Spatial Distribution and Abundance of Chrysomya bezziana in Jazan Province, Saudi Arabia Using GIS

Azzam Alahmed, Mohamed Nasser*, Salah Kheir, Mohamed Sallam, Hassan Dawah, Sara Alashaal, Eslam M. Hosni

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Myiasis caused by Calliphorid and Oestrid flies constitute a major threat to the development of livestock industry in Kingdom of Saudi Arabia. Increased veterinary concerns recently paid to the frequent reports on enteric pathogens caused by the larvae of these flies. Although the importance of sheep and goats in Saudi Arabia, the geographic distribution limits of Calliphorid and Oestrid infestation in caprine livestock has never been highlighted. ArcGIS software was used to assess the spatial distribution of myiasis causing flies in Jazan Province, Saudi Arabia. Implemented Evolutionary algorithms in maximum entropy (MaxEnt) was used to predict the distribution map for myiasis causing flies. Bioclimatic and topographic data layers from Worldclim was analyzed to estimate the percent contribution of variables predicting suitable habitats of flies causing myiasis. Field validation was occurred to evaluate the habitat suitability produced by the model. The predictive ecological niche model was found high with an AUC value of 0.95 and 0.93 for train and test occurrence records, respectively, with a standard deviation equal 0.032. About eighteen variables were found to ↱contribute in spatial predictive occurrence of myiasis causing flies. Precipitation variables enhanced the model predictive power with (57.7%) in Jackknife test. Besides, elevation, NDVI and tree cover shared reduced effect in predicting myiasis causing flies distribution.

Original languageEnglish
Pages (from-to)439-453
Number of pages15
JournalJournal of the Entomological Research Society
Issue number2
StatePublished - 2023
Externally publishedYes


  • Field validation
  • MaxEnt
  • Myasis
  • Oestrid flies
  • Spatial distribution modelling


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