Biotypes and spatial distribution of Tylenchulus semipenetrans in Southern Iran
- 1Department of Plant Protection, College of Agriculture, Shiraz University, Shiraz, Iran
- 2Department of Soil Science, College of Agriculture, Shiraz University, Shiraz, Iran
Abstract
Soil and root samples were collected from 189 citrus orchards in Fars Province, Iran, during summer to early autumn 2018 to assess the spatial distribution and biotype identity of Tylenchulus semipenetrans. This study also evaluated the applicability of Geographic Information Systems (GIS)-based spatial modeling for predicting nematode distribution. A stratified field survey was conducted across major citrus-producing regions, and nematode densities were determined using the tray extraction method. Overall, 67.7% of orchards were infested, with 32.3% nematode-free, 11.6% having low (1–800 nematodes/100 cm3 soil), 20.6% moderate (800–3,600 nematodes), and 35.4% high (>3,600 nematodes) population densities. To evaluate biotype identity, 19 representative populations were inoculated onto sour orange, trifoliate orange, grapevine, and olive under greenhouse conditions. All populations reproduced on citrus but failed to reproduce on trifoliate orange and had minimal reproduction on olive and grapevine, consistent with the Mediterranean biotype. In a subset of 46 orchards in Jahrom County, soil physicochemical properties were analyzed and related to nematode density using GIS and Random Forest modeling. Spatial interpolation and predictive mapping were conducted using ArcGIS. The Random Forest model explained 82.6% of the variation in nematode density (R 2 = 0.826), with soil pH identified as the most important predictor, followed by silt and sand content, whereas clay content and calcium carbonate had lower importance. These results demonstrate the widespread distribution of T. semipenetrans in Fars Province and the utility of GIS and machine learning for identifying environmental drivers of nematode abundance and supporting spatially targeted management strategies.
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BibTeX
@article{mohammad2026biotypes,
title = {{Biotypes and spatial distribution of Tylenchulus semipenetrans in Southern Iran}},
author = {Rumiani Mohammad and Karegar Akbar and Hamzehzarghani Habiballah and Pourghasemi Hamid Reza},
journal = {Journal of Nematology},
year = {2026},
month = oct,
doi = {10.2478/jofnem-2026-0036},
url = {https://doi.org/10.2478/jofnem-2026-0036},
note = {CC BY},
}
RIS
TY - JOUR
TI - Biotypes and spatial distribution of Tylenchulus semipenetrans in Southern Iran
AU - Rumiani Mohammad
AU - Karegar Akbar
AU - Hamzehzarghani Habiballah
AU - Pourghasemi Hamid Reza
PY - 2026
DA - 2026/10/26
JO - Journal of Nematology
DO - 10.2478/jofnem-2026-0036
UR - https://doi.org/10.2478/jofnem-2026-0036
AB - Soil and root samples were collected from 189 citrus orchards in Fars Province, Iran, during summer to early autumn 2018 to assess the spatial distribution and biotype identity of Tylenchulus semipenetrans. This study also evaluated the applicability of Geographic Information Systems (GIS)-based spatial modeling for predicting nematode distribution. A stratified field survey was conducted across major citrus-producing regions, and nematode densities were determined using the tray extraction method. Overall, 67.7% of orchards were infested, with 32.3% nematode-free, 11.6% having low (1–800 nematodes/100 cm3 soil), 20.6% moderate (800–3,600 nematodes), and 35.4% high (>3,600 nematodes) population densities. To evaluate biotype identity, 19 representative populations were inoculated onto sour orange, trifoliate orange, grapevine, and olive under greenhouse conditions. All populations reproduced on citrus but failed to reproduce on trifoliate orange and had minimal reproduction on olive and grapevine, consistent with the Mediterranean biotype. In a subset of 46 orchards in Jahrom County, soil physicochemical properties were analyzed and related to nematode density using GIS and Random Forest modeling. Spatial interpolation and predictive mapping were conducted using ArcGIS. The Random Forest model explained 82.6% of the variation in nematode density (R 2 = 0.826), with soil pH identified as the most important predictor, followed by silt and sand content, whereas clay content and calcium carbonate had lower importance. These results demonstrate the widespread distribution of T. semipenetrans in Fars Province and the utility of GIS and machine learning for identifying environmental drivers of nematode abundance and supporting spatially targeted management strategies.
SN - 2640-396X
SN - 0022-300X
ER -