Descripción
Registros
Los datos en este recurso de registros biológicos han sido publicados como Archivo Darwin Core(DwC-A), el cual es un formato estándar para compartir datos de biodiversidad como un conjunto de una o más tablas de datos. La tabla de datos del core contiene 1.263 registros.
también existen 1 tablas de datos de extensiones. Un registro en una extensión provee información adicional sobre un registro en el core. El número de registros en cada tabla de datos de la extensión se ilustra a continuación.
Este IPT archiva los datos y, por lo tanto, sirve como repositorio de datos. Los datos y los metadatos del recurso están disponibles para su descarga en la sección descargas. La tabla versiones enumera otras versiones del recurso que se han puesto a disposición del público y permite seguir los cambios realizados en el recurso a lo largo del tiempo.
Versiones
La siguiente tabla muestra sólo las versiones publicadas del recurso que son de acceso público.
¿Cómo referenciar?
Los usuarios deben citar este trabajo de la siguiente manera:
MZNA (2024). Literature records in MZNA-LIT: primary biodiversity records in environmental assessments in Spain. v1.2. University of Navarra, Museum of Zoology. Occurrence dataset. https://doi.org/10.15470/bvznpy
Derechos
Los usuarios deben respetar los siguientes derechos de uso:
El publicador y propietario de los derechos de este trabajo es University of Navarra – Department of Environmental Biology. Esta obra está bajo una licencia Creative Commons de Atribución/Reconocimiento (CC-BY 4.0).
Registro GBIF
Este recurso ha sido registrado en GBIF con el siguiente UUID: 3d6fbb6d-8699-4f93-9adc-c1fd6bc03f4e. University of Navarra – Department of Environmental Biology publica este recurso y está registrado en GBIF como un publicador de datos avalado por GBIF Spain.
Palabras clave
Occurrence; Observation; Environmental Assessment; Protected species; Public archives; Dark Data
Contactos
- Proveedor De Los Metadatos ●
- Originador ●
- Punto De Contacto
- PhD student
https://orcid.org/0009-0004-1833-9720
- Originador
- Institution
- Curador
- Autor
- Custodio De Los Datos
- Curador
- Curador
- Curador
Cobertura geográfica
The data set primarily comprises occurrence records from Peninsular Spain (99.84%). It also includes two other records from the Balearic and Canary Islands.
| Coordenadas límite | Latitud Mínima Longitud Mínima [28,951, -13,61], Latitud Máxima Longitud Máxima [43,659, 2,729] |
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Cobertura taxonómica
The data set comprises records of 59 species corresponding to five classes, 16 orders, and 23 families. The species correspond to 31 non-Chiroptera threatened species listed in the Spanish Catalogue of Threatened Species (11 endangered and 20 vulnerable) and 28 Chiroptera species (one endangered, 11 vulnerable, and 16 listed in the List of Wild Species under Special Protection Regime).
| Especie | Aegypius monachus (Buitre negro), Aphanius iberus (Fartet), Aquila adalberti (Águila imperial ibérica), Aquila fasciata (Águila perdicera), Ardeola ralloides (Garcilla cangrejera), Aythya nyroca (Porrón pardo), Barbastella barbastellus (Murciélago de bosque), Botaurus stellaris (Avetoro común), Charadrius alexandrinus (Chorlitejo patinegro), Charadrius morinellus (Chorlito carambolo), Chersophilus duponti (Alondra de Dupont o Ricotí), Chioglossa lusitanica (Salamandra rabilarga), Ciconia nigra (Cigüeña negra), Circus pygargus (Aguilucho cenizo), Emys orbicularis (Galápago europeo), Eptesicus isabellinus (Murciélago hortelano mediterráneo), Eptesicus serotinus (Murciélago hortelano), Erythropygia galactotes (Alzacola), Fulica cristata (Focha moruna), Gypaetus barbatus (Quebrantahuesos), Hypsugo savii (Murciélago montañero), Larus audouinii (Gaviota de Audouin), Marmaronetta angustirostris (Cerceta pardilla), Microtus cabrerae (Topillo de Cabrera), Milvus milvus (Milano real), Miniopterus schreibersii (Murciélago de cueva), Myotis alcathoe (Murciélago ratonero bigotudo pequeño), Myotis bechsteinii (Murciélago ratonero forestal), Myotis blythii (Murciélago ratonero mediano), Myotis capaccinii (Murciélago ratonero patudo), Myotis daubentonii (Murciélago ratonero ribereño), Myotis emarginatus (Murciélago ratonero pardo), Myotis myotis (Murciélago ratonero grande), Myotis mystacinus (Murciélago ratonero bigotudo), Myotis nattereri (Murciélago de Natterer), Nyctalus lasiopterus (Nóctulo grande), Nyctalus leisleri (Nóctulo pequeño), Nyctalus noctula (Nóctulo mediano), Oxyura leucocephala (Malvasía cabeciblanca), Pandion haliaetus (Águila pescadora), Phalacrocorax aristotelis (Cormorán moñudo), Phoenicurus phoenicurus (Colirrojo real), Pipistrellus kuhlii (Murciélago de borde claro), Pipistrellus nathusii (Murciélago de Nathusius), Pipistrellus pipistrellus (Murciélago enano), Pipistrellus pygmaeus (Murciélago de Cabrera), Plecotus auritus (Murciélago orejudo dorado), Plecotus austriacus (Murciélago orejudo gris), Pterocles alchata (Ganga común), Pterocles orientalis (Ganga ortega), Rana pyrenaica (Rana pirenaica), Rhinolophus euryale (Murciélago mediterráneo de herradura), Rhinolophus ferrumequinum (Murciélago grande de herradura), Rhinolophus hipposideros (Murciélago pequeño de herradura), Rhinolophus mehelyi (Murciélago mediano de herradura), Testudo graeca (Tortuga mora), Tetrax tetrax (Sisón común) |
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Cobertura temporal
| Periodo de formación | 07/2012-01/2023 |
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Datos del proyecto
This thesis aims to enhance the efficiency of biodiversity data management to improve conservation efforts. It examines the dark data generated from environmental management-related activities, which often remain misused due to accessibility challenges. By identifying barriers to data flow, assessing data mobilization impacts on national biodiversity understanding, and developing improved data protocols, the project seeks to make critical biodiversity information more accessible and usable. The ultimate goal is to ensure that high-quality biodiversity data is available for informed decision-making and effective conservation planning, following FAIR data principles (Findable, Accessible, Interoperable, Reusable).
| Título | DATA for BiodivERsity Governance: looking for the efficiency of biodiversity data management for conservation (DATABerG). |
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Personas asociadas al proyecto:
Métodos de muestreo
We searched environmental Records of Decision (RODs) in the Official State Gazette (https://www.boe.es/) to identify pronouncements with biodiversity data. We processed these reports and automatically detected species citations. Those fieldwork-based records, so-called Primary Biodiversity Records, constitute this published data set.
| Área de Estudio | The data set contains species records from 232 Spanish localities or municipalities where environmental assessments have been conducted, in 90% of cases, locations suitable for installing a photovoltaic solar plant or a wind farm. Spain is a country located in southwestern Europe and includes most of the Iberian Peninsula, the Balearic Islands, the Canary Islands, and five small areas in North Africa. Due to its geographical position, its varied topography, and the influence of different climates, Spain is characterized by the presence of four biogeographical regions: Mediterranean bioregion, Atlantic bioregion, Alpine bioregion, and Macaronesian region. |
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| Control de Calidad | The performance of automatic biodiversity data detection was assessed by calculating precision and recall (Kohavi & Provost, 1998; Fahmy Amin, 2022) based on correctly detected, incorrectly detected, and undetected records. Precision, representing the accuracy of the detections, was 0.937, while recall, reflecting the proportion of total records detected, was 0.948. False positives, such as species names within organization titles, affected precision, while recall was compromised by misspelled or incomplete species names. Despite these issues, the detection system performed well overall. Georeferencing uncertainty was evaluated following Marcer et al. 2020. |
Descripción de la metodología paso a paso:
- Environmental Records of Decision (RODs) were collected from the Official State Gazette by searching for “evaluación ambiental” in the “Other provisions” database. Using the Octoparse data scraper (Octoparse, n.d.), we extracted the content of these pronouncements as text strings for further analysis. Species citations were automatically detected using RStudio (R Core Team, 2022), considering scientific names, common names, and possible synonyms included in the CEEA and the LESRPE. These species records were manually reviewed and categorized into three types: Primary Biodiversity Record (PBR, based on fieldwork), absence (species not recorded despite fieldwork), and literature-based. PBRs were georeferenced a posteriori using Google Maps (https://www.google.com/maps), calculating their uncertainty following best practice guidelines (Chapman & Wieczorek, 2020; Marcer et al., 2020). The data were incorporated into the MZNA database (Zootron v4.5; Ariño, 1991) and were standardized following the Darwin Core Standard (Darwin Core Maintenance Group, 2023), resulting in a database with 32 fields.
Referencias bibliográficas
- Kohavi, R. & Provost, F. (1998). Glossary of term. Machine Learning, 30: 271‑274. https://doi.org/10.1023/a:1017181826899. https://doi.org/10.1023/a:1017181826899
- Fahmy Amin, M. (2022). Confusion Matrix in Binary Classification Problems: A Step-by-Step Tutorial. Journal of Engineering Research, 6(5). https://doi.org/10.21608/erjeng.2022.274526. https://doi.org/10.21608/erjeng.2022.274526
- Marcer, A., Haston, E., Groom, Q., Ariño, A., Chapman, A., Bakken, T., Braun, P., Dillen, M., Ernst, M., Escobar, A., Fichtmüller, D., Livermore, L., Nicolson, N., Paragamian, K., Paul, D., Pettersson, L., Phillips, S., Plummer, J., Rainer, H., Rey, I., Robertson, T., Röpert, D., Santos, J., Uribe, F., Waller, J., Wieczorek, J. (2020). Quality issues in georeferencing: From physical collections to digital data repositories for ecological research. Diversity and distributions, 27(3): 564‑567. https://doi.org/10.1111/ddi.13208. https://doi.org/10.1111/ddi.13208
- Octoparse. (n.d.). Web scraping tool & free web crawlers. https://www.octoparse.com/. https://www.octoparse.com/
- R Core Team. (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. URL: https://www.R-project.org. https://www.R-project.org
- Chapman, A. & Wieczorek, J. (2020). Georeferencing Best Practices. GBIF Secretariat, Copenhagen. https://doi.org/10.15468/doc-gg7h-s853. https://doi.org/10.15468/doc-gg7h-s853
- Ariño, A. H. (1991). Bibliography of Iberian Polychaetes: a data base. Ophelia, suppl. 5: 647–652. https://doi.org/10.1163/9789004629745_068. https://doi.org/10.1163/9789004629745_068
- Darwin Core Maintenance Group (2023) Darwin Core List of Terms. Biodiversity Information Standards (TDWG). http://rs.tdwg.org/dwc/doc/list/2023-09-18. http://rs.tdwg.org/dwc/doc/list/2023-09-18
Metadatos adicionales
| Propósito | The aim of the present data set is to make the dark data generated during environmental assessments FAIR (Findable, Accessible, Interoperable, and Reusable). Publishing these data in a publicly accessible platform creates an opportunity for their potential reuse in future conservation decisions, ensuring that these decisions are based on the best available evidence. |
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| Identificadores alternativos | 10.15470/bvznpy |
| 3d6fbb6d-8699-4f93-9adc-c1fd6bc03f4e | |
| https://ipt.gbif.es/resource?r=mzna-lit |