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. 2024 Feb 3;14(1):2828.
doi: 10.1038/s41598-024-53166-1.

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

Behnam Meshgi  1 , Ahmad Ali Hanafi-Bojd  2   3 , Saeid Fathi  4 , Galia Modabbernia  5 , Kourosh Meshgi  6 , Mohammad Shadman  5
Affiliations

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

Behnam Meshgi et al. Sci Rep. .

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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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
A total of 93 sampling sites were selected as inputs for calibrating the ecological niche models in three provinces (A), points/locations are shown on map as black dots (B), as well as three bioclimatic zones. The spatial patterns of some of environmental variables applied in the MaxEnt model were also shown in the figure (C–E).
Figure 2
Figure 2
Iran's climatic zones, spatial patterns of the some climatic and environmental variables applied in the MaxEnt model, and Iran's land cover.
Figure 3
Figure 3
Model response curves for the area under the curve for gastrointestinal nematodes of sheep in three climates zone, Iran. Training test and random prediction AUC values provided by performed models in the modeling analysis. (A) Azerbaijan province, (B) Kerman province, (C): Guilan province.
Figure 4
Figure 4
MaxEnt habitat suitability maps of GINs in three climatic regions, Iran. Areas depicted as red are of high suitability and areas depicted as blue are of low suitability. Areas depicted as green to red show suitability of regions from 0 (unsuitable habitat) to 1 (highly suitable habitat), visualizing the potential risk of GINs. (A): Azerbaijan province, (B): Kerman province, (C): Guilan province.
Figure 5
Figure 5
The Jackknife test for environmental variables’ contribution in modeling in three climates zone, Iran. Training and AUC gains is based upon the three scenarios including without variable, with only variable and with all variable. Blue bar depicts regularized training gain of individual environmental variable relative to all environmental variables (Red bar). (A) Azerbaijan province, (B) Kerman province, (C) Guilan province.
Figure 6
Figure 6
Response curves of MaxEnt models for dominant variables; Bio17 (Precipitation of the driest quarter; mm), slope and NDVI contributed remarkably to the suitability model, indicating the most significant influence of variables on presence-only habitat suitability of GINs. (A): East Azerbaijan province, (B): Kerman province, (C): Guilan province.
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