气候变化领域集成服务门户(CCPortal): Multi-pronged abundance prediction of bee pests' spatial proliferation in Kenya

CCPortal

DOI	10.1016/j.jag.2024.103738
	Multi-pronged abundance prediction of bee pests' spatial proliferation in Kenya
	Makori, David Masereti; Abdel-Rahman, Elfatih M.; Odindi, John; Mutanga, Onisimo; Landmann, Tobias; Tonnang, Henri E. Z.
发表日期	2024
ISSN	1569-8432
EISSN	1872-826X
起始页码	128
卷号	128
英文摘要	Bee farming and beehealth are threatened by climate change, agricultural and agrochemicals intensification, and bee pests and diseases. Among these threats, bee pests have particularly been identified as a major obstacle to beehealth. Although previous studies have endeavoured to establish bee pests' spatial distribution, their seasonal abundance in the landscape remains poorly understood. Hence, this study sought to determine factors that influence the abundance and spatial proliferation of bee pests in Kenya. Abundance data on Varroa destructor, Oplostomus haroldi, Galleria mellonella and Aethina tumida were collected from apiaries in Kenya during the wet and dry seasons. The abundance data were fitted to non -conflating human footprint datasets, satellite derived vegetation phenological, topographical and bioclimatic variables. The results indicated a significant (p <= 0.05) seasonal influence on bee pests' abundance, while precipitation was the most relevant on most bee pests' abundance prediction models. Topographic and vegetation phenological influence varied across the landscapes while anthropogenic influence was comparatively low. High seasonality in bioclimatic variables influenced the projected (year 2055) spatial and abundance risk levels of bee pests across the study area. The V. destructor and A. tumida prediction models for current and future epochs ranked excellent in their performance, while O. haroldi and G. mellonella were ranked good and fair, respectively. Due to their precision, this study concluded that these models could reliably be used to establish bee pests' high -risk areas for management and mitigation purposes.
英文关键词	Beehealth; Food security; Climate change; Human footprint; Machine learning; Bee pest abundance
语种	英语
WOS研究方向	Remote Sensing
WOS类目	Remote Sensing
WOS记录号	WOS:001207584200001
来源期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/288830
作者单位	International Centre of Insect Physiology & Ecology (ICIPE); University of Kwazulu Natal
推荐引用方式 GB/T 7714	Makori, David Masereti,Abdel-Rahman, Elfatih M.,Odindi, John,et al. Multi-pronged abundance prediction of bee pests' spatial proliferation in Kenya[J],2024,128.
APA	Makori, David Masereti,Abdel-Rahman, Elfatih M.,Odindi, John,Mutanga, Onisimo,Landmann, Tobias,&Tonnang, Henri E. Z..(2024).Multi-pronged abundance prediction of bee pests' spatial proliferation in Kenya.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,128.
MLA	Makori, David Masereti,et al."Multi-pronged abundance prediction of bee pests' spatial proliferation in Kenya".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 128(2024).