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Delineating ecological boundaries of Hanuman langur species complex in peninsular India using MaxEnt modeling approach.

ABSTRACT: Hanuman langur is one of the widely distributed and extensively studied non-human diurnal primates in India. Until recently it was believed to be a single species - Semnopithecus entellus. Recent molecular and morphological studies suggest that the Hanuman langurs consists of at least three species S. entellus, S. hypoleucos and S. priam. Furthermore, morphological studies suggested that both S. hypoleucos and S. priam have at least three subspecies in each. We explored the use of ecological niche modeling (ENM) to confirm the validity of these seven taxa and an additional taxon S. johnii belonging to the same genus. MaxEnt modeling tool was used with 19 bioclimatic, 12 vegetation and 6 hydrological environmental layers. We reduced total environmental variables to 14 layers after testing for collinearity and an independent test for model prediction was done using ENMTools. A total of 196 non-overlapping data points from primary and secondary sources were used as inputs for ENM. Results showed eight distinct ecological boundaries, corroborating the eight taxa mentioned above thereby confirming validity of these eight taxa. The study, for the first time provided ecological variables that determined the ecological requirements and distribution of members of the Hanuman langur species complex in the Indian peninsula.

SUBMITTER: Nag C

PROVIDER: S-EPMC3912124 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Delineating ecological boundaries of Hanuman langur species complex in peninsular India using MaxEnt modeling approach.

Nag Chetan C Karanth K Praveen KP Gururaja Kotambylu Vasudeva KV

PloS one 20140203 2

Hanuman langur is one of the widely distributed and extensively studied non-human diurnal primates in India. Until recently it was believed to be a single species - Semnopithecus entellus. Recent molecular and morphological studies suggest that the Hanuman langurs consists of at least three species S. entellus, S. hypoleucos and S. priam. Furthermore, morphological studies suggested that both S. hypoleucos and S. priam have at least three subspecies in each. We explored the use of ecological nic ...[more]

PMID: 24498377

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Project description:The emergence of potentially life-threatening zoonotic malaria caused by Plasmodium knowlesi nearly two decades ago has continued to challenge Malaysia healthcare. With a total of 376 P. knowlesi infections notified in 2008, the number increased to 2,609 cases in 2020 nationwide. Numerous studies have been conducted in Malaysian Borneo to determine the association between environmental factors and knowlesi malaria transmission. However, there is still a lack of understanding of the environmental influence on knowlesi malaria transmission in Peninsular Malaysia. Therefore, our study aimed to investigate the ecological distribution of human P. knowlesi malaria in relation to environmental factors in Peninsular Malaysia. A total of 2,873 records of human P. knowlesi infections in Peninsular Malaysia from 1st January 2011 to 31st December 2019 were collated from the Ministry of Health Malaysia and geolocated. Three machine learning-based models, maximum entropy (MaxEnt), extreme gradient boosting (XGBoost), and ensemble modeling approach, were applied to predict the spatial variation of P. knowlesi disease risk. Multiple environmental parameters including climate factors, landscape characteristics, and anthropogenic factors were included as predictors in both predictive models. Subsequently, an ensemble model was developed based on the output of both MaxEnt and XGBoost. Comparison between models indicated that the XGBoost has higher performance as compared to MaxEnt and ensemble model, with AUCROC values of 0.933 ± 0.002 and 0.854 ± 0.007 for train and test datasets, respectively. Key environmental covariates affecting human P. knowlesi occurrence were distance to the coastline, elevation, tree cover, annual precipitation, tree loss, and distance to the forest. Our models indicated that the disease risk areas were mainly distributed in low elevation (75-345 m above mean sea level) areas along the Titiwangsa mountain range and inland central-northern region of Peninsular Malaysia. The high-resolution risk map of human knowlesi malaria constructed in this study can be further utilized for multi-pronged interventions targeting community at-risk, macaque populations, and mosquito vectors.

Project description:BackgroundKyasanur forest disease (KFD), known as monkey fever, was for the first time reported in 1957 from the Shivamogga district of Karnataka. But since 2011, it has been spreading to the neighbouring state of Kerala, Goa, Maharashtra, and Tamil Nadu. The disease is transmitted to humans, monkeys and by the infected bite of ticks Haemaphysalis spinigera. It is known that deforestation and ecological changes are the main reasons for KFD emergence, but the bio-climatic understanding and emerging pathways remain unknown.MethodsThe present study aims to understand the bio-climatic determinants of distribution of tick vector of KFD in southern India using the Maximum Entropy (MaxEnt) model. The analysis was done using 34 locations of Haemaphysalis spinigera occurrence and nineteen bio-climatic variables from WorldClim. Climatic variables contribution was assessed using the Jackknife test and mean AUC 0.859, indicating the model performs with very high accuracy.ResultsMost influential variables affecting the spatial distribution of Haemaphysalis spinigera were the average temperature of the warmest quarter (bio10, contributed 32.5%), average diurnal temperature range (bio2, contributed 21%), precipitation of wettest period (bio13, contributed 17.6%), and annual precipitation (bio12, contributed 11.1%). The highest probability of Haemaphysalis spinigera presence was found when the mean warmest quarter temperature ranged between 25.4 and 30 °C. The risk of availability of the tick increased noticeably when the mean diurnal temperature ranged between 8 and 10 °C. The tick also preferred habitat having an annual mean temperature (bio1) between 23 and 26.2 °C, mean temperature of the driest quarter (bio9) between 20 and 28 °C, and mean temperature of the wettest quarter (bio8) between 22.5 and 25 °C.ConclusionsThe results have established the relationship between bioclimatic variables and KFD tick distribution and mapped the potential areas for KFD in adjacent areas wherein surveillance for the disease is warranted for early preparedness before the occurrence of outbreaks etc. The modelling approach helps link bio-climatic variables with the present and predicted distribution of Haemaphysalis spinigera tick.

Dataset Information

Delineating ecological boundaries of Hanuman langur species complex in peninsular India using MaxEnt modeling approach.

Publications

Delineating ecological boundaries of Hanuman langur species complex in peninsular India using MaxEnt modeling approach.

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