在中国Ceracris kiangsu Tsai的潜在分布预测
Chun Fu1, Xuanye Wen2, Zhaopeng Shi3
1Key Laboratory of Sichuan Province for Bamboo Pests Control and Resource Development, Leshan Normal University, Leshan, 614000, People's Republic of China.
Scientific reports
|June 11, 2024
概括
森林害虫Ceracris kiangsu Tsai,其适合的息地主要位于林 - 河以南. 未来的气候变化可能会改变其分布,一些地区变得更适合,而另一些地区则减少.
科学领域:
- 生态生态学 生态生态学
- 气候变化生物学 气候变化生物学
- 侵入性物种研究 侵入性物种研究
背景情况:
- (Ceracris kiangsu Tsai) 是一个重要的森林害虫,影响着许多植物物种.
- 了解它的分布对于农业和生态管理至关重要.
研究的目的:
- 预测中国C.kiangsu目前和未来适合分布的地区.
- 确定影响其息地适宜性的关键环境因素.
- 评估在各种气候变化情景下分布的潜在变化.
主要方法:
- 从草药库,文献和现场调查中利用了314种物种的分布点.
- 采用了两个生态利基模型,GARP和Maxent.
- 来自三个未来气候场景 (CMIP6) 的纳入数据.
主要成果:
- 最干燥月份的降雨量 (bio14) 和最冷月份的最低温度 (bio6) 是关键的环境驱动因素.
- 目前适合的地区主要位于林 - 河以南,面积约为160.65×10^4平方公里.
- 未来的预测表明适合地区的转变,像云南南部和四川东南部这样的部分地区在变暖条件下变得非常适合.
结论:
- C. kiangsu 的分布受到特定气候因素的强烈影响.
- 预计气候变化将改变其适合的息地,需要在新适合的地区集中监测.
- 中国南部很可能仍然是一个主要的息地,在变暖情景下,可能会扩展到新的地区.
更多相关视频
11:41Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
Published on: February 1, 2020
20.3K
09:03Field-Deployable Candidatus Liberibacter asiaticus Detection Using Recombinase Polymerase Amplification Combined with CRISPR-Cas12a
Published on: December 23, 2022
2.6K
相关概念视频
Receiver Operating Characteristic Plot
137
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
137
Finding Critical Values for Chi-Square
2.9K
Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...
2.9K
