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

資料紀錄

此資源出現紀錄的資料已發佈為達爾文核心集檔案（DwC-A），其以一或多組資料表構成分享生物多樣性資料的標準格式。 核心資料表包含 1,263 筆紀錄。

亦存在 1 筆延伸集的資料表。延伸集中的紀錄補充核心集中紀錄的額外資訊。 每個延伸集資料表中資料筆數顯示如下。

此 IPT 存放資料以提供資料儲存庫服務。資料與資源的詮釋資料可由「下載」單元下載。「版本」表格列出此資源的其它公開版本，以便利追蹤其隨時間的變更。

版本

以下的表格只顯示可公開存取資源的已發布版本。

如何引用

研究者應依照以下指示引用此資源。:

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

權利

研究者應尊重以下權利聲明。:

此資料的發布者及權利單位為 University of Navarra – Department of Environmental Biology。 This work is licensed under a Creative Commons Attribution (CC-BY 4.0) License.

GBIF 註冊

此資源已向GBIF註冊，並指定以下之GBIF UUID: 3d6fbb6d-8699-4f93-9adc-c1fd6bc03f4e。  University of Navarra – Department of Environmental Biology 發佈此資源，並經由GBIF Spain同意向GBIF註冊成為資料發佈者。

關鍵字

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

聯絡資訊

地理涵蓋範圍

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

分類群涵蓋範圍

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).

時間涵蓋範圍

計畫資料

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).

參與計畫的人員:

取樣方法

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.

方法步驟描述:

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.

引用文獻

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

額外的詮釋資料