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Bridging Knowledge and Data Gaps in Odonata Rarity:一个韩国案例研究,使用多个物种占用模型和拉比诺维茨框架

Sungsoo Yoon^1,2, Wanmo Kang³

¹Department of Forest Resources, Graduate School of Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea.

|November 26, 2024

概括

数据驱动的模型和生态知识揭示了物种的稀有性. 调查结果突出了差异,强调需要进行标准化调查和数据共享,以准确地评估韩国奥多纳塔的保护状况.

科学领域:

生态生态学生态生态学
保护生物学保护生物学
生物多样性评估评估

背景情况:

精确的物种稀有性和保护状况评估需要将数据驱动模型与生态知识相结合.
(龙和) 是淡水生态系统健康的重要指标.
现有的评估可能会受到调查偏见和数据可访问性限制的影响.

研究的目的:

应用多物种占用 (MSO) 和隐性因子多物种占用 (LFMSO) 模型来估计韩国Odonata的发生情况.
使用拉比诺维茨框架进行基于数据的稀有性评估,并将其与传统生态知识进行比较.
识别差异,并了解影响稀有性评估的因素,用于保护规划.

主要方法:

应用多种占用 (MSO) 和隐性因子多种占用 (LFMSO) 模型对133种Odonata物种.
实施拉比诺维茨稀有性框架,使用模型输出来进行数据驱动的稀有性评估.
基于数据的评估与地理范围,息地偏好,区域红色名单状态和公民科学数据的比较.

主要成果:

在数据驱动的稀有物种分类和区域红色名单状态 (接近危,脆弱) 之间进行总体调整.
确定了显著的差异,特别是在被认为是常见的镜形类 (Odonata) 物种.
调查中的空间偏差和对受保护物种的有限数据访问被确定为准确评估的潜在障碍.

结论:

数据驱动的稀有性评估提供了有价值的见解,但需要与生态知识进行验证.
标准化的调查协议和改进的数据共享政策对于可靠的生物多样性评估至关重要.
在稀有性评估中提高准确性对于有效的淡水生态系统保护规划和管理至关重要.

关键词:

在 Odonata 的位置上.拉比诺维茨稀有性框架知识数据不一致性占用率建模的使用情况.种类稀有性品种稀有性

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