Literature records in MZNA-LIT: primary biodiversity records in environmental assessments in Spain

Occurrence Observation
最新バージョン University of Navarra – Department of Environmental Biology により出版 11月 19, 2024 University of Navarra – Department of Environmental Biology
公開日:
2024年11月19日
ライセンス:
CC-BY 4.0

DwC-A形式のリソース データまたは EML / RTF 形式のリソース メタデータの最新バージョンをダウンロード：

DwC ファイルとしてのデータ ダウンロード 1,263 レコード English で (89 KB) - 更新頻度: as needed
EML ファイルとしてのメタデータ ダウンロード English で (37 KB)
RTF ファイルとしてのメタデータ ダウンロード English で (19 KB)

説明

The data set comprises primary biodiversity records (PBR) on protected species identified during environmental assessments in Spain from 2013 to 2023. The data were extracted from Records of Decisions (RODs) published in the Spanish Official State Gazette, focusing on species listed under the Spanish Catalogue of Threatened Species and the List of Wild Species under Special Protection Regime. These EA-related data belong to dark data, stuck in Records of Decision and thus rarely accessible, limiting their availability for other conservation purposes. Through automated data extraction and manual verification, this data set offers standardized and georeferenced EA-related dark data for future conservation planning and decision-making.

データ レコード

この オカレンス（観察データと標本) リソース内のデータは、1 つまたは複数のデータ テーブルとして生物多様性データを共有するための標準化された形式であるダーウィン コア アーカイブ (DwC-A) として公開されています。 コア データ テーブルには、1,263 レコードが含まれています。

拡張データ テーブルは1 件存在しています。拡張レコードは、コアのレコードについての追加情報を提供するものです。 各拡張データ テーブル内のレコード数を以下に示します。

Occurrence (コア)
1263
Reference 
1263

この IPT はデータをアーカイブし、データ リポジトリとして機能します。データとリソースのメタデータは、 ダウンロード セクションからダウンロードできます。 バージョン テーブルから公開可能な他のバージョンを閲覧でき、リソースに加えられた変更を知ることができます。

バージョン

次の表は、公にアクセス可能な公開バージョンのリソースのみ表示しています。

引用方法

研究者はこの研究内容を以下のように引用する必要があります。:

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

権利

研究者は権利に関する下記ステートメントを尊重する必要があります。:

パブリッシャーとライセンス保持者権利者は University of Navarra – Department of Environmental Biology。 This work is licensed under a Creative Commons Attribution (CC-BY 4.0) License.

GBIF登録

このリソースをはGBIF と登録されており GBIF UUID: 3d6fbb6d-8699-4f93-9adc-c1fd6bc03f4eが割り当てられています。   GBIF Spain によって承認されたデータ パブリッシャーとして GBIF に登録されているUniversity of Navarra – Department of Environmental Biology が、このリソースをパブリッシュしました。

キーワード

Occurrence; Observation; Environmental Assessment; Protected species; Public archives; Dark Data

連絡先

Maite Telletxea
  • メタデータ提供者 ●
  • 最初のデータ採集者 ●
  • 連絡先
  • PhD student
University of Navarra
31008 Pamplona
Navarra
ES
MZNA Museum of Zoology
  • 最初のデータ採集者
  • Institution
University of Navarra
31008 Pamplona
Navarra
ES
David Galicia
  • キュレーター
University of Navarra
31008 Pamplona
Navarra
ES
Rafael Miranda
  • 論文著者
University of Navarra
31008 Pamplona
Navarra
ES
Arturo H. Ariño
  • Custodiansteward(保管者）
University of Navarra
31008 Pamplona
Navarra
ES
Ángel Chaves
  • キュレーター
University of Navarra
31008 Pamplona
Navarra
ES
Ana Amézcua
  • キュレーター
University of Navarra
31008 Pamplona
Navarra
ES
María Imas
  • キュレーター
University of Navarra
31008 Pamplona
Navarra
ES

地理的範囲

The data set primarily comprises occurrence records from Peninsular Spain (99.84%). It also includes two other records from the Balearic and Canary Islands.

座標（緯度経度） 南 西 [28.951, -13.61], 北 東 [43.659, 2.729]

生物分類学的範囲

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).

Species 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)

時間的範囲

生成(収集）期間 07/2012-01/2023

プロジェクトデータ

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).

タイトル DATA for BiodivERsity Governance: looking for the efficiency of biodiversity data management for conservation (DATABerG).

プロジェクトに携わる要員:

Maite Telletxea Martínez
  • 論文著者
Rafael Miranda Ferreiro
  • 論文著者
Arturo H. Ariño Plana
  • 論文著者
David Galicia Paredes
  • 論文著者

収集方法

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.

Study Extent 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.
Quality Control 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.

Method step description:

  1. 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.

書誌情報の引用

  1. 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
  2. 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
  3. 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
  4. Octoparse. (n.d.). Web scraping tool & free web crawlers. https://www.octoparse.com/. https://www.octoparse.com/
  5. 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
  6. 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
  7. 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
  8. 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

追加のメタデータ

目的 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.
代替識別子 10.15470/bvznpy
3d6fbb6d-8699-4f93-9adc-c1fd6bc03f4e
https://ipt.gbif.es/resource?r=mzna-lit