Cumulated dam impact in France and the Iberian Peninsula (SUDOANG project)

Zusammenfassung

1. SUDOANG PROJECT The SUDOANG project has provided common tools and assessment methods to managers to support the eel conservation in the SUDOE zone (Southern France, Spain and Portugal). One of the goals of the project was to develop an eel abundance and distribution atlas in the three countries, based on the results of the implementation of Eel Density Analysis (EDA). This model extrapolates eel abundance from a range of river segments sampled by electrofishing, to the whole river and lake network, by considering how eel abundance, size and sex vary according to different parameters related to eel habitat. To do this, we have created a dataset of "cumulated dam impact" which compiles different ways of calculating cumulated height from the sea. 2. SUDOANG DATABASE The dataset on cumulated impact was first derived from information on obstacles collected by the SUDOANG project. Obstacles data for the three countries were imported in the SUDOANG database (deliverable 2.2.1), whose structure is inherited from the DataBase for EEl (DBEEL), developed during a European research project (POSE - Pilot projects to estimate potential and actual escapement of silver eel, Walker et al., 2011). This database is designed to contain all data relative to eel biology and anthropogenic pressures applying to eel. During the course of SUDOANG, this database was used and ameliorated.  In France the obstacles were compiled from three pre-existing different databases: the Referential of flow obstacles (ROE) , the Information of Ecological Continuity (ICE) and the Flow Obstruction Database (Base de Données des Obstacles à l'Ecoulement, (BDOE). The data we have integrated into the SUDOANG 1.0.4. database came from a database dump of the 12th September 2019. The inventory includes bridges that have a significant impact on river continuity. In Spain, data came from: the MITECO Ministry the Basque Water Agency (URA) - Basque Country the Catalan Water Agency (ACA) - Catalonia the University of Girona - Catalonia the University of Córdoba - Andalusia Xunta de Galicia, Consellería de Medio Ambiente, Territorio e Vivenda - Galicia the AMBER project In Portugal the data came from: the Portuguese Water Agency (APA) MARE-ULisboa (University of Lisbon) CIIMAR, the University of Porto the AMBER project. In the case of the transboundary river Minho, the data came from: CIIMAR, the University of Porto (Portuguese area) (report) EHEC, the University of Santiago de Compostela (Spanish area) (report) 3. DATA DESCRIPTION 3.1. Data collected on artificial obstacles Artificial obstacles were classified into 10 types according to the Adaptive Management of Barriers in European Rivers (AMBER) project. Some additional types (e.g., penstock pipes) were added to identify other obstacles in national databases that did not fit the AMBER classification (see the list below). Sometimes dams from different branches are connected, creating a dam-network. In those cases, we have only kept the dam(s) in the main course and use a hierarchical classification of the dams to only consider the cumulated height from the sea to the reference dam. We included only obstacles that are presently standing, i.e., not planned, under construction, or destroyed. Dikes, longitudinal control structures and grates were excluded. Obstacle classification according to the data collected and the AMBER project: BR - Bridge: A structure that is built over a river to allow people or vehicles to cross CU - Culvert: A tunnel or pipe carrying a stream or open drain under a road or railway DI - Dike: An embankment used to hold back water DA - Dam: Structure that blocks the river and extends across the river bed to the flood plain FO - Ford: A shallow crossing-place in a river PP - Penstock: pipe Group of pipes that transport pressurised water from a reservoir (dam) to the turbines installed in a hydro-electric power plant RR - Rock ramp: A weir made of rocks WE - Weir: Structure across a river that does not extend to the flood plain OT - Other: Structure that is not covered by previous definitions UN - Unknown: Unknown We have projected obstacles on the SUDOANG river network at the nearest point within 300 m. To avoid projecting large obstacles in the wrong location in the southwestern France, SUDOANG experts have reviewed and corrected this information. We have also used an algorithm that extracts the best obstacle height data from the three existing databases in France. In the Iberian Peninsula, data providers validated and corrected obstacle location and height using a Shiny application developed by the project, in which they could directly correct the height of obstacles. The variables in the obstacles table (csv delimiter ",") are: op_id: Identifier of the observation place name op_gis_layername: Original data source op_placename: Name of the dam op_op_id: If the dam is linked within a complex (e.g. when there are multiple channels for the same river) the name of the parent dam id_original: Original id of the dam (in the raw table) country: Country code ('SP', 'ES' or 'FR') dp_name: Name of the data provider obstruction_type_code: Type of obstruction (see table obstruction type code) obstruction_type_name: Name of the dam po_obstruction_height: Difference of level of water between the downstream and the upstream part of the dam po_presence_eel_pass: Presence of a pass suitable for eel (see paper) po_date_presence_eel_pass: Date of construction of the eel (or eel compatible) pass fishway_type_code: Code of the fishway type fishway_type_name: Name of the fishway type googlemapscoods: Link to google map x_espg_4326: Longitude (with ESPG 4326) y_espg_4326: Latitude (with ESPG 4326) 3.2. Modeling missing data and estimating the cumulative impact on obstacles For those obstacles missing height information, we have calculated height using a Generalized Linear Models (GLM of log transformed height, family = gaussian, link = identity. In France the model was based on river segment slope, river segment median flow and hydrographic basin. In the Iberian Peninsula, we have implemented a simpler model based on obstacle type, as information about flow or slope was not available for all river segments. The cumulated impact of obstacles was assessed by creating a table joining each river segment with all the dams located in the downstream course. Using this, various metrics were computed using different assumptions concerning the effect of obstacles. The heights were power transformed to test for a different effect of obstacle's height (the cumulated effect of two obstacles of 1 m might be different than the cumulated effect of a single obstacle of 2 m), and functions were developed to calculate cumulated obstacle transformed variables. Other variables were also tested. In fact, tests in France have shown that factors such as presence of a fish pass, and eel passability did not improve the model performance. For this reason, but also because in the Iberian Peninsula this type of information was too limited, we used dam height to model the cumulative height of obstacles at a given river segment.  The variables in the cumulated_dam_impact_SUDOANG table (format Rdata - to be read with the R software, this will load as a data.frame called datadam) are: cs_height_08_n: Cumulated height from the sea,  dam height transformed with power 0.8, no prediction for missing values cs_height_08_n.: Same variable but truncated to 300 cs_height_08_p: Cumulated height from the sea,  dam height transformed with power 0.8, with prediction for missing values cs_height_08_p.: Same variable but truncated to 300 cs_height_08_pps 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 cs_height_10_FR: 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 cs_height_10_n: Cumulated height from the sea, no transformation, no prediction for missing values cs_height_10_n.: Same variable but truncated to 200 cs_height_10_p: Cumulated height from the sea, no transformation, missing height are extrapolated from two different models in France and the Iberian Peninsula cs_height_10_p.: Same variable but truncated to 200 cs_height_10_pass0: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without pass are used to build the cumulated value cs_height_10_pass1: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams with pass are used to build the cumulated value cs_height_10_pp: 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 cs_height_10_ppass0: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without pass are used to build the cumulated value cs_height_10_ppass1: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams with pass are used to build the cumulated value cs_height_10_pps: 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 cs_height_10_pscore0: Cumulated height from the sea, no transformation, including prediction for missing values, only the dams without score are used to build the cumulated value cs_height_10_pscore1: 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 cs_height_10_PT: 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 cs_height_10_score0: Cumulated height from the sea, no transformation, no prediction for missing values, only the dams without score are used to build the cumulated value cs_height_10_score1: 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 cs_height_10_SP: 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 cs_height_12_n: Cumulated height from the sea, dam height transformed with power 1.2, no prediction for missing values cs_height_12_n: Same variable but truncated to 500 cs_height_12_p: Cumulated height from the sea, dam height transformed with power 1.2, with prediction for missing values cs_height_12_p.: Same variable but truncated to 500 cs_height_12_pp: 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 cs_height_12_pps: 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 cs_height_15_n: Cumulated height from the sea, dam height transformed with power 1.5, no prediction for missing values cs_height_15_n: Same variable but truncated to 800 cs_height_15_p: Cumulated height from the sea, dam height transformed with power 1.5, with prediction for missing values cs_height_15_p.: Same variable but truncated to 800 cs_height_15_pp: 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 cs_height_15_pps: 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  cumnbdamp: Cumulated number of dam from the sea cumnbdamso: duplicate of cumnbdamp idsegment: Unique identifier of the segment [data type: UUID]. Use the Atlas to link with spatial table in PostgreSQL 4. VERSIONS 10.5281/zenodo.7825552 1.0.0 - 2023-04-15 - Initial Upload (closed access) 10.5281/zenodo.8348374 1.0.1 - 2023-09-15 - Update provider and names (closed access) 10.5281/zenodo.8348374 1.0.1 - 2023-11-08 -  Final version (open access) 5. READ MORE Atlas of European Eel Distribution (Anguilla anguilla) in Portugal, Spain and France (10.5281/zenodo.7546419) Electrofishing data for eel in the Iberian Peninsula (SUDOANG project) (10.5281/zenodo.8348353) Eel data (Anguilla anguilla) and associated environment variables used to fit the EDA model in the SUDOE area (SUDOANG project) (10.5281/zenodo.6397009) 6. FUNDING Project co-financed by the INTERREG SUDOE Programme through the European Regional Development Fund (ERDF).