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opencc-by-4.0Mar 2024View details →

Trait values of European tetrapods archetypes

This dataset contains trait values used to identify archetype groups among European tetrapods. The file includes 8 data sheets, one for each homogeneous group of tetrapods and one sheet about references.

opencc-zeroMar 2024View details →

With or without you: Gut microbiota does not predict aggregation behavior in European earwig females

The reasons why some individuals are solitary and others gregarious are the subject of ongoing debate as we seek to understand the emergence of sociality. Recent studies suggest that the expression of aggregation behaviors may be linked to the gut microbiota of the host. Here, we tested this hypothesis in females of the European earwig. This insect is ideal for addressing this question, as adults both naturally vary in the degree to which they live in groups and show inter-individual variation in their gut microbial communities. We video-tracked 320 field-sampled females to quantify their natural variation in aggregation and then tested whether the most and least gregarious females had different gut microbiota. We also compared the general activity, boldness, body size, and body condition of these females and examined the association between each of these traits and the gut microbiota. Contrary to our predictions, we found no difference in the gut microbiota between the most and least gregarious females. There was also no difference in activity, boldness, and body condition between these two types of females. Independent of aggregation, gut microbiota was overall associated with female body condition, but not with any of our other measurements. Overall, these results demonstrate that a host's gut microbiota is not necessarily a major driver or a consequence of aggregation behavior in species with inter-individual variation in group living and call for future studies to investigate the determinants and role of gut microbiota in earwigs.

opencc-by-4.0Mar 2024View details →

Distribution of Panorpa (Mecoptera) in the territory of European Russia

Dataset represents occurrences of Panorpa (Insecta: Mecoptera) in European Russia in 2008, 2009, 2011, 2015, and 2017-2023.

Database to: Effectiveness of soil management strategies for mitigation of N2O emissions in European arable land: A meta-analysis

Database to a meta-analysis studing the effects of adding different organic matter inputs (crop residues, green manure, livestock manure, slurry, digestate, compost or biochar) to soils on N2O emissions. Database consists of over 50 field experiments conducted in 15 European countries. Diverse arable crops, mainly cereals, were cultivated in monoculture or in crop rotations on mineral soils.  Cumulative N2O emissions per unit land area were monitored during periods of 30 to 1,070 days in treatments, which received organic matter inputs, alone or in combination with mineral N fertiliser; and in controls fertilised with mineral N. The original results appeared in 46 articles published between 1993 and 2022 in peer-reviewed scientific journals, as well as a project report, and a PhD thesis.

opencc-zeroApr 2024View details →

Carpathian tree-ring network for European beech and Norway spruce

Basic ecological theory suggests that a tradeoff between competitiveness and stress tolerance dictates species range limits at regional extents. However, empirical support for this key theory remains deficient because the necessary spatial and temporal coverage and scalability of field observations have rarely been achieved. We harnessed an extensive dendroecological network (>22,000 tree-ring samples from 816 forest inventory plots) to disentangle competition-limited from climate-limited growth in both overstory and understory trees. Growth synchrony among trees thereby served as an integral metric of climate sensitivity, an approach that we justify in supplementary analyses of growth responses to temperature, precipitation, and the standardized precipitation-evapotranspiration index. Sampling plots were arranged along elevational climate and vegetation gradients throughout the Carpathian Mountains, ranging from mixed-species lowland forests to coniferous forests at high elevations. With mixed-effect modelling, we also identified non-climatic factors (stand characteristics, species diversity, and disturbance history) that modulate spatial patterns in the growth rate and synchrony of European beech (Fagus sylvatica L.) and Norway spruce (Picea abies (L.) Karst.). Beech exhibited reduced growth and increased climate sensitivity towards higher elevations but performed better when species diversity was higher. The growth of spruce increased towards its lower range boundary, but understory cohorts grew poorly under interspecific competition. Overall, climate sensitivity was lower in more productive stands with benign climatic conditions and in recently disturbed sites with reduced stand density. These contrasting performances at mid-elevations where the two species overlap (900 – 1300 m a.s.l.) reflect their evolutionary history, which enables them to be competitive (beech) or cold-stress tolerant (spruce). This history will affect interactions between the two species under climate warming and shape macroecological patterns in the Carpathian ecoregion and likely other parts of Europe. Our findings point to a growing advantage of competitively stronger species in montane and subalpine vegetation zones.

Calibrated and uncalibrated projection data from the paper "Assessing observational constraints on future European climate in an out-of-sample framework"

Individual uncalibrated and calibrated projections for each of the five methods (A-E) in the following folders: MethodA_proj/ MethodB_proj/ MethodC_proj/ MethodD_proj/ MethodE_proj/   Also included are the out-of-sample data from the "pseudo-observations" (taken from CMIP6 models) used for the verification (see paper for full details): FUTUREverif/

opencc-by-4.0Oct 2023View details →

Figures 1–2. Ameroseius lidiae Bregetova, 1977 in Description of Ameroseius lidiae male (Mesostigmata: Ameroseiidae) from Iran with a key to males of European species within the genus

Figures 1–2. Ameroseius lidiae Bregetova, 1977 (male) – 1. Dorsal idiosoma; 2. Ventral idiosoma.

opencc-by-4.0Jul 2023View details →

Quality and Utility of European Cardiovascular and Orthopaedic Registries for the Regulatory Evaluation of Medical Device Safety and Performance Across the Implant Lifecycle: A Systematic Review - Dataset

Background: The European Union Medical Device Regulation (MDR) requires manufacturers to undertake post-market clinical follow-up (PMCF) to assess the safety and performance of their devices following approval and Conformité Européenne (CE) marking. The quality and reliability of device registries for this Regulation have not been reported. As part of the Coordinating Research and Evidence for Medical Devices (CORE-MD) project, we identified and reviewed European cardiovascular and orthopaedic registries to assess their structures, methods, and suitability as data sources for regulatory purposes. Methods: Regional, national and multi-country European cardiovascular (coronary stents and valve repair/replacement) and orthopaedic (hip/knee prostheses) registries were identified using a systematic literature search. Annual reports, peer-reviewed publications, and websites were reviewed to extract publicly available information for 33 items related to structure and methodology in six domains and also for reported outcomes. Results: Of the 20 cardiovascular and 26 orthopaedic registries fulfilling eligibility criteria, a median of 33% (IQR: 14%-71%) items for cardiovascular and 60% (IQR: 28%-100%) items for orthopaedic registries were reported, with large variation across domains. For instance, no cardiovascular and 16 (62%) orthopaedic registries reported patient/ procedure-level completeness. No cardiovascular and 5 (19%) orthopaedic registries reported outlier performances of devices, but each with a different outlier definition. There was large heterogeneity in reporting on items, outcomes, definitions of outcomes, and follow-up durations. Conclusion: European cardiovascular and orthopaedic device registries could improve their potential as data sources for regulatory purposes by reaching consensus on standardised reporting of structural and methodological characteristics to judge the quality of the evidence as well as outcomes.

Data for "Willingness of rural and urban citizens to undertake pollinator conservation actions across three contrasting European countries"

Data for: "Willingness of rural and urban citizens to undertake pollinator conservation actions across three contrasting European countries" by Costanza Geppert, Cristiano Franceschinis, Thijs P.M. Fijen, David Kleijn, Jeroen Scheper, Ingolf Steffan-Dewenter, Mara Thiene, Lorenzo Marini (2024) People and Nature. This dataset was obtained by administering an online questionnaire in Germany, Italy, and the Netherlands.

Data on the occurrence of Anisakids in fishery products from aquaculture in European countries (Jan 2010 – Sept 2023)

This file contains data on Anisakids in fishery products from aquaculture in European countries covering studies published between January 2010 and September 2023. The systematic review protocol used to identify and extract the information is available at <a href="../records/10270810">https://zenodo.org/records/10270810</a>.

opencc-zeroApr 2024View details →

Data from: Timing of egg-laying in relation to a female's social environment in European starlings

It is widely assumed that female birds use non-photic supplemental cues, including social factors, to fine-tune timing of egg-laying to local conditions, but our knowledge of the nature of these social cues and how they operate remains limited. We analyzed the relationship between a female's social environment (nearest neighbor distances, residency, female -and- network familiarity, synchrony) and variation in timing of egg-laying in European starlings (Sturnus vulgaris) using individual, residual laying date (controlling for annual variation) and temperature-independent residual laying date (accounting for the effect of ambient temperature on laying date). Female social environment varied systematically with overall spatial distribution of nest-boxes (linear vs clumped boxes) but this was not associated with spatial variation in laying date or temperature-independent residual laying date. We found no evidence for any relationships between individual variation in social environment and individual, residual laying date and only weak evidence for any association with individual, temperature-independent residual laying date. The latter was associated with a) nearest neighbor distances in the linear habitat, with females nesting closer to neighbors laying earlier than predicted by temperature, but not in the two clumped habitats, and b) neighbor familiarity: females with an intermediate number of returning females (3/8) laid closest to the predicted date. Finally, despite the fact that synchrony was not associated with other social environment metrics, females with lower laying synchrony among neighbors laid earlier than predicted by temperature. This suggests that some components of the female-female social environment could act as supplemental cues for timing of egg-laying.

A stocktaking of European mid- and long-term experiments dealing with the application of external organic matter (EJP SOIL - EOM4SOIL - D 3.1.1)

Extending and optimizing recycling of organic wastes in agriculture is a key element in shifting conventional agriculture towards systems adapted to both energy depletion and climate change. Long-term field experiments (LTEs) play a crucial role in assessing and modelling the effects of repeated exogenous organic matter (EOM) application to soil, which allows to formulate locally adapted recommendations of use.   Nevertheless, a specific database focussing on LTEs dealing with organic fertilization in Europe were missing. To close this gap, we listed LTEs dealing with repeated application of EOM from existing online databases, and collected and harmonized all available metadata. The aim of this work was threefold: (1) to facilitate connections between comparable LTEs to foster data harmonization and compilation, (2) to map the diversity of pedoclimatic contexts and experimental designs in the LTE list and, (3) to highlight current knowledge gaps and research needs.  Data were collected from five online databases, allowing us to describe 201 LTEs. Key characteristics such as trial name, responsible institution, location, pedoclimatic context, duration, crop type and availability of online resources are well-described in contrast to LTE goal and owner contact, experimental design, soil type, studied EOM and monitored parameters (EOM characteristics, soil and crop properties), which are more difficult to gather and harmonize. The analysis of LTE metadata highlighted first that substantial harmonization efforts are required, particularly regarding the reporting of soil, crop and EOM properties over time. Second, the survey outlines that some European regions are poorly represented in the database, which may result either from an absence of LTE or from a lack of reporting. To close this gap, we call LTE managers to complete the current database with any missing relevant LTE or additional metadata, using the editable online repository attached to this document. In the future, improvement of predictive models could contribute to provide recommendations of EOM use to uncovered situations, whether in terms of soil, climate or type of EOM. Third, long-term effects on soil properties such as changes in soil biology composition or accumulation of organic contaminants (PFAS, microplastics, antibiotics, ...) appear to be poorly documented. LTEs have a key role to play in answering these emerging questions, having the potential to provide the rationale to fix acceptable thresholds in soils and EOMs for emerging pollutants and accordingly provide the best possible guidelines for the use of EOM in agriculture.  See related report at <a href="https://doi.org/10.5281/zenodo.14161379" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14161379</a>.

Data for: Seasonal differences in observed versus modeled new particle formation at two European boreal stations

Model dataset variables produced from the IFS and TM5 modules in EC-Earth3 version 3.4.0 which contains the control case and four experiments with the NPF lookup table. This paper is under review. The files contain: <ul> <li>NetCDF files from TM5 general output for each simulation at the two location grid points.</li> </ul> File-name description of the EC-Earth3 experiments:  "Hyyt" = Hyytiälä, "Mossa" = Hyltemossa. "ricc2" = CLUST-HIGH case "dlpno" = CLUST-Low case "bono" = CLUST-Low+Riccobono case "ctrl"   = Model control run case "nonpf" = No NPF case <ul> <li>Compressed tar file of NetCDF data from IFS output for all four simulations. All IFS data have been averaged to monthly means from 3-hourly grib datasets.</li> </ul>   <ul> <li>ADCHEM base-case particle number size distributions from Hyytiälä and Hyltemossa.</li> </ul>

Classification and structural analysis of value chain contracts for biodiversity conservation in the European Union

The data provided by this dataset are the raw data published in the paper "Classification and structural analysis of value chain contracts for biodiversity conservation in the European Union" (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.sftr.2024.100372" target="_blank" rel="noopener">https://doi.org/10.1016/j.sftr.2024.100372</a>).

opencc-by-4.0Nov 2022View details →

European Starling categorical perception chronic ephys and behavior dataset

This dataset corresponds to the currently in-press paper "Expectation-driven sensory adaptations support enhanced acuity during categorical perception" in Nature Neuroscience.    This dataset corresponds to the code at <a href="https://github.com/timsainb/cdcp_paper">https://github.com/timsainb/cdcp_paper</a> Please refer to the readme for this GitHub repo, which contains all the necessary information for reproducing our analyses or using this data for additional analyses.      <pre> </pre>

EEAR-Clim: A high density observational dataset of daily precipitation and air temperature for the Extended European Alpine Region

Data, metadata and code for paper published in Earth System Science Data: A high density observational dataset of daily precipitation and air temperature for the Extended Alpine Region   Code (working copy all written in R statistical software): scripts.zip <ul> <li>to read and process data in from different sources</li> <li>to perform intra and inter-stations quality control</li> <li>to perform break detection and homogenization</li> <li>to read results of quality control and homogenization</li> </ul> Data: <ul> <li>Daily time series of air temperature (mean, minimum and maximum) and precipitation as .zip files, grouped by data provider.</li> <li>Information on column content is provided in separate files "data_readme.txt"</li> <li>about 10000 stations from Italy, France, Switzerland, Austria, Germany, Slovenia, Croatia, Bosnia-Herzegovina, Czech Republic, Slovakia and Hungary</li> <li>Meta data (code, name, longitude, latitude, elevation, measurements availability for each variable, starting date, ending date) in "metadata.zip", including a file for each data provider</li> <li>If you use the data you agree to adhere to the respective data provider's terms as listed in "License.pdf"</li> <li>The license terms especially (and additionally to any other terms of the single data providers) include: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. [from <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>] </li> </ul>   Version history: v1.0: initial upload v2.0: update of data policies; addition of France and Croatia time series

opencc-by-4.0Jun 2024View details →

Data & codes for "Changes in abundance and distribution of European forest bird populations depend on biome, ecological specialisation and traits"

<h1>1.    Selection of European forest bird species and classification of their biome preferences</h1> We selected all species that are related to forest and woodland based on two data sources: Storchová & Hořák (2018) and Tobias et al. (2022), resulting in 107 bird species studied (Data S1). We defined forest bird species as those using environments ranging from closed-canopy forests to more open-canopy woodlands (A. Lehikoinen & Virkkala, 2018; Storchová & Hořák, 2018; Tobias et al., 2022). We determined their biome specialisation using breeding distribution centroids and the overall breeding distribution of each of the species, using the global map of terrestrial ecoregions from Olson et al. (2001) and range data from European Breeding Bird Atlas 1 and 2 (Hagemeijer & Blair, 1997; Keller et al., 2020). We categorised species as Mediterranean, temperate, or boreal based on their predominant biogeographic region. We considered species commonly occurring over several biomes as “generalists”. For instance, we reclassified the two typically boreal species Glaucidium passerinum Linnaeus and Strix uralensis Pallas as “generalists” due to significant range expansions into central and southern Europe in recent decades, therefore no longer restricted to the boreal region. For the complete list of species, biome specialisation, traits, and specialisation indices, refer to Data S1. <h1>2.    Changes in abundance and distribution of European forest bird species</h1> We assessed long-term changes in European forest bird populations through two approaches: (i) changes in estimated total European-level species abundance over a 40-year timeframe; and (ii) changes in species spatial distribution over a 30-year timeframe (Fig. 1). We utilized the estimated trends in European-level population size (i.e., the total number of individuals) for each common native European bird species from 1980 to 2017, as reported by Burns et al. (2021). Three species out of the 107 studied forest species were missing in the original manuscript and we used data generated with the same method from 1980 to 2018 from the European assessment, Article 12 (https://nature-art12.eionet.europa.eu/article12/). These abundance trends were calculated by Burns et al. (2021) using multi-sourced annual times series. For each species, they gathered population estimates and trends from each European country as well as European Union (EU)-level population trends. They analysed these data with a Bayesian hierarchical model to reconstruct EU-level smoothed species population time series. The model outputs include an average annual rate of abundance change and an associated 95% credible interval (Burns et al., 2021). Therefore, we did not directly use the average annual rate of abundance change, as this would have led us to consider species with low uncertainty as similar to those with high uncertainty. To account for the uncertainty, we categorised species as (i) declining, i.e., annual rates below one, (ii) increasing, i.e., annual rates above one and (iii) stable, i.e., annual rate whose 95% CI overlap one, i.e., no significant change. To better acknowledge the magnitude of the abundance change, significant changes with rates below 0.98 were labelled as “strongly declining” (i.e., 6.5% of the 107 species), while those above 1.02 were labelled as “strongly increasing” (i.e., 11% of the 107 species). To evaluate the sensitivity of the decision to categorised abundance change data, we also analysed abundance trend as continuous variable (see Supporting Information Fig. S8). To determine changes in species distributions, we used a comparison of species distributions between two periods (i.e., 1985-1988 and 2013-2017) using the European Breeding Bird Atlas 1 and 2 (EBBA 1 & 2; Hagemeijer & Blair, 1997; Howard et al., 2023; Keller et al., 2020). Howard et al. (2023) provided calculations of observed colonisation and extinction areas at a 50 x 50 km resolution across Europe. We measured changes in range as the difference between colonisations and extinctions of each species, with negative values indicating contracting ranges and positive values indicating expanding ranges. Additionally, we calculated the shift in the centre of gravity of the distribution range between the two periods, as a distance (km) along the south-north gradient for each species (Howard et al., 2023). <h1>3.    Trait and specialisation data for European forest bird species</h1> We extracted data for six functional traits from several sources (Table 1). (i) The species temperature index (STI)represents the long-term average temperature within the species’ breeding range (A. Lehikoinen et al., 2021). (ii) Diet data during the breeding season were obtained from Storchová & Hořák (2018), classifying species into binary variables as vertebrate carnivorous, invertebrate carnivorous, and herbivores (combining the leaf and seed eaters). Storchová & Hořák (2018) classified species into a diet category when the corresponding food resource represented at least 10% of the species diet throughout the breeding season. Therefore, one species can be in several categories (i.e., omnivores). (iii) We obtained nesting site data from Pearman et al. (2014), classifying species into binary variables as ground nesters, tree hole nesters, or elevated nesters (> 1 m in a tree or shrub). We also included data on (iv) species dependence on old-growth forests (Data S1; mostly from Fraixedas et al. (2015) and Mönkkönen et al. (2014), if present on both references, we classified them as “1” and if only in one reference as “0.5”), (v) migration distance (Howard et al., 2023), and (vi) body mass (Tobias et al., 2022). Finally, we extracted and developed seven species specialisation indices. (i) We used an overall specialisation index based on multiple traits (i.e., temperature, diet, foraging behaviour and substrate, habitat, and nesting site), and (ii) a nesting specialisation index, both obtained from Morelli et al. (2019). Both indices represent species specialization based on the dispersion of trait preferences for each species: e.g., nesting specialism equal 0 for species that nest in all habitat type and equal 1 for species that nest in only one habitat type). They are both calculated using the Gini index of inequality, which measures overall dispersion across, e.g., all traits for the overall specialization, based on data from Pearman et al. (2014) and Storchová & Hořák (2018). For additional information, see Morelli et al. (2019). We also used (iii) the diet specialisation index, (iv) the species distribution range during the breeding season (hereafter “breeding range area”) and (v) the climatic niche breadth from Reif et al. (2016). The diet specialisation index was calculated as the coefficient of variation for diet preferences for each species, where high values denotes specialized species (Reif et al., 2016). The breeding range area was evaluated as the number of 50-km squares in the distribution maps in Europe occupied by each species during the reproduction period, and is based on EBBA 1 (Hagemeijer & Blair, 1997). The climatic niche breadth was calculated as the difference between the 5% hottest and the 5% coldest mean temperature between April and June in which each species occurs, using EBBA 1 (Hagemeijer & Blair, 1997; Reif et al., 2016). Additionally, (vi) we calculated a broadleaf forest specialisation index based on binary forest habitat preferences (Storchová & Hořák, 2018), assigning values of one for species found only in broadleaf forests; zero for those in coniferous forests, and 0.5 for those found in both. Lastly, (vii) we created a forest specialisation index based on the species habitat preferences (Storchová & Hořák, 2018). The forest specialisation index was calculated as the mean of species affinity across habitats. We used increasing habitat weights along a gradient of tree dominance: open habitats as 1, shrubland as 1.5, woodland as 2 (i.e., species associated with habitats structured by trees in lower density than in forest), forest generalist (found in both coniferous and broadleaf dense forests) as 3, and forest specialist (found only either in coniferous or broadleaf dense forests) as 4. For instance, the index value for species occurring either in shrubland, woodland or both broadleaf and coniferous forests is 2.167. <h1>4.    Data analysis</h1> Data analyses were conducted with R software version 4.4.1. (R Core Team, 2024). Given the non-independence of species due to their genetic relatedness, we accounted for interspecific phylogenetic distance in all models. We constructed the phylogenetic tree for the 107 European forest bird species using ‘rotl’ and ‘ape’ R-packages (Michonneau et al., 2022; Paradis et al., 2023). We used rotl as an interface with the "Open Tree of Life", employing tol_induced_subtree R-function to generate the phylogenetic tree and compute.brlen R-function to set branch lengths using Grafen’s computation. We generated separate phylogenetic trees for boreal (17), temperate (15), Mediterranean (16) and “generalist” (59) species to perform biome-specific analysis (see Supplementary Information, Figs. S1 & S2). To investigate the effects of functional traits and specialisation indices on abundance, range changes, and distribution shift, we used two regression methods. All methods were based on the relationships between a measure of change and a functional trait or specialisation index. Our sample unit is an individual forest bird species (i.e., one value for each species, either abundance or range change, or distribution shift). Abundance change was a categorical variable (i.e., strong decline – decline – stable – increase – strong increase), while range change (i.e., difference between colonisation and extinction) and distribution shift (i.e., south-north shift) were continuous variables. Therefore, to study abundance changes, we used proportional-odds linear mixed effects model using (Phylo)clmm R-function from the ‘ordinal’ R-package (Christensen, 2022). Interspecific phylogenetic relatedness was included as a random effect, reflecting the correlation between species based on phylogenetic distances (see also Hagge et al. (2021) and Seibold et al. (2015)). For distribution changes, we employed phylogenetic generalised least squares regression (PGLS) using the gls R-function from the ‘nlme’ R-package (Pinheiro et al., 2023). The phylogenetic correlation structure was integrated into PGLS using Pagel’s lambda parameter (λ; Pagel (1999)) a widely used measured of phylogenetic signal strength (see, e.g., Hagge et al., 2021; Triviño et al., 2013). Furthermore, we included latitude, a key driver of bird communities at broad scales (Luoto et al., 2007), as a fixed covariable (centroid latitude of the species’ breeding distribution) in all global models (i.e., species from all biomes together), except for the STI model due to strong correlation. For biome-specific analysis, we included latitude only in boreal species models for range change and distribution shift, as it significantly improved model fit (ΔAIC < -2). We did not add latitude for models specific to temperate, Mediterranean, and generalist species since it did not improve model fits (ΔAIC > -2). Additionally, we included breeding range area in range change and distribution shift models, assuming that species with larger ranges would exhibit larger shifts. We scaled predictors to a mean of 0 and standard deviation of 1 to facilitate effect size comparisons. We adjusted p-values using the Holm method (for n=3) to account for multiple testing of traits and specialisation indices on three response variables.

opencc-by-4.0Feb 2024View details →

FORSITE-Clim Europe: European-wide climate indicators for historical periods and climate projections at high resolution

<h2>Overview</h2> This meteorological data set consists of climatologies (climate indicators) on 30-year average basis for Europe and covers two historical periods as well as two periods for three selected climate scenarios with a high spatial resolution of less than 1 km. The two 30-year periods provided for the observations allow the analysis of the climate change that has already happened.  Resolution: 30x30 arcsec Projection: EPSG 4326 Extent for historical data: 10.67°W – 47.67°E, 33.68°N – 71.33°N Extent for scenario data: 10.67°W – 39.33°E, 33.68°N – 71.33°N Periods for historical data: 1961-1990 and 1991-2020 Periods for scenario data: 2036-2065 and 2071-2100 Format: GeoTIFF List of climatologies (climate indicators)   <table> <tbody> <tr> <td> # </td> <td> Short name </td> <td> Name </td> <td> Description </td> <td> Unit </td> <td> Yearly (Y) or monthly (M) </td> </tr> <tr> <td> 1 </td> <td> tasmin </td> <td> Average daily minimum temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 2 </td> <td> tasmax </td> <td> Average daily maximum temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 3 </td> <td> tas </td> <td> Average temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 4 </td> <td> tas_warmest_month </td> <td> Average temperature mean in the warmest month </td> <td> Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 5 </td> <td> tas_coldest_month </td> <td> Average temperature mean in the coldest month </td> <td> Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 6 </td> <td> tasmin_coldest_month </td> <td> Average temperature minimum in the coldest month </td> <td> Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 7 </td> <td> tasmax_warmest_month </td> <td> Average temperature maximum in the warmest month </td> <td> Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 10 </td> <td> GSL </td> <td> Average length of the growing season </td> <td> The growing season is the duration in days of the longest continuous period of days with an average temperature of at least 5°C. However, an earlier or later period of such warm days is included in the growing season if it lasts longer than the sum of all intervening cooler days </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 12 </td> <td> GDD </td> <td> Average Growing Degree Days per year above 5°C </td> <td> Σ(Tmean – 5°C) per year.  </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 13 </td> <td> FD_first </td> <td> Average date of the first frost occurrence </td> <td> Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate </td> <td> day of year </td> <td> Y </td> </tr> <tr> <td> 14 </td> <td> FD_last </td> <td> Average date of the last frost occurrence </td> <td> Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate </td> <td> day of year </td> <td> Y </td> </tr> <tr> <td> 20 </td> <td> GLO_hori </td> <td> Average sum of global radiation </td> <td>   </td> <td> kWh </td> <td> Y, M </td> </tr> <tr> <td> 33 </td> <td> pr </td> <td> Average precipitation sum </td> <td>   </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 39 </td> <td> ET0 </td> <td> Average annual potential evapotranspiration </td> <td> Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 40 </td> <td> WBAL </td> <td> Average climatic water balance </td> <td> Precipitation minus potential evapotranspiration </td> <td> mm </td> <td> Y, M </td> </tr> </tbody> </table> <h2>Data sources</h2> The raw historical data is a combination or extension of daily CHELSA (Climatologies at high resolution for the earth’s land surface areas) with ERA5-Land to fully cover 1961-2020.  <ul> <li>CHELSA-W5E5 v1.0 (https://doi.org/10.5194/essd-15-2445-2023) for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), maximum temperature (tasmax) and minimum temperature (tasmin) for the period 1979-2016</li> <li>CHELSA V2.1 for climatological average monthly wind speed (sfcWind_01, ..., sfcWind_12) and for climatological mean vapor pressure deficit (vpd_01, ..., vpd_12)</li> <li>ERA5-Land for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), dew point (tds), and wind speed (sfcWind) for the period 1961-2020</li> <li>v2.0: WorldClim version 2.1 for climatological monthly minimum, maximum, and average temperatures.</li> </ul> Climate models from EURO-CORDEX (doi.org/10.1007/s10113-013-0499-2): <ul> <li>MPI-M-MPI-ESM-LR_rcp45_r1i1p1_CLMcom-CCLM4-8-17</li> <li>MPI-M-MPI-ESM-LR_rcp85_r1i1p1_CLMcom-CCLM4-8-17</li> <li>ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4</li> </ul>

Systemic Failure of European Fisheries Management

Supporting data, R scripts, and resulting figures for Froese et al. (submitted): Systemic Failure of European Fisheries Management   Authors: Rainer Froese 1*, Noa Steiner 2, Eva Papaioannou 1, Liam MacNeil 1, Thorsten Reusch 1, Marco Scotti 1,3   Affiliations:   1 GEOMAR Helmholtz Centre for Ocean Research Kiel, Wischhofstraße 1-3, 24148 Kiel, Germany  2 Institute of Agricultural Economics, University of Kiel, Olshausenstraße 40, 24118 Kiel, Germany 3 Institute of Biosciences and Bioresources, National Research Council of Italy, Via Madonna del Piano 10, 50019 Sesto Fiorentino (Firenze), Italy *Corresponding author. Email: rfroese@geomar.de