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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

Resultados interactivos de una investigación llevada a cabo sobre la implementación de estrategias de rehabilitación optimizadas aplicadas al parque residencial social del sur de España en diferentes zonas climáticas (A3, A4, B4 y C3), ante escenarios climáticos futuros de cambio climático. En concreto, se ha realizado un análisis multiobjetivo en el que se optimizan, a partir de la aplicación de algoritmos genéticos, los costes de intervención de diversas soluciones de rehabilitación y el confort térmico interior en verano e invierno de las viviendas sociales, bajo escenarios futuros de calentamiento global. Todo ello se realiza mediante modelos parametrizados y validados a nivel de conjunto edificatorio, implementando información real contenida en una base de datos facilitada por la Agencia de Vivienda y Rehabilitación de Andalucía (AVRA) en los modelos de simulación dinámica de conjunto. Los resultados de esta investigación están vinculados con el proyecto: Optimización Paramétrica de Fachadas de Doble Piel en Clima Mediterráneo para la Mejora de la Eficiencia Energética ante Escenarios de cambio Climático (BIA2017-86383-R). La visualización de los resultados de esta investigación se realiza a través de ficheros .html que pueden ser accedidos fácilmente mediante cualquier navegador web. Existen tres tipos de figuras interactivas: gráficas de dispersión en 3d, gráficas de dispersión en 2d y gráficas de ejes paralelos. Se ha generado por cada zona climática analizada estos tres tipos de gráficos. En el caso de los gráficos de dispersión en 3d, el entorno web permite girar y aumentar la figura, para facilitar su visualización en el espacio. Además, colocando el cursor sobre cada punto, pueden consultarse los valores específicos de las variables de optimización (porcentaje de horas con temperaturas por encima del límite superior e inferior del confort y costes de inversión en €/m2 construido). En los gráficos en dispersión en 2d, colocando el cursor sobre cada punto, se despliega una ventana en la que pueden visualizarse los diferentes valores asociados a ese punto. En lo referente a las figuras de coordenadas paralelas, las variables de optimización pueden filtrase, seleccionando y arrastrando el cursor sobre un rango de valores buscado. Lo mismo puede realizarse con el resto de variables combinatorias. Hecho esto, la herramienta web mostrará la combinación de los paquetes de rehabilitación óptimos (variables de rehabilitación ligadas a la mejora energética de la envolvente térmica y las variables operacionales analizadas). Cada combinación, tendrá asociado un valor concreto de horas fuera del confort en verano e invierno, así como de costes de inversión. Por consiguiente, es posible realizar una comparación rápida y genérica entre diferentes actuaciones y seleccionar, de forma acorde, valorando los resultados, las medidas de rehabilitación que mejor se ajusten a los Programas e Iniciativas rehabilitadoras consideradas. v.1

Peer reviewed 1 table.

Survey data used in a perception study of stalked barnacle harvesters on the effectiveness of fisheries management practices in Spain, Portugal and France. Harvesters from the following six regions along the Atlantic Arc participated: Morbihan in Brittany (France), Asturias-East, Asturias-West and Galicia (Spain), the Reserva Natural das Berlengas (RNB; Portugal) and the Parque Natural do Sudoeste Alentejano e Costa Vicentina (PNSACV; Portugal). We administered 184 surveys from October 2019 to September 2020 and each region was treated as an independent population. The data includes: general demographic data (Region, Age, Gender, Level of Education, Main income source, Years of Experience); perception data of the effectiveness of the currently implemented management strategies in each region (coded: e_name_of_strategy – using Likert Scale with scores ranging from 1 = completely ineffective to 5 = very effective); data of the willingness for change of the currently implemented management (Yes, No, NA); and data of harvesters’ perceptions regarding the most important strategy to achieve sustainability in the fishery. Because the surveys were conducted both before and during the Covid-19 pandemic (the column Covid indicates whether the data was collected before or during the pandemic), we had to make adjustments in our data collection methods. We provided the following options for survey completion (see the Recollection_of_data column): by hand in a written format, online, or via an oral interview conducted with the assistance of a scientist per telephone. Our results indicate that the majority of harvesters in the regions in Portugal and France were willing to make changes to current management strategies, reflecting their awareness of the need for improvement. Based on the AIC model selection analysis results, the model with the single variable region explained 83% of the cumulative model weight. The variable region was the best predictor of the trends in management strategy preferences, and presented a highly significant goodness-of-fit result (p<0.001), suggesting that regional differences play a significant role in shaping these preferences. No clear trend emerged regarding a single "optimal" management strategy preferred by harvesters across regions. Harvesters in less developed co-management systems favored general input and output restrictions and expressed a desire for greater involvement in co-management processes. Conversely, harvesters in highly developed co-management systems with Territorial User Rights for Fishers (TURFs) preferred the most restrictive and spatially explicit management strategies, such as implementing harvest bans and establishing marine reserves. Our findings emphasise that management strategies do not only need to be tailored to each region's particular practices, needs, and characteristics, but that resource users’ readiness for specific strategies also needs to be considered.

Input files for the ForClim model (version 4.0.1) used in the associated paper. They can be used to to reproduce results of the simulation study. The ForClim model, including the source code, executable and documentation, is freely available under an Open Access license from the website of the original developers at https://ites-fe.ethz.ch/openaccess/. The original climatic dataset used to generate the ForClim input climate files at each site in South Tyrol is freely available at https://doi.pangaea.de/10.1594/PANGAEA.924502 while the CHELSA climate data for future scenarios are available at https://www.chelsa-climate.org. If interested in using this dataset for a research study or a project, please contact Marco Mina ----------------------------------------------------------------------- Hillebrand L, Marzini S, Crespi A, Hiltner U & Mina M (2023) Contrasting impacts of climate change on protection forests of the Italian Alps. Frontiers in Forests and Global Change, 6, 2023 https://doi.org/10.3389/ffgc.2023.1240235 ABSTRACT. Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.

The Aneto Glacier, is the largest glacier in the Pyrenees. Its shrinkage and wastage have been continuous in recent decades, and there are signs of accelerated melting in recent years. In this study, changes in the surface and ice thickness of the Aneto Glacier from 1981 to 2022 are investigated using historical aerial imagery, airborne LiDAR point clouds, and UAV imagery. A GPR survey conducted in 2020, combined with data from photogrammetric analyses, allowed us to reconstruct the current ice thickness and also the existing ice distribution in 1981 and 2011. Over the last 41 years, the total glaciated area has shrunk by 64.7% and the ice thickness has decreased, on average, by 30.5 m. The mean remaining ice thickness in autumn 2022 was 11.9 m, as against the mean thicknesses of 32.9 m, 19.2 m reconstructed for 1981 and 2011 and 15.0 m observed in 2020 respectively. The results demonstrate the critical situation of the glacier, with an imminent segmentation into two smaller ice bodies and no evidence of an accumulation zone. We also found that the occurrence of an extremely hot and dry year, as observed in the 2021–2022 season, leads to a drastic degradation of the glacier, posing a high risk to the persistence of the Aneto Glacier, a situation that could extend to the rest of the Pyrenean glaciers in a relatively short time.

The version 1.0 contains the supporting data for the work (still under submission) "Last century changes in annual precipitation in a Mediterranean area and their spatial variability. Insights from northern Tuscany (Italy)". The following files are here available (all file are georeferenced in EPSG: 3003): - AVG_Rainfall_1990-2019.tif -> Raster map of the mean annual precipitation for the northern Tuscany, Italy. It encompasses the portion of the Tuscany region northern of the cities of Livorno - Florence. The interpolation was validated via a leave one out cross-validation procedure. - D3-1_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1921 to 1950 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy). - D3-2_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1951 to 1980 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy). - DeltaSHP_Points_AVG_Annual_Rainfall.zip -> Shape file of the raingauges locations with the mean annual precipitation values of the period 1990 to 2019. - RaingaugesSHP_Points_AVG_Annual_Rainfall_1990-2019.zip -> Shape file of the raingauges locations with the following information: differences in the mean annual precipitation values between the two 3-decades periods 1951 to 1980 and 1990 to 2019 (named D3-2); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1951 to 1980 and 1990 to 2019; difference in the mean annual precipitation values between the two 3-decades periods 1921 to 1950 and 1990 to 2019 (named D3-1); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1921 to 1950 and 1990 to 2019.

[ES] Se ha aplicado la clasificación de tipos de tiempo (Weather Types) de Jenkinson y Collison a la malla de presiones diaria del reanálisis NCAR-NCEP (periodo Enero 1950-Diciembre 2023) correspondiente a la Península Ibérica y Baleares. Por la resolución de dicha malla (2.5º x 2.5º lat/long) el total de nodos de control es de 12. Los tipos de tiempo resultantes incluyen los 8 direccionales puros, Anticiclónico y Ciclónico puro, y la combinación de 8 tipos híbridos entre las categorías previas. Los casos indeterminados fueron distribuidos proporcionalmente entre las clases previas. [EN] It has been applied the Jenkinson & Collison classification of Weather Types to Iberian Peninsula and Balearic Island by using the daily NCAR-NCEP grid surface pressure dataset (January-1950/December-2023). Grid resolution/2.5ºx2.5 lat/long) produces 12 series. Weather Types classification includes 8 directional pure, Anticyclonic and Cyclonic pure types, and combination of previous ones in the hybrid types. Non determines cases were spread homogeneously. [EN] WETYDAS contains 12 TXT archives localized by their coordinates in NCAR grid; information include year, month, day and code of weathee type. [ES] WETYDAS consta de 12 archivos formato TXT geolocalizados por sus coordenadas en la malla NCAR; la información incluye el año, mes y día, así como el código del tipo de tiempo resultante.

# sRGB Reflectances from Iberian butterflies [https://doi.org/10.5061/dryad.1g1jwsv0q](https://doi.org/10.5061/dryad.1g1jwsv0q) Data on wing reflectance (visible spectrum, mean standard RGB values (243.7= white, to 52= black) from 224 species of butterflies (Lepidoptera, Papilionoidea): 223 from the Iberian Peninsula and one (*C. webbianus*) from the Canaries. Average of male and female, sample size as indicated in column n. The data from *C. webbianus* and *C. marshalli* were not included in our analyses of reflectance. Text file, CSV format, columns delimited by periods, 225 rows (including headings) and 38 columns. Any means presented are weighted averages taking into account the areas of the parts involved. Wing reflectances refer to the parts of the wings exposed in a living butterfly (except FW\_AREA and HW\_AREA which are total wing surfaces). * **Ord**, row number (roughly a taxonomic arrangement) * **Species**, species name (abbreviated genus, contains a blank space, e.g., *Heteropterus morpheus*) * **N**, sample size * **FWL**, forewing length (mm) * **DFT**, reflectance, dorsal forewing * **DFp**, reflectance, dorsal forewing, proximal area * **DFd**, reflectance, dorsal forewing, distal area * **DHT**, reflectance, dorsal hindwing * **DHp**, reflectance, dorsal hindwing, proximal area * **DHd**, reflectance, dorsal hindwing, distal area * **DB**, reflectance, dorsal body area * **D(Tp+B)**, reflectance of the exposed dorsal body plus proximal wing surfaces * **DT**, reflectance of the dorsal areas (body plus whole wing) * **DTp**, reflectance of the dorsal, proximal wing areas * **DTd**, reflectance of the dorsal, distal wing areas * **VFT**, reflectance, ventral forewing * **VFp**, reflectance, ventral forewing, proximal area * **VFd**, reflectance, ventral forewing, distal area * **VHT**, reflectance, ventral hindwing * **VHp**, reflectance, ventral hindwing, proximal area * **VHd**, reflectance, ventral hindwing, distal area * **VB**, reflectance, ventral body area * **V(Tp+B)**, reflectance of the exposed ventral body plus proximal wing surfaces * **VT**, reflectance of the ventral areas (body plus whole wing) * **VTp**, reflectance of the ventral, proximal wing areas * **VTd**, reflectance of the ventral, distal wing areas * **Mean**, mean total reflectance (dorsal and ventral surfaces) * **p\_Mean**, mean reflectance of the proximal (dorsal and ventral) wing areas * **p\_Otimum**, mean reflectance of the proximal dorsal (for dorsal baskers) or ventral (for lateral basking species) wing areas. * **FW\_area**, total forewing area (mm2) * **HW\_area**, total hindwing area (mm2) * **T\_Mean\_Iberia\_10km**, Iberian mean species temperature, Centigrade degrees, 10 x 10 km resolution * **P\_Mean\_Iberia\_10km**, mean species annual precipitation, mm, Iberian Peninsula, 10 x 10 km resolution * **T\_Mean\_Ibera\_50km**, mean species temperature, Centigrade degrees, Iberian Peninsula, 50 x 50 km resolution * **P\_Mean\_Iberia\_50km**, mean species annual precipitation, mm, Iberian Peninsula, 50 x 50 km resolution Data on wing reflectance (visible spectrum, mean standard RGB values (243.7= white, to 52= black) from 224 species of butterflies (Lepidoptera, Papilionoidea): 223 from the Iberian Peninsula and one (Cyclyrius webbianus) from the Canary Islands. Average of male and female, sample size as indicated in column n. The data from C. webbianus and Cacyreus marshalli are provided although these species were not included in our analyses of reflectance. The data were measured from digital images of set (collection) specimens taken in fixed conditions, with grey (average RGB) values standardized a posteriori to fit the scale white= 243.7= white, to black= 52. The data set includes the mean length of the forewing (mm) and the total areas (mm2) of the fore and hind wings.

This readme file provides all data and R codes used to perform the analyses presented in Figs. 2-4 of the main text and Supplementary Information Figures S1-S2-S3. FIGURE 2 - Seasonally_dated_GDs.txt: Contains information on the timing (Season) of rockfall (GD) in a given tree (Id) and a given year (yr) over the past 100 years. Inv refers to the operators which analyzed growth disturbances in the tree-ring series. Lat / Long refers to the position of the tree in CH1903/ Swiss Grid projection. Intensity (1-4) refers to (1), intermediate (2) and strong (3) GD. Intensity 4 was attributed to injuries (I). Only the 408 GD rated 3 (strong TRD) and 4 (injuries) were used in Fig. 2. Acronyms used for Response_type read as follows: TRD: Tangential rows of traumatic resin ducts; I: Injuries. Acronyms used for Season refer to Dormancy (1_D), early (2_EE), middle (3_ME) and late (4_LE) earlywood, whereas a GD found in the latewood was attributed to either the early (5_EL) or late (6_LL) latewood. - Trends_in_seasonality_R1.R: The data contained in "Seasonally_dated_GDs" were processed with the R script "Trends_in_Seasonality.R". This seasonal trend analysis code is inspired by work published by Schlögl et al. (2021; https://doi.org/10.1016/j.crm.2021.100294) and Heiser et al. (2022; https://doi.org/10.1029/2011JF002262). FIGURE 3-4-S1 - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - Rockfall_frequency_climate.R: The data contained in "Tasch_GD.txt" were processed with the R script "Rockfall_frequency_climate.R". - The temperature (Imfeld23_tmp.txt) and precipitation (Imfeld23_prc.txt) data used in Fig. 3 are from the Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Täschgufer site. - The script set with temperature series enables to compute Fig. 4 (l.149:216) and Fig. 3 (l. 216:330); the script set with precipitation series enables to compute Fig. S1 FIGURE S2 - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) at the Täschgufer site in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - Rockfall_frequency_borehole.R: is adapted from "Rockfall_frequency_climate.R" to work with the borehole dates. - Corvatsch0_6R1: Contains the Corvatsch borehole temperature series (2000-2020, 0.6m depth) (Hoelzle, M. et al. https://doi.org/10.5194/essd-14-1531-2022, 2022). FIGURE S3 - Plattje_GD.txt: Contains the raw data on rockfall impacts (GD) at the Plattje site in a given year (yr) as found all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - - Rockfall_frequency_climate_Plattje.R: The data contained in "Plattje_GD.txt" were processed with the R script "Rockfall_frequency_climate_Plattje.R". - The temperature (Imfeld23_tmp_Plattje.txt) and precipitation (Imfeld23_prc_Plattje.txt) data used in Fig. 3 are from Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Plattje site.