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CASCADE is a global dataset for 139 extant coccolithophore taxonomic units. CASCADE includes a trait database (size and cellular organic and inorganic carbon contents) and taxonomic-specific global spatiotemporal distributions (Lat/Lon/Depth/Month/Year) of coccolithophore abundance and organic and inorganic carbon stocks. CASCADE covers all ocean basins over the upper 275 meters, spans the years 1964-2019 and includes 33,119 taxonomic-specific abundance observations. Within CASCADE, we characterise the underlying uncertainties due to measurement errors by propagating error estimates between the different studies. Full details of the data set are provided in the associated Scientific Data manuscript. The repository contains five main folders: 1) "Classification", which contains YAML files with synonyms, family-level classifications, and life cycle phase associations and definitions; 2) "Concatenated literature", which contains the merged datasets of size, PIC and POC and which were corrected for taxonomic unit synonyms; 3) "Resampled cellular datasets", which contains the resampled datasets of size, PIC and POC in long format as well as a summary table; 4) "Gridded data sets", which contains gridded datasets of abundance, PIC and POC; 5) "Species lists", which contains spreadsheets of the "common" (>20 obs) and "rare" (<20 obs) species and their number of observations. The CASCADE data set can be easily reproduced using the scripts and data provided in the associated github repository: https://github.com/nanophyto/CASCADE/ (zenodo.12797197) Correspondence to: Joost de Vries, joost.devries@bristol.ac.uk v.0.1.2 has some fixes: 1. The wrongly specified S. neapolitana was removed from synonyms.yml (this species is now S. nana)2. Longitudes were corrected for Guerreiro et al., 20233. A double entry for Dimizia et al., 2015 was fixed4. Units in Sal et al., 2013 were correct to cells/L (previously cells/ml)5. Data from Sal et al., 2013 was re-done, as some species were missing6. Duplicate entries from Baumann et al., 2000 were dropped

This data contents information about parental wood and charcoal characteristics of 16 tropical species growing in fast-growth condictions. The data details tha following characterist of parental wood: moisture content (PMC) and wood density (PWD). On the others hand the charcoal characteristics are: Density (CD), moisture content (CMC) and compression strength of charcoal, gross calorific value (GCV), ash and volatile matter and fixed carbon, Carbon (C), nitrogen (N), hydrogen (H), and oxygen (O) contents, C/N ratio, O/Cmol ratio and H/Cmol ratio. Besides it is presented FTIR spectra and the ignition temperature (Ti), the burnout temperature (Tf), the characteristic combustion index (S), the ignition index (Di), the time corresponding to the maximum combustion rate (tp), the ignition time (tig), and the average rate of combustion.

The growing search for alternative energy sources is not only due to the present shortage of non-renewable energy sources, but also due to their negative environmental impacts. Therefore, a lot of attention is drawn to the use of biomass as a renewable energy source. However, using biomass in its natural state has not proven to be an efficient technique, giving rise to a wide range of processing treatments that enhance the properties of biomass as an energy source. Torrefaction is a thermal process that enhances the properties of biomass through its thermal decomposition at temperatures between 200 and 300 °C. The torrefaction process is defined by several parameters, which also have impacts on the final quality of the torrefied biomass. The final quality is measured by considering parameters, such as humidity, heating value (HV), and grindability. Studies have focused on maximizing the torrefied biomass’ quality using the best possible combination for the different parameters. The main objective of this article is to present new information regarding the conventional torrefaction process, as well as study the innovative techniques that have been in development for the improvement of the torrefied biomass qualities. With this study, conclusions were made regarding the importance of torrefaction in the energy field, after considering the economic status of this renewable resource. The importance of the torrefaction parameters on the final properties of torrefied biomass was also highly considered, as well as the importance of the reactor scales for the definition of ideal protocols.

The European Union Water Framework Directive (WFD), a new regulation aiming to achieve and maintain a clean and well-managed water environment, refers to phytoplankton as one of the biological quality elements that should be regularly monitored, and upon which the reference conditions of water quality should be established. However, the use of phytoplankton as a biological quality element will result in several constraints, which are analyzed in this article with examples from Portuguese waters. Specifically, the establishment of reference conditions of water quality may be difficult in some water bodies for which no historical data exists. The sampling frequency proposed for phytoplankton monitoring does not seem suitable to assess phytoplankton succession, and may preclude the detection of algal blooms. Finally, the use of chlorophyll a as a proxy of phytoplankton biomass and abundance has been proposed by some authors, but it may overlook blooms of pico- and small nanophytoplankton, and overestimate the importance of large microphytoplankton. Furthermore, most studies in Portugal have used only inverted microscopy for phytoplankton observation and quantification; this method does not permit the distinction between autotrophic and heterotrophic cells, especially in samples preserved with Lugol's solution, and does not allow the observation of smaller-sized cells. Finally, some techniques, such as remote sensing and chemotaxonomic analysis, are proposed to be used as supplements in phytoplankton monitoring programs.

Marcelo A. Aizen, Gabriela R. Gleiser, Thomas Kitzberger, Ruben Milla. Being a tree crop increases the odds of experiencing yield declines irrespective of pollinator dependence (to be submitted to PCI) Data and R scripts to reproduce the analyses and the figures shown in the paper. All analyses were performed using R 4.0.2. Data 1. FAOdata_21-12-2021.csv This file includes yearly data (1961-2020, column 8) on yield and cultivated area (columns 6 and 10) at the country, sub-regional, and regional levels (column 2) for each crop (column 4) drawn from the United Nations Food and Agriculture Organization database (data available at http://www.fao.org/faostat/en; accessed July 21-12-2021). [Used in Script 1 to generate the synthesis dataset] 2. countries.csv This file provides information on the region (column 2) to which each country (column 1) belongs. [Used in Script 1 to generate the synthesis dataset] 3. dependence.csv This file provides information on the pollinator dependence category (column 2) of each crop (column 1). 4. traits.csv This file provides information on the traits of each crop other than pollinator dependence, including, besides the crop name (column1), the variables type of harvested organ (column 5) and growth form (column 6). [Used in Script 1 to generate the synthesis dataset] 5. dataset.csv The synthesis dataset generated by Script 1. 6. growth.csv The yield growth dataset generated by Script 1 and used as input by Scripts 2 and 3. 7. phylonames.csv This file lists all the crops (column 1) and their equivalent tip names in the crop phylogeny (column 2). [Used in Script 2 for the phylogenetically-controlled analyses] 8.phylo137.tre File containing the phylogenetic tree. Scripts 1. dataset This R script curates and merges all the individual datasets mentioned above into a single dataset, estimating and adding to this single dataset the growth rate for each crop and country, and the (log) cumulative harvested area per crop and country over the period 1961-2020. 2. analyses This R script includes all the analyses described in the article’s main text. 3. figures This R script creates all the main and supplementary figures of this article. 4. lme4_phylo_setup R function written by Li and Bolker (2019) to carry out phylogenetically-controlled generalized linear mixed-effects models as described in the main text of the article. References Li, M., and B. Bolker. 2019. wzmli/phyloglmm: First release of phylogenetic comparative analysis in lme4- verse. Zenodo. https://doi.org/10.5281/zenodo.2639887.

[Methods for processing the data] The Easyflux datalogger program (Easyflux-DL, Campbell Scientific, 2020) corrected the raw high-frequency data from the EC tower using the full suite of standard corrections and adjustments, including spike filtering, measurement quality control flags and applying correction for high/low frequency losses, to generate corrected half-hourly turbulent fluxes. More details of EC data post-processing are available in the EasyFlux-DL product manual (Campbell Scientific, 2020). UAV images were processed using OpenDroneMap (https://www.opendronemap.org/), an open-source drone processing software. Raw TIR H20T image tiles (i.e. in R-JPEG format) were first converted to single band radiometric temperatures using the open-source DJI Thermal SDK software (https://www.dji.com/downloads/softwares/dji-thermal-sdk). These individual temperature image tiles were then mosaicked together with OpenDroneMap. Congruently, multispectral images from Sequoia+ were radiometrically calibrated using camera corrections, such as vignetting, black level and gain/exposure compensations, using the available routines developed for OpenDroneMap (https://github.com/OpenDroneMap/ODM/blob/master/opendm/multispectral.py). [Description of methods used for collection/generation of data] For the data located in 'meteo' folder, an Eddy-Covariance (EC) tower was used to sample all variables described above. The tower was instrumented with an integrated open-path infrared gas analyzer and 3D Sonic anemometer Campbell Scientific1 (IRGASON, Campbell Scientific, Logan, UT, USA) to measure ecosystem-level carbon and water gas exchanges alongwith meterological forcings. The raw data were sampled at a frequency of 20 Hz and recorded using a CR6 datalogger (Campbell Scientific, Logan, UT, USA). Regarding the images from the UAV system in the 'inputs' folder, a DJI Matrice-300 UAV (DJI Technology Co., Ltd, Shenzhen, China) was used to acquire visible near infrared (VNIR), thermal (TIR) and RGB imagery using the sensors Parrot Sequoia+ (Parrot S.A., Paris, France), DJI’s Zenmuse H20T and DJI’s Zenmuse P1, respectively. Regarding the images in the 'outputs' folder, these are the images resulting from applying the different versions of TSEB as shown in the python script ('run_tseb_main.py') All the inputs and outputs of the two-source energy balance (TSEB) model are in the 'inputs' and 'outputs folder. Another readme file explicitely describes this data. Also described below: ## inputs ### meteo A csv file with meterological and EC measurements during UAV overpass time. ### UAV UAV imagery are stored in seperate folders for each date (in YYYYMMDD). Each input is available over the study site at 2m spatial resolution. ## outputs The model outputs are available for both TSEB-PT and TSEB-2T versions using pyTSEB (https://github.com/hectornieto/pyTSEB). In each folder, both Main ('Main') and ancillary ('Anc') output data is made available.

- Sampling plots methodology. The number of systematic sampling plots, setting a maximum error of 6% was 256. The sampling plots centres were located at the nodes of a 55 m side square net. The corresponding UTM coordinates (Datum WGS84) were identified and located in field with a sub-metric precision GPS. Circular plots of 4 m diameter were marked on the terrain and the measurements of the following data were concentrated on them: shrub crown cover (CC, %), species composition, number of plants per hectare (N), and average shrub height (H, m). Subsequently, all the plants were cut at ground level and were weighted with a 40 kg ± 10 g digital dynamometer. Four samples per shrubland (2.5 kg per sample), including rockrose trunk, branches and leaves, were collected and sent to the Laboratory of Biomass Characterization (LCB) at CEDER-CIEMAT in Soria (Centre for the Development of Renewable Energy Sources) to measure moisture content in order to estimate dry biomass weight per study area. - The dataset includes field data from 290 rockrose sampling plots (Ø 4 m). 256 plots were used for developing a biomass weight per hectare equation and the rest of them for completing validation process. - Location of the four studied shrublands in Soria. Coordinates of sampling areas central point (Datum WGS84): - Lubia: Latitud 41º35´57.69” N, Longitud 2º29´53.57” W; Huso UTM: 30, Coord. X: 541816.48, Coord. Y: 4605416.65 - Acrijos: Latitud 42º2´38.99” N, Longitud 2º31´44.46” W; Huso UTM: 30, Coord. X: 566565.63, Coord. Y: 4654992.73 - Navalcaballo: Latitud 41º40´8.51” N, Longitud 2º31´36.14” W; Huso UTM: 30, Coord. X: 539399.58, Coord. Y: 4613138.42 - Centenera: Latitud 41º30´48.89” N, Longitud 2º41´31.76” W; Huso UTM: 30, Coord. X: 525688.09, Coord. Y: 4595817.82 Descripción del proyecto: The purpose of field data collection is building weight equations to predict dry biomass weight per hectare (t/ha) and dry biomass weight per plant (kg/plant) of rockrose (Cistus laurifolius L.) shrublands in Central Spain. Descripción del dataset: The dataset contains 2 data files with dasometric measurements from 290 rockrose Ø 4 m sampling plots (shrub crown cover, shrub mean height, green and oven-dried biomass weight), and individual measurements from 426 rockrose plants (plant height, mean crown diameter and oven-dried plant weight). The sampling date and the location of the study areas are also included. in. Excel, 10