Multi-pronged abundance prediction of bee pests’ spatial proliferation in Kenya
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S156984322400092X |
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| author | David Masereti Makori Elfatih M. Abdel-Rahman John Odindi Onisimo Mutanga Tobias Landmann Henri E.Z. Tonnang |
| author_facet | David Masereti Makori Elfatih M. Abdel-Rahman John Odindi Onisimo Mutanga Tobias Landmann Henri E.Z. Tonnang |
| author_sort | David Masereti Makori |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T19:10:53Z |
| format | Article |
| id | doaj.art-fc44802e71094ed18d22965188335ff5 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-24T13:52:06Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-fc44802e71094ed18d22965188335ff52024-04-04T05:03:41ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-04-01128103738Multi-pronged abundance prediction of bee pests’ spatial proliferation in KenyaDavid Masereti Makori0Elfatih M. Abdel-Rahman1John Odindi2Onisimo Mutanga3Tobias Landmann4Henri E.Z. Tonnang5International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, Kenya; School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa; Corresponding author at: International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, Kenya.International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, Kenya; School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South AfricaSchool of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South AfricaSchool of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South AfricaInternational Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, KenyaInternational Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, Kenya; School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South AfricaBee 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.http://www.sciencedirect.com/science/article/pii/S156984322400092XBeehealthFood securityClimate changeHuman footprintMachine learningBee pest abundance |
| spellingShingle | David Masereti Makori Elfatih M. Abdel-Rahman John Odindi Onisimo Mutanga Tobias Landmann Henri E.Z. Tonnang Multi-pronged abundance prediction of bee pests’ spatial proliferation in Kenya International Journal of Applied Earth Observations and Geoinformation Beehealth Food security Climate change Human footprint Machine learning Bee pest abundance |
| title | Multi-pronged abundance prediction of bee pests’ spatial proliferation in Kenya |
| title_full | Multi-pronged abundance prediction of bee pests’ spatial proliferation in Kenya |
| title_fullStr | Multi-pronged abundance prediction of bee pests’ spatial proliferation in Kenya |
| title_full_unstemmed | Multi-pronged abundance prediction of bee pests’ spatial proliferation in Kenya |
| title_short | Multi-pronged abundance prediction of bee pests’ spatial proliferation in Kenya |
| title_sort | multi pronged abundance prediction of bee pests spatial proliferation in kenya |
| topic | Beehealth Food security Climate change Human footprint Machine learning Bee pest abundance |
| url | http://www.sciencedirect.com/science/article/pii/S156984322400092X |
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