Abundance and diversity of insects in grassland and woodlot area of Danby Woods
收藏DataCite Commons2020-09-04 更新2024-07-25 收录
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The data was collected on Thursday, October 1, 2015 at around 3:00pm. The location was at Danby Woodlot on both the grassland and woodlot area at York University – Keele Campus. The weather at the time of collecting data was sunny with winds and roughly 15 degrees Celsius. The collection of data was done in a group of four, which included – Vivian Do, Amandeep Bains, Stephanie Achaiya, and Alexander Karakatganis. To collect the data, 1m x 1m quadrats were randomly placed on the grassland and woodlot area. 20 quadrats were sampled - 10 in the grassland and 10 in the woodlot. When each quadrat was placed down, a timer was set to observe each quadrat for one minute. While making observations, the abundance – number of total species within that quadrat as well as RTU (Recognizable Taxonomic Units) – number of different species within that same quadrat was counted using a quick eye count and data was recorded. The different species were determined visually based on easily recognized features. This was repeated until a total of 20 plots were sampled and data was recorded. However, this data may be prone to error, depending on the sample, sorter and species. Due to the small size of insects, characteristics - such as camouflage or due to the fact that the insects may be hidden, will hinder our ability to count them in. This leads to the total count and RTU being affected, therefore, resulting in a lower insect count. The purpose of this dataset was to find correlations between the variables studied using quadrats as a sampling technique.
本数据集采集于2015年10月1日周四下午3时左右,采集地点为约克大学(York University)基尔校区(Keele Campus)的丹比林地(Danby Woodlot),涵盖草地与林地两类区域。当日采集时段天气晴朗且伴有风力,气温约15摄氏度。
本次数据采集工作由四人小组完成,成员包括Vivian Do、Amandeep Bains、Stephanie Achaiya与Alexander Karakatganis。
采样过程中,研究人员将1米×1米的样方(quadrats)随机布设至草地与林地区域,共布设20个样方——草地与林地各10个。每个样方放置就位后,启动计时器开展1分钟的观测。观测期间,通过快速目视计数记录两类数据:丰度(abundance,即该样方内的物种总个体数)与可识别分类单元(RTU, Recognizable Taxonomic Units,即同一样方内的不同物种数量),物种识别依托直观易辨的形态特征完成。上述流程重复进行,直至完成全部20个样方的采样与数据记录。
不过本数据集存在潜在误差,误差程度受样本、观测者与物种属性影响。由于昆虫体型微小,加之保护色或隐匿行为等特征,会干扰计数工作,导致总个体数与可识别分类单元数量被低估,最终呈现出偏低的昆虫计数结果。
本数据集旨在以样方法为采样手段,探究所研究变量间的相关关系。
提供机构:
figshare
创建时间:
2016-01-20



