dataesr/scientific-paragraphs-categorization huggingface dataset:
However, more precise evaluations still evade us. Global estimates of SGD-derived fluxes for key elements such as carbon and iron remain unavailable. d) Muddy shorelines Most SGD datasets were obtained from sandy shorelines. Muddy saltmarshes and mangroves are widespread along global coastlines but remain understudied. Their muddy soils are often permeable due to abundant animal burrows and can produce extremely high porewater concentrations of carbon and heavy metals. To develop global estimates of groundwater and porewater influences on marine biogeochemical cycles, a stronger focus on mangroves and saltmarshes may be required. e) Sustainability SGD is a relatively stable water discharge through a subterranean estuary into the coastal environment. Thus, the "stability" of SGD may be much different from river discharge and shape the environment in a number of ways.
Data and methods Data were obtained during the DACCIWA aircraft and ground-based campaigns in southern West Africa during June and July 2016  (Flamant et al. , 2018; Kalthoff et al. , 2018). The chemical data presented here were collected by the British Antarctic Survey Twin Otter aircraft, from 11 flights carried out between 6 and 15 July. The Twin Otter's operating area during the campaign was from 0. 6 • W to 2. 7 • E and from 5. 4 to 8. 0 • N. During DACCIWA, airborne measurements were made of aerosol properties with a focus on both regional aerosol properties and the effects of urban emissions. Flight patterns consisted of a series of profiles, and straight and level runs at different altitudes. The focus of the Twin Otter aircraft was on low-tropospheric measurements, below 3 km. Instrumentation on board the aircraft measured submicron aerosol chemical composition, optical properties and physical properties.
An Aerodyne Compact Time-of-Flight Aerosol Mass Spectrometer (AMS; Aerodyne Research Inc. , Billerica, MA, USA) (see  Canagaratna et al. , 2007; Drewnick et al. , 2005)  was used to measure the chemical composition of the submicron non-refractory aerosol mass. Measurements of organics, nitrate, sulfate and ammonium were collected; chloride concentrations were found to be consistently below the instrument's detection limit and so are not included here. The ionisation efficiency of the instrument was calibrated several times during the campaign using ammonium nitrate and once with ammonium sulfate. A collection efficiency of 0. 5 was applied to the data, which is typical for ambient data  (Middlebrook et al. , 2011). Comparisons between the AMS and the volume convolved size distribution show that other components, for example mineral dust, contribute little to the submicron aerosol mass and that coarse mode concentrations were low.
Comparing aerosol profiles from different times of day did not reveal any up-or downwards trend in the aerosol concentrations as a function of the time of day, although there was some variability in mass loadings from one day to the next. Similarly, there was no evidence in the data for a trend in the proportional distribution of aerosol species across the diurnal cycle. For example, the average organic-to-sulfate ratio varied by less than 10 % across the diurnal cycle. The boundary layer aerosol during the DACCIWA campaign was found to have been significantly influenced by long-distance transport from biomass burning in central and southern Africa, which is likely to account for the homogeneity in aerosol properties observed here. This phenomenon will be explored in future work. All DACCIWA flights took place between the hours of 07:00 and 19:00 and were therefore unable to provide information about potential changes in aerosol properties occurring during the night.
As the objective of this work is to examine the effects of high RH, and given the lack of sufficient evidence in this dataset suggesting a trend in aerosol concentrations over the diurnal cycle, a median aerosol profile and chemical composition are used here to explore the effects of changes in RH on the AOD. However, it is important to note that the resultant calculations only provide an indication of the effect of RH on the AOD and are unable to capture the true range of AOD that would result from including variations in the aerosol loading, which would scale the resulting AOD linearly. Figure  4  shows RH measurements from radiosondes released from the ground site in Savè. These plots show the mean RH and standard deviations at every 100 m interval up to 3 km, averaged across four times of day.
These data show a clear diurnal cycle in the RH, with a humid boundary layer during the night and early morning becoming drier towards noon and into the evening. These features are characteristic of the Gulf of Guinea maritime inflow  (Adler et al. , 2017; Deetz et al. , 2018b) , a coastal air mass that propagates inland in the evening, bringing cool air to the southern West African region. The increase of RH in the evening is mainly caused by cooling due to this cold air advection  (Adler et al. , 2019; Babić et al. , 2018). The RH in the pre-frontal region is generally around 75 %, while post-frontal RH rises to be almost consistently above 90 %  (Deetz et al. , 2018a)  and leads to low-level cloud formation in the night  (Adler et al. , 2019; Babić et al. , 2018). The dataset of RH measurements used here is valuable, as models are often unable to replicate the vertical RH profile  (Hannak et al.
Calculating aerosol optical depths In order to establish the impact of humid layers on column optical properties, the sub-3 km aerosol optical depth (AOD) was calculated for each of the 63 unsaturated radiosonde profiles taken from the Savè ground site during the DACCIWA campaign. The steps of the approach used to establish the AOD are outlined below using a case study RH profile from 8 July at 10:57 UTC (local time = UTC+1 h). This approach was then applied to all RH profiles in the dataset: 1. Establish the average aerosol profile. The median aerosol mass concentration in the area of interest was  calculated for each 100 m bin from 300 to 3000 m. Data were not collected from lower than this within this region, so an average data point from further south was used here to represent the 200 m bin.
The sun photometer dataset includes 319 measurements taken between 8 June and 29 July 2016 as part of the DACCIWA field campaign. There is good general agreement between the AOD values calculated here and those collected by sun photometer from the Savè ground site over a similar period. The median AOD is 0. 36 for both datasets. Values of AOD in the early morning have a larger spread, being reasonably well distributed between 0. 3 and 0. 7 in both cases, while AOD later in the evening in both cases was distributed more normally around the median. Both datasets have a high tail that is dominated by data from the mornings and the highest value is around 0. 7 in the calculated dataset and 0. 8 from the sun photometers. The assumption of a constant aerosol profile prevents the model from capturing the full spread that would result from variations in aerosol loadings.
As a result, the sun photometer dataset has a larger interquartile range of 0. 14, compared with 0. 09 for the calculated dataset. The minimum value in the sun photometer dataset was a little less than 0. 2, while that of the calculated dataset was closer to 0. 3. This likely represents days with lower aerosol loadings, which are not captured by the model. The calculated dataset is based solely on observations in the lowest 3 km, and as such, any highlevel aerosol layers are not taken into account. However, this does not appear to have significantly biased the calculated AODs. It is important to note that the calculated values here are not expected to reproduce the real-world AOD for any given RH profile. A number of values are assumed to be constant here, including the shape and magnitude of the aerosol profile, the aerosol chemical composition, the shape of the accumulation mode size distribution and simplifications generally assumed in Mie calculations, such as that particles are internally mixed spheres.
A realistic calculation would require variation in such quantities to be taken into account. This dataset, instead, gives an estimate of the variability in the AOD measurements that is due entirely to changes in RH. Despite this limitation, this approach is able to replicate a large part of the measured AOD frequency distribution; in particular, it is able to reproduce the high values of AOD observed during the mornings. While the inclusion of aerosol variation would be necessary in order to recreate the full spectrum of AODs, there are no observations of aerosol concentrations from anywhere across the region that are sufficiently high enough to be the sole cause of the enhancements in the ambient AOD observed by sun photometers. Although the omission of variations in aerosol properties has resulted in a narrower spread of calculated values compared with direct measurements, these results do show that the highly humid layers are the root cause of the very highest AODs being observed during the mornings.
In reality, some aerosol will exist at higher altitudes. For example, intrusions of biomass burning are known to become advected into this region regularly between 3 and 4 km at this time of year  (Mari et al. , 2008). Second, the focus on accumulation mode aerosol necessarily excludes the impact from other size modes from the calculations. Although very little coarse mode aerosol was observed by the Twin Otter aircraft during the DACCIWA campaign, it must be noted that any present aerosol in this mode would increase the region's AOD. The Twin Otter flew above 2750 m only once, but at this time, a higher concentration of sulfate aerosol was observed that this altitude. If this data point is representative of the altitude in general, then it is likely that this study has underestimated the HGF at the higher altitudes.
Summary and conclusions Aircraft and radiosonde data collected during the DACCIWA field campaign were used to estimate the hygroscopic growth of aerosols under different RH conditions, based on aerosol chemical composition and using the ZSR mixing rule. A consistent aerosol composition and vertical profile was assumed across the region of interest, due to the relative homogeneity of aerosol observations from the Twin Otter aircraft in the region. The RH profiles displayed a diurnal cycle, with higher RH generally observed during the night and early morning and drier conditions in the afternoon. These are features associated with the Gulf of Guinea maritime inflow, described by  Adler et al. (2017)  and  Deetz et al. (2018b) , which brings cool air inland in the evening  (Adler et al. , 2019; Babić et al.
Acknowledgements. The research leading to these results has received funding from the  European Union   7th Framework Programme  ( FP7/2007-2013)  under grant agreement no. 603502  ( EU  project  DACCIWA: Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa ). The lead author was supported by the  NERC Doctoral Training Programme  (grant ref:  NE/L002469/1 ). Thanks to the  German Weather Service (DWD)  for providing access to the ICON forecast data. We thank  British Antarctic Survey (BAS , operator of the Twin Otter aircraft), the  Service des Avions Français Instrument's pour la Recherche en Environnement (SAFIRE)  and the  Deutsches Zentrum für Luft-und Raumfahrt  for their support during the aircraft campaign. Edited by: Dominick Spracklen Reviewed by: two anonymous referees.
Data availability. All data used in this study are publicly available on the SErvice de DOnnées de l'OMP (SEDOO) database  (Derrien et al. , 2016; Handwerker et al. , 2016; Kohler et al. , 2016; Wieser et al. , 2016) , which can be found at http://baobab. sedoo. fr/DACCIWA (last access: 1 February 2019).
We demonstrate on simulated data the utility of the spectral density profile to successfully cluster phylogenetic trait data into meaningful groups and to characterise the phenotypic patterning within those groups. We furthermore demonstrate how the spectral density profile is a powerful tool for visualising phenotypic space across traits and for assessing whether distinct trait evolution models are distinguishable on a given empirical phylogeny. We illustrate the approach in two empirical datasets: a comprehensive dataset of traits involved in song, plumage and resource-use in tanagers, and a high-dimensional dataset of endocranial landmarks in New World monkeys. Considering the proliferation of morphometric and molecular data collected across the tree of life, we expect this approach will benefit big data analyses requiring a comprehensive and intuitive framework.
Together, these applications show the strength of applying the graph Laplacian. However, despite its widespread utility, no such framework has been developed for characterizing and comparing phylogenetic trait data. We first describe how to construct the spectral density profile of the normalized graph Laplacian for phylogenetic trait data and demonstrate how to interpret it in terms of specific properties of phenotypic evolution. We use simulations to show how the profiles relate to conventional metrics of phylogenetic signal and models of trait evolution. We show how to compute the distance between profiles and cluster phylogenetic trait data based on those distances. Finally, we illustrate the utility of this approach for assessing whether distinct trait evolution models are distinguishable using the Cetacean phylogeny.
Implementation Below, we describe how to use the normalized modified graph Laplacian (nMGL) to construct a spectral density profile for traits (i. e. , unidimensional continuous extant tip data) on a phylogeny, how to characterize the profile in terms of evolutionary patterning, and how to compute the distance between profiles. We implemented these functionalities in the R package RPANDA freely available on CRAN  (Morlon et al. 2016 ). In the analyses detailed below, phylogenies were simulated using the R package T ESS  (Höhna 2013) ; trait data for BM, OU and ACDC models were simulated using mvM ORP H (mvSIM function  Clavel et al. (2015) ) and for DD and MC models with RP AN DA (sim t comp function). Blombergs K was computed using phytools (Revell 2012); and MDI was computed using geiger  (Harmon et al.
We simulated a single birth-death tree with 100 tips at 20 million years under constant speciation (0. 2) and extinction (0. 05) rates (throughout, rates of speciation and extinction are expressed in event per lineage per million year) and simulated 500 trait datasets on that tree under a Brownian motion (BM) model of trait evolution with variance σ 2 = 0. 01 (Cavalli-Sforza and Edwards 1967), an exponentially accelerating (AC) model with rate value β = 1. 5 and σ 2 = 0. 01, an exponentially decelerating (DC) model with rate value β = -0. 1 and σ 2 = 0. 01  (Blomberg et al. 2003; Harmon et al. 2010) , and a white-noise model by randomly drawing trait values from a normal distribution (with a mean of zero and standard deviation of one). For each of the three first models, we set the root value at 0.
For each dataset, we estimated Blomberg's K, which measures the partitioning of variance using a BM model as reference, where K > 1 means close relatives resemble one another more than expected under BM, and K < 1 means they resemble one another less  (Blomberg et al. 2003) , and the morphology disparity index (MDI), which is a measure of the difference between the observed diversity through time curve and that expected under a BM model, where a higher MDI indicates that higher subclade disparity than expected under a BM model  (Foote 1997; Harmon et al. 2003; Slater et al. 2010). We fit OLS regression models between n η and both Blomberg's K and MDI for the 500 trait datasets. The fragmenter is the skewness of the SDP, denoted n ψ, and computed as the ln-transformed µ 3 µ 3/2 2 , where µ i is the ordinary ith moment of the distribution; it represents the relative abundance of small and large n λ.
Therefore, as trait space becomes more clustered, irrespective of phylogenetic signal, the proportion of small n λ increases and so does the fragmenter. Therefore, we expect the fragmenter to be a good measure of the discreteness of trait space. In order to test whether the fragmenter captures discrete clusters of extant trait data, we simulated a single birth-death tree with 200 tips at 20 million years under constant speciation (0. 2) and extinction (0. 05) rates and simulated 200 datasets of discrete trait space under low and high phylogenetic signal. For low phylogenetic signal, we simulated trait data on four macroevolutionary landscapes  (Boucher et al. 2017) , each defined by a different polynomial function: V (x) = x 2 , V (x) = x 4 -0. 5x 2 , V (x) = x 6 -0. 5x 2 , and V (x) = x 8 -0.
5x 2 , where the landscape is estimated as e -V (x). Here, an increase in the exponent of the first term generates a more discretized trait distribution (i. e. , a deeper well in the macroevolutionary landscape). We set σ 2 = 0. 5, the root value equal to 5, and the trait boundaries at [0, 10]. We plotted the landscapes as defined by the polynomial functions, histograms of the trait data for each landscape as realized in the simulations, and the spectral density profiles of each dataset. For high phylogenetic signal, we simulated trait data on the same birth-death tree under a DC model with rate value β = -0. 6, -0. 3, 0 and σ 2 = 0. 1, where more negative values of β indicate more decelerated rates  (Blomberg et al. 2003; Harmon et al. 2010 ). This generated trait data distributed in discrete monophyletic clusters across the tree.
For the low and high phylogenetic signal datasets, we computed the fragmenter and compared values as a function of macroevolutionary landscape and of β. The splitter is the principal n λ, denoted ( n λ * ); it is diagnostic of the disjointedness of a graph, where larger splitter values imply a more bipartite structure  (Banerjee and Jost 2008; Bauer and Jost 2009). In macroevolutionary terms, as traits become increasingly bimodally distributed with a strong phylogenetic signal, the splitter increases. As n λ ≤ 2 for the nMGL, the splitter 2 when a clade is composed of two phylogenetically distinct subclades with different mean trait values. To assess the relationship between the spectral density profile and differences in mean trait values on a phylogeny, we simulated a single birth-death tree with constant speciation (0.
We then calculated the average branch-length distance between tips in cluster 1 and tips in cluster 2. For phylogenetic trait data that can be separated into two monophyletic clusters, the average between-group distance will equal two times the crown age of the tree. We present this as a heuristic test of the monophyly of trait values when the splitter 2. To test the effect of phylogenetic signal on the splitter value, we simulated 10 trait datasets with one difference in mean trait values on a 100-tip constant-rate birth-death tree as above. We then randomized the distribution of tip data within each cluster 100 times and compared the resulting splitter value for the randomized trees against the original splitter value. We compared the splitter values for the two-cluster BM datasets and the randomized two-cluster datasets to 100 datasets simulated under a simple BM process (with no clusters and σ = 0.
01). To test the effect of erroneous data on the nMGL, we simulated trait data under a BM process on a 100-tip constant-rate birth-death tree (σ = 0. 01). We tested both the effect of increasing the amount of error and increasing the number of tips with error. For the former, we introduced error on 10% of randomly drawn tips as a sampling variance equal to n times the standard error for n = 1, 2, 3. For the latter, we introduced error on 20, 30, 40, 100% of tips as a sampling variance equal to the standard error. We simulated 100 datasets for each scenario. We compared the resulting splitter, tracer, and fragmenter values to BM datasets (σ = 0. 01) and ACDC datasets (β = -1. 1, σ = 0. 01) simulated on the same tree and with no introduced error.
Clustering nMGLs from Their Spectral Density Profiles To demonstrate whether we can distinguish phylogenetic trait data simulated under trait models we know are distinguishable, we clustered nMGLs constructed for trait data on the same phylogeny under different trait models. To cluster nMGLs, we computed the Jensen-Shannon distance between spectral density profiles. The Jensen-Shannon distance is defined as ∆(Λ 1 , Λ 2 ) = 1 2 KL(f * 1 , f * ) + 1 2 KL(f * 2 , f * ) (1) where f * 1 and f * 2 are spectral densities for profiles 1 and 2, f * = 1 2 (f * 1 + f * 2 ) , and KL is the Kullback-Leibler divergence measure for the probability distribution  (Endres and Schindelin 2003). We then cluster the matrix of Jensen-Shannon distances for each profile pair using hierarchical clustering with boostrap resampling and k-medoids clustering using optimal silhouette width, s(i), which is a measure of the between/within-variance of each datapoint i assigned to a cluster; data are typically considered structured at s > 0.
51  (Szekely and Rizzo 2005; Reynolds et al. 2006 ). In each case, the number of clusters is not set a priori. We tested the efficacy of clustering on profiles using trait datasets simulated on birth-death trees. We simulated a total of 1500 trait datasets under a BM model of trait evolution with variance σ 2 = 0. 01 (Cavalli-Sforza and Edwards 1967), an exponentially AC model with rate value β = 1. 5 and σ 2 = 0. 01, and an exponentially DC model with rate value β = -0. 1 and σ 2 = 0. 01  (Blomberg et al. 2003; Harmon et al. 2010 ). For each model, we set the root value at 0. We visualised this clustering by plotting the profiles in a multidimensional space defined by n λ * , n ψ, and n η. We tested the ability of the spectral density profile to find meaningful clusters of trait models on different tree shapes and sizes.
To test for the effect of tree shape, we simulated trait datasets on 200-tip birth-death trees (with a max age of 20Ma) with constant speciation (0. 2) and extinction (0. 02) rates, with decreasing speciation (0. 1 * e -0. 2t ) and constant extinction (0. 02) rates, and with increasing speciation (0. 1 * e 0. 1t ) and constant extinction (0. 05) rates. We conducted analyses on identical trees without pruning extinct lineages, resulting therefore in non-ultrametric trees, to test whether the profiles of different models were more distinguishable on a non-ultrametric tree compared to an ultrametric tree, as is expected from likelihood-based approaches  (Cooper et al. 2015). To test for the effect of tree size, we simulated 6000 trait datasets under the same BM, AC, and DC trait model parameters on birth-death trees with constant speciation (0.
Applications To illustrate our approach, we demonstrate three applications. First, we used the Cetacean phylogeny (87 spp. )  (Steeman et al. 2009)  to illustrate how our approach can be used to assess the distinguishability of different trait evolution models in a particular clade. We simulated six trait models under a range of parameter values on the Cetacean phylogeny: BM with σ 2 = 0. 1 -5; Ornstein-Uhlenbeck (OU) with strength of pull towards an optimum α = 1 -20 and σ 2 = 0. 1; exponential diversity-dependence (DD) with slope parameter r = -1. 1 --0. 1 and σ 2 = 0. 1; AC with rate value β = 1. 1 -1. 5 and σ 2 = 0. 1; DC with rate value β = -0. 5 --0. 1 and σ 2 = 0. 1; and matching competition (MC) with the strength of competition S = -1. 1 --0. 1 and σ 2 = 0. 1. For each model, we simulated 500 datasets with the root value set to zero.