說明
This dataset documents the presence of alien invasive plant species collected through systematic field surveys conducted in the Xiaoxing'an Mountains region of Heilongjiang Province, China, between July 2022 and July 2023. The collection is comprised of 4,408 unique sampling event records and 4,773 species occurrence records. The data follows the Darwin Core Archive standard and is published as a Sampling Event Core, providing detailed information on sampling efforts, precise geographical coordinates (with a spatial accuracy of 10 meters), temporal coverage, and species composition. The dataset is a vital resource for regional ecological risk assessment, species distribution modeling, and the management of invasive species in this crucial temperate forest ecosystem.
資料紀錄
此資源sampling event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 4,408 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。:
ZHANG Y (2025). Distribution and Abundance Dataset of Invasive Plants in the Xiaoxing'an Mountains, Heilongjiang (2022–2023). Version 1.5. Northeast Forestry University. Samplingevent dataset. https://ipt.taibif.tw/resource?r=lesser_khingan_invasives&v=1.5
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 Northeast Forestry University。 To the extent possible under law, the publisher has waived all rights to these data and has dedicated them to the Public Domain (CC0 1.0). Users may copy, modify, distribute and use the work, including for commercial purposes, without restriction.
GBIF 註冊
此資源已向GBIF註冊，並指定以下之GBIF UUID: 41c1c15f-fff8-45a8-a781-b1b24d81b9cc。 Northeast Forestry University 發佈此資源，並經由Chinese Academy of Sciences同意向GBIF註冊成為資料發佈者。
關鍵字
Sampling event; Alien Invasive Species; Xiaoxing'an Mountains; Occurrence Data; Vegetation Plot
聯絡資訊
地理涵蓋範圍
Xiaoxing'an Mountains, China
| 界定座標範圍 | 緯度南界 經度西界 [45.198, 125.611], 緯度北界 經度東界 [52.056, 130.781] |
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分類群涵蓋範圍
無相關描述
| Kingdom | Plantae |
|---|---|
| Phylum | Angiospermae |
| Class | Magnoliopsida |
| Order | Malvales, Rosales, Fabales, Solanales, Caryophyllales, Asterales, Poales, Malpighiales, Myrtales, Vitales |
| Family | Onagraceae, Poaceae, Vitaceae, Convolvulaceae, Malvaceae, Cannabaceae, Fabaceae, Solanaceae, Amaranthaceae, Asteraceae, Euphorbiaceae |
時間涵蓋範圍
| 起始日期 / 結束日期 | 2022-06-15 / 2023-09-26 |
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計畫資料
無相關描述
| 計畫名稱 | Survey on Alien Invasive Plants in Heilongjiang Province |
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取樣方法
Grid Survey: The study area is divided into 20km × 20km grids, focusing on areas with complex vegetation. A 300m × 300m survey is conducted around each grid point to locate invasive plants. Targeted Plot Setup and Quantification: If invasive plants are found during the reconnaissance, plots are set up for further investigation. The plot size depends on the type of invasive species: Herbaceous plants: 1m × 1m quadrats are used to record species and coverage. Shrub plants: 5m × 5m quadrats are used for the same purpose. The reconnaissance also recorded occurrence details of all invasive plants within the 300m × 300m area. Transect Survey: Sampling sections of road, free from major disturbances, are chosen for at least 5km stretches. A sample point is established every 1km along one side of the road. A reconnaissance survey is conducted at each sample point, and quadrats are placed randomly to record the species and cover of invasive plants.
| 研究範圍 | Xiaoxing'an Mountains region, Heilongjiang Province, China, focusing on core forest areas and roadside environments. |
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方法步驟描述:
- Using high-precision GPS to locate each sampling event, recording the scientific name, date, and percent cover of invasive species.
- Entering field data into spreadsheets, checking for transcription errors, and standardizing scientific names.
- Formatting cleaned data into the Darwin Core (DwC) structure, separating into Event Core (event information) and Occurrence Extension (occurrence information).
額外的詮釋資料
| 替代的識別碼 | 41c1c15f-fff8-45a8-a781-b1b24d81b9cc |
|---|---|
| https://ipt.taibif.tw/resource?r=lesser_khingan_invasives |