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Eel data (Anguilla anguilla) and associated environment variables for eel in the SUDOE area (SUDOANG project)
<h2><strong>1. DESCRIPTION</strong></h2><h3><strong>1.1. THE SUDOANG PROJECT</strong></h3><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). </p><p>Three main datasets have been used to implement EDA.</p><ul><li>A database of rivers and their attributes along with tools for chaining</li><li>A database of dams.</li><li>A database of electrofishing (current dataset)</li></ul><p>Electrofishing data include site locations, fishing operations which can be done several times at one site, and fish data collected during each operation. The operations are attached to stretches of river or river segments whose characteristics describe the conditions for presence, density, size structure, or silvering rate of the eels. </p><p>The electrofishing operation are classified by type:</p><ul><li>com full two pass fishing</li><li>coa full two pass electrofishing for eel</li><li>iaa eel abundance point sampling</li><li>ber bank sampling</li><li>gm point fishing for large streams</li><li>oth other, or unspecified.</li></ul><h3><strong>1.2. TEMPORAL SCOPE </strong></h3><p>From 1985 to 2018, beware incomplete dataset after 2015 in France.</p><h3><strong>1.3. GEOGRAPHICAL RANGE</strong></h3><p>The SUDOE area including Iberian Peninsula and France.</p><h2><strong>2. DATASETS DESCRIPTION</strong></h2><h3><strong>2.1. DENSITIES AND PRESENCE ABSENCE</strong></h3><h4>Dataset: <a href="https://zenodo.org/api/files/c576c330-0219-4710-a831-717762b117aa/frsppt_12_2020.Rdata?versionId=ad5867d9-1a51-413d-9d27-d269462df9a2">frsppt_12_2020.Rdata</a></h4><p>Most variables are built <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/de433830a1a483fff6404e0f668a38e8e88663a6/eda2.3/report/report2.3/EDA_build.Rnw#L1233">here. </a></p><p>The script to create cumulated values for dams can be found <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/S4/BaseEdaRiosRiversegmentsDam.R#L624">here.</a></p><p>For a technical description see the <a href="https://sudoang.eu/wp-content/uploads/2022/02/E411_Briand_et_al_2022_EDA_report_opt-1.pdf">report.</a></p><p>This file contains the following datasets:</p><ul><li>ddd => dataset used to calibrate the presence absence model, 46147 lines</li><li>ddg => dataset used to calibrate the gamma model (only positive values retained), 19993 lines.</li><li>tdd => dataset corresponding to places where transport operation have been identified, presence absence model, 6582 lines.</li><li>tdg => dataset corresponding to places where transport operation have been identified, gamma model (only positive values) 984 lines.</li></ul><p>And the following columns (in alphabetical order):</p><ul><li><i>altitudem</i>: Altitude in meter truncated to 800 m</li><li><i>area_sudo</i>: Area for recruitment (Drouineau et al., 2021)</li><li><i>codesea</i>: Code of the Sea, factor A = Atlantic, M = Mediterranean</li><li><i>country</i>: A factor (FR, SP, PT) for France, Spain and Portugal</li><li><i>country2</i>: Country with grouping for the Iberian Peninsula (SPPT). Other level is France (FR)</li><li><i>cs_height_08_n</i>: Cumulated height from the sea, dam height transformed with power 0.8, no prediction for missing values</li><li><i>cs_height_08_n</i>: Same variable but truncated to 300</li><li><i>cs_height_08_p</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values</li><li><i>cs_height_08_p</i>: Same variable but truncated to 300</li><li><i>cs_height_08_pp</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_08_pps</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_10_FR</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from France are considered when building on a transnational water course</li><li><i>cs_height_10_n</i>: Cumulated height from the sea, no transformation, no prediction for missing values</li><li><i>cs_height_10_n</i>: Same variable but truncated to 200</li><li><i>cs_height_10_p</i>: Cumulated height from the sea, no transformation, missing height are extrapolated from two different models in France and the Iberian Peninsula</li><li><i>cs_height_10_p</i>: Same variable but truncated to 200</li><li><i>cs_height_10_pass0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_pass1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_pp</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_10_ppass0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_ppass1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_pps</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_10_pscore0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_pscore1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_PT</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Portugal are considered when building on a transnational water course</li><li><i>cs_height_10_score0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_score1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_SP</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Spain are considered when building on a transnational water course.</li><li><i>cs_height_12_n</i>: Cumulated height from the sea, dam height transformed with power 1.2, no prediction for missing values.</li><li><i>cs_height_12_n</i>.: Same variable but truncated to 500</li><li><i>cs_height_12_p</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values.</li><li><i>cs_height_12_p</i>.: Same variable but truncated to 500</li><li><i>cs_height_12_pp</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_12_pps</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_15_n</i>: Cumulated height from the sea, dam height transformed with power 1.5, no prediction for missing values</li><li><i>cs_height_15_n</i>.: Same variable but truncated to 800</li><li><i>cs_height_15_p</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values</li><li><i>cs_height_15_p</i>.: Same variable but truncated to 800</li><li><i>cs_height_15_pp</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_15_pps</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cumnbdamp</i>: Cumulated number of dam from the sea</li><li><i>cumnbdamso</i>: duplicate of cumnbdamp</li><li><i>cumwettedsurfacebothkm2</i>.: Surface of water downstream from the segment in the river. Corresponds to both riversegment and waterbodies</li><li><i>cumwettedsurfacekm2</i>.: Surface of water downstream from the segment in the river. Corresponds only to rivers</li><li><i>cumwettedsurfaceotherkm2</i>.: Surface of water downstream from the segment in the river. Corresponds only waterbodies (water surfaces, associated with the segment).</li><li><i>densCS</i>: Density from Carle and Strub, number in second pass extrapolated from efficiency if only one pass</li><li><i>dist_from_gibraltar_km</i>: Distance to Gibraltar calculated using an enveloppe along the coastline (e.g. the estuaries ingress inland are not counted for this distance).</li><li><i>distanceseakm</i>: Distance to the sea</li><li><i>distanceseakm</i>.: Distance to the sea, truncated at 500</li><li><i>distancesourcem</i>: distance to the source in meters</li><li><i>downstdrainagewettedsurfaceboth</i>.: Percentage of wetted surface downstream for both rivers and associated waterbodies (cumwettedsurfacebothkm2) divided by land surface for all segments within the basin located at a same or lesser distance to the sea.</li><li><i>downstreamwettedsurface</i>: Percentage of wetted surface downstream (<i>cumwettedsurfacekm2</i>) divided by land surface for all segments within the basin located at a same or lesser distance to the sea</li><li><i>drainage_density_perm</i>.: River length / surface of basin in the basin downstream (m-1) Numeric, multiplied by $10^4$, truncated to 20</li><li><i>ef_fishingmethod</i>: Fishing method. See details in text and Briand et al., (2022)</li><li><i>ef_wetted_area</i>: Surface of the electrofishing station</li><li><i>emu</i>: Eel management unit. See <a href="https://github.com/ices-eg/wg_WGEEL/wiki">Git WGEEL</a></li><li><i>hydraulicdensityperm2</i>.: Number of riversegments per surface of basin (m-2). Numeric Multiplied by 10^6, truncated to 1.5</li><li><i>idsegment</i>: Unique identifier of the segment TEXT</li><li><i>laltitudem</i>.: Log transformed value of altitude (truncated)</li><li><i>lcs_height_10_n</i>.: Log transformed value of cumulated height</li><li><i>lddws</i>: Log transformed value of <i>downstdrainagewettedsurfaceboth</i></li><li><i>ldownstreamwettedsurface</i>: Log of previous column</li><li><i>lriverwidthm</i>.: Log transformed value of river width</li><li><i>month</i>: Month</li><li><i>NCS</i>: Number of eels estimated by Carle and Strub</li><li><i>ob_id</i>: Operation (observation) identifier</li><li><i>op_id</i>: Station (observation place) identifier</li><li><i>riverwidthm</i>: Width of the river in m (comes from various sources: see <a href="https://doi.org/10.5281/zenodo.7546419">Atlas</a> and Briand et al., 2022).</li><li><i>seaidsegment</i>: Identifier of the sea idsegment</li><li><i>temperature</i>: Average temperature from 1960-2000 from the CCM (Vogt, 2007)</li><li><i>temperature.1</i>: duplicate of temperature</li><li><i>transport</i>: code of transport operation</li><li><i>year</i>: year of electrofishing</li></ul><h3><strong>2.2. SIZE STRUCTURE OF EELS</strong> </h3><h4>Dataset <a href="https://zenodo.org/api/files/c576c330-0219-4710-a831-717762b117aa/table_ind.Rdata?versionId=a81c7bb4-0f36-44e3-82ea-86d9a2caac8e">table_ind.Rdata</a> </h4><p>A dataset of 494163 lines. In this dataset, one line correspond to one eel.</p><ul><li><i>altitudem</i>: Altitude in meter</li><li><i>altitudem</i>.: Altitude in meter truncated to 800 m</li><li><i>area_sudo</i>: Area for recruitment (Drouineau et al., 2021)</li><li><i>basin</i>: Name (or code from bd_carthage France) of the basin</li><li><i>codesea</i>: Code of the Sea, factor A = Atlantic, M = Mediterranean</li><li><i>country</i>: A factor (FR, SP, PT) for France, Spain, and Portugal</li><li><i>cs_height_08_n</i>: Cumulated height from the sea, dam height transformed with power 0.8, no prediction for missing values.</li><li><i>cs_height_08_n</i>.: Same variable but truncated to 300</li><li><i>cs_height_08_p</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values.</li><li><i>cs_height_08_p</i>.: Same variable but truncated to 300</li><li><i>cs_height_08_pp</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_08_pps</i>: Cumulated height from the sea, dam height transformed with power 0.8, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_10_FR</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from France are considered when building on a transnational water course.</li><li><i>cs_height_10_n</i>: Cumulated height from the sea, no transformation, no prediction for missing values.</li><li><i>cs_height_10_n</i>.: Same variable but truncated to 200</li><li><i>cs_height_10_p</i>: Cumulated height from the sea, no transformation, missing height are extrapolated from two different models in France and the Iberian Peninsula</li><li><i>cs_height_10_p</i>.: Same variable but truncated to 200</li><li><i>cs_height_10_pass0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_pass1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_pp</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_10_ppass0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with pass are used to build the cumulated value</li><li><i>cs_height_10_ppass1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without pass are used to build the cumulated value</li><li><i>cs_height_10_pps</i>: Cumulated height from the sea, no transformation, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_10_pscore0</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_pscore1</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_PT</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Portugal are considered when building on a transnational water course</li><li><i>cs_height_10_score0</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with score (that have been expertised as no or small barrier for eel) are used to build the cumulated value</li><li><i>cs_height_10_score1</i>: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without score are used to build the cumulated value</li><li><i>cs_height_10_SP</i>: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams from Spain are considered when building on a transnational water course</li><li><i>cs_height_12_n</i>: Cumulated height from the sea, dam height transformed with power 1.2, no prediction for missing values</li><li><i>cs_height_12_n</i>.: Same variable but truncated to 500</li><li><i>cs_height_12_p</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values</li><li><i>cs_height_12_p</i>.: Same variable but truncated to 500</li><li><i>cs_height_12_pp</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_12_pps</i>: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cs_height_15_n</i>: Cumulated height from the sea, dam height transformed with power 1.5, no prediction for missing values</li><li><i>cs_height_15_n</i>.: Same variable but truncated to 800</li><li><i>cs_height_15_p</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values</li><li><i>cs_height_15_p</i>.: Same variable but truncated to 800</li><li><i>cs_height_15_pp</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if equiped with an efficient fishway for eel</li><li><i>cs_height_15_pps</i>: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values, the height of dam is set to zero if a score of efficient passage was attributed for eel on this structure</li><li><i>cumheightdam</i>: Cumulated height of dam (ignore)</li><li><i>cumnbdamp</i>: Cumulated number of dam from the sea</li><li><i>cumnbdamso</i>: Cumulated number of dam from the sea only for dams whose height is larger than zero</li><li><i>densCS</i>: Density from Carle and Strub, number in second pass extrapolated from efficiency if only one pass.</li><li><i>dist_from_gibraltar_km</i>: Distance to Gibraltar calculated using an enveloppe along the coastline (e.g. the estuaries ingress inland are not counted for this distance).</li><li><i>distanceseakm</i>: Distance to the sea in kilometers</li><li><i>distanceseakm</i>.: Distance to the sea, truncated at 500</li><li><i>distanceseam</i>: Distance to the sea in meters</li><li><i>distancesourcem</i>: Distance to the source in meters</li><li><i>ef_electrofishing_mean</i>: Percentage of wetted surface downstream (<i>cumwettedsurfacekm2</i>) divided by land surface for all segments within the basin located at a same or lesser distance to the sea</li><li><i>ef_fished_length</i>: Length of the fishing operation</li><li><i>ef_fished_width</i>: Width of the fishing operation</li><li><i>ef_fishingmethod</i>: Method of electrofishing</li><li><i>ef_nbpas</i>: Number of pass in the electrofishing operation</li><li><i>ef_wetted_area</i>: Surface of the electrofishing station</li><li><i>emu</i>: Eel Management Unit</li><li><i>id</i>: Comes from uncout R function, which transforms counts into lines</li><li><i>idsegment</i>: Unique identifier of the segment [data type: UUID]. Use the <a href="https://doi.org/10.5281/zenodo.7546419">Atlas</a> to link with spatial table in PostgreSQL</li><li><i>isendoreic</i>: Is the riversegment coming from an endoreic river?</li><li><i>issea</i>: Is the riversegment a sea outlet?</li><li><i>laltitudem</i>.: Log transformed value of altitude (truncated)</li><li><i>lcs_height_10_n</i>.: Log transformed value of cumulated height</li><li><i>lengthm</i>: Length of the electroshing station in meters</li><li><i>lengthriverm</i>: length of the riversegment in meters</li><li><i>lriverwidthm</i>.: Log transformed value of river width</li><li><i>medianflowm3ps</i>: Median flow of the river in cubic meter per second</li><li><i>month</i>: Month</li><li><i>name</i>: Name of the river (Spain and Portugal)</li><li><i>nb_size_measured</i>: Number of size measured in the electrofishing operation</li><li><i>nbp1</i>: Number of eel collected in the first pass</li><li><i>nbp2</i>: Number of eel collected in the second pass</li><li><i>nbp3</i>: Number of eel collected in the third pass</li><li><i>NCS</i>: Number estimated during the electrofishing operation by Carle and Strubb and interpolation of first pass efficiency if only one pass</li><li><i>nextdownidsegment</i>: Code of the next downstream <i>idsegment</i></li><li><i>Npred</i>: Number of eel predicted on the riversegment <i>pdeltagamma </i>* <i>watersurface</i></li><li><i>ob_dp_name</i>: Data provider for the operation</li><li><i>ob_id</i>: Operation (observation) identifier</li><li><i>ob_starting_date</i>: Date of the operation</li><li><i>op_id</i>: Station (observation place) identifier</li><li><i>pdelta</i>: prediction of the delta (presence absence model)</li><li><i>pdeltagamma: pdelta</i>*<i>pgamma</i></li><li><i>pgamma</i>: prediction of the gamma (positive densities model)</li><li><i>rdelta</i>: residuals of the delta (presence absence model)</li><li><i>rdeltagamma</i>: <i>rdelta</i>*<i>rgamma</i></li><li><i>rgamma</i>: residuals of the gamma (positive densities model)</li><li><i>riverwidthm</i>: River width in meters</li><li><i>riverwidthm</i>.: same variable as riverwidthm (no truncation for riverwidth)</li><li><i>rN</i>: residuals in number (difference in number of eels between observed and predicted there are some NA where we had to get the data from "density" as there was no water surface available</li><li><i>seaidsegment</i>: Identifier of the sea idsegment</li><li><i>sector</i>: please ignore</li><li><i>shreeve</i>: Shreve rank of the segment</li><li><i>size</i>: Size class, "1 - <150", "2 - [150-300[", "3 - [300-450[", "4 - [450-600[", "5 - [600-750[", "6 - >=750"</li><li><i>strahler</i>: Strahler rank of the segment</li><li><i>surfacebvkm2</i>: surface of the watershed in m2</li><li><i>surfacebvm2</i>: surface of the watershed in km3</li><li><i>surfaceunitbvm2</i>: surface of the unit basin surrounding the segment in m2</li><li><i>temperature</i>: Average temperature from 1960-2000 from the CCM (Vogt 2007)</li><li><i>temperaturejan</i>: January temperature (France)</li><li><i>temperaturejul</i>: July temperature (France)</li><li><i>totalnumber</i>: Total number of eel caught during the operation</li><li><i>transport</i>: transport area</li><li><i>wettedsurfacem2</i>: Surface of water downstream from the segment in the river. Corresponds only to rivers</li><li><i>wettedsurfaceotherm2</i>: Surface of water downstream from the segment in the river. Corresponds only waterbodies (water surfaces, associated with the segment).</li><li><i>year</i>: Year of electrofishing operation</li></ul><h3><strong>2.3. SILVER EEL DATA</strong></h3><h4>Dataset <a href="https://zenodo.org/api/files/c576c330-0219-4710-a831-717762b117aa/silver_eel.Rdata?versionId=9df184ef-241d-46af-989e-2bddc0179df0">silver_eel_2020.Rdata</a></h4><p>A dataset of 20101 lines corresponding to yellow and silver eel along with their measurements for silvering for eel > 150 mm.</p><p>This dataset has been built <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/eda2.3/report/report2.3/EDA_build.Rnw#L6454">here</a>, and validated <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/eda2.3/report/report2.3/EDA_build.Rnw#L6812">here</a> and <a href="https://forgemia.inra.fr/pole-migrateurs/eda/eda_model/-/blob/main/eda2.3/report/report2.3/EDA_build.Rnw#L8409">here</a>, For more information look at the <a href="https://sudoang.eu/wp-content/uploads/2022/02/E411_Briand_et_al_2022_EDA_report_opt-1.pdf">report</a> especially the annexes.</p><p>The columns are:</p><ul><li><i>BL</i>: Body length</li><li><i>Dv</i>: Vertical eye diameter</li><li><i>FL</i>: Pectoral fin length</li><li><i>MD</i>: Mean eye diameter</li><li><i>W</i>: Eel weight</li><li><i>contrast</i>: Body contrast for the eel</li><li><i>Dh</i>: Horizontal eye diameter</li><li><i>diam_max</i>: Max of horizontal and vertical eye diameter</li><li><i>diam_min</i>: Min of horizontal and vertical eye diameter</li><li><i>distance_foyer_c</i>: sqrt(diam_max^2 - diam_min^2)</li><li><i>excentricite</i>: Eye excentricity = distance_foyer_c / diam_max</li><li><i>IO</i>: Occular index Pankhurst = 100*((Dh+Dv/2)^2*pi/BL</li><li><i>K_ful</i>: Fulton coefficient= 100*W/(BL/10)^3</li><li><i>maturite_durif</i>: Durif (2009) maturity class</li><li><i>oc_surface</i>: Occular surface</li><li><i>sexe_durif</i>: Sex according to Durif (2009)</li><li><i>silver</i>: Is it a silver eel, corresponds to one of "MII", "FIV", "FV" in Durif stage [data type: Boolean]</li><li><i>stade_pankhurst</i>: Stage according to Pankhurst</li><li><i>stage_Durif</i>: I, MII, FII, FIII, FIV, FV see Durif (2009)</li><li><i>pdeltagamma</i>: Density predicted from the delta gamma model in EDA (Briand et al., 2022)</li><li><i>dist_from_gibraltar_km</i>: Distance from Gibraltar along the coastline, negative in the Mediterranean</li><li><i>surfacebvm2</i>: Surface of the watershed in m2</li><li><i>altitudem.</i>: Altitude in m truncated at 400</li><li><i>temperature</i>: Average temperature from 1960-2000 from the CCM (Vogt, 2007)</li><li><i>distanceseakm</i>.: Distance to the sea in km truncated at 500</li><li><i>strahler</i>: Strahler order of the stream</li><li><i>month2</i>: Month of electrofishing with values grouped for <=7 or >=10</li><li><i>month</i>: Month of electrofishing</li><li><i>year</i>: Year of electrofishing</li></ul><h2><strong>3. VERSIONS</strong></h2><ul><li><a href="https://doi.org/10.5281/zenodo.6023561">10.5281/zenodo.6023561 </a>1.0.0 - 2022-02-09 - Initial upload (open access)</li><li><a href="https://doi.org/10.5281/zenodo.6397009">10.5281/zenodo.6397009 </a>1.0.1 - 2023-08-02 - Fixed description (open access)</li></ul><h2><strong>4. READ MORE</strong></h2><ul><li>Atlas of European Eel Distribution (<i>Anguilla anguilla</i>) in Portugal, Spain and France (<a href="https://doi.org/10.5281/zenodo.7546419">10.5281/zenodo.7546419</a>)</li><li>Electrofishing data for eel in the Iberian Peninsula (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.8348353">10.5281/zenodo.8348353</a>)</li><li>Cumulated dam impact in France and the Iberian Peninsula (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.8348374">10.5281/zenodo.8348374</a>)</li></ul><h2><strong>5. FUNDING</strong></h2><p>Project co-financed by the INTERREG SUDOE Programme through the European Regional Development Fund (ERDF).</p>
Experimental evaluation of genetic variability based on DNA metabarcoding from the aquatic environment: Insights from the Leray COI fragment
<p>Intraspecific genetic variation is important for the assessment of organisms' resistance to changing environments and anthropogenic pressures. Aquatic DNA metabarcoding provides a non-invasive method in biodiversity research, including investigations at the within-species level. Through the analysis of eDNA samples collected from the Peter the Great Gulf of the Japan Sea, in this study we aimed to evaluate the identification of Amplicon Sequence Variants (ASVs) in marine eDNA among abundant species of the <em>Zostera</em> sp. community: <em>Hexagrammos octogrammus</em>, <em>Pholidapus dybowskii</em> (Teleostei: Perciformes), and <em>Pandalus latirostris</em> (Arthropoda: Decapoda). These species were collected from two distant locations to produce mock communities and gather aquatic eDNA both on the community and individual level. Our approach highlights the efficacy of eDNA metabarcoding in capturing haplotypic diversity and the potential for this methodology to track genetic diversity accurately, contributing to conservation efforts and ecosystem management. Additionally, our results elucidate the impact of nuclear mitochondrial DNA segments (NUMTs) on the reliability of metabarcoding data, indicating the necessity for cautious interpretation of such data in ecological studies. Moreover, we analyzed 83 publicly available <em>COI</em> sequence datasets from common groups of multicellular organisms (Mollusca, Echinodermata, Crustacea, Polychaeta, and Actinopterygii). The results reflect the decrease in population diversity that arises from using the metabarcode compared to the <em>COI</em> barcode.</p>
Novel environments induce variability in fitness-related traits
<p class="MsoNormal">Environmental change from anthropogenic activities threatens individual organisms, the persistence of populations, and entire species. Rapid environmental change puts organisms in a double bind, they are forced to contend with novel environmental conditions but with little time to respond. Phenotypic plasticity can act quickly to promote establishment and persistence of individuals and populations in novel or altered environments. In typical environmental conditions, fitness-related traits can be buffered, reducing phenotypic variation in expression of traits, and allowing underlying genetic variation to accumulate without selection. In stressful conditions, buffering mechanisms can break down, exposing underlying phenotypic variation and permitting the expression of phenotypes that may allow populations to persist in the face of altered or otherwise novel environments. Using reciprocal transplant experiments of freshwater snails, we demonstrate that novel conditions induce higher variability in growth rates and, to a lesser degree, morphology (area of the shell opening) relative to natal conditions. Our findings suggest a potentially important role of phenotypic plasticity in population persistence as organisms face a rapidly changing, human-altered world.</p>
Morphological variability decreases in populations living in less suitable environments and close to the range edges
<p><strong>Aim:</strong> Geographic range expansion depends on ecological and evolutionary processes that may hamper local adaptations in populations living at range edges by constraining phenotypic variability. This study investigates the spatial patterns of the intraspecific variability of skull traits throughout the geographic range of a marsupial from the Brazilian Atlantic Forest. We aimed to answer whether the distance from the range edge and the environmental suitability explain the geographic variation of morphological variability of the species.</p> <p><strong>Location:</strong> Atlantic Forest, Brazil.</p> <p><strong>Taxon:</strong> <em>Marmosops incanus</em> (Didelphimorphia, Didelphidae).</p> <p><strong>Methods:</strong> We analysed adult specimens deposited in the main biological collections in southeastern Brazil. The morphological variability extent and integration within populations were characterised by 13 linear measures of the skull, using a multivariate approach. Environmental suitability for the species' occurrence was estimated by Ecological Niche Models, using climatic and vegetation productivity as predictors and three different modelling methods. Distance from the range edge was calculated based on the minimum linear distance between populations and the closest range limit. We fitted linear regressions and selected the best models that explained the spatial variation of morphological variability based on the Akaike information criterion.</p> <p><strong>Results:</strong> The extent of morphological variability of <em>M. incanus</em> is positively correlated with morphological integration and increases with local environmental suitability and distance from the range edges. However, the relationship between morphological variability and environmental suitability depends on the niche modelling method.</p> <p><strong>Main conclusions: </strong>Unfavourable environmental conditions constrain morphological variability within populations of the Gray slender opossum and may hamper local adaptation in peripheral populations living in less suitable environments. In addition to environmental conditions, geographic location of populations also plays an important role in phenotypic variability among populations. We stress the boundary effect of the species range on the local adaptation of peripheral populations and its possible consequences for the range expansion dynamics.</p>
Resolving the consequences of gradual phenotypic plasticity for populations in variable environments
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Morphological variability decreases in populations living in less suitable environments and close to the range edges
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Data from: Diet variability among insular populations of Podarcis lizards reveals diverse strategies to face resource-limited environments
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Data from: Gene expression plasticity as a mechanism of coral adaptation to a variable environment
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Data from: The adaptive role of melanin plasticity in thermally variable environments
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Experimental evaluation of genetic variability based on DNA metabarcoding from the aquatic environment: Insights from the Leray COI fragment
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Novel environments induce variability in fitness-related traits
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Data from: Multiple signaling in a variable environment: expression of song and color traits as a function of ambient sound and light
Many animals communicate using more than one signal, and several hypotheses exist to explain the evolution of multiple signals. However, these hypotheses typically assume static selection pressures and previous work has not addressed how spatial and temporal environmental variation can shape variation in signaling systems. In particular, environmental variability, such as ambient lighting or noise, may affect efficacy (e.g. detectability/perception by receivers) of signals. To examine how signal expression varies intraspecifically as a function of habitat characteristics, we evaluated relationships between spatial environmental variation and song and plumage color expression in a tropical songbird, the red-throated ant-tanager (Habia fuscicauda) in Panama. We recorded male ant-tanager song, plucked feathers to measure coloration, and recorded the acoustic and light environments from each male's territory. In addition, we took several morphometric measurements from each male to assess the potential information content of song and plumage color. We found that males with redder and more saturated crowns occurred on darker territories, and males that sang shorter and lower frequency songs occurred on noisier territories. We also found that more colorful males tended to sing longer and lower frequency songs. Finally we found that song and color correlated similarly with male morphology (e.g. tarsus length, body mass). Altogether these results indicate that spatial variation in the environment is related to male coloration and song, and that males might be optimizing color and song expression for their particular territorial environment.
Data from: Extreme precipitation variability, forage quality and large herbivore diet selection in arid environments
Nutritional ecology forms the interface between environmental variability and large herbivore behaviour, life history characteristics, and population dynamics. Forage conditions in arid and semi-arid regions are driven by unpredictable spatial and temporal patterns in rainfall. Diet selection by herbivores should be directed towards overcoming the most pressing nutritional limitation (i.e. energy, protein [nitrogen, N], moisture) within the constraints imposed by temporal and spatial variability in forage conditions. We investigated the influence of precipitation-induced shifts in forage nutritional quality and subsequent large herbivore responses across widely varying precipitation conditions in an arid environment. Specifically, we assessed seasonal changes in diet breadth and forage selection of adult female desert bighorn sheep Ovis canadensis mexicana in relation to potential nutritional limitations in forage N, moisture and energy content (as proxied by dry matter digestibility, DMD). Succulents were consistently high in moisture but low in N and grasses were low in N and moisture until the wet period. Nitrogen and moisture content of shrubs and forbs varied among seasons and climatic periods, whereas trees had consistently high N and moderate moisture levels. Shrubs, trees and succulents composed most of the seasonal sheep diets but had little variation in DMD. Across all seasons during drought and during summer with average precipitation, forages selected by sheep were higher in N and moisture than that of available forage. Differences in DMD between sheep diets and available forage were minor. Diet breadth was lowest during drought and increased with precipitation, reflecting a reliance on few key forage species during drought. Overall, forage selection was more strongly associated with N and moisture content than energy content. Our study demonstrates that unlike north-temperate ungulates which are generally reported to be energy-limited, N and moisture may be more nutritionally limiting for desert ungulates than digestible energy.
Data from: Are dormant plants hedging their bets? Demographic consequences of prolonged dormancy in variable environments
During the growing season, some individuals in perennial plant populations may remain alive below ground while others emerge. This phenomenon, known as prolonged dormancy, seems maladaptive, because prolonged dormancy delays growth and reproduction. However, prolonged dormancy may offer the benefit of safety while below ground, leading to the hypothesis that prolonged dormancy is a bet hedging strategy. We evaluated this hypothesis using a 25-year demographic study of Astragalus scaphoides, an iteroparous perennial plant. First, we determined the relationship between prolonged dormancy and fitness using data from individuals in our population. This analysis showed that prolonged dormancy decreased arithmetic mean fitness and reduced variance in fitness. Geometric mean fitness was maximized at intermediate levels of prolonged dormancy. Empirical patterns of lifetime reproductive success confirm this relationship. We also compared fitness of plants in our population to hypothetical plants without prolonged dormancy, which generally revealed benefits of prolonged dormancy, even if plants could forego prolonged dormancy without costs to other vital rates. Therefore, prolonged dormancy may indeed function as a bet hedging strategy, but the benefits of remaining below ground only outweigh the costs for a subset of individuals. Bet hedging has been demonstrated in plants with simple life histories, such as annuals and monocarpic perennials; we present evidence that bet hedging may be important for plants with more complex life histories.
Data from: Assortative mating by flowering time and its effect on correlated traits in variable environments
Reproductive timing is a key life history trait that impacts the pool of available mates, the environment experienced during flowering, and the expression of other traits through genetic covariation. Selection on phenology, and its consequences on other life history traits, has considerable implications in the context of ongoing climate change and shifting growing seasons. To test this, we grew field-collected seed from the wildflower Mimulus guttatus in a greenhouse to assess the standing genetic variation for flowering time and covariation with other traits. We then created full-sib families through phenological assortative mating and grew offspring in three photoperiod treatments representing seasonal variation in daylength. We find substantial quantitative genetic variation for the onset of flowering time, which covaried with vegetative traits. In the assortatively-mated offspring, we discover over 2-hours variation in critical photoperiod, so that families differed in the probability of flowering across treatments. Allocation to flowering and vegetative growth changed across the daylength treatments, with consistent direction and magnitude of covariation amongst flowering time and other traits. Our results suggest that future studies of flowering time evolution should consider the joint evolution of correlated traits and shifting seasonal selection to understand how environmental variation influences life histories.
Supplementary material 1 from: Serafim-Júnior M, Perbiche-Neves G, Lansac-Toha F (2019) Environments and macrophytes as main variables controlling rotifers in a river/lake system before Porto Primavera Reservoir construction. Zoologia 36: 1-8. https://doi.org/10.3897/zoologia.36.e24191
Table S1. List of species found in our study, separated by their habitat (H) occurrence in the pelagic (P) or littoranean (L) region
Environmental variability as a predictor of behavioral flexibility in urban environments
<p>Global urbanization processes have highlighted the importance of understanding the effects of urban habitats on animal behavior. Behavioral changes are usually evaluated along an urbanization gradient, comparing urban and rural populations. However, this metric fails to consider heterogeneity between urban habitats that can differ significantly in their characteristics, such as their level of environmental variability. We suggest incorporating dimensions of environmental variability into the urbanization metric. We tested the importance of both level of urbanization and level of urban stability (the rate of anthropogenic changes) on animals' behavioral flexibility by comparing reversal learning abilities in house sparrows from sites differing in the rate of urban development over time. We show that at least for males, urban stability better explains levels of behavioral flexibility than urbanization level. We further show that urban stability corresponds to other behavioral traits such as scrounging behavior and foraging activity. Thus, considering environmental stability and predictability in the form of urban changes can help better understand the mechanisms allowing behavioral changes and adaptations to urban environments. Evaluating the dynamics of the urban built environment could provide a better metric with which to understand urbanization effects on wildlife behavior and an important next step in urban ecology.</p>
Data from: Experience-mediated plasticity in mate preferences: mating assurance in a variable environment
An individual's prior experience of sexual signals can result in variation in mate preferences, with important consequences for the course of sexual selection. We test two hypotheses about the evolution of experience-mediated plasticity in mate preferences: mating assurance and mismating avoidance. We exposed female Enchenopa binotata treehoppers (Hemiptera: Membracidae) to treatments that varied their experience of signal frequency, the most divergent sexual signal trait in the E. binotata species complex. Treatments consisted of (1) signals matching the preferred frequency, (2-3) signals deviating either 100 Hz above or 100 Hz below the preferred frequency, and (4) no signals. Females experiencing preferred signals showed the greatest selectivity. However, experience had no effect on peak preference. These results support the hypothesis that selection has favored plasticity in mate preferences that ensures that mating takes place when preferred mates are rare or absent, while at the same time ensuring choice of preferred types when those are present. We consider how experience-mediated plasticity may influence selection on sexual advertisement signals, patterns of reproductive isolation, and the maintenance of genetic variation. We suggest that the plasticity we describe may increase the likelihood of successful colonization of a novel environment, where preferred mating types may be rare.
Data from: Melanin in a changing world: brown trout coloration reflects alternative reproductive strategies in variable environments
Melanins are the most widespread pigments in animals but their adaptive significance remains elusive. Recent studies suggest that intraspecific variation in melanin-based coloration reflects individual genetic-based alternative strategies to cope with environment variability, which could be crucial for their responses to climate changes. However, empirical evidence is still scarce. In this study, we tested how skin coloration in natural populations of brown trout Salmo trutta fario would reflect alternative reproductive strategies in different environments. We experimentally manipulated the flow regime (constant vs. variable) in artificial streams and compared the reproductive investment (body mass and plasma triglyceride variations), innate immunity (variations in plasma peroxidase and lysozyme activity) and reproductive success (number of mates and offspring) of differently colored brown trout over 2 reproductive seasons. Results show that darker males had a higher reproductive investment, but similar immune variations during reproduction compared to paler males. In addition, this reproductive investment was higher in variable environments. However, this did not translate into a higher reproductive success in variable environments, as darker males had a similar number of mates and offspring compared to their paler counterparts under a variable water flow. Since climate change will likely lead to an increased flow variability in the next decades, this suggests that darker brown trout could incur a higher energetic cost of reproduction and could be more impacted by climate changes than their paler counterparts. This highlights the need to take into account intraspecific variability to better forecast the response of natural populations to climate changes.
Foraging in a dynamic environment: response of four sympatric sub-Antarctic albatross species to interannual environmental variability
Seasonal and annual climate variations are linked to fluctuations in the abundance and distribution of resources, posing a significant challenge to animals that need to adjust their foraging behaviour accordingly. Particularly during adverse conditions, and while energetically constrained when breeding, animals ideally need to be flexible in their foraging behaviour. Such behavioural plasticity may separate 'winners' from 'losers' in light of rapid environmental changes due to climate change. Here, the foraging behaviour of four sub-Antarctic albatross species was investigated from 2015/16 to 2017/18, a period characterized by pronounced environmental variability. Over three breeding seasons on Marion Island, Prince Edward Archipelago, incubating wandering (WA, Diomedea exulans; n=45), grey-headed (GHA, Thalassarche chrysostoma; n=26), sooty (SA, Phoebetria fusca; n=23) and light-mantled (LMSA, P. palpebrata; n=22) albatrosses were tracked with GPS loggers. The response of birds to environmental variability was investigated by quantifying inter-annual changes in their foraging behaviour along two axes: spatial distribution, using kernel density analysis, and foraging habitat preference, using generalized additive mixed models and Bayesian mixed models. All four species were shown to respond behaviourally to environmental variability, but with substantial differences in their foraging strategies. WA was most general in its habitat use defined by sea surface height, eddy kinetic energy, wind speed, ocean floor slope and sea level anomaly, with individuals foraging in a range of habitats. In contrast, the three smaller albatrosses exploited two main foraging habitats, with habitat use varying between years. Generalist habitat use by WA and inter-annually variable use of habitats by GHA, SA and LMSA would likely offer these species some resilience to predicted changes in climate such as warming seas and strengthening of westerly winds. However, future investigations need to consider other life history stages coupled with demographic studies, to better understand the link between behavioural plasticity and population responses.
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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.