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FIGURE 2. Chelifera giraudae Vaillant. a in New synonyms and new species of European aquatic dance flies (Diptera, Empididae)
FIGURE 2. Chelifera giraudae Vaillant. a, slide with holotype; b, holotype, lateral view; c, male terminalia, lateral view (photos MZLS).
FIGURE 1. Chelifera pallida Vaillant. a in New synonyms and new species of European aquatic dance flies (Diptera, Empididae)
FIGURE 1. Chelifera pallida Vaillant. a, slide with holotype; b, holotype, lateral view; c, male terminalia, lateral view (photos MZLS).
FIGURE 10 in New synonyms and new species of European aquatic dance flies (Diptera, Empididae)
FIGURE 10. Wiedemannia rudolfi Wagner & Ivković sp. nov. a, slide with holotype, upperside; b, slide with holotype, underside; c, male terminalia, lateral view; d, clasping cercus, inner view. (photos RW).
Identification of local thresholds of TWL for triggering the European coastal flood awareness system, Deliverable 4.3 – Report on the identification of local thresholds of TWL for triggering coastal flooding - ECFAS project (GA 101004211). www.ecfas.eu
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p><p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. </p><p><i><strong>Description of the product</strong></i></p><p>The ECFAS Deliverable 4.3 - Report on the identification of local thresholds of TWL for triggering coastal flooding aims to describe the methodology developed to identify local thresholds that will trigger the coastal flood mapping activity. To this end, it was necessary to identify both a total water level triggering threshold, used as a local reference to trigger the system in case of forecasted TWL exceedence, and a duration threshold, used to set the storm duration. In order to compute both thresholds, an Extreme Value Analysis (EVA) and a Duration Analysis (DA) were performed on the ECFAS combined hindcast. As the local TWL thresholds (triggering and duration) were identified using the ECFAS combined hindcast, and the system will instead be operative with the input of CMEMS forecast, a methodology was developed to establish a correction to be applied before integrating the thresholds into the warning system. The document also describes some limitations and possible future improvements of the employed methodology.</p><p>The Deliverable 4.3 - Report on the identification of local thresholds of TWL for triggering coastal flooding is accompanied by an accessory data file. This file, named "ThresholdsFile.csv", contains the values of the triggering and duration thresholds for all the ECFAS combined hindcast of TWL points and their coordinates.</p><p>This <strong>ECFAS Thresholds Dataset</strong> is made available under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</a>. Any rights in individual contents of the ECFAS Thresholds Dataset are licensed under the Database Contents License: <a href="http://opendatacommons.org/licenses/dbcl/1.0/">http://opendatacommons.org/licenses/dbcl/1.0/</a>.</p><p>This <strong>Report</strong> on the identification of thresholds is made available under the <strong>Creative Commons Attribution 4.0 International License</strong>.</p><p><i><strong>Disclaimer:</strong></i></p><p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p><p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p><p> </p>
Atlas of European Eel Distribution (Anguilla anguilla) in Portugal, Spain and France
<p><strong>DESCRIPTION</strong></p> <p><strong>----------------</strong></p> <p>VERSIONS</p> <p>version1.0.1 fixes problem with functions</p> <p>version1.0.2 added table dbeel_rivers.rn_rivermouth with GEREM basin, distance to Gibraltar and link to CCM.</p> <p>version1.0.3 fixes problem with functions</p> <p>version1.0.4 adds views rn_rna and rn_rne to the database</p> <p> </p> <p>----------------</p> <p>The <a href="https://sudoang.eu/en/">SUDOANG</a> project aims at providing common tools to managers to support eel conservation in the SUDOE area (Spain, France and Portugal). <a href="https://sudoang.eu/fr/visuang/">VISUANG</a> is the SUDOANG Interactive Web Application that host all these tools . The application consists of an eel distribution atlas (GT1), assessments of mortalities caused by turbines and an atlas showing obstacles to migration<br> (GT2), estimates of recruitment and exploitation rate (GT3) and escapement (chosen as a target by the EC for the Eel Management Plans) (GT4). In addition, it includes an interactive map showing sampling results from the pilot basin network produced by GT6.</p> <p>The eel abundance for the eel atlas and escapement has been obtained using the Eel Density Analysis model (EDA, GT4's product). EDA extrapolates the abundance of eel in sampled river segments to other segments taking into account how the abundance, sex and size of the eels change depending on different parameters. Thus, EDA requires two main data sources: those related to the river<br> characteristics and those related to eel abundance and characteristics.</p> <p>However, in both cases, data availability was uneven in the SUDOE area. In addition, this information was dispersed among several managers and in different formats due to different sampling sources: Water Framework Directive (WFD), Community Framework for the Collection, Management and Use of Data in the Fisheries Sector (EUMAP), Eel Management Plans, research groups, scientific<br> papers and technical reports. Therefore, the first step towards having eel abundance estimations including the whole SUDOE area, was to have a joint river and eel database. In this report we will describe the database corresponding to the river’s characteristics in the SUDOE area and the eel abundances and their characteristics.</p> <p>In the case of rivers, two types of information has been collected: </p> <ul> <li><strong>River topology</strong> (RN table): a compilation of data on rivers and their topological and hydrographic characteristics in the three countries.</li> <li><strong>River attributes</strong> (RNA table): contains physical attributes that have fed the SUDOANG models.</li> </ul> <p>The estimation of eel abundance and characteristic (size, biomass, sex-ratio and silver) distribution at different scales (river segment, basin, Eel Management Unit (EMU), and country) in the SUDOE area obtained with the implementation of the EDA2.3 model has been compiled in the <strong>RNE table (eel predictions)</strong>.</p> <p><strong>CURRENT ACTIVE PROJECT</strong></p> <p>The project is currently active here : <a href="https://forgemia.inra.fr/pole-migrateurs/eda">gitlab forgemia</a></p> <p><strong>TECHNICAL DESCRIPTION TO BUILD THE POSTGRES DATABASE</strong></p> <p><strong>1. Build the database in postgres.</strong></p> <p>All tables are in ESPG:3035 (European LAEA). The format is postgreSQL database. You can download other formats (shapefiles, csv), here <a href="https://azti.sharepoint.com/sites/Proyectos/SUDOANG/Documentos%20compartidos/Forms/AllItems.aspx?id=%2Fsites%2FProyectos%2FSUDOANG%2FDocumentos%20compartidos%2FSUDOANG%20Database&p=true">SUDOANG gt1 database</a>.</p> <p>Initial command</p> <pre><code class="language-bash"># open a shell with command CMD # Move to the place where you have downloaded the file using the following command cd c:/path/to/my/folder # note psql must be accessible, in windows you can add the path to the postgres #bin folder, otherwise you need to add the full path to the postgres bin folder see link to instructions below createdb -U postgres eda2.3 psql -U postgres eda2.3 # this will open a command with # where you can launch the commands in the next box </code></pre> <p>Within the psql command</p> <pre><code class="language-sql"> create extension "postgis"; create extension "dblink"; create extension "ltree"; create extension "tablefunc"; create schema dbeel_rivers; create schema france; create schema spain; create schema portugal; -- type \q to quit the psql shell</code></pre> <p>Now the database is ready to receive the differents dumps. The dump file are large. You might not need the part including unit basins or waterbodies. All the tables except waterbodies and unit basins are described in the Atlas. You might need to understand what is inheritance in a database. <a href="https://www.postgresql.org/docs/12/tutorial-inheritance.html">https://www.postgresql.org/docs/12/tutorial-inheritance.html</a></p> <p><strong>2. RN (riversegments)</strong></p> <p>These layers contain the topology (see Atlas for detail)</p> <ul> <li>dbeel_rivers.rn</li> <li>france.rn</li> <li>spain.rn</li> <li>portugal.rn</li> </ul> <p>Columns (see Atlas)</p> <table> <tbody> <tr> <td>gid</td> </tr> <tr> <td>idsegment</td> </tr> <tr> <td>source</td> </tr> <tr> <td>target</td> </tr> <tr> <td>lengthm</td> </tr> <tr> <td>nextdownidsegment</td> </tr> <tr> <td>path</td> </tr> <tr> <td>isfrontier</td> </tr> <tr> <td>issource</td> </tr> <tr> <td>seaidsegment</td> </tr> <tr> <td>issea</td> </tr> <tr> <td>geom</td> </tr> <tr> <td>isendoreic</td> </tr> <tr> <td>isinternational</td> </tr> <tr> <td>country</td> </tr> </tbody> </table> <p> </p> <p>dbeel_rivers.rn_rivermouth</p> <table> <tbody> <tr> <td>seaidsegment</td> </tr> <tr> <td>geom (polygon)</td> </tr> <tr> <td>gerem_zone_3</td> </tr> <tr> <td>gerem_zone_4 (used in EDA)</td> </tr> <tr> <td>gerem_zone_5</td> </tr> <tr> <td>ccm_wso_id</td> </tr> <tr> <td>country</td> </tr> <tr> <td>emu_name_short</td> </tr> <tr> <td>geom_outlet (point)</td> </tr> <tr> <td>name_basin</td> </tr> <tr> <td>dist_from_gibraltar_km</td> </tr> <tr> <td>name_coast</td> </tr> <tr> <td>basin_name</td> </tr> </tbody> </table> <pre><code class="language-bash"># dbeel_rivers.rn ! mandatory => table at the international level from which # the other table inherit # even if you don't want to use other countries # (In many cases you should ... there are transboundary catchments) download this first. # the rn network must be restored firt ! #table rne and rna refer to it by foreign keys. pg_restore -U postgres -d eda2.3 "dbeel_rivers.rn.backup" #france pg_restore -U postgres -d eda2.3 "france.rn.backup" # spain pg_restore -U postgres -d eda2.3 "spain.rn.backup" # portugal pg_restore -U postgres -d eda2.3 "portugal.rn.backup" # rivermouth and basins, this file contains GEREM basins, distance to Gibraltar, the link to CCM id for each basin flowing to the sea. pg_restore -U postgres -d eda2.3 "dbeel_rivers.rn_rivermouth.backup" # with the schema you will probably want to be able to use the functions, but launch this only after # restoring rna in the next step psql -U postgres -d eda2.3 -f "function_dbeel_rivers.sql"</code></pre> <p><strong>3. RNA (Attributes)</strong></p> <p>This corresponds to tables</p> <ul> <li>dbeel_rivers.rna</li> <li>france.rna</li> <li>spain.rna</li> <li>portugal.rna</li> </ul> <p>Columns (See Atlas)</p> <table> <tbody> <tr> <td>idsegment</td> </tr> <tr> <td>altitudem</td> </tr> <tr> <td>distanceseam</td> </tr> <tr> <td>distancesourcem</td> </tr> <tr> <td>cumnbdam</td> </tr> <tr> <td>medianflowm3ps</td> </tr> <tr> <td>surfaceunitbvm2</td> </tr> <tr> <td>surfacebvm2</td> </tr> <tr> <td>strahler</td> </tr> <tr> <td>shreeve</td> </tr> <tr> <td>codesea</td> </tr> <tr> <td>name</td> </tr> <tr> <td>pfafriver</td> </tr> <tr> <td>pfafsegment</td> </tr> <tr> <td>basin</td> </tr> <tr> <td>riverwidthm</td> </tr> <tr> <td>temperature</td> </tr> <tr> <td>temperaturejan</td> </tr> <tr> <td>temperaturejul</td> </tr> <tr> <td>wettedsurfacem2</td> </tr> <tr> <td>wettedsurfaceotherm2</td> </tr> <tr> <td>lengthriverm</td> </tr> <tr> <td>emu</td> </tr> <tr> <td>cumheightdam</td> </tr> <tr> <td>riverwidthmsource</td> </tr> <tr> <td>slope</td> </tr> <tr> <td>dis_m3_pyr_riveratlas</td> </tr> <tr> <td>dis_m3_pmn_riveratlas</td> </tr> <tr> <td>dis_m3_pmx_riveratlas</td> </tr> <tr> <td>drought</td> </tr> <tr> <td>drought_type_calc</td> </tr> </tbody> </table> <p>Code :</p> <pre><code class="language-bash">pg_restore -U postgres -d eda2.3 "dbeel_rivers.rna.backup" pg_restore -U postgres -d eda2.3 "france.rna.backup" pg_restore -U postgres -d eda2.3 "spain.rna.backup" pg_restore -U postgres -d eda2.3 "portugal.rna.backup" </code></pre> <p><strong>4. RNE (eel predictions)</strong></p> <p>These layers contain eel data (see Atlas for detail)</p> <ul> <li>dbeel_rivers.rne</li> <li>france.rne</li> <li>spain.rne</li> <li>portugal.rne</li> </ul> <p>Columns (see Atlas)</p> <table> <tbody> <tr> <td>idsegment</td> </tr> <tr> <td>surfaceunitbvm2</td> </tr> <tr> <td>surfacebvm2</td> </tr> <tr> <td>delta</td> </tr> <tr> <td>gamma</td> </tr> <tr> <td>density</td> </tr> <tr> <td>neel</td> </tr> <tr> <td>beel</td> </tr> <tr> <td>peel150</td> </tr> <tr> <td>peel150300</td> </tr> <tr> <td>peel300450</td> </tr> <tr> <td>peel450600</td> </tr> <tr> <td>peel600750</td> </tr> <tr> <td>peel750</td> </tr> <tr> <td>nsilver</td> </tr> <tr> <td>bsilver</td> </tr> <tr> <td>psilver150300</td> </tr> <tr> <td>psilver300450</td> </tr> <tr> <td>psilver450600</td> </tr> <tr> <td>psilver600750</td> </tr> <tr> <td>psilver750</td> </tr> <tr> <td>psilver</td> </tr> <tr> <td>pmale150300</td> </tr> <tr> <td>pmale300450</td> </tr> <tr> <td>pmale450600</td> </tr> <tr> <td>pfemale300450</td> </tr> <tr> <td>pfemale450600</td> </tr> <tr> <td>pfemale600750</td> </tr> <tr> <td>pfemale750</td> </tr> <tr> <td>pmale</td> </tr> <tr> <td>pfemale</td> </tr> <tr> <td>sex_ratio</td> </tr> <tr> <td>cnfemale300450</td> </tr> <tr> <td>cnfemale450600</td> </tr> <tr> <td>cnfemale600750</td> </tr> <tr> <td>cnfemale750</td> </tr> <tr> <td>cnmale150300</td> </tr> <tr> <td>cnmale300450</td> </tr> <tr> <td>cnmale450600</td> </tr> <tr> <td>cnsilver150300</td> </tr> <tr> <td>cnsilver300450</td> </tr> <tr> <td>cnsilver450600</td> </tr> <tr> <td>cnsilver600750</td> </tr> <tr> <td>cnsilver750</td> </tr> <tr> <td>cnsilver</td> </tr> <tr> <td>delta_tr</td> </tr> <tr> <td>gamma_tr</td> </tr> <tr> <td>type_fit_delta_tr</td> </tr> <tr> <td>type_fit_gamma_tr</td> </tr> <tr> <td>density_tr</td> </tr> <tr> <td>density_pmax_tr</td> </tr> <tr> <td>neel_pmax_tr</td> </tr> <tr> <td>nsilver_pmax_tr</td> </tr> <tr> <td>density_wd</td> </tr> <tr> <td>neel_wd</td> </tr> <tr> <td>beel_wd</td> </tr> <tr> <td>nsilver_wd</td> </tr> <tr> <td>bsilver_wd</td> </tr> <tr> <td>sector_tr</td> </tr> <tr> <td>year_tr</td> </tr> <tr> <td>is_current_distribution_area</td> </tr> <tr> <td>is_pristine_distribution_area_1985</td> </tr> </tbody> </table> <p>Code for restauration</p> <pre><code class="language-bash">pg_restore -U postgres -d eda2.3 "dbeel_rivers.rne.backup" pg_restore -U postgres -d eda2.3 "france.rne.backup" pg_restore -U postgres -d eda2.3 "spain.rne.backup" pg_restore -U postgres -d eda2.3 "portugal.rne.backup"</code></pre> <p><strong>5. Unit basins</strong></p> <p>Units basins are not described in the Altas. They correspond to the following tables :</p> <ul> <li>dbeel_rivers.basinunit_bu</li> <li>france.basinunit_bu</li> <li>spain.basinunit_bu</li> <li>portugal.basinunit_bu</li> <li>france.basinunitout_buo</li> <li>spain.basinunitout_buo</li> <li>portugal.basinunitout_buo</li> </ul> <p>The unit basins is the simple basin that surrounds a segment. It correspond to the topography unit from which unit segment have been calculated. ESPG 3035. Tables bu_unitbv, and bu_unitbvout inherit from dbeel_rivers.unit_bv. The first table intersects with a segment, the second table does not, it corresponds to basin polygons which do not have a riversegment.</p> <p>Source :</p> <ul> <li>Portugal</li> </ul> <p><a href="https://sniambgeoviewer.apambiente.pt/Geodocs/gml/inspire/HY_PhysicalWaters_DrainageBasinGeoCod.zip">https://sniambgeoviewer.apambiente.pt/Geodocs/gml/inspire/HY_PhysicalWaters_DrainageBasinGeoCod.zip</a><a href="https://sniambgeoviewer.apambiente.pt/Geodocs/gml/inspire/HY_PhysicalWaters_DrainageBasinGeoCod.zip">https://sniambgeoviewer.apambiente.pt/Geodocs/gml/inspire/HY_PhysicalWaters_DrainageBasinGeoCod.zip</a></p> <ul> <li>France</li> </ul> <p>In france unit bv corresponds to the RHT (Pella et al., 2012)</p> <ul> <li>Spain</li> </ul> <p><a href="http://www.mapama.gob.es/ide/metadatos/index.html?srv=metadata.show&uuid=898f0ff8-f06c-4c14-88f7-43ea90e48233">http://www.mapama.gob.es/ide/metadatos/index.html?srv=metadata.show&uuid=898f0ff8-f06c-4c14-88f7-43ea90e48233</a></p> <pre><code class="language-bash">pg_restore -U postgres -d eda2.3 'dbeel_rivers.basinunit_bu.backup' # france pg_restore -U postgres -d eda2.3 "france.basinunit_bu.backup" pg_restore -U postgres -d eda2.3 "france.basinunitout_buo.backup" # spain pg_restore -U postgres -d eda2.3 "spain.basinunit_bu.backup" pg_restore -U postgres -d eda2.3 "spain.basinunit_bu.backup" # portugal pg_restore -U postgres -d eda2.3 "portugal.basinunit_bu.backup" pg_restore -U postgres -d eda2.3 "portugal.basinunitout_buo.backup" </code></pre> <p><strong>6- Waterbodies</strong></p> <p>In these tables we have have kept the structure from the source table in WISE or from the bd_topage.</p> <ul> <li>dbeel_rivers.waterbody_unitbv </li> <li>portugal.waterbody_unitbv</li> <li>france.waterbody_unitbv</li> <li>spain.waterbody_unitbv</li> </ul> <p>In France, corresponds to the hydrographic surface from <a href="https://bdtopage.eaufrance.fr/page/documents-ressources">bd_topage</a>. <br> <br> <a href="https://bdtopage.eaufrance.fr/page/documents-ressources">https://bdtopage.eaufrance.fr/page/documents-ressources</a></p> <p>In spain it corresponds to . <a href="http://www.mapama.gob.es/ide/metadatos/index.html?srv=metadata.show&uuid=b3114bb6-4a0c-4bf2-90e2-240b9de82ad0">Cuencas hidrográficas de los principales ríos definidos en el artículo 3 de la Directiva Marco del Agua (DMA)</a>, in Portugal to <a href="https://sniambgeoviewer.apambiente.pt/Geodocs/gml/inspire/HY_PhysicalWaters_DrainageBasinMAgua.zip">HY_PhysicalWaters_DrainageBasinMAgua</a>.</p> <p> </p> <pre><code class="language-bash">pg_restore -U postgres -d eda2.3 "dbeel_rivers.waterbody_unitbv.backup" pg_restore -U postgres -d eda2.3 "portugal.waterbody_unitbv.backup" pg_restore -U postgres -d eda2.3 "france.waterbody_unitbv.backup" </code></pre> <p> </p> <p><br> 7- functions</p> <p>The functions can be found in dbeel_rivers_functions.sql you can read the examples there and description of the functions, here is a quick example showing the functionalities.</p> <p>dbeel_rivers.get_path provides the path between two idsegments of the same basin :</p> <pre><code class="language-sql">select dbeel_rivers.get_path (113670,114115,'FR') -- FR113618.FR114065.FR114053.FR114042</code></pre> <p>dbeel_rivers.get_distance calculates the distance between two rivers segments (including the distance of the idsegments themselves)</p> <pre><code class="language-sql">select dbeel_rivers.get_distance (113670,114115,'FR'); --12669</code></pre> <p>dbeel_rivers.upstream_segments_rn(TEXT) takes an upstream segment and returns a vector of idsegments attention this function is slower than national counterparts, check in schema spain portugal and france for quicker functions. It is use for instance to calculate all eels coming from the upstream basin.</p> <pre><code class="language-sql">SELECT dbeel_rivers.upstream_segments_rn('FR114042'); /* FR114042 FR114053 FR113982 FR114034 ... */</code></pre> <p>dbeel_rivers.upstream_segments_rn_sti(TEXT) takes an upstream segment and a TABLE with idsegment, target, source, this in more convenient for later use of routing functions (like get path) which require source and target<br> dbeel_rivers.downstream_segments_rn(TEXT) takes a segment and returns the path to the sea.</p> <pre><code class="language-sql">SELECT dbeel_rivers.downstream_segments_rn('SP227795');</code></pre> <p><strong>7- READ MORE </strong></p> <ul> <li>Eel data (Anguilla anguilla) and associated environment variables used to fit the EDA model in the SUDOE area (SUDOANG project) (10.5281/zenodo.7964967) </li> <li>Electrofishing data for eel in Spain and Portugal (SUDOANG project) (10.5281/zenodo.8207785)</li> <li>Cumulated dam impact in France, Spain and Portugal (SUDOANG project) (10.5281/zenodo.7825552)</li> </ul> <p> </p>
Data from: Tracing the origin of Oriental beech stands across Western Europe and reporting hybridization with European beech – implications for assisted gene flow
<p>The benefits and risks of human-aided translocation of individuals within the species range, assisted gene flow (AGF), depend on the genetic divergence, on the rate and direction of hybridization, and on the climate transfer distance between the host and donor populations. In this study, we explored the use of Oriental beech (<em>Fagus</em> <em>sylvatica</em> subsp. <em>orientalis</em>), growing from Iran to the Balkans, for AGF into European beech populations (<em>F</em>. <em>sylvatica</em> subsp. <em>Sylvatica</em>) that increasingly suffer from climate warming. Using samples from natural populations of Oriental and European beech and microsatellite loci, we identified 5 distinct genetic clusters in Oriental beech with a divergence (FST) of 0.15 to 0.25 from European beech. Using this knowledge, we traced the origin of 11 Oriental beech stands in Western Europe established during the 20<sup>th</sup> century. In two stands of Greater Caucasus origin, we found evidence for extensive hybridization, with 18% and 41% of the seedlings having hybrid status. Climate data revealed higher seasonality with warmer and drier summers across the native Oriental beech sites in comparison to the planting sites in Western Europe. Accordingly, we found that bud burst of Oriental beech occurred four days earlier than in European beech. Overall, our results suggest that AGF of Oriental beech could increase the genetic diversity of European beech stands and may foster introgression of variants adapted to expected future climatic conditions. Our study showcases the evaluation of the benefits and risks of AGF and call for similar studies on other native tree species.</p>
Supplementary material 1 from: Craves JA, Anich NM (2023) Status and distribution of an introduced population of European Goldfinches (Carduelis carduelis) in the western Great Lakes region of North America. NeoBiota 81: 129-155. https://doi.org/10.3897/neobiota.81.97736
Records of European Goldfinches in North America, 2001–2021, by state/province with county totals. Records represent observations, not individual birds. Regions included in the western Great Lakes region are in bold
Supplementary material 2 from: Craves JA, Anich NM (2023) Status and distribution of an introduced population of European Goldfinches (Carduelis carduelis) in the western Great Lakes region of North America. NeoBiota 81: 129-155. https://doi.org/10.3897/neobiota.81.97736
Mapped locations of confirmed breeding European Goldfinches in the western Great Lakes region, 2001–2021
Can Forest Management Practices Counteract Species Loss Arising from Increasing European Demand for Forest Biomass under Climate Mitigation Scenarios?
<p>Here are stored two datasets belonging to the model build for the following research article:</p> <p><strong>Can Forest Management Practices Counteract Species Loss Arising from Increasing European Demand for Forest Biomass under Climate Mitigation Scenarios?</strong></p> <p>Francesca Rosa, Fulvio Di Fulvio, Pekka Lauri, Adam Felton, Nicklas Forsell, Stephan Pfister, Stefanie Hellweg</p> <p><em>Environmental Science and Technology</em>, <strong>2023</strong></p> <p><a href="https://doi.org/10.1021/acs.est.2c07867">https://doi.org/10.1021/acs.est.2c07867</a></p> <p>The rest of the data, the code and further documentation are provided in this github repository: <a href="https://github.com/francesca-git/EU28-ForestMng-Climate">https://github.com/francesca-git/EU28-ForestMng-Climate</a> </p> <p> </p>
Data and code for: Red-listed plants are contracting their elevational range faster than common plants in the European Alps
<p>Dataset of the publication Geppert C., Bertolli A., Prosser F., Marini L., (2023): Red-listed plants are contracting their elevational range faster than common plants in the European Alps. PNAS. Contacts: costanza.geppert@unipd.it - lorenzo.marini@unipd.it.</p> <p>This data repository consists of plant records collected in the Trento Province from 1990 to 2019, species' elevational range shifts, ecological traits, hotspots' files and R script. </p> <p>Description of the dataset: please see ReadMe.docx containing information on each file and instructions for use.</p>
Sensitivity of glaciers in the European Alps to anthropogenic atmospheric forcings: case study of the Argentière glacier
<p>This Zenodo repository contains all datasets (IPSL CMIP6 data, glaciological data, SAFRAN data), ElmerIce codes and Python Jupyter Notebook used in the study reported in the article <strong><em>"Sensitivity of glaciers in the European Alps to anthropogenic atmospheric forcings: case study of the Argentière glacier"</em></strong></p>
Data for: Perennial biomass cropping and use: Shaping the policy ecosystem in European countries
<p><span>Demand for sustainably produced biomass is expected to increase with the need to provide renewable commodities, improve resource security, and reduce greenhouse gas emissions in line with COP26 commitments. Studies have demonstrated additional environmental benefits of using perennial biomass crops (PBCs), when produced appropriately, as a feedstock for the growing bioeconomy, including utilisation for bioenergy (with or without carbon capture and storage). PBCs can potentially contribute to Common Agricultural Policy (CAP) (2023–27) objectives provided they are carefully integrated into farming systems and landscapes. Despite significant R&D investment over decades in herbaceous and coppiced woody PBCs, deployment has largely stagnated due to social, economic and policy uncertainties. This paper identifies the challenges in creating policies that are acceptable to all actors. Development will need to be informed by measurement, reporting and verification (MRV) of greenhouse gas emissions reductions and other environmental, economic and social metrics. It discusses interlinked issues that must be considered in the expansion of PBC production: i) available land; ii) yield potential; iii) integration into farming systems; iv) research and development (R&D) requirements; v) utilisation options; and vi) market systems and the socioeconomic environment. It makes policy recommendations that would enable greater PBC deployment: 1) incentivise farmers and land managers through specific policy measures, including carbon pricing, to allocate their less productive and less profitable land for uses which deliver demonstrable greenhouse gas reductions; 2) enable GHG mitigation markets to develop and offer secure contracts for commercial developers of verifiable low carbon bioenergy and bio-products; 3) support innovation in biomass utilisation value chains; and 4) continue long-term, strategic R&D and education for positive environmental, economic and social sustainability impacts.</span></p>
Data for: Spring emergence and canopy development strategies in miscanthus hybrids in Mediterranean, continental and temperate European climates
<p class="MsoNormal"><span>Due to its versatility and storability, biomass is an important resource for renewable materials and energy. Miscanthus hybrids combine high yield potential, low input demand, tolerance of certain marginal land types and several ecosystem benefits. To date, miscanthus breeding has focussed on increasing yield potential by maximising radiation interception through: 1) selection for early emergence, 2) increasing the growth rate to reach canopy closure fastest possible, and 3) delayed flowering and senescence. The objective of this paper is to compare early season re-growth in miscanthus hybrids cultivated at across Europe. Determination of differences in early canopy development on end-of-year yield traits are required to provide information for breeding decisions to improve future crop performance. Therefore, a trial was planted with four miscanthus hybrids (two novel seed-based hybrids <em>M. sinensis×sinensis</em> (<em>M sin×sin</em>) and <em>M. sacchariflorus×sinensis </em>(<em>M sac×sin</em>), a novel rhizome-based <em>M sac×sin</em> and a standard <em>Miscanthus</em>×<em>giganteus </em>(<em>M</em>×<em>g</em>) clone) in the UK, Germany, Croatia and Italy and was monitored in the third and fourth growing season. We determined differences in base temperature, frost sensitivity and emergence strategy between the hybrids. <em>M×g</em> and <em>M sac×sin</em> mainly emerged from belowground plant organs, producing fewer but thicker shoots at the beginning of the growing season, but these shoots were susceptible to air frosts (as determined by recording 0°C at 2 m above ground surface). By contrast, <em>M sin×sin</em> emerged 10 days earlier avoiding damage by late spring frosts with a high number of thinner shoots from aboveground shoots. Therefore we recommend cultivating <em>M sac×sin</em> at locations with low risk and <em>M sin×sin</em> at locations with higher risk of late spring frosts. Selecting miscanthus hybrids producing shoots throughout the vegetation period is an effective strategy to limit the risk of late frost damages and avoid a reduction in yield due to a shortened growing season. </span></p>
Figure 15 in A comparative atlas of selected skeletal elements of European urodeles (Amphibia: Urodela) for palaeontological investigations
Figure 15. Precaudal vertebrae of Ichthyosaura (A, B) and Lissotriton (C, D), as an example of the intrageneric variation of the posterodorsal area of the neural arch. Notice that in A and C, the posterodorsal area is vertical and followed by the forked neural crest, whereas in B and D the posterodorsal area is roof-shaped, and the neural crest terminates in the middle of the incisura dorsalis. In contrast, the medial edges of the prezygapophyses (dashed lines in A, C) are a less variable character (divergent in the case of Ichthyosaura and parallel in Lissotriton; see diagnoses section). Scale bars: 1 mm.
Figure 16 in A comparative atlas of selected skeletal elements of European urodeles (Amphibia: Urodela) for palaeontological investigations
Figure 16. Phylogenetic trees of European genera of urodeles. A, 50% majority rule consensus, morphology-based tree (present study). Numbers at the nodes represent posterior probabilities. B–D, trees based on molecular analyses: B, maximum likelihood analysis based on mitochondrial and nuclear sequences from the study by Pyron & Wiens (2011); C, Bayesian inference analyses based on four nuclear genes from the study by Veith et al. (2018); and D, based on amino acid sequences of mitochondrial genes under a CAT-GTR model from the study by Rancilhac et al. (2021).
Figure 14 in A comparative atlas of selected skeletal elements of European urodeles (Amphibia: Urodela) for palaeontological investigations
Figure 14. Caudal vertebrae of European urodeles. A, Ommatotriton ophryticus (MNCN 40462). B, C, Pleurodeles waltl (MDHC 253). D, Triturus carnifex (MDHC 299). E, Triturus carnifex (MDHC 145). From left to right: anterior, dorsal, lateral (right lateral for A, C; left lateral for B, D, E), posterior and ventral views. Scale bars: 1 mm.
Figure 13 in A comparative atlas of selected skeletal elements of European urodeles (Amphibia: Urodela) for palaeontological investigations
Figure 13. Caudal vertebrae of European urodeles. A, Calotriton asper (MNCN 16122). B, Euproctus platycephalus (MDHC 405). C, D, Ichthyosaura alpestris (MDHC 391). E, Lissotriton vulgaris (MDHC 135). From left to right: anterior, dorsal, lateral (left lateral for A, E; right lateral for B–D), posterior and ventral views. Scale bars: 1 mm.
Figure 12 in A comparative atlas of selected skeletal elements of European urodeles (Amphibia: Urodela) for palaeontological investigations
Figure 12. Caudal vertebrae of European urodeles. A, Salamandrella keyserlingii (BSPGM 5451). B, Proteus anguinus (BSPGM 4538). C, Speleomantes strinatii (MDHC 225). D, Salamandrina perspicillata (MDHC 407). E, Mertensiella caucasica (BSPGM 2730). F, Salamandra salamandra (MDHC 234). G, Salamandra salamandra (MDHC 396). From left to right: anterior, dorsal, lateral (right lateral for A, B, D–F; left lateral for C, G), posterior and ventral views. Scale bars: 1 mm.
Figure 10 in A comparative atlas of selected skeletal elements of European urodeles (Amphibia: Urodela) for palaeontological investigations
Figure 10. Precaudal vertebrae of European urodeles. A, Mertensiella caucasica (MNCN 23821). B–D, Salamandra salamandra (MDHC 396): B, one of the first trunk vertebrae; C, trunk vertebra close to the pelvis; and D, sacral vertebra. E, Calotriton asper (MNCN 16122). From left to right: anterior, dorsal, lateral (right lateral for B, D; left lateral for A, C, E), posterior and ventral views. Scale bars: 1 mm.
Figure 9 in A comparative atlas of selected skeletal elements of European urodeles (Amphibia: Urodela) for palaeontological investigations
Figure 9. Precaudal vertebrae of European urodeles. A, Salamandrella keyserlingii (BSPGM 5451). B, Proteus anguinus (BSPGM 4539). C, first precaudal vertebra of Speleomantes strinatii (MDHC 225). D, precaudal vertebra of Speleomantes strinatii (MDHC 225) close to the pelvis. E, Salamandrina perspicillata (MDHC 228). F, Chioglossa lusitanica (MNCN 16099). From left to right: anterior, dorsal, lateral (right lateral for A, C–F; left lateral for B), posterior and ventral views. Scale bars: 1 mm.
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.