The 2nd and 3rd National Survey on the Natural Environment: Vegetation Survey (common species)

Descrição

Registros de Dados

Os dados deste recurso de evento de amostragem foram publicados como um Darwin Core Archive (DwC-A), que é o formato padronizado para compartilhamento de dados de biodiversidade como um conjunto de uma ou mais tabelas de dados. A tabela de dados do núcleo contém 6.769 registros.

Também existem 1 tabelas de dados de extensão. Um registro de extensão fornece informações adicionais sobre um registro do núcleo. O número de registros em cada tabela de dados de extensão é ilustrado abaixo.

This IPT archives the data and thus serves as the data repository. The data and resource metadata are available for download in the downloads section. The versions table lists other versions of the resource that have been made publicly available and allows tracking changes made to the resource over time.

Versões

A tabela abaixo mostra apenas versões de recursos que são publicamente acessíveis.

Como citar

Pesquisadores deveriam citar esta obra da seguinte maneira:

Biodiversity Center of Japan (2023) The 2nd and 3rd National Survey on the Natural Environment: Vegetation Survey from 1957 to 1988 (common species). Ministry of the Environment, Government of Japan. Dataset/Sampling-event.

Direitos

Pesquisadores devem respeitar a seguinte declaração de direitos:

O editor e o detentor dos direitos deste trabalho é National Institute of Genetics, ROIS. This work is licensed under a Creative Commons Attribution (CC-BY 4.0) License.

GBIF Registration

Este recurso foi registrado no GBIF e atribuído ao seguinte GBIF UUID: d1d4931c-ba3a-4ebe-9e64-0b544d0d3271.  National Institute of Genetics, ROIS publica este recurso, e está registrado no GBIF como um publicador de dados aprovado por GBIF Japan.

Palavras-chave

Samplingevent; Plants; Plantae; the 2nd and 3rd National Survey on the Natural Environment; Vegetation Survey; Japan

Contatos

Cobertura Geográfica

Japan

Cobertura Taxonômica

Identified taxon for Plantae were as follows: 46 families, 136 genera, 2638 species, 184 subspecies, 1135 varieties, 114 forms and 9 cultivars. Identified taxon for Fungi were as follows: 3 genera, 10 species, 3 subspecies.

Cobertura Temporal

Métodos de Amostragem

The surveys were conducted based on phytosociological methods. Species compositions were recorded using arbitrary placed quadrats. For each quadrat, above ground vegetation were horizontally divided into several layers (e.g., herb layer, shrub layer, subcanopy layer, canopy layer). All vascular plants, some mosses and fungi were recorded for each layer. Details for the National Survey on the Natural Environment are available on the website of Biodiversity Center of Japan, Ministry of the Environment (https://www.biodic.go.jp/ne_research_e.html).

Descrição dos passos do método:

1. Geographic coordinates were generalized from original locality to representative coordinates in consideration of protecting sensitive species. The center of either secondary mesh (10x10km), the prefectural capitals or the capital of Japan was used as the representative coordinates. The coordinates were obtained from National Land Numerical Data (MLIT 2022a, MLIT 2022b). The closest terrestrial point from the center was chosen as an alternative point instead of the point in the sea for secondary mesh level coordinates. The maximum distance from the coordinates to the polygon edge was calculated as the accuracy of coordinates. R programs and ArcGIS were used for calculation (竹中 2014, Hijmans 2021, Karney 2013, Pebesma 2018).
2. Recorded Japanese common names were cleaned since some obvious typos scattered throughout the data. Data deletion was avoided as much as possible during data cleaning, however 161 occurrence records with incomplete species names such as blanks, "?" and "? sp" were deleted. Ambiguous search using R package: stringdist（van der Loo 2014) was conducted against the checklist of Japanese plant names (Yamanouchi et al. 2019), which is available on the JBIF website, to get candidates during data cleaning. The checklist was also used to get scientific names based on Japanese common names. Scientific names in Green List (Ebihara et al. 2016, Ito et al. 2016) and YList (based on Yonekura and Kajita 2003–) were selected in priority order, referring to other sources (Ebihara 2016a, 2016b, 大橋ほか（編）2015、2016a、2016b、2017a、2017b) in the checklist as appropriate. Taxon of fungi and algae was checked against literatures (広瀬・山岸 1977, 吉田ほか2015, National Museum of Nature and Science 2018, NIES 2022, Ohmura and Kashiwadani 2018, Suzuki 2016). YList (Yonekura and Kajita 2003–) was also referred to identify names for moss plants. Family names were mostly extracted from the checklist (Yamanouchi et al. 2019) and higher taxon was extracted from GBIF Backbone Taxonomy using GBIF Species API (GBIF 2023, GBIF Secretariat 2023) in Python programs.
3. Spelling variants in event dates, prefectures, quadrat areas and vegetation layers were fixed if obvious. Prefecture names were checked against the Digital Agency Registry Catalog (Digital Agency 2023). Vegetation layers were cleaned and normalized based on the outline of the 2nd National Survey on the Natural Environment (MoE 1979). All data including spatial and taxonomic information were organized into occurrence data using a MySQL database. Total of 413 occurrence records were deleted from original data, because either species names were invalid or relations between occurrences and events were incomplete.

Citações bibliográficas

1. Digital Agency (2023) Japan Prefecture Master Dataset. Digital Agency Registry Catalog. CC BY 4.0. https://catalog.registries.digital.go.jp/rsc/address/mt_pref_all.csv.zip [accessed on 2023-01-30].
2. Ebihara, A. (2016a) The Standard of Ferns and Lycophytes in Japan, Volume 1. Gakken Publishers, Tokyo.
3. Ebihara, A. (2016b) The Standard of Ferns and Lycophytes in Japan, Volume 2. Gakken Publishers, Tokyo.
4. Ebihara, A., Ito, M., Nagamasu, H., Fujii, S., Katsuyama, T., Yonekura, K., Yahara, T. (2016) Fern GreenList ver. 1.01. http://www.rdplants.org/gl/
5. GBIF (2023) Species API. Available from https://www.gbif.org/developer/species [accessed on 2023-01-18].
6. GBIF Secretariat (2023) GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei Accessed via. GBIF.org [accessed on 2023-01-18].
7. Hijmans R (2021) geosphere: Spherical Trigonometry. R package version 1.5-14. https://CRAN.R-project.org/package=geosphere [accessed on 2022-10-18].
8. Ito, M., Nagamasu, H., Fujii, S., Katsuyama, T., Yonekura, K., Ebihara, A., Yahara, T. (2016) GreenList ver. 1.01. http://www.rdplants.org/gl/
9. Karney, C. F. F. (2013) Algorithms for geodesics, J. Geodesy 87: 43-55.
10. Ministry of Land, Infrastructure, Transport and Tourism, Japan (MLIT) (2022a) National Land Numerical Information Institutional National and Prefectural Agency Data. https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-P28.html#prefecture00 [accessed on 2022-10-17].
11. Ministry of Land, Infrastructure, Transport and Tourism, Japan (MLIT) (2022b) National Land Numerical Information Administrative Area Data. N03-20220101_GML.zip https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-N03-v3_1.html#prefecture00 [accessed on 2022-10-17].
12. Ministry of the Environment (MoE) (1979) The outline of the 2nd National Survey on the Natural Environment (vegetation, lakes and rivers). https://www.biodic.go.jp/reports/2-12/index.html [Accessed on 2022-10-05].
13. Ministry of Environment (MoE) (2012) The 4th Version of the Japanese Red Lists on 9 Taxonomic Groups. 2012-08-28 Press release. https://www.env.go.jp/en/headline/1841.html [accessed on 2023-03-13].
14. National Institute for Environmental Studies (NIES) (2022) Invasive Species of Japan. https://www.nies.go.jp/biodiversity/invasive/index.html [accessed on 2022-08- 24].
15. National Museum of Nature and Science (2018) Science Museum Net (S-Net). http://science-net.kahaku.go.jp/ [accessed on 2023-03-01].
16. Ohmura, Y. and Kashiwadani, H. (2018) Checklist of Lichens and Allied Fungi of Japan. National Museum of Nature and Science Monographs 49: 1-140.
17. Pebesma, E. (2018) Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10: 439-446.
18. Suzuki, T. (2016) A Revised New Catalog of the Mosses of Japan. Hattoria 7: 9-223.
19. van der Loo M (2014) The stringdist package for approximate string matching. The R Journal 6: 111-122. https://CRAN.R-project.org/package=stringdist
20. Yamanouchi, T., Shutoh, K., Osawa, T., Yonekura, K., Kato, S., Shiga, T. (2019) A checklist of Japanese plant names. https://gbif.jp/activities/checklist/wamei_checklist_110 [accessed on 2022-11-25].
21. Yonekura K, Kajita T (2003-) BG Plants: index for Japanese scientific plant names: Ylist. http://ylist.info [accessed on 2022-05-25].
22. 大橋広好, 門田裕一, 木原浩, 邑田仁, 米倉浩司（編）(2015) 改訂新版 日本の野生植物 1 ソテツ科～カヤツリグサ科. 平凡社, 東京.
23. 大橋広好, 門田裕一, 木原浩, 邑田仁, 米倉浩司（編）(2016a) 改訂新版 日本の野生植物 2 イネ科～イラクサ科. 平凡社, 東京.
24. 大橋広好, 門田裕一, 木原浩, 邑田仁, 米倉浩司（編）(2016b) 改訂新版 日本の野生植物 3 バラ科～センダン科. 平凡社, 東京.
25. 大橋広好, 門田裕一, 木原浩, 邑田仁, 米倉浩司（編）(2017a) 改訂新版 日本の野生植物 4 アオイ科～キョウチクトウ科. 平凡社, 東京.
26. 大橋広好, 門田裕一, 木原浩, 邑田仁, 米倉浩司（編）(2017b) 改訂新版 日本の野生植物 5 ヒルガオ科～スイカズラ科. 平凡社, 東京.
27. 竹中 (2014) メッシュコード変換プログラム for R. http://takenaka-akio.org/etc/meshcode/r_code.html [参照2022年10月19日].
28. 広瀬弘幸, 山岸高旺（編）(1977) 日本淡水藻図鑑. pp. 778-779. 内田老鶴圃, 東京.
29. 吉田忠生, 鈴木雅大, 吉永一男 (2015) 日本産海藻目録 (2015 年改訂版). 藻類 63: 129-189.

Metadados Adicionais