Of these, 34 taxa belonged to Diptera (10 of which were from the family Chironomidae), 13 to Trichoptera, 11 to Coleoptera, 5 to Lepidoptera, 4 to Rhynchobdellida, 3 to Haplotaxida, 2 to Hemiptera, and 1 to Lumbriculida, Tricladida, Gordioidea, Hydracarina, Basommatophora, Podocopida, Amphipoda, Veneroida, Plecoptera, and Ephemeroptera. Removing rare taxa (see data analysis section) left us with 89 767 specimens and 40 taxa, representing 99. 7% of total abundance and 48. 8% of total richness, respectively. Kryal (K1 and K2) and mixed (M) site types had an important proportion of rare taxa (singletons and doubletons) (K1 = 9, K2 = 13 and M = 19, Fig.
Detailed analyses of benthic communities and the development of databases are key for conservation strategies. In general, water management municipalities and/or enterprises tend to focus on mountain streams' flows, but this study highlights that water managers should also consider water quality and stream site types for more sustainable management of these important ecosystems. Ethical statement: authors declare that they all agree with this publication and made significant contributions; that there is no conflict of interest of any kind; and that we followed all pertinent ethical and legal procedures and requirements. All financial sources are fully and clearly stated in the acknowledgements section. A signed document has been filed in the journal archives. la presencia de taxones raros y la rotación de taxones, la influencia de los glaciares puede aumentar la biodiversidad en cuencas con influencia glaciar.
ACKNOWLEDGMENTS We thank everyone who assisted us with field and laboratory work, especially to the members of the  Limnology Laboratory of PUCE , who assisted in the sorting and identification of aquatic macroinvertebrates. Funding by  Ecofondo  (grant no. 034-ECO8-inv1 ) and by a  WWF-Novozymes  (grant  2008 ) is greatly appreciated. A special thanks to  Kirstynn Joseph  for reviewing the English writing. We thank  Mr. Jose Delgado  and  Antisana Ecological Reserve  for granting us access to the study sites. Specimens were collected under permit  N° 008-09 IC-FAU-DNB/MA  from  Ministerio de Ambiente of Ecuador.
, 2003; Read et al. , 2012; Wang et al. , 2019). Several potential additional acetaldehyde sources have been proposed, including new hydrocarbon oxidation mechanisms, aerosol-related sources, and oceanic sources. One possible source of acetaldehyde in the remote marine boundary layer is oceanic emission from the photodegradation of coloured dissolved organic matter (CDOM) in sea-surface water, where acetaldehyde could be produced together with other low-molecular-weight carbonyl compounds  (Kieber et al. , 1990; Zhou and Mopper, 1997; Sinha et al. , 2007; Dixon et al. , 2013). Nevertheless, due to both limited airborne and seawater measurements of acetaldehyde, the importance of oceanic emission is still under debate  (Millet et al. , 2010; Wang et al. , 2019). In order to better understand the atmospheric budgets of acetaldehyde (and the other carbonyl compounds), it is informative to analyse a dataset of multiple carbonyl compounds in both polluted and clean environments, with influence from marine emis- sions, varying particulate loadings, and high rates of oxidation as shown in Fig.
1 , which demonstrates the main formation pathways of acetaldehyde during this campaign. During the AQABA (Air Quality and Climate Change in the Arabian Basin) shipborne research campaign, carbonyl compounds were continuously measured by PTR-ToF-MS onboard a research vessel that circumnavigated the Arabian Peninsula. During the campaign, chemically distinct air masses were sampled, which had been influenced by primary emissions of hydrocarbons and inorganic pollutants (NO x , SO 2 , and CO) from petroleum industries and marine transport  (Bourtsoukidis et al. , 2019; Celik et al. , 2019) , by pollution from urban areas  (Pfannerstill et al. , 2019) , and clean marine-influenced air  (Edtbauer et al. , 2020). It is a unique dataset of carbonyl compounds encompassing starkly different environmental conditions from a region with few (or no) available in situ measurements to date.
Data analysis The data were initially processed by the PTR Analyzer software  (Müller et al. , 2013)  to identify and integrate the peaks. After obtaining the raw data (counts per second for each mass identified), a custom-developed python-based program was used to further process the data to final mixing ratios. For compounds present in the standard gas cylinder, interpolated sensitivities based on the five in-campaign calibrations were applied to derive the mixing ratios, while mixing ratios of the other masses were calculated by using a proton transfer reaction rate constant (k PTR ) of 2. 0 × 10 -9 cm 3 s -1. The uncertainty associated with the mixing ratios of the calibrated compounds was around 6 %-17 % (see Table  S1 ). For the mixing ratios derived by assuming k PTR , the accuracy was around ±50 %  (Zhao and Zhang, 2004).
Meteorological data and other trace gases The meteorological data were obtained by using a commercial weather station (Sterela) which monitored wind speed, wind direction, RH, temperature, speed of the vessel, GPS, etc. The actinic flux was measured by a spectral radiometer (Metcon GmbH;  Meusel et al. , 2016). Non-methane hydrocarbon (NMHC) mixing ratios were measured by a gas chromatograph with a flame ionization detector (GC-FID) online with a time resolution of 50 min. It measured hydrocarbons (C 2 -C 8 ) and aromatics (C 6 -C 8 ) with an average LOD < 10 ppt for most of the compounds. For a detailed instrumental description, see  Bourtsoukidis et al. (2019). Formaldehyde mixing ratios were determined by a modified and optimized version of the commercially available AL4021 (Aero-Laser, Germany), which utilizes the Hantzsch technique  (Stickler et al.
, 2006). Methane and carbon monoxide (CO) levels were monitored by a cavity ring-down spectroscopy analyser (Picarro G2401). Ozone was measured with an absorption photometer (Model 202 Ozone Monitor, 2B Technologies, Boulder, Colorado). Due to the potential interference from sampling our own ship exhaust in which carbonyl compounds may be present  (Reda et al. , 2014) , a filter was applied to the dataset based on the wind direction and NO x , SO 2 , and ethene levels.
Results and discussion Around the Arabian Peninsula, the mixing ratios of individual carbonyl compounds varied over a wide range, from tens of ppt to ppb levels. In this study, we divided the dataset geographically into eight regions (Fig. 2 , middle graph) to classify and characterize the primary and secondary origins of carbonyl compounds. The regional delineations were the Mediterranean Sea (MS), Suez, Red Sea North (RSN), Red Sea South (RSS), Gulf of Aden (GA), Arabian Sea (AS), Gulf of Oman (GO), and Arabian Gulf (AG), the same as those described by  Bourtsoukidis et al. (2019). Figure  2  shows the abundance of aliphatic, aromatic, and unsaturated carbonyl compounds (carbonyls) for each region. Generally, aliphatic carbonyls were present at much higher mixing ratios than aromatic and unsaturated carbonyls, with smaller carbonyl compounds (formaldehyde, acetaldehyde, C 3 and C 4 carbonyls) dominating the distribution.
The mixing ratios of aliphatic carbonyls decreased dramatically from C 5 carbonyls with increasing carbon number. The box plots (Fig. 2 ) also show that carbonyl compounds were measured at higher mixing ratios and were more variable over the Suez region and the Arabian Gulf. The abundance of carbonyl compounds varied markedly from region to region, with the highest and lowest values found in the Arabian Gulf and the Arabian Sea, respectively. Table  1  shows the mean, standard deviation, and the median values for carbonyls in each region. In the following sections, each class of carbonyl compounds is investigated in greater detail. Bourtsoukidis et al. (2019). The data used for map plotting were from public domain GIS data found on the Natural Earth website (http://www. naturalearthdata. com, last access: 23 January 2019) and were read into Igor using the IgorGIS XOP beta.
Moreover, acetaldehyde was found to have a much worse correlation with ozone during the nighttime compared to the correlation during the daytime over the Arabian Gulf (Fig. 3b  and c ), which also indicates that inlet generation of acetaldehyde was insignificant. Over other regions, especially the remote area (the Arabian Sea and Gulf of Aden), ozone was relatively constant and low, with poor correlation with acetaldehyde mixing ratios. Although we cannot completely exclude the possible existence of artifacts, the interference is likely to be insignificant in this dataset.
, 2014; Griffith et al. , 2013) : an increasing concentration trend of pyruvic acid was observed in marine aerosols over the western North Pacific Ocean  (Boreddy et al. , 2017). Therefore, due to limited terrestrial biogenic sources of pyruvic acid for the AQABA campaign, the gas-phase level of pyruvic acid was expected to be low. Limited studies reported pyruvic acid level in the marine boundary layer and  Baboukas et al. (2000)  measured 1. 1 ± 1. 0 ppt of pyruvic acid above the Atlantic Ocean. Pyruvic acid was measured by  Jardine et al. (2010)  using a PTR-MS at m/z 89 in a forested environment. For the AQABA PTR-ToF-MS dataset, enhanced signals were observed at m/z 89. 024, with a mean mixing ratio of 35-110 ppt over different regions (Table  S4 ), which is much more abundant than reported pyruvic acid levels by  Baboukas et al.
Data availability. The data used in this study are available to all scientists agreeing to the AQABA protocol at https://doi. org/10. 5281/zenodo. 3974228 (Wang et al. , 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-20-10807-2020-supplement. Author contributions. AE and CS performed PTR-ToF-MS measurement and preliminary data processing. NW conducted data analysis and drafted the article. AP performed EMAC model simulation. EB and LE were responsible for NMHC measurements and data. DD, BH, and HF provided formaldehyde data. Ozone and actinic flux data were contributed by JS and JNC. Methane and carbon monoxide data were provided by JDP. JL designed and realized the campaign. JW supervised the study. All the authors contributed to editing the draft and approved the submitted version.
Acknowledgements. We are grateful for the collaboration with the  King Abdullah University of Science and Technology (KAUST) , the  Kuwait Institute for Scientific Research (KISR) , and the  Cyprus Institute (CyI)  to fulfill the campaign. We would like to thank  Captain Pavel Kirzner  and the crew for their full support onboard the  Kommandor Iona, Hays Ships Ltd. We are grateful for the support from all members involved in the AQABA campaign, especially  Hartwig Harder  for his general organization onboard the campaign, and  Marcel Dorf ,  Claus Koeppel ,  Thomas Klüpfel , and  Rolf Hofmann  for logistical organization and their help with preparation and set-up. We would like to express our gratitude to  Ivan Tadic  and  Philipp Eger  for the use of a ship exhaust contamination flag. Financial support.
predisposition in sending (outbound connectivity) and receiving (inbound connectivity) propagules to/from neighboring reefs  17. In this study, we predicted spatial patterns of heat stress adaptation and connectivity for over 1000 km of coral reefs of New Caledonia, in the Southern Pacific (Fig. 1 ). The study area encompassed the barrier reef surrounding Grande Terre, the main islands of the Archipelago, as well as the intermediary and fringing enclosed in the lagoon. We also considered reefs surrounding the Loyalty Islands (Ouvéa, Lifou and Maré) and the Astrolabe (east of Grande Terre) and those in the Entrecasteaux and Petri atolls (north of Grande Terre). We first used remote sensing data to (1) evaluate the thermal variability of the study area and (2) estimate patterns of sea current connectivity between reefs.
Despite evidence of effects of thermal patterns on coral cover reported by previous studies, there might be other environmental constraints that are asymmetrical between the two coasts of Grande Terre and modulate coral cover changes. Further validation remains necessary and could be achieved via experimental assays of heat stress resistance  8  in colonies sampled at reefs with different PA HEAT. This approach would also enable disentangling of the possible confounding role of acclimatization in heat stress adaptive responses  12, 33. Another important aspect to consider in future studies is the resolution of remote sensing datasets used for predictions. Here, we worked at a resolution of ~ 5 km for thermal variables and ~ 8. 5 km for sea current data. While the overall environmental patterns appeared consistent with those characterized in previous studies, it is likely that small scale phenomena were neglected.
Conclusions. In this study, we combined remote sensing of environmental conditions with genomic data to predict spatial patterns of heat stress adaptation and connectivity for the coral reefs of New Caledonia. We then retrieved field survey data and showed that recent heat stress was associated with a decrease in living coral cover, but also that such association appeared to be mitigated at reefs predicted with (1) high probability of heat stress adaptation and (2) high levels of incoming dispersal. The metrics computed in this work resumes the adaptive potential of corals against heat stress, and therefore represents valuable indices to support spatial planning of reef conservation.
Methods Remote sensing of sea surface temperature. Satellite data characterizing sea surface temperature (SST) were retrieved from a publicly available database (dataset: ESA SST CCI reprocessed sea surface temperature analyses)  48, 49. This dataset provides daily records of SST at a ~ 5 km resolution from the years 1981 to 2017 across the whole study area (Fig. 1 ). The shapes of the reef of the region  50  were transformed into a regular grid (1,284 cells with maximal size of 5 × 5 km), and for each reef cell we extracted the average temperature for every day of the observational period using QGIS software (v. 3. 14)  51. We performed calculations of heat stress patterns in the R environment (v. 3. 5) using the raster package (v. 3. 0)  52, 53. For each reef cell, patterns of heat stress were computed using the bleaching alert definition developed by the Coral Reef Watch briefly described hereafter  20.
Seascape connectivity graph. For the estimation of connectivity we applied a method based on spatial graphs previously employed to study coral reef connectivity  17  and briefly outlined hereafter. We retrieved a publicly available dataset describing the eastward and northward surface water velocity (Global Ocean Physics Reanalysis)  48. This dataset provided daily records at ~ 8. 5 km resolution from 1993 to 2017. Since this resolution can be inaccurate close to coastlines, we increased the resolution to 1 km using the "resample" function ("bilinear" method) of the raster R package, and used high resolution bathymetry data (100 m resolution  54  ) to remove the sea velocity value from pixels located on land. We then used the R package gdistance (v. 1. 2)  55  to create a matrix describing the transition costs between each adjacent pixel in the study area.
SNPs dataset. We retrieved genomic data employed in previous seascape genomics analyses on three coral species of New Caledonia: Acropora millepora, Pocillopora damicornis and Pocillopora acuta  23. This dataset encompassed more than one hundred individuals per population (167 in A. millepora, 118 in P. damicornis, 110 in P. acuta), collected at multiple sampling sites around Grande Terre (20 sites for A. millepora, 17 for P. damicornis, 17 for P. acuta) and genotyped using a Genotype-By-Sequencing approach  56  characterizing thousands of single-nucleotide-polymorphisms (SNPs; 11,935 in A. millepora, 7,895 in P. damicornis and 8,343 in P. acuta). Of note, SNPs in this dataset were already filtered for rare allelic variants (minor allele frequency < 0. 05%) and linkage disequilibrium (LD-pruning threshold = 0.
3  57  ). Probability of heat stress adaptation. The previous seascape genomics study investigated the genotype-environment associations between SNPs and 47 environmental descriptors (among which is BAF overall ) using LFMM software  23, 58. In each of the three species, the analysis reported significant associations (q < 0. 01) of BAF overall with potentially adaptive SNPs (10 in A. millepora, 18 in P. damicornis, and 4 in P. acuta). We employed these genotype-environment associations to predict the probability of heat stress adaptation (PA HEAT ) from BAF overall values. We used a method based on logistic regressions  21, 59  that was previously applied to corals  17  , with some modifications outlined hereafter. First, we evaluated the accuracy of the approach. For each individual used in the analysis, we retrieved the BAF overall value at the sampling location.
As a comparison, we applied the same cross-validation method for 1000 genotypes randomly selected in each of the three species. Next, we employed a generalize linear mixed model (GLMM) to build an overall genotype-environment association model combining all the putatively adaptive genotypes across the three species. This was done through the R package glmmTMB (v 1. 0)  60  , using a logistic regression model where genotype identifier, sample identifier and species were introduced as random factor. The resulting model then was used to transform BAF overall values associated with each of the 2,284 reef cells of New Caledonia in PA HEAT. The model was plotted using the visreg R package (v. 2. 6. 1)  61. Reef connectivity and genetic structure of coral populations. The SNPs dataset was used to evaluate whether reef connectivity predictions based on seascape graph were representative proxies of the genetic structure of three coral populations.
Next, a principal component analysis (PCA) of the allele frequencies matrix was performed using the "prcomp" R function (stats package, v. 3. 5). The first principal components (PCs) that cumulatively explained 80% of the total variation were kept and used as response table for the RDA. The seascape graph was used to calculate the least-cost-path between every pair of sampling sites using the "costDistance" function (R package gdistance, v. 1. 2)  55. Next, we computed the distance-based Moran's eigenvector maps (dbMEMs)  65  based on the least-cost-path between sites using the "pcnm" function of the vegan R package (v. 2. 5)  66. dbMEMs are spatial orthogonal variables decomposing the distances between sites as described in the least-cost-path matrix. The table of dbMEMs was used as explanatory table for the RDA.
Overall, we used data from 74 survey stations distributed across the Archipelago of New Caledonia (Fig. 1 ). At each station, yearly coral cover surveys were performed along the same 100 m transect using the "point intercept" technique. Surveys covered the period from 2003 to 2017, where 18 sites have been visited for less than five years, 27 for five to ten years, and 29 for more than ten years. The exact coordinates of survey stations were retrieved from the geographic information web-portal of New Caledonia (https ://geore p. nc/). Environmental characterization of survey sites. The coordinates of survey stations were used to find the corresponding reef cells and the associated values of the connectivity indices (OCI and ICI). For each survey record (i. e. survey at a given station in a specific year) we also calculated BAF overall as the average BAF since 1985 to two years before the survey.
Analysis of coral cover change. We investigated the association of BAF previous year , PA HEAT and connectivity indices (ICI and OCI; in total 4 explanatory variables) with coral cover rates (response variable) using GLMMs. This analysis focused on the coral cover rates of every survey record (total of 574 records). The computation of GLMMs was performed using the R package glmmTMB  60  , which allowed us to model coral cover rates via beta regression  67. We accounted for the non-independence of survey records originated at the same station but on different years by setting the station effect as random factor on the coral cover rate  68. This approach is recommended for studies of longitudinal data with irregular time points  69. To avoid bias due to scale differences between explanatory variables, each variable was standardized to mean 0 and standard deviation 1 using the R "scale" function.
In edge-of-field surface waters, CPF may show relatively fast dissipation in the water compartment, resulting in a single or a few repeated exposure peaks shorter than 28 d (see, e. g. ,  Crum and Brock 1994; López-Mancisidor et al. 2008). The 8 theoretical aquatic exposure profiles in Figure  1  are arranged in the order from the highest expected risk (AEP1, with the highest overall exposure) to the lowest expected risk (AEP8, with the lowest overall exposure). Panels AEP5, AEP6, and AEP7 are characterized by 2 pulse exposures and the same 28-d overall exposure, but the time intervals between pulses increase from AEP5 to AEP7. We included AEP1 characterized by a constant exposure regime (and consequently the highest overall exposure) because in standardized laboratory toxicity tests, from which the data used for the GUTS-RED model calibration initially originate, the exposure concentration is assumed to be constant.
Model equations and calibration Model calibration for the 13 arthropod taxa was done using immobility data and concentration measurements of external exposure. Scaled internal damage was used (Eqn. 1) and linked to the stochastic death model (GUTS-RED-SD; Eqns. 2 and 3) or to the individual tolerance model (GUTS-RED-IT; Eqns. 4 and 5) to describe the survival frequencies over time (see  Jager et al. 2011). * ( ) = ( ( )- ( )) dD t dt k C t D t. w D e x t w (1) In Equation 1, D w (t) is the scaled internal concentration in concentration units, C ext (t) the external concentration in the water (concentration units), and k D the dominant rate constant (per time unit). ( ) ( ) = + τ β ( ) - τ ≤ ≤ F t 1 1 , D m max t w w 0 (4) * * ( ) = ( -( )) - S t F t e 1. IT h t b (5) In Equations 4 and 5, F(t) is the cumulative distribution function at time t, h b the background mortality rate constant (per time unit), k D the dominant rate constant (per time unit), m w the median threshold (concentration unit), and β the spread of the threshold distribution (dimensionless).
(2011)  database for illustrative purposes, whereas in a realistic regulatory risk assessment the geomean would not be the chosen option if sufficient species (>8) for an SSD are available. The selected crustaceans comprised D. magna, Asellus aquaticus, and Gammarus pulex, whereas the insects comprised Chaoborus obscuripes, Cloeon dipterum, and Plea minutissima (these taxa are relatively frequently tested in insecticide studies;  Van Wijngaarden et al. 2015). Following the procedure proposed by EFSA PPR (2013), the geometric mean 96-h EC50 values were calculated separately for crustaceans and insects, and the lowest value (either for crustaceans or insects) was selected for Tier-2A TER derivation. The Tier-2A TER is thus the lowest geometric mean 96-h EC50 value divided by the PEC max. If this Tier-2A TER is larger than 100 (the Tier-1 AF), acute risk is considered low.