ArticleScientific reports2024
Multi-scale habitat modeling framework for predicting the potential distribution of sheep gastrointestinal nematodes across Iran's three distinct climatic zones: a MaxEnt machine-learning algorithm.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 4 citations in OpenAlex.
- The Role of Artificial Intelligence and Machine Learning in Advancing Animal Biotechnology: A Review.Archives of Razi Institute · 2025Review
- Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review.Biology · 2025Review
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6 authors at 4 institutions in 1 country.
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Abstract
Ecological niche models (ENMs) serve as valuable tools in assessing the potential species distribution, identifying crucial habitat components for species associations, and facilitating conservation efforts. The current study aimed to investigate the gastrointestinal nematodes (GINs) infection in sheep, predict and analyze their ecological niches and ranges, and identify the key bioclimatic variables influencing their distribution across three distinct climatic regions in Iran. In a cross-sectional study, a total of 2140 fecal samples were collected from semi-arid (n = 800), arid (n = 500), and humid-subtropical (n = 840) climates in East Azerbaijan, Kerman, and Guilan provinces, respectively. The flotation method was employed to assess stool samples, whereby the fecal egg count (the number of parasite eggs per gram [EPG]) was ascertained for each individual specimen. Employing a presence-only approach, the multi-scale maximum entropy (MaxEnt) method was used to model GINs' habitat suitability using 93 selected points/locations. The findings revealed that Guilan (34.2%) and East Azerbaijan (19.62%) exhibited the utmost proportion of Strongyle-type eggs. East Azerbaijan province also displayed the highest proportion of Marshallagia and Nematodirus, respectively (approximately 40% and 27%), followed by Guilan and Kerman provinces, while Kerman province had the highest proportion of Trichuris (approximately 15%). Ecological niche modeling revealed that the precipitation of the driest quarter (Bio17) exerted the most significant influence on Marshallagia, Nematodirus, Trichuris, and ُSُُُtrongyle-type eggs' presence in East Azerbaijan and Kerman provinces. For Guilan province, the most influential factor defining habitat suitability for Strongyle-type eggs, Marshallagia, and Nematodirus was increasing slope. Additionally, the distribution of Trichuris was most affected by the variable Bio2 in Guilan province. The study highlights the response of GINs to climate drivers in highly suitable regions, providing insights into ecologically favorable areas for GINs. In conclusion, this study provides a better understanding of GINs and the environmental factors influencing their transmission dynamics.
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