dataesr/scientific-paragraphs-categorization huggingface dataset:
| Phylogeny of BCI angiosperms and genetic distance measurement We constructed a molecular phylogeny encompassing 730 species of the 759 genera and 1,263 lesser taxa of angiosperms known from BCI  (Croat, 1978)  (Figure  S1 ). DNA sequence data were obtained from GenBank using the PHLAWD system  (Smith & Walker, 2019). Four chloroplast regions, rbcL, trnL, ndhF, and matK were used alongside the nuclear internal transcribed spacer (ITS) region. These sequences were aligned using MAFFT version 7  (Katoh & Stanley, 2013)  and concatenated into a supermatrix. The maximum-likelihood BCI angiosperm phylogeny was reconstructed in RAxML, version 8  (Stamatakis, 2014)  using the general time reversible model with Gamma site heterogeneity. There are several standard calculations of phylogenetic diversity (for a review of approaches see  Vellend et al.
| Description of pollen morphology and measurement of morphological distance Pollen morphology data for each angiosperm taxon in our phylogeny and pollination strategy datasets were extracted from Mander (2016), which describes angiosperm pollen grains from BCI plants. The  Mander (2016)  data were derived from a monograph containing morphological descriptions and images of BCI pollen grains  (Roubik & Moreno, 1991)  by scoring each pollen grain for the following six discrete morphological characters: apertures (60 character states), exine stratification (4 character states), shape (10 character states), dispersal unit (6 character states), primary surface ornamentation (22 character states), and secondary surface ornamentation (23 character states) (see  Punt et al. , 2007 for terminology).
For each pollen type, these six characters were concatenated so that the pollen morphology of each taxon was represented by a string of length 6. Each unique string constitutes a pollen morphotype. The morphological distance between taxa was measured by computing the Hamming distance between their strings using a script written in the Python programming language (following Mander, 2018, see the Supplementary Information). The Hamming distance between two strings of equal length can be defined as the number of positions at which the corresponding characters are different  (Hamming, 1950). For example, the following two taxa and their strings (in brackets) are from the dataset used in this paper:  19, 4, 7, 1, 2, 23). Piper aequale (Piperaceae) = ( Prioria copaifera (Fabaceae) =  (44, 3, 8, 1, 8, 19).
| Statistical tests Statistical significance tests were performed in R (R DCT, 2013). We tested the association between morphological and genetic distance using Spearman's rho, which is non-parametric and computed using cor. test (x, y, method="spearman"). We tested for differences in the morphological diversity of each pollination group-abiotic, mixed, biotic-using pairwise Wilcoxon rank sum tests, which are non-parametric and computed using wilcox. test(x, y) with continuity correction and without p-value adjustment. We visualized the distribution of taxa in morphological space using a linear discriminant analysis, which was performed in the R package MASS using lda(x, data). We standardized sample sizes across each pollination group using bootstrap analyses (random resampling with replacement) of mean pairwise morphological diversity and the proportion of unique pollen morphotypes, which were performed using a script written in the Python programming language (see the Supplementary Information).
17 or 0. 83 (Figure  1 ). There is considerable variation in the genetic distance of plants that have morphological distance values of 0. 33 and 0. 50, whereas plants that have morphological distance values of 0 and 0. 83 have less variation in their genetic distances (Figure  1 ). As a group, taxa that are separated by a morphological distance of 0. 83 are all further apart genetically than taxa that are identical in terms of their pollen morphology (Figure  1 ). The association between morphological and genetic distance is statistically significant (Figure  1 , Spearman's rho = 0. 097, p =. 014). In our dataset, there are 14 families and 125 species in the abiotic pollination category, 6 families and 72 species in the mixed pollination category, and 104 families and 795 species in the biotic pollination category (Table  1 ).
The three groups do not form discrete clusters, and there are representatives of each pollination strategy distributed throughout morphological space (Figure  4 , see also Figure  S2  and Figure  S3 ). Despite this, the abiotic and biotic pollination groups both contain a high number of unique pollen morphotypes. In our dataset of BCI angiosperms, there are 125 abiotically pollinated plants and these produce 47 pollen morphotypes, 36 of which (77%) are unique to this pollination strategy and are not produced by plants that employ a mixed or biotic pollination strategy (Table  1 ). Similarly, there are 795 biotically pollinated BCI plants in our dataset and these produce 406 pollen morphotypes, 386 of which (95%) are only found in this pollination group (Table  1 ). Since the numbers of taxa in each pollination group vary considerably, we undertook a resampling analysis in which 25 taxa were randomly selected from each pollination group and the proportion of unique pollen morphotypes in each group was calculated.
In our dataset, many more taxa employ a biotic pollination strategy (n = 795) than an abiotic strategy (n = 125) (Table  1 ) and consequently comparing the numbers of morphotypes produced by each group is difficult. Our classifications of biotic and abiotic pollination strategies are based on a limited number of pollination studies and because wind pollination may not be a viable strategy for plants that occur in low population densities, or in the relatively windless forest understory, it is possible that we have overestimated abiotic pollination. Even with those caveats, however, the abiotically pollinated plants in our dataset produce just 47 pollen morphotypes (compared to 406 produced by biotically pollinated plants), but 36 (77%) of these morphotypes are unique to this pollination strategy (Table  1 , see also Figure  5 ).
Agradecimientos Los autores agradecen las aportaciones y comentarios realizados por los revisores, que sin duda permitieron mejorar la calidad de este trabajo. Este estudio se realizó en el marco del Programa ANLAC (Amenazas Naturales en Los Andes Centrales) dirigido por S. M. Moreiras y formó parte de la tesis de maestría de M. Correas González, financiada por el programa Bec. Ar del Ministerio de Educación, Cultura, Ciencia y Tecnología de la Nación Argentina.
The shell is preserved as voucher at the Bordeaux Natural History Museum (catalog number: MHNBx 2018. 19. 1). The status of new occurrence was established by consulting the lists of the island's terrestrial snail inventories  (Van der Schalie, 1948; Aguayo, 1961 Aguayo, , 1966;; Grana Raffucci, 2007)  as well as the naturalist databases documenting terrestrial molluscs in North America and the Caribbean (iNaturalist) or compiling the catalogues of natural history collections, including that of the University of Puerto Rico (iDigBio, GBIF). The examination of the seven shells collected makes it possible to recognize two species. Six shells are identified as Bulimulus guadalupensis (Bruguière, 1789), a widespread species in the Caribbean region (e. g. Breure, 1974)  and in Puerto Rico (  Van der Schalie, 1948; Grana Raffucci, 2007)  where it is reported since 19th century.
| Microclimatic data Because conditions at the microsites in which decomposition takes place are poorly represented by macroclimatic parameters F I G U R E 1 Geographic location of sites included in our litter transplant experiment in Chile. AD, Arid-Dry; AF, Arid-Fog; SA, Semi-Arid; ME, Mediterranean; TU, Temperate upland; TL, Temperate Lowland  (Bradford et al. , 2016 (Bradford et al. , , 2017)) , we measured local soil moisture and temperature directly next to the litterbags (see next section) in each of the three plots per site for the duration of the experiment. We measured soil temperature at a depth of 2 cm using HOBO Micro Station dataloggers (H21-002) with two sensors (S-TMB-M002) and volumetric soil moisture at a depth between 0 and 14 cm using TMS-3 dataloggers (TOMST, Czech Republic).
Based on the clay and sand content of our study sites  (Bernhard et al. , 2018) , calibrations for sandy loam (AD and AF), loamy sand (SA and ME) and loamy soils (TH and TL) were used for the soil moisture measurements, as suggested by the provider  (Wild et al. , 2019). Sensors recorded data every 30 (temperature) or 15 min (moisture). We calculated mean soil temperature (MST) and soil volumetric water content (henceforth, mean soil moisture, MSM) for each decomposition period (0-3, 0-6, 0-9, 012 and 0-24 months, from June 2016 to June 2018). MST and MSM data were aggregated at the level of the plot (mean of two sensors) and site (mean of three plots, Table  1 ). Additionally, to characterize the radiation environment in the plots, canopy cover and leaf area index (LAI) were estimated. LAI was measured with a Licor TA B L E 1 Description of study sites across the climatic gradient considered in this study, including dominant vegetation, selected species for the litter transplant experiment, latitude, meteorological and soil microclimatic data.
We analysed how litter mass loss changed among sites (as a proxy for climate) and through time, by performing a two-way ANOVA with mass loss as a function of site and time, both as factors, followed by Tukey pairwise-comparisons as post hoc analyses. To determine how the relative importance of climate and traits for litter mass loss and their interaction changed with decomposition stage, we used linear mixed models (LMMs) to explain mass loss for each decomposition stage separately, comparing the models to detect changes in the driving factors through time. We tested MSM, functional traits and their interaction terms as predictor variables, with study sites as a random factor, using the lmer function from the lme4 package  (Bates et al. , 2014)  in r. For each decomposition stage, model selection was performed using forward selection, selecting the most parsimonious model following the Akaike information criterion (AIC).
At each step, we quantified the variance inflation of the added variable, using the check_collinearity function (vif threshold = 10) from the performance package in r  (Lüdecke et al. , 2020)  to restrict multicollinearity in the model. For each selected decomposition model (i. e. for each decomposition stage), the explained variance per predictor was approached using dominance analysis  (Azen & Budescu, 2006; Luo & Azen, 2013)  and calculated using the S&B R 2 metric at level 1  (Snijders & Bosker, 1994)  with the dominanceanalysis package in r  (Bustos & Soares, 2020). For each decomposition model, we calculated the relative importance of traits to MSM (RI Traits) as the total variance explained by traits (traits R 2 ) divided by the variance explained by MSM (MSM R 2 ). To evaluate the effect of trait variation (expressed as the SD) on the importance of traits for litter decomposition, we used randomization-based statistical procedures.
ABSTRACT. Acquiring information on snow depth distribution at high spatial and temporal resolution in mountain areas is time consuming and generally these acquisitions are subjected to meteorological constrains. This work presents a simple approach to assess snow depth distribution from automatically observed snow variables and a pre-existing database of snow depth maps. By combining daily observations of in-situ snow depth, georectified time-lapse photography (snow presence or absence) and information on snowpack distribution during annual snow peaks determined with a Terrestrial Laser Scanner (TLS), a method was developed to simulate snow depth distribution on day-by-day basis. This method was tested is Izas Experimental Catchment, in the Central Spanish Pyrenees, a site with a large database of TLS observations, time-lapse images and nivo-meteorological variables for six snow seasons  (from 2011 to 2017).
, 2013). The large database of snow depth distribution maps generated with LiDAR technology worldwide has been exploited to better understand snowpack dynamics  (Mott et al. , 2010; Schirmer et al. , 2011; Revuelto et al. , 2015; Schön et al. , 2015). Nevertheless data acquisition and data processing are time consuming and require highly specialized work teams and expensive measurement devices. Recently developed methods currently applied to mountain areas, such as unmanned aerial vehicles (UAVs) and high spatial resolution stereo satellite images  (De Michele et al. , 2016; Marti et al. , 2016) , have overcome some TLS/ALS limitations, but also have important meteorological and technical limitations that prevent the determination of snowpack distribution on a regular basis. Thereby, there is a scientific need to formulate methods that allow the generation of snow depth distribution maps without remote sensing observations, by combining certain automatically acquired snowpack variables.
The present study sought to develop a novel and simple method of generating daily snow depth maps from observations automatically obtained in mountain areas. The methodology was designed and its results evaluated in the Izas Experimental Catchment (Central Spanish Pyrenees). This site is equipped with an AWS that can determine changes in snow depth over time on a precise location, as well as a time-lapse camera that can be used to assess snow cover extent on a daily basis. Moreover, a TLS database of snow depth at high spatial resolution is available for particular dates from 2011 to 2017  (Revuelto et al. , 2017). This approach utilizes mean values of the TLS annual peak accumulation to calculate the ratio among snow depths pixels. These ratios were subsequently used to generate daily snow depth distribution maps, by combining snow depth from the AWS and the snow cover extent derived from time-lapse images.
Methods and database The methodology to generate daily snow depth distribution maps is mainly sustained on the strong topographic control of snowpack distributions observed in mountain areas. These areas show strong inter-and intra-annual consistency  (Revuelto et al. , 2014a; López-Moreno et al. , 2017) , indicating that the main snow distribution patterns are repeated from year to year  (Sturm and Wagner, 2010). Once a repeat distribution pattern is confirmed in an area, the proposed method is designed to generate a daily database of snow depth distribution from automatically obtained observations of snow depth at a particular location and the distribution of snow presence or absence over the target area. The database available to test and define the methodology at the Izas Experimental Catchment was composed of 25 TLS snow depth distribution maps, georectified timelapse photographs of daily changes in snow covered area, and in-situ observations of changes in snow depth from the AWS.
The procedure used to generate the database of the distribution of snow covered areas (snow presence/absence) in the Izas Experimental Catchment has been described previously  (Revuelto et al. , 2016b). The final product had the same geometry as the snow depth distribution obtained with the TLS. Both databases are available in UTM 30N (ETRS89 datum) with the same limits and the same spatial resolution (1 m x 1 m), enhancing the applicability of the methods defined in this study. Table  1  includes information on the Snow Covered Area (SCA) of TLS scanning dates derived from georectified time-lapse photographs. Cuadernos de Investigación Geográfica 46 (1), 2020, pp. 59-79.
Generation of daily snow depth distribution maps Several steps were required to generate daily databases of snow depth distribution: 1. In the first step, the ratios of average snow depth for each pixel to the maximum average snow depths at various yearly snow peak observation times were calculated. Average snow depth distributions were obtained from the selected scans (note that various combinations of annual TLS observations were tested). Subsequently, the ratio (P(x,y)) between the mean snow depth for each grid cell (SD avg (x,y)) and the maximum mean snow depth value (SD max ) was calculated using the equation: (1) Thus, P(x,y) is a ratio that falls in the interval (0, 1]. Moreover, all yearly snow peaks in the study area were entirely covered with snow. 2. In the second step, the P(x,y) ratios from step 1 for each time with valid timelapse photographs were rescaled to their original interval of (0, 1] for snow covered pixels, with snow free pixels assigned a value of 0.
Synthetic in-situ snow depth The methodology presented in this study has an important limitation. In the absence of snow at an AWS location, it was impossible to generate the snow depth distribution. This was applicable to seven of the 25 TLS snow depth maps available, mostly at the end of the snow season. Despite the ability of the methods presented in this study to generate a daily database of snow depth distributions, the only alternative is to evaluate the results with snow distribution obtained from TLS observations. Moreover, as the average snow depth distribution at peak snow accumulation was calculated based on two to six TLS observations, the number of independent observations available to evaluate the performance of the methodology was drastically reduced. To overcome these limitations, and to be able to test simulated results for all TLS observation dates, a procedure was established to generate synthetic in-situ snow depth observations for dates on which AWS showed no snow.
This was mainly observed following scans early in the snow season (i. e. 06/11/2014) and late in the snow season after long periods of melting (i. e. 24/05/2012). At these times, the simulation approach is unable to generate the snowpack distribution observed. Moreover the methodology is unable to reproduce areas of high snow accumulation, underestimating snow accumulation during peak snow periods and overestimating snow depth during melting periods. The results presented here are encouraging when compared with other simulation approaches (detailed snowpack models) with higher computational costs, methods that must be combined with TLS observations to obtain similar or superior performance  (Revuelto et al. , 2016a)  than the methodology presented here. Nevertheless, the simulation procedure presented here is applicable only to simulating small-scale snowpack dynamics (from 1 m to ~500 m), in study areas with a pre-existent database of snow distribution dynamics.
Simulation results have been tested against 25 TLS observations available at the study site. The method is supported by the strong topographic control that terrain exerts on snowpack distribution in mountainous areas. The major findings of this research can be summarized as follows: Combining time-lapse photographs with in-situ snow depth observations and a preexistent database of snow depth maps, following the methodology proposed here; allows simulating snow depth distribution on almost a daily basis in remote mountain areas. 1. Superior results are obtained when using information from a long lasting database of TLS observations (averaging all annual TLS peak observations). However, almost same results are obtained when data from average snow accumulation seasons are used in the algorithm.
Acknowledgments J. Revuelto is supported by a  Post-doctoral Fellowship  from the  AXA  research fund (le Post-Doctorant Jesús Revuelto est bénéficiaire d'une bourse postdoctorale du Fonds AXA pour la Recherchem Ref:  CNRM 3. 2. 01/17 ). Esteban Alonso-González is supported by the  Spanish Ministry of Economy and Competitiveness  ( BES-2015-071466 ).
• Web of Science In Web of Science, MMNP was indexed in the categories Mathematical & Computation Biology (Q4); Mathematics, Interdisciplinary Applications (Q4); and Multidisciplinary Sciences (Q4). In August 2019, we sent a request to Clarivate Analytics, which maintains Web of Science, to add MMNP in the category Mathematics, Applied. The Editorial team of Clarivate evaluated the change of category request and determined that Mathematics, Applied"is a sufficiently relevant category for this journal and as a result have added this category to the journal's categorization. The decision was also made to drop the Multidisciplinary Sciences category because of the primary focus of the journal being on mathematical methods. "Clarivate publishes the Impact Factor, which is a measure of the frequency with which the average article in a journal has been cited in a two-year period, based on the Journal Citation Report database.
• Scopus A similar request was sent to Scopus in August 2019. Until then, MMNP was indexed in a single category in Scopus: Modeling and Simulation (Q3). We requested the addition of the category Applied Mathematics. Scopus evaluated and approved this request, so that the journal is now indexed in both categories: Modeling and Simulation, and Applied Mathematics. Furthermore, we are pleased to report that in the new category of Applied Mathematics, MMNP is ranked #222/461, which puts the journal in the second quartile in the category of Applied Mathematics (Q2). The CiteScore is a measure of a frequency in which the average article 1 Senior Publisher, EDP Sciences ZA Courtaboeuf, 91944 Les Ulis, France. 2 Editor-in-Chief, Mathematical Modelling and Natural Phenomena, Villeurbanne, France. EDITORIAL in a journal has been cited in a three-year period, based on the Scopus database.
Agradecimientos Los socios financiadores que han apoyado esta investigación incluyen el programa de investigación e innovación H2020 de la Unión Europea (proyecto SINCERE), la Iniciativa Climática Internacional (IKI) del Ministerio Federal de Medio Ambiente, Conservación de la Naturaleza, Construcción y Seguridad Nuclear de Alemania (BMUB), la Agencia Noruega para la Cooperación al Desarrollo, el Ministerio de Transición Ecológica e Inclusiva de Francia y el Programa de Investigación del CGIAR sobre Bosques, Árboles y Agroforestería (CRP-FTA) con apoyo financiero del Fondo CGIAR. Los autores agradecen a Helvetas, Bosques Andinos, CEDES e IDMA por su apoyo logístico y ayuda en la organización de los talleres y entrevistas; así como a todos los entrevistados por su tiempo e información útil.
However, metallurgical exploitation prioritised mineralisations rich in arsenic and other elements, even when other sources were more readily accessible: for the case of Las Pilas, the exploitation of Pinar de Bédar sources instead of Sierra Cabrera, closer to the site; for the cases of Santa Bárbara and Almizaraque, the sources of Cerro Minado. The possibility that Almizaraque and Las Pilas also exploited the minerals of Herrerías, although to a lesser extent, remains open. Broader exchange networks are indicated by the data from finished objects, from which greater mobility can be inferred.
THE VERA BASIN: ARCHAEOLOGICAL AND GEOLOGICAL BACKGROUND There are three main settlements with evidence of the full chaîne opératoire of metallurgical production in the Vera Basin: Las Pilas, Santa Bárbara and Almizaraque; there is also partial evidence of some stages of metallurgical works in other settlements such as Zájara and Campos. Along with these sites, there is a series of collective burial necropolises that have traditionally been associated with the populations that lived in the Vera depression and the Almanzora River basin, such Zájara; 5. Almizaraque; 6. La Encantada; 7. Las Pilas; 8. Loma de Belmonte; 9. Gatas; 10. El Argar. Design M. Murillo-Barroso. Base map modified from CGIAR-CSI SRTM 90m 53 Digital Elevation Data (http://srtm. csi. cgiar. org/), with permission from SRTM  (Jarvis et al.