Title: Multi-scale Habitat Modeling for Predicting Sheep Gastrointestinal Nematodes in Iran’s Climate Zones
A new study titled Multi-scale habitat modeling framework for predicting the potential distribution of sheep gastrointestinal nematodes across Iran’s three distinct climatic zones sheds light on the potential geographical distribution of gastrointestinal nematodes (GINs) in Iran. The study aims to provide valuable insights into the transmission dynamics of GINs and help decision-makers implement effective surveillance and control measures in the future.
To conduct the study, researchers employed the MaxEnt machine-learning algorithm and Arc-geographical information system (Arc-GIS) platforms. They focused on three different climatic regions in Iran: East Azerbaijan, Kerman, and Guilan provinces, representing semi-arid, arid, and humid-subtropical zones, respectively.
In Guilan Province, located along the southern strip of the Caspian Sea, the researchers obtained an area with elevations ranging from 15 meters below to 300 meters above sea level. This province experiences a temperate Caspian climate with significant rainfall, making it ideal for dense vegetation.
East Azerbaijan province, situated in the northwest of Iran, has a Mediterranean continental and cold semi-arid climate. The capital city, Tabriz, receives an average annual rainfall of 310mm. Kerman province, located in the south-central part of Iran, experiences a semi-arid to dry climate.
The researchers conducted a cross-sectional study from April to September 2020, collecting fresh fecal samples from native sheep in the three provinces. A total of 2140 fecal samples were collected, with each sample sent to the Parasitology Department of the Faculty of Veterinary Medicine at the University of Tehran for identification.
Based on the identification of GIN species and analysis of their transmission dynamics, the researchers aimed to predict suitable habitats for these species and identify the critical bioclimatic and environmental/topographic variables impacting their geographic distribution.
For the ecological niche modeling, various bioclimatic, topographic, and environmental variables were used. Bioclimatic data was extracted from the WorldClim database, while topographic variables were derived from the digital elevation model (DEM) of Iran. The maximum entropy modeling (MaxEnt) algorithm was then applied to estimate the probability of species presence.
The accuracy of the model was evaluated using the receiver-operating characteristic (ROC) curve, considering both training and test datasets. Additionally, the contribution of each variable in the model was identified using jackknife analysis. The resulting maps were loaded into ArcGIS for further analysis.
This study provides valuable insights into the potential distribution of sheep gastrointestinal nematodes in different climatic zones of Iran. By understanding the geographical distribution and identifying key factors affecting transmission dynamics, decision-makers can implement effective surveillance and control measures to mitigate the potential risks posed by these parasites.
The findings of this study contribute to the knowledge base on the effects of climate change and environmental factors on nematode ranges in Iran. This research also highlights the importance of considering the potential risk of species invasions and the need for future national surveillance and control measures.
In conclusion, the multi-scale habitat modeling framework developed in this study provides valuable information on the potential distribution of sheep gastrointestinal nematodes in Iran’s diverse climatic zones. The identification of critical factors impacting their geographic distribution will help decision-makers implement effective surveillance and control measures to minimize the impact of these parasites on sheep populations in Iran.
