Next Article in Journal
Applying OGC Standards to Develop a Land Surveying Measurement Model
Next Article in Special Issue
Development and Comparison of Species Distribution Models for Forest Inventories
Previous Article in Journal
Linking Neighborhood Characteristics and Drug-Related Police Interventions: A Bayesian Spatial Analysis
Previous Article in Special Issue
Effect of the Long-Term Mean and the Temporal Stability of Water-Energy Dynamics on China’s Terrestrial Species Richness
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(3), 66; doi:10.3390/ijgi6030066

Predicting Spatial Distribution of Key Honeybee Pests in Kenya Using Remotely Sensed and Bioclimatic Variables: Key Honeybee Pests Distribution Models

1
International Center for Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, Kenya
2
Discipline of Geography, School of Agricultural, Earth and Environment Sciences, University of Kwa Zulu Natal, Pietermaritzburg 3209, South Africa
3
Department of Agronomy, Faculty of Agriculture, University of Khartoum, Khartoum North 13314, Sudan
*
Author to whom correspondence should be addressed.
Academic Editors: Duccio Rocchini and Wolfgang Kainz
Received: 21 September 2016 / Revised: 10 February 2017 / Accepted: 21 February 2017 / Published: 28 February 2017
(This article belongs to the Special Issue Spatial Ecology)
View Full-Text   |   Download PDF [5420 KB, uploaded 28 February 2017]   |  

Abstract

Bee keeping is indispensable to global food production. It is an alternate income source, especially in rural underdeveloped African settlements, and an important forest conservation incentive. However, dwindling honeybee colonies around the world are attributed to pests and diseases whose spatial distribution and influences are not well established. In this study, we used remotely sensed data to improve the reliability of pest ecological niche (EN) models to attain reliable pest distribution maps. Occurrence data on four pests (Aethina tumida, Galleria mellonella, Oplostomus haroldi and Varroa destructor) were collected from apiaries within four main agro-ecological regions responsible for over 80% of Kenya’s bee keeping. Africlim bioclimatic and derived normalized difference vegetation index (NDVI) variables were used to model their ecological niches using Maximum Entropy (MaxEnt). Combined precipitation variables had a high positive logit influence on all remotely sensed and biotic models’ performance. Remotely sensed vegetation variables had a substantial effect on the model, contributing up to 40.8% for G. mellonella and regions with high rainfall seasonality were predicted to be high-risk areas. Projections (to 2055) indicated that, with the current climate change trend, these regions will experience increased honeybee pest risk. We conclude that honeybee pests could be modelled using bioclimatic data and remotely sensed variables in MaxEnt. Although the bioclimatic data were most relevant in all model results, incorporating vegetation seasonality variables to improve mapping the ‘actual’ habitat of key honeybee pests and to identify risk and containment zones needs to be further investigated. View Full-Text
Keywords: honeybee pests; bioclimatic variables; remotely sensed variables; phenology; ecological niche modelling honeybee pests; bioclimatic variables; remotely sensed variables; phenology; ecological niche modelling
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Makori, D.M.; Fombong, A.T.; Abdel-Rahman, E.M.; Nkoba, K.; Ongus, J.; Irungu, J.; Mosomtai, G.; Makau, S.; Mutanga, O.; Odindi, J.; Raina, S.; Landmann, T. Predicting Spatial Distribution of Key Honeybee Pests in Kenya Using Remotely Sensed and Bioclimatic Variables: Key Honeybee Pests Distribution Models. ISPRS Int. J. Geo-Inf. 2017, 6, 66.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top