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55 results for “European eel”
Fig. 1 in Temporal dynamics of species associations in the parasite community of European eels, Anguilla anguilla, from a coastal lagoon
Fig. 1. Abundance (mean number of parasites per host, including non-infected hosts) of the six most common helminth parasites of eels, Anguilla anguilla, in Comacchio Lagoons, during three sampling periods: 2005–2006 (N = 140 eels), 2010–2013 (N = 131), and 2015–2017 (N = 30). Note that some values for the time period 2015–2017 are based on very few fish; see Table 1 for actual numbers and for full species names.
Genome-wide methylation in the panmictic European eel (Anguilla anguilla)
Open the record for dataset details and reuse information.
Fig. 2 in Metazoan Parasites And Health State Of European Eel, Anguilla Anguilla (Anguilliformes, Anguillidae From Tonga Lake And El Mellah Lagoon In The Northeast Of Algeria
Fig. 2. Distribution of the swimbladder degenerative index (SDI) in eels from the two localities.
Fig. 1 in Metazoan Parasites And Health State Of European Eel, Anguilla Anguilla (Anguilliformes, Anguillidae From Tonga Lake And El Mellah Lagoon In The Northeast Of Algeria
Fig. 1. Northeast of Algeria; stars show localization of sampling sites of eels.
Data from: Signatures of natural selection between life cycle stages separated by metamorphosis in European eel
Background: Species showing complex life cycles provide excellent opportunities to study the genetic associations between life cycle stages, as selective pressures may differ before and after metamorphosis. The European eel presents a complex life cycle with two metamorphoses, a first metamorphosis from larvae into glass eels (juvenile stage) and a second metamorphosis into silver eels (adult stage). We tested the hypothesis that different genes and gene pathways will be under selection at different life stages when comparing the genetic associations between glass eels and silver eels. Results: We used two sets of markers to test for selection: first, we genotyped individuals using a panel of 80 coding-gene single nucleotide polymorphisms (SNPs) developed in American eel; second, we investigated selection at the genome level using a total of 153,423 RAD-sequencing generated SNPs widely distributed across the genome. Using the RAD approach, outlier tests identified a total of 2413 (1.57 %) potentially selected SNPs. Functional annotation analysis identified signal transduction pathways as the most over-represented group of genes, including MAPK/Erk signalling, calcium signalling and GnRH (gonadotropin-releasing hormone) signalling. Many of the over-represented pathways were related to growth, while others could result from the different conditions that eels inhabit during their life cycle. Conclusions: The observation of different genes and gene pathways under selection when comparing glass eels vs. silver eels supports the adaptive decoupling hypothesis for the benefits of metamorphosis. Partitioning the life cycle into discrete morphological phases may be overall beneficial since it allows the different life stages to respond independently to their unique selection pressures. This might translate into a more effective use of food and niche resources and/or performance of phase-specific tasks (e.g. feeding in the case of glass eels, migrating and reproducing in the case of silver eels).
Data from: Is the continental life of the European eel Anguilla anguilla affected by the parasitic invader Anguillicoloides crassus?
Quantifying the fitness cost that parasites impose on wild hosts is a challenging task because the epidemiological history of field-sampled hosts is often unknown. In this study we used an internal marker of the parasite pressure on individual hosts to evaluate the costs of parasitism with respect to host body condition, size increase and reproductive potential of field-collected animals for which we also determined individual age. In our investigated system, the European eel Anguilla anguilla and the parasitic invader Anguillicoloides crassus, high virulence and severe impacts are expected because the host lacks an adaptive immune response. We demonstrated a nonlinear relationship between the severity of damage to the affected organ (i.e. the swimbladder, our internal marker) and parasite abundance and biomass, thus showing that the use of classical epidemiological parameters was not relevant here. Surprisingly, we found that the most severely affected eels (with damaged swimbladder) had greater body length and mass (+11% and +41%, respectively) than unaffected eels of same age. We discuss mechanisms that could explain this finding and other counter-intuitive results in this host–parasite system, and highlight the likely importance of host panmixia in generating great inter-individual variability in growth potential and infection risk. Under that scenario, the most active foragers would not only have the greatest size increase, but also the highest probability of becoming repeatedly infected –via trophic parasite transmission– during their continental life.
Speciation history of European (Anguilla anguilla) and American eel (A. rostrata), analyzed using genomic data
<p>Speciation in the ocean could differ from terrestrial environments due to fewer barriers to gene flow. Hence, sympatric speciation might be common, with American and European eel being candidates for exemplifying this. They show disjunct continental distributions on both sides of the Atlantic, but spawn in overlapping regions of the Sargasso Sea from where juveniles are advected to North American, European and North African coasts. Hybridization and introgression is known to occur, with hybrids almost exclusively observed in Iceland. Different speciation scenarios have been suggested, involving either vicariance or sympatric ecological speciation. Using RAD sequencing and whole-genome sequencing data from parental species and F1 hybrids, we analyzed speciation history based on the Joint Allele Frequency Spectrum (JAFS) and PSMC (pairwise sequentially Markovian coalescent) plot. JAFS supported a model involving a split without gene flow 150,000 – 160,000 generations ago, followed by secondary contact 87,000 – 92,000 generations ago, with 64% of the genome experiencing restricted gene flow. This supports vicariance rather than sympatric speciation, likely associated with Pleistocene Glaciation cycles and ocean current changes. Whole genome PSMC analysis of F1 hybrids from Iceland suggested divergence 200,000 generations ago and indicated subsequent gene flow rather than strict isolation. Finally, simulations showed that results from both approaches (JAFS and PSMC) were congruent. Hence, there is strong evidence against sympatric speciation in North Atlantic eels. These results reiterate the need for careful consideration of cases of possible sympatric speciation, as even in seemingly barrier-free oceanic environments palaeoceanographic factors may have promoted vicariance and allopatric speciation.</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>
Age of European silver eels during a period of declining abundance in Norway
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Data from: Is the continental life of the European eel Anguilla anguilla affected by the parasitic invader Anguillicoloides crassus?
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Data from: Historical data to plan the recovery of the European eel
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Data from: All roads lead to home: panmixia of European eel in the Sargasso Sea
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Data from: Signatures of natural selection between life cycle stages separated by metamorphosis in European eel
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Speciation history of European (Anguilla anguilla) and American eel (A. rostrata), analyzed using genomic data
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Figure 2 in Diet of the European eel Anguilla anguilla (Linnaeus, 1758) in two transitional waters of Southwestern Mediterranean
Figure 2. – Evolution of the digestive vacuity coefficient (Cv %) of Anguilla anguilla according to season and development stages in Mellah lagoon (in black) and Wadi El Kebir (in gray). The values above the bars mean the number of eels examined.
Figure 1 in Rivers in search of the European eel. Distribution and threats of the Critically Endangered Anguilla anguilla (Linnaeus, 1758) in Sicily: the province of Ragusa as a case study
Figure 1. – Distribution of the European eel Anguilla anguilla in the rivers of the Ragusa province. (D: Dirillo, A: Amerillo, Mz: Mazzarronello, Ip: Ippari, R: Rifriscolaro, I: Irminio, V: Volpe, M: Mastratto, C: Ciaramite, SL: San Leonardo, MS: Modica-Scicli, F: Favara, Tr: Tellaro, Tl: Tellesimo, Pr: Prainito; m: mortality; p: poaching).
Data from: Response of seaward migrating European eel (Anguilla anguilla) to manipulated flow fields
Open the record for dataset details and reuse information.
Data from: Deep sequencing of the olfactory epithelium reveals specific chemosensory receptors are expressed at sexual maturity in the European eel Anguilla anguilla
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Data from: Genome-wide signatures of local selection in the panmictic European eel
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European eel Anguilla anguilla individuals from high (Tiber river, Italy) and low pollution (Bolsena lake, Italy) environments
GEO Series GSE35055. Anguilla anguilla. 23 samples. Type: Expression profiling by array.
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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.