Predicting ixodid tick distribution in Tamil Nadu domestic mammals using ensemble species distribution models
Abstract Background Tick borne diseases are re-emerging around the world, including India. Information about the occurrence of the tick vectors in different geographical locations is essential for controlling the diseases. Tick surveys have not been conducted in many parts of India and information o...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-02-01
|
| Series: | Ecological Processes |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13717-025-00578-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823863261523607552 |
|---|---|
| author | Ayyanar Elango Hari Kishan Raju Ananganallur Nagarajan Shriram Ashwani Kumar Manju Rahi |
| author_facet | Ayyanar Elango Hari Kishan Raju Ananganallur Nagarajan Shriram Ashwani Kumar Manju Rahi |
| author_sort | Ayyanar Elango |
| collection | DOAJ |
| description | Abstract Background Tick borne diseases are re-emerging around the world, including India. Information about the occurrence of the tick vectors in different geographical locations is essential for controlling the diseases. Tick surveys have not been conducted in many parts of India and information on the current prevalence of tick vectors is not available in all states of India. Many studies have been carried out utilizing modelling methods to predict the distribution of tick species in other countries. The MaxEnt model is widely used for predicting tick species distribution using bioclimatic variables. Lyme disease vectors such as Ixodes sp., Amblyomma sp., and Dermacentor sp. are the most commonly predicted tick species. However, very few studies have been carried out to predict the distribution of tick species in India. Haemaphysalis spinigera, the primary Kyasanur Forest Disease vector, was predicted along the Western Ghats using the MaxEnt model. Rhipicephalus (Boophilus) microplus was predicted across India using the generalized linear model (GLM). Identifying the tick vectors in transmitting the infection through conventional survey and identification methods is cumbersome due to the less number of experienced persons available. Prediction of tick vectors of public health concern, including other tick species in different geographical regions of Tamil Nadu, India, is essential for the prevention and control of tick-borne disease in humans and domestic animals. The present study adopts the package ‘SSDM’ (stacked species distribution models) with R software containing ensemble species distribution models to predict the distribution of tick species using different available environmental and climatic data. Results The categorical variables such as land use and land cover (LULC), soil type, elevation, Bio1, Bio10, Bio15, Bio19 and Bio8 contributed more to modelling the distribution of tick species. MaxEnt, GLM, GBM and GAM are suitable models for predicting the tick species distribution in the present study. Among these models, MaxEnt is the most suitable model for predicting tick species distribution in Tamil Nadu, India. Conclusions Our results suggest that MaxEnt is a suitable model for predicting the distribution of tick species. Both environmental factors such as LULC, elevation and soil type and bioclimatic factors such as temperature and precipitation contribute significantly to predicting tick species distribution in domestic animals in Tamil Nadu. The SSDM package is very useful and user-friendly graphical user interface for modelling the distribution of tick species. However, the package can be further improved by using higher resolution raster variables in larger areas, which is not currently supported. The predicted elevation range of Ha. spinigera distribution could not be provided due to software limitations. |
| format | Article |
| id | doaj-art-3dfdc9bf06e146bab0f27bae28e7b1db |
| institution | Kabale University |
| issn | 2192-1709 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Ecological Processes |
| spelling | doaj-art-3dfdc9bf06e146bab0f27bae28e7b1db2025-02-09T12:12:37ZengSpringerOpenEcological Processes2192-17092025-02-0114111710.1186/s13717-025-00578-0Predicting ixodid tick distribution in Tamil Nadu domestic mammals using ensemble species distribution modelsAyyanar Elango0Hari Kishan Raju1Ananganallur Nagarajan Shriram2Ashwani Kumar3Manju Rahi4Division of Vector Biology and Control, ICMR-Vector Control Research Centre, Department of Health Research, Ministry of Health & Family Welfare, GOIClimate Change, GIS and VBD Stratification/Mapping, ICMR-Vector Control Research Centre, Department of Health Research, Ministry of Health & Family Welfare, GOIDivision of Vector Biology and Control, ICMR-Vector Control Research Centre, Department of Health Research, Ministry of Health & Family Welfare, GOICentre for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, ChennaiICMR-Vector Control Research Centre, Department of Health Research, Ministry of Health & Family Welfare, GOIAbstract Background Tick borne diseases are re-emerging around the world, including India. Information about the occurrence of the tick vectors in different geographical locations is essential for controlling the diseases. Tick surveys have not been conducted in many parts of India and information on the current prevalence of tick vectors is not available in all states of India. Many studies have been carried out utilizing modelling methods to predict the distribution of tick species in other countries. The MaxEnt model is widely used for predicting tick species distribution using bioclimatic variables. Lyme disease vectors such as Ixodes sp., Amblyomma sp., and Dermacentor sp. are the most commonly predicted tick species. However, very few studies have been carried out to predict the distribution of tick species in India. Haemaphysalis spinigera, the primary Kyasanur Forest Disease vector, was predicted along the Western Ghats using the MaxEnt model. Rhipicephalus (Boophilus) microplus was predicted across India using the generalized linear model (GLM). Identifying the tick vectors in transmitting the infection through conventional survey and identification methods is cumbersome due to the less number of experienced persons available. Prediction of tick vectors of public health concern, including other tick species in different geographical regions of Tamil Nadu, India, is essential for the prevention and control of tick-borne disease in humans and domestic animals. The present study adopts the package ‘SSDM’ (stacked species distribution models) with R software containing ensemble species distribution models to predict the distribution of tick species using different available environmental and climatic data. Results The categorical variables such as land use and land cover (LULC), soil type, elevation, Bio1, Bio10, Bio15, Bio19 and Bio8 contributed more to modelling the distribution of tick species. MaxEnt, GLM, GBM and GAM are suitable models for predicting the tick species distribution in the present study. Among these models, MaxEnt is the most suitable model for predicting tick species distribution in Tamil Nadu, India. Conclusions Our results suggest that MaxEnt is a suitable model for predicting the distribution of tick species. Both environmental factors such as LULC, elevation and soil type and bioclimatic factors such as temperature and precipitation contribute significantly to predicting tick species distribution in domestic animals in Tamil Nadu. The SSDM package is very useful and user-friendly graphical user interface for modelling the distribution of tick species. However, the package can be further improved by using higher resolution raster variables in larger areas, which is not currently supported. The predicted elevation range of Ha. spinigera distribution could not be provided due to software limitations.https://doi.org/10.1186/s13717-025-00578-0Land use and land cover (LULC)ElevationPredictionModelIndiaMaxEnt |
| spellingShingle | Ayyanar Elango Hari Kishan Raju Ananganallur Nagarajan Shriram Ashwani Kumar Manju Rahi Predicting ixodid tick distribution in Tamil Nadu domestic mammals using ensemble species distribution models Ecological Processes Land use and land cover (LULC) Elevation Prediction Model India MaxEnt |
| title | Predicting ixodid tick distribution in Tamil Nadu domestic mammals using ensemble species distribution models |
| title_full | Predicting ixodid tick distribution in Tamil Nadu domestic mammals using ensemble species distribution models |
| title_fullStr | Predicting ixodid tick distribution in Tamil Nadu domestic mammals using ensemble species distribution models |
| title_full_unstemmed | Predicting ixodid tick distribution in Tamil Nadu domestic mammals using ensemble species distribution models |
| title_short | Predicting ixodid tick distribution in Tamil Nadu domestic mammals using ensemble species distribution models |
| title_sort | predicting ixodid tick distribution in tamil nadu domestic mammals using ensemble species distribution models |
| topic | Land use and land cover (LULC) Elevation Prediction Model India MaxEnt |
| url | https://doi.org/10.1186/s13717-025-00578-0 |
| work_keys_str_mv | AT ayyanarelango predictingixodidtickdistributionintamilnadudomesticmammalsusingensemblespeciesdistributionmodels AT harikishanraju predictingixodidtickdistributionintamilnadudomesticmammalsusingensemblespeciesdistributionmodels AT ananganallurnagarajanshriram predictingixodidtickdistributionintamilnadudomesticmammalsusingensemblespeciesdistributionmodels AT ashwanikumar predictingixodidtickdistributionintamilnadudomesticmammalsusingensemblespeciesdistributionmodels AT manjurahi predictingixodidtickdistributionintamilnadudomesticmammalsusingensemblespeciesdistributionmodels |