dataesr/scientific-paragraphs-categorization huggingface dataset:
La barra de tamaño en la parte inferior derecha de cada panel corresponde a 2. 5 μm. TARA_80 superficie 20-180 µm: I (diatomea Hemiaulus con un filamento de la cianobacteria Richelia fijadora de N2); TARA_81 máx. Chl a 5-20 µm: D (diatomea céntrica), C y G (diatomeas pennadas), J (dinoflagelados, orden Gonyaulacales), L (prasinofito, género Pterosperma); TARA_82 superficie 5-20 µm: A, B y E (diatomeas pennadas), F (diatomea, probablemente género Chaetoceros), M (haptofito, probablemente género Phaeocystis); TARA_82 superficie 20-180 µm: H (coscinodiscales), K (dinoflagelados). ₈₄₂ FM I�������  confocal microscopy dataset  (Colin et al. 2017). We found images coming from the nano (5-20 μm) and micro (20-180 μm) size fractions assigned to diatoms, dinoflagellates, haptophytes and chlorophytes (Figure  2 ).
Additionally, a state-of-the-art microscopy imaging dataset and a precise metagenomic analysis aimed at the nifH gene helped us to point at symbioses between eukaryotic phytoplankton and nitrogen-fixing bacteria. Biological interactions involving phytoplankton are still being unveiled (e. g. Vincent et al. 2018)  and for this, microscopic observations remain crucial  (Pierella Karlusich et al. 2020 ). To our knowledge, the Tara Ocean samples represent the first 18S rRNA gene metabarcoding survey of the SWAO plankton in the vicinity of the Argentine Sea and, in spite of the limited number of samples, they provide relevant regional data. The regional diversity that we found reflects latitudinal gradients observed for the SWAO for different microscopic organisms such as diatoms, tintinnids and copepods, with decreasing diversity in the poleward direction  (Thompson 2004; Olguin Salinas et al.
A���������������. This work was written in the frame of a ECOS Sud bilateral project (AT08ST18) and supported by Assemble+ (PID8395), University of Buenos Aires (UBACYT 20020170100620BA), ANPCyT (PICT 2017-3020) and Programa INVESTIGACIÓN, DESARROLLO E INNOVACIÓN EN CIENCIAS DEL MAR A6 BioMMAr. We thank Martin Saraceno and Viviana Alder for their help during the manuscript preparation. CB acknowledges funding from ERC Advanced Awards Diatomite and Diatomic, the FFEM -French Facility for Global Environment (Fonds Français pour l'Environnement Mondial) to JJPK, and the French Government "Investissements d'Avenir" Programmes MEMO LIFE (Grant ANR-10-LABX-54), Université de Recherche Paris Sciences et Lettres (PSL) (Grant ANR-1253 11-IDEX-0001-02), and OCEANOMICS Grant ANR-11-BTBR-0008. This article is contribution number 138 of Tara Oceans.
Survey target population identification The survey population target was companies in Colombia whose main economic activity is software development. To identify this population, we got information from the Colombian chamber of commerce. This entity has a complete database that includes information that allows us to identify this population. We got information from the Colombian chamber of commerce of companies established in Colombia grouped by economic activity type. Using the database, we got 5818 companies whose principal economic activity was "Software development". Filters used are available in a replication package on (L. F. Restrepo Gutierrez, 2021). From these 5818 software companies, a sample of 361 companies was determined to be surveyed, considering a confidence level of 95% and a margin error of 5% (according to the general rule to acceptable margins of error mentioned by  Kotrlik & Higgins, 2001).
The predictor variables associated with Likert scale answers could be entered as nominal or ordinal; for this reason, four models were created with the variation of taking response variable and predictor variables as ordinal and nominal. In order to create the models, the polr and multinom commands from the MASS package of the R tool  (Ripley, 2020)  were used. For estimating the best logistic regression model, an iterative model evaluation was made with the Step command from the R tool's stats package to fit the model. Step is an automated method based on the AIC (Akaike Information Criterion) model selection that aims to find out the best fit model from all possible subset models and returns back the optimal set of features  (Hosmer et al. , 2013). The four models were evaluated with the AIC.
Agradecimientos Este documento fue financiado por el Programa de Trabajo Económico y Sectorial (RG-E1741) del del Banco Interamericano de Desarrollo (BID). Los autores agradecen a Nadine Andrieu, Allen Blackman, Ana María Ibanez, Catalina Marinkovic, Ana Ríos, Graham Watkins, a un revisor anónimo, y a los numerosos participantes del taller inicial celebrado en 2021 por sus útiles comentarios y opiniones. Agradecemos especialmente a Romina Ordoñez por su revisión exhaustiva. Todos los demás errores son de los autores. Gráfico 6 del Apéndice: Emisiones del cambio del uso del suelo, escenarios combinados de producción y dieta por subregión Gráfico 7 del Apéndice: Emisiones de producción, producción combinada y escenarios de dieta Cuadro 2 del Apéndice: Coeficiente de la necesidad de piensos, calculado como el ingreso total de materia seca de los piensos, dividido por la cantidad de producción del producto principal en toneladas de materia fresca (carne de res para la carne de res y leche cruda para los lácteos), en toneladas de materia seca/toneladas, en el año base y en las proyecciones de línea de base.
Analysis of densitometric data Densitometric data were fitted with the residual method to remove the effect of cambial age, using independent models for each location  (Rozenberg et al. , 2020). Rings close to the pith were excluded in the analysis to avoid variation due to juvenile effect  (Cerano-Paredes et al. , 2016). Since the last ring (  2018 ) had not been fully formed, it was also not included in the analysis. Based on these criteria and availability of instrumental climate data, it was determined that the most appropriate period to distinguish the effects of climatic factors was from 1960 to 2017. With the average values of the growth ring components per tree, a two-way ANOVA was carried out with the MIXED procedure of SAS version 9. 4 (SAS Institute, 2013), to estimate average values per location and evaluate the effect of elevation, mountain, and their interaction.
Agradecimientos Se agradece el apoyo del programa de cooperación académica y científica CONACYT-ECOS Nord entre México y Francia a través del proyecto número 0276777 'Impacto del cambio climático en la adaptación y plasticidad fenotípica del crecimiento en árboles forestales'-M16A01 'Impact du changement climatique sur la plasticité phénotypique de la microdensité du xylème'.
A systematic review of plant hydraulic studies in Mexico To explore the development of published research on plant water relations conducted in Mexico, we performed a systematic review of the primary literature covering ca. 40 years, based on the criteria of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols  (Page et al. 2021). Between November 30th, 2021, and December 3rd, 2021, using the Clarivate Web of Science database, we performed searches using the following terms: Mexico hydraul*, Mexico water-use, and Mexico water relations. The initial display of results offered 2,551, 1,311, and 1,744 entries, respectively. From this total, we filtered the articles of the following disciplines: biology, agronomy, ecology, plant sciences, forestry, and biological conservation.
This filter yielded 262, 377, and 420 entries. Subsequently, we reviewed the titles and abstracts and excluded secondary literature (reviews and comments) and articles not related to plant water transport (918 exclusions) (Figure  1 ). From this filtered dataset (141 articles), we reviewed the methods to exclude works that were not performed within Mexico. From this further exclusion, we obtained 83 entries (Figure  1 ). These were reviewed and the following information was extracted: location and state where the research was mainly conducted, the vegetation type according to  Rzedowski (2006) , the growth-form investigated, and finally, the plant genera studied. We correlated time vs the number of publications, and integrated the information of the frequency of occurrence of genera within scientific publications using a semantic cloud generator (wordcloud.
cs. arizona. edu). Figures and analyses were performed in SigmaPlot 11. 0 (Systat Software Inc. Chicago IL, U. S. A. ). For the relation between number of publications per year, the normality of data was tested using Shapiro-Wilks test. As the dataset did not fulfill the normal distribution, a nonparametric Spearman correlation analysis was performed using SigmaPlot 11. 0.
The semantic cloud showed that Quercus is the most studied tree genus in our dataset, Tillandsia the most studied epiphytic genus and Capsicum the most studied crop genus (Figure  4 ). Most of the studied species appear only in one study. With exception of Manilkara, four of the five most speciose tree genera in Mexico (Cavender-Bares 2019) are represented at least two times (Quercus, Pinus, Bursera, and Lysiloma). Limited research on plant hydraulics has been performed for the most conspicuous crops in our country, namely maize and beans, and nearly none in squash, edible fruits, and other economically important crops (Figure  4 ).
Acknowledgments MEO appreciates the support of  Consejo Nacional de Ciencia y Tecnología  ( CONACyT ), project  A1-S-26934 , and  Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica (PAPIIT, UNAM) , grant  IN210719. H. P. appreciates support of  CONACyT , project  CB-240607 , and  PAPIIT ,  UNAM  grant  IN207618. J. López-Portillo  provided valuable impulse to produce this document.
Meiofaunal and nematode analyses All samples were sieved through 1000 µm and 32 µm mesh sizes. Meiofaunal organisms were extracted by Ludox centrifugation following  Danovaro (2010) , counted and classified to the highest taxonomic level using a stereomicroscope. At least 100 nematodes from each replicate were picked out randomly and mounted on permanent slides after formalin-ethanol-glycerol treatment for identification to the genus level with an optical microscope according to  Platt and Warwick (1988)  and the recent literature available (NeMys database,  Bezerra et al. 2021). Species richness (SR), expected number of genera (51) and diversity indices (Shannon, H', Margalef D, Pielou evenness and J') were calculated for both meiofauna and nematodes with the DIVERSE routine (PRIMER 6+;  Clarke and Gorley 2006).
Statistical analysis Differences in total meiofaunal abundance, total nematode biomass, nematode diversity between layers, total meiofaunal abundance and total nematode biomass between the maerl bed and the sandy beach were assessed by one-way analyses of variance (ANO-VA). Prior to the ANOVAs, the homogeneity of variances was assessed by the Anderson-Darling test and, when necessary, data were square-root transformed. Tukey's HSD test was performed to assess significant  between-level effects. The ANOVAs and Turkey's test were performed using the STATISTICA V. 10 software. A one-way analysis of similarities (ANO-SIM) was used to assess between-layer differences in meiofauna community structure and nematode composition. The SIMPER routine (cut-off of 90%, on fourth-root transformed data) was used to determine the contribution of each meiofaunal taxon and nematode species to the total dissimilarity.
Permeability index The first index was developed from the latest available land-use map for Nord and Pasde-Calais. The map is based on two sets of aerial photographs taken in 2009 at a spatial resolution of 20 cm in the visible and near-infrared regions of the electromagnetic spectrum. The map uses European Corine Land Cover (CLC) nomenclature with three nested levels and also includes a fourth level, giving a total of 54 categories to refine the distinctions within each of the following areas: artificial surfaces, varying with building density and use intensity (11 categories in the CLC vs 26 in the database we used), agricultural areas, varying with crop assortment and cultivation practices (4 categories vs 6), natural areas, with for example a distinction between forested areas according to planting or harvest date (13 categories vs 22).
This typology was compared with the expert knowledge of three ecologists on three test areas where they had conducted surveys (Table  1 ). Ten levels of permeability to movement were selected with a wider range between levels, particularly for areas to be avoided  (Kinldmann, Burel, 2008) : Table 1 also contains the change in the number of polygons in the initial land cover database after its modification following the scoring of the eight indices. The number of polygons increased from 125,000 to nearly 520,000, providing a finer estimate of biodiversity potential. Table  1  also contains the mean, median and standard deviation of CBPI for all land-use categories. The highest scores correspond to the least favourable habitats and the lowest values to the most favourable habitats. Variations in CBPI for the same habitat type or land-use category are explained by spatial variations in quality, shape and configuration indices.
Production methods and techniques are thus more easily adopted. Nevertheless, and despite substantial literature, the various calculations can be long to implement, especially when using complex databases. It is often a question of finding a compromise between the degree of precision, the calculation time and the computational capacities  (Foltête et al. , 2012). The spatial resolution of the landscape matrix in this study, however, allows analyses to be conducted from regional to local scales in a unified reference frame. This unification can help to limit misinterpretation and increase sharing between different administrative levels  (Alphandéry, Fortier, 2015). CBPI also uses expert knowledge in a diagnostic approach shared between several disciplinary fields. This type of approach can be transposed during the development of urban planning tools.
Conclusion 33 The CBPI is a multifactorial index of biodiversity potential. It is composed of quality, shape and configuration indices from landscape ecology. It combines a structural and functional approach, focusing, in this article, on a generalist medium-sized mammal. It can be recalculated to (i) measure the effects of urban planning projects on biodiversity potential and (ii) provide a tool for monitoring biodiversity over time. It can also be a reference in the determination of roughness coefficients (cost of passage) in the identification of ecological corridors by spatial analysis (distance cost according to species and patches of the landscape matrix). Finally, it can be readily updated following the creation of new databases describing the coverage, use, morphology and characteristics of the landscape on a smaller scale 8.
Towards an index of biodiversity potential: example in an anthropised and fra. Cybergeo: European Journal of Geography , Espace, Société, Territoire NOTES 1. Modelling outputs are not always produced at the scale of political territories and their spatial resolution is not always compatible with spatial planning tools. Generally, they are applied in a 'project-by-project approach'  (Bigard et al. , 2020 -p. 8). 2. Most of the indices have been calculated in Python language or use common spatial operators (overlap, join, etc. ) 3. Six indices were calculated on the land use/cover polygons in the database presented below. Two indices (shape and isolation) were calculated from the grouping of these polygons according to their adjacency and permeability index value or score. 4. Mustelidae (weasels, European badgers, ermines).
ABSTRACTS Promoting biodiversity is a major issue in urban planning. Estimating the biodiversity potential of patches in the landscape matrix often uses the tools of spatial analysis and landscape ecology indices, especially in large study areas. In this paper, we present the construction of a composite biodiversity potential index (CBPI) based on structural and functional approaches of landscape ecology. The CBPI is composed of eight indices (quality: permeability, fragmentation, environmental management; shape: size, shape complexity; configuration: contrast between neighbouring patches, distance to the most favourable habitats, distance from built-up areas). The CBPI appears to be suitable for anthropised and fragmented landscape matrices. It can be used to identify ecological networks in an 'Avoid, Reduce, Compensate' approach and can be readily updated with new map databases.
l'analyse spatiale et les indicateurs de l'écologie du paysage, notamment dans de vastes zones d'étude. Dans cet article, nous exposons la constitution d'un indice composite (CBPI) mobilisant les approches structurelle et fonctionnelle de l'écologie du paysage. Il est composé de 8 indicateurs (de qualité : perméabilité, fragmentation, gestion environnementale ; de forme : taille, complexité de la forme ; de configuration : contraste avec les polygones voisins, distance aux habitats les plus favorables, distance aux zones bâties). Le CBPI apparait adapté aux matrices paysagères anthropisées et fragmentées. Il peut servir dans l'identification des réseaux écologiques, dans la démarche Eviter, Réduire, Compenser et il peut être actualisé avec de nouvelles bases de données cartographiques.
To adequately manage forest ecosystems, it is necessary to have a comprehensive understanding of the effect of mixing species on the temporal stability of productivity and the way in which it is influenced by climate conditions across large geographical areas. 2. Here, we used a unique dataset of 261 stands combining pure and two-species mixtures of four relevant tree species over a wide range of climate conditions in Europe to examine the effect of species mixing on the level and temporal stability of productivity. Structural equation modelling was employed to further explore the direct and indirect influence of climate, overyielding, species asynchrony and additive effect (i. e. temporal stability expected from the species growth in monospecific stands) on temporal stability in mixed forests.
, 2008). In this study, we examine the temporal stability of stand growth (as an indicator of productivity) and its main drivers in monocultures and two-species mixtures along a climate gradient in Europe. We use a unique dataset of 261 plots including beech (Fagus sylvatica L. ), oak (Quercus robur L. and Q. petraea  [Matt. ] Liebl. ) or spruce (Picea abies [L. ] H. Karst. ) in combination with pine (Pinus sylvestris L. ). All these tree species are native, productive and economically valuable for the European forestry sector, with the greatest values of growing stocks  (Freudenschuss et al. , 2020). In a previous study, we used 93 beech and pine plots to study temporal stability of productivity in mixed and monospecific stands at different organizational levels  (tree, population, community; del Río et al.
| Dataset The data used come from 87 triplets distributed in three transects with different tree species compositions across Europe (Figure  2 ). S2 ). Fieldwork permits were obtained from the respective forest owners when required. Ethical approval was not required. The annual stand basal area increment (BAI, m 2 ha -1 year -1 ) was used as an indicator of community productivity  (del Río et al. , 2017; Dolezal et al. , 2020) , as this variable can be precisely estimated from field measurements and in forest stands is highly correlated with net above-ground productivity. The studied period was 2000-2013 for the beech-pine transect and 2004-2017 for the oak-pine and spruce-pine transects, the last year corresponding to triplet establishment. The series of annual stand basal area increments for the studied period (14 years) were estimated based on measured tree diameters and tree ring width series.
Using data from cored trees, tree diameter increment-diameter models were fitted by year, species and plot to estimate annual diameter increments of noncored trees. Annual climate data were obtained from meteorological weather stations located in the proximity of each triplet (50 triplets). When local station data were not available, national digital climatic atlas data (24 triplets) or more general gridded data (13 triplets) were used (see Table  S1 ). For analyses, we considered the average mean annual temperature (T), total annual precipitation (P) and the Martonne aridity index (M = P/[T + 10])  (Martonne, 1926)  during the studied period, as they describe the large variability of climates in the study area (Figure  2 ), and are related to productivity variation at large scales in a simple way  (Huang & Xia, 2019).
| Climate effects on TS To analyse the influence of climate on TS, climate variables were added to Equation  4 , including the interaction with the type of species composition. where Cli j represents the different climate variables tested (T, P, M, although M was not significant in the final models) in triplet j, and all other terms are defined analogously to those in Equation  4. Then, we explored the overall climate effect on TS when considering the identity of species composition instead of the type of composition. We analysed all data together, including the seven levels of identity of species composition (Id: beech, oak, spruce, pine, beech-pine, oak-pine, spruce-pine). To test whether the climate effect varied among the three mixtures, we included the climate in Equation 6 and fitted it using only mixed plots.
All authors contributed critically to the drafts and gave final approval for publication. ACK N OWLED G EM ENTS This study was supported by the projects  REFORM (ERA-Net SUMFOREST ,  PCIN2017-026/-027 ,  MICIN, Spain) ,  CARE4C  ( Marie Skłodowska-Curie No   778322 ,  HORIZON2020 ) and  CLU-2019-01 -iuFOR-UVa  and  VA183P20-SMART. J. C. was supported by the  National Agency of Agricultural Research  (Project No. QK21020307 ); K. B. by the  Polish Government MNiSW   2018-2021   Matching  Fund No. 117/H2020/2018 ; M. M. by  Estonian Research Council  grant ( PRG1586 ), and  EMÜ  Projects  P180024MIME ,  P200029MIME ; R. S. by the  Slovak Research and Development Agency , project No. APVV-18-0390.
Period 2013 -2017 The physical Coastal and Regional Ocean Community (CROCO) model was used for this period and solved the same equations as ROMS. It also has a non-hydrostatic component that improves the representation of the dynamics at high resolution (which was not used in this study). The CROCO code (version 1. 0) used is similar to that of ROMS (version 3. 1). The model is coupled to the same version of PISCES used in the RP-EN simulation. The simulation ranged from 5°N to 20°S and from 92°W to 70°W, between January 2013 and June 2017. The horizontal resolution was ~12 km (1/9°) and 32 vertical sigma levels were used. ASCAT satellite data provided the atmospheric forcing for winds  (Bentamy et al. , 2009) , and for heat fluxes, they came from ERA-Interim  (Dee et al. , 2011). The Mercator model  (Lellouche et al.
Satellite data Chlorophyll-a (Cl-a) satellite data were used for model validation and productivity analysis during the ENs and 2017 CEN. Surface Cl-a came from the coupling of satellite data from SeaWiFS  (Sept. 1997 -Dec. 2010 , O´Reilly et al. , 1998)  and  MODIS (Jan. 2011 -Dec. 2017 , Ocean Biology Processing Group, 2003). Given the bias they present compared to SeaWIFS, the MODIS data were corrected  (Demarcq & Benazzouz, 2015; Espinoza-Morriberón et al. , 2017b). The output has a spatial resolution of ~4 km. Cl-a anomalies were estimated based on monthly climatology between January 1998 -December 2015. Furthermore, we calculated the mean Cl-a diciembre 2015. Además, utilizando el producto SeaWIFS-MODIS, con el cual se tienen ~20 años de observaciones, y diferentes eventos EN, se calculó anomalías promedio de Cl-a para los eventos EN y EN Costero 2017, como indicador de productividad.