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Maxent Modelling for Predicting the Spatial Distribution and Habitat Suitability of Long-billed Vulture Gyps Indicus (Scopoli, 1786) in Arunachal Pradesh, India

Jacob Ngukir, Abprez Thungwon Kimsing, Talo Biju, Daniel Mize
Applied Ecology and Environmental Sciences. 2022, 10(3), 147-153. DOI: 10.12691/aees-10-3-10
Received February 12, 2022; Revised March 15, 2022; Accepted March 23, 2022

Abstract

Vultures are ecologically important primarily because of their scavenging role in cleaning carcasses of the environment. The Long-billed vulture Gyps indicus has suffered catastrophic declines in parts of its range and, thus, information about its global distribution and factors influencing its occurrence within this range are essential for its conservation. To this end, we estimated the spatial distribution of Long-billed vulture (LBV) and variables affecting this distribution. We used occurrence points (n = 10) from field survey conducted during 2016-2018 and past records and available literature, environmental variables related to these sites and Maximum Entropy (MaxEnt) software to predict the distribution of this species and its relationship to environmental variables. Out of ~82167.58 km2 study area, the LBV had a predicted range of 1856.79 Km2 i.e. 2.26 % of study area. The district with densest potential distribution was in East Siang, followed by Namsai, Lower Dibang Valley and a scanter potential distribution was around lower part of Papumpare, Changlang, and areas adjacent to the boundaries of neighboring state Assam. Elevation was related to the vulture’s most probable range: in particular higher temperature and low precipitation were important variables regardless of the season of observations examined. Conservation of identified habitats and mitigation of anthropogenic impacts are recommended for immediate and long-term conservation of the LBV in Arunachal Pradesh, India.

1. Introduction

Long-billed vulture (LBV) Gyps indicus is an old world vulture with a long robust neck, a strong bill a rounded crown and regal aquiline bearing. LBV is listed as critically endangered by IUCN, as it suffered catastrophic decline of population of over 97% in the wild. The past record shows that occurrence of LBV has been reported from different parts of Arunachal Pradesh (A.P) namely Namsai, Papumpare, East Siang and Daying Ering Memorial Wildlife Sanctuary 1, 2, 3. A.P considers as biological frontier and mostly an unexplored land in terms of wildlife is located in North-East India within the North-Eastern Himalayan biodiversity hotspot and eastern edges lies in the confluence of Eastern Himalayan, Indo –Malayan & Mountain of South West China biodiversity hotspot 4, 5. Due to the rough terrain, high unexplored forest areas, high altitudinal gradient, unpredictable climatic condition and poor road connectivity across the state, there is very limited past research work in vultures which is not enough to study the distribution and ecology of vultures in A.P. An efficient solution to this issue is the use of species distribution models (SDMs), i.e. mathematical models for estimating species distributions based on the recorded presence of focal species and the environmental and/or spatial characteristics of potential sites 6. Maximum Entropy (MaxEnt) model is a popular type of SDM which can perform spatial prediction modeling and estimate the potential geographic distributions of a species across the landscape based on the relationship between the species occurrence data and environmental variables 7. MaxEnt requires a proper distribution of occurrence points in the ecological space rather than the geographical space 8, 9. The advantages of MaxEnt are that it offers acceptable results even with a limited available sample size 10 and is also capable of projecting shifts in species distribution under various climate change scenarios 11, 12, 13 and thus extensively used for calculating potential species distributions of plants and animals for many purposes in biogeography, conservation biology and ecology 14, 15. The species occurrence sites are regarded as suitable habitat to meet species’ ecological requirements which are taken as the reference data for the favourable environmental variables determining the occurrence of the species. These data are used by MaxEnt to calculate the constraints and explores the possible distribution of maximum entropy under this constraint condition, and then predicts the habitat suitability of the species at the study area 10, 16. Thus, the objectives of this research work are to construct a spatial distribution map and habitat suitability areas of LBV in A.P using MaxEnt modeling prediction. The study will also identify the key environmental factors influencing the distribution ranges. The findings will facilitate the formulation of appropriate conservation measures for LBV in A.P in near future.

2. Materials and Methods

2.1. Study Area

The study area i.e. Arunachal Pradesh (A.P) lies between 260 28’N and 290 30’N latitude and 910 20’E and 970 30’ longitude and has 83,743 Km2 area in the extreme North-Eastern part of India (Figure 1). Nestled amid the foothills of the Shivalik ranges the topography in A.P is characterized and marked by lofty hill slopes, sparsely populated mountainous area, enchanting river valleys, and majestic peaks. The forest types can be divided into tropical rainforests, subtropical, temperate forests and alpine meadows and its unique location at the junction of the Eastern Himalaya and Indo-Burma bio geographical zones has contributed to the rich bio-diversity the state 17. A.P can be divided into 3 major climatic zones- (i) The hot–humid subtropical climatic zone (ii) The cooler or Micro-thermal climatic zone. (iii) The Himadri or Alpine type climate zone, which shows variation in the climatic and geographical topography in the state 18. Topographically, the average elevation gradient ranges from 97m to 6629m (based on SRTM Digital Elevation Model). The temperature ranges from -19.25° - 28.5°C (WorldClim). In proportion to the altitude, the weather of A.P differ i.e. areas at higher altitude experience tundra climate, areas at lesser elevation enjoy temperate weather, and sea-level areas experience a sub-tropical climate. Precipitation in the state generally follows the wet-dry monsoon pattern where the annual rainfall ranges from 8 mm to 2078 mm (WorldClim).

2.2. Species Occurrence Data

The three year field survey for the study of distribution of Long-billed vulture was conducted between January 2016- December 2018 in A.P. 14 routes of maximum 100 km for each route were laid on the state’s motor able roadways (Figure 1) for which Road count method 19 was used to record vulture presence data. Addition to it, the line transect method 20 was used to record the occurrence of vultures in protected areas of the state. The surveys were carried out between 0800 hours to 1600 and the coordinates of LBV occurrence points and the coordinates of survey areas were recorded by using GPS device Garmin Montana 680. Since the study also aims to predict the habitat suitability of LBV in the state; occurrence records of LBV from past records 1, 2, 3 were also taken into consideration. A total of 10 georeferenced points (occurrence points of LBV) were taken. For better accuracy and precision of MaxEnt modelling, filtering of occurrence points was done by using “Spatially rarefy occurrence data for SDMs” tool from the SDM Toolbox in ArcMap in which points with distances less than one km between the two points were removed randomly 21.

2.3. Environmental Variables and Processing

Bioclimatic data on temperature and precipitation were procured using WorldClim data 22 which provides the averaged weather data over 30 years (1970 to 2000). For the data on vegetation cover, Normalised Differentiated Vegetation Index (NDVI) 23 was used. For elevation data, Digital elevation model (DEM) data was downloaded using SRTM data 24. Land use Land cover (LULC) data were procured from ESRI 25. The LULC data is comprised of 10 components (Table 1).

For this study, we used four bioclimatic variables, two NDVI data; one DEM data and one LULC data for running MaxEnt modeling. The downloaded Bioclim data file had “0.0083333333 x 0.0083333333” cell size spatial resolution and “GCS_WGS_1984” projection. The NDVI data had spatial resolution of “926.6254331 x 926.6254331” and “sinusoidal grid” projection. DEM layer had a resolution of “0.00027777778 x 0.00027777778” and “GCS_WGS_1984”. The NDVI and DEM layers were resampled to “0.0083333333 x 0.0083333333” spatial resolution and GCS_WGS_1984 projection to match the resolution of bioclimatic variable layers. For DEM, 19 separate tiles of elevation data were laid to cover the study area. Two tiles of LULC layers were laid to cover the study area of “10 x 10” spatial resolution with “WGS_1984_UTM_Zone” projection. The LULC layer projection was also resampled to “0.0083333333 x 0.0083333333” spatial resolution by using “GCS_WGS_1984”. Finally, all the layers were masked to shapefile of the study area and were converted to “ascii” file. The processing of data was done using ArcMap 10.4.

2.4. MaxEnt Modelling

MaxEnt was chosen for the present study to predict habitat suitability of LBV in A.P. This algorithm is highly précis and seems to outperform other modeling methods in quality and predicting power when there is limited information on geographic records 26. The output quality of MaxEnt depends on the optimization in different parameters of MaxEnt settings 27. Therefore, in MaxEnt interface, five characteristic parameters (linear, quadratic, product, threshold, and hinge features) are combined. Output format was set to “Cloglog”. Regularization multiplier set to 4, random test percentage set to 25%, maximum iteration set to 5000 times and bootstrap replication with 10 repetitions was used. Jackknife analysis was used to analyze the contribution rate and importance of environmental variables. Area Under the Receiver operating characteristic curve (AUC) (threshold independent) was used to evaluate the accuracy of the model.

3. Results and Discussion

3.1. Model Performance

We evaluated the accuracy of the predicted model using AUC. AUC with higher values than 0.5 shows that the model is good and that significantly differs from randomly predicted one. The average AUCs of habitat suitability of LBV in A.P after 10 repetitions are AUCtraining = 0.989 (Figure 3) and AUCtest = 0.952. This indicates high accuracy of the prediction model.

3.2. Prediction of Habitat Suitability and Spatial Distribution of LBV in A.P

To build the habitat suitability map maximum test sensitivity plus specificity cloglog threshold of 0.37 was used as the suitability threshold. The habitat suitability was reclassified into four classes: unsuitable (< 0.37), lowly suitable (0.37 - 0.6), moderately suitable (0.6 - 0.8) and highly suitable (> 0.8) (Figure 4). The result show that out of total area of 82167.58 Km2, 267.95 Km2 i.e. 0.33 % falls under highly suitable habitat area, 422.23 Km2 i.e. 0.51% falls under moderately suitable habitat area and 1166.61 Km2 i.e. 1.42% falls under lowly suitable habitat area for the distribution of LBV in A.P. Further 80310.79 Km2 i.e. 97.74 % of the total area is predicted as unsuitable habitat area for the distribution of LBV in A.P (Table 2). The interpretation of MaxEnt output predicts the spatial distribution of LBV in all lower altitude regions near the border of Assam. Most possible occurrence areas for LBV are predicted in East Siang, Lower Dibang valley, Namsai, some part of Changlang and Papumpare districts.

3.3. Influence of Environmental Variables

The contribution table of MaxEnt output indicated that the DEM (59.6 %) has the highest influence on distribution of LBV, followed by LULC (13%), Bio16 (9.5%), NDVI12 (7.6 %), Bio17 (6.9%), Bio10 (6%), NDVI06 (4.8%) and lastly Bio11 (1.1%) (Table 3). According to the Jackknife analysis, variables that had a moderate or least impact on model training gain are Bio16 and NDVI06 (Figure 5). DEM has the highest regularized training gain when used alone to train the model (Figure 5).

This is the first attempt to describe the spatial distribution of the LBV and the factors related to this distribution in A.P. The result show that the potential habitat suitability mostly lies at the area with lesser vegetation cover, foothill and areas at lower altitude. The MaxEnt Modeling predicted that most of the part of A.P is not suitable for the occurrence of LBV. Only 1856.79 Km2 i.e. 2.26 % of total areas are falling under the suitable habitat category. Such sparsely potential distribution area of LBV in A.P is seems to be highly influence by the environmental variables like climatic factors, landscape pattern and anthropogenic disturbance. This can be understood by the response curves developed by MaxEnt output (Figure 6). The curves show how the predicted probability of presence changes as each environmental variable is varied, keeping all other environmental variables at their average sample value. The output result shows that vultures prefer lower elevation with elevation range of 90-200 metre, regions with higher temperature with temperature range of above 280 C during warmest quarter of the year and 180 C in coldest quarter of the year. Also the favourable NDVI value lies between range of 0.1-0.5 for both months of June and December. Areas with NDVI value between ranges of 0.3-0.4 shows highly suitable whereas areas with NDVI value of below 0.1 and above 0.5 shows unsuitable habit for vulture distribution. LULC output predicts that built area and bare area are the highest influential and trees and snow areas are the least influential habitat variables in that effecting the distribution of LBV.

According to Birdlife International, LBV is a Critically Endangered vulture whose population has been declining rampantly across the globe. Understanding a species distribution and the factors affecting its distributional patterns always play an essential role for effective conservation planning 28. To date, the ecological biogeography of LBV i.e. spatial distribution, habitat suitability and factors affecting it, have never been examined in the past in A.P. This research work is the first attempt to address these objectives at local scales and thus will facilitate in effective management and development of conservation strategies of LBV in A.P.

4. Conclusion

Arunachal Pradesh is home to LBV and though a very small portion of area (1856.79 Km2 i.e. 2.26 % of the total area) is predicted to be suitable for its occurrence, it has enough environmental factors and other requirements for its survival in the state. The strong relationship between environmental factors and vulture occurrence identified in this study can help in management of vulture population and conservation approaches in the state. Availability of food, landscape pattern, riverine vegetation, presence of large open field and lower altitudinal habitat give a safe haven for the sheltering and occurrence of the species in the study area. Regular monitoring of vulture population, complete ban on use of diclofenac, social awareness among local residents, vulture conservation project by government and NGOs are some of the key measures that can be used for the conservation of this threated species in the state. Further rampant habitat destruction, rapid climatic change and disturbance by human activities can play a crucial role in determining the distribution of LBV in A.P.

Acknowledgments

We take this opportunity to thank Centre with Potential for Excellence in Biodiversity (CPEB), Rajiv Gandhi University, Doimukh Arunachal Pradesh for providing financial support during the survey. The authors are indebted to Principal Chief Conservator of Forest (WL & BD), Department of Environment and Forest, Govt. of Arunachal Pradesh for granting us permission to conduct the vulture survey inside protected areas. We also like to thank DFOs and other forest officials for their logistic and technical support during the survey. We thank Department of Geography, Rajiv Gandhi University for the initial help in GIS processing of the data.

References

[1]  Singh P, Recent birds’ record from Arunachal Pradesh. Forktail, 1994: 10: 65-104.
In article      
 
[2]  Mize D, Taba R, Chetry R and Payum T. Evaluation of the avian diversity survey in D’Ering Memorial wildlife sanctuary, Arunachal Pradesh. Journal of Bioresourses. 2014:1 (1): 4-10.
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[12]  Yang X, Kushwaha S, Saran S, Xu J and Roy P. Maxent modeling for predicting the potential distribution of medicinal plant. Justicia adhatoda L. in Lesser Himalayan foothills. Ecological Engineering. 2013: 51: 83-8.
In article      View Article
 
[13]  Remya K, Ramachandran A and Jayakumar S. Predicting the current and future suitable habitat distribution of Myristica dactyloides Gaertn using MaxEnt model in the Eastern Ghats, India. Ecological Engineering. 2015:9:184-188.
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[14]  Phillips S J and Dudik, M. Modeling of Species Distributions with MaxEnt: New Extensions and a Comprehensive Evaluation. Ecography. 2008: 31: 161-175.
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[15]  Sergio C, Figureueira R and Munt D D. Modelling bryophyte distribution based on ecological information for extent of occurrence assessment. Biological Conservation. 2007: 135(3): 341-351.
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[16]  Cory Merow C, Smith M J and Silander Jr J A. A practical guide to MaxEnt for modeling species' distributions: What it does, and why inputs and settings matter. Ecography. 2013: 36: 1058-1069.
In article      View Article
 
[17]  Mishra C, Madhusudan M D and Datta A. Mammals of the high altitudes of western Arunachal Pradesh, eastern Himalaya: an assessment of threats and conservation needs. Oryx. 2006: 40: 29-35.
In article      View Article
 
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[19]  Fuller M R and Mosher J A. Methods of detecting and counting raptors: A review. Studies in Avian Biology.198:6:235-246.
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[20]  Bibby C J, Burgess N D, Hill, D A and Mustoe S H. Bird Census Techniques, 2nd edition. Academic Press, London 2000.
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[21]  Keliang Z, Yao L, Meng J and Tao Z. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Science of The Total Environment. 2018: 634:1326-1334.
In article      View Article  PubMed
 
[22]  Available online at https://www.worldclim.org/.
In article      
 
[23]  Available online at https://lpdaac.usgs.gov/products/mod13a3v006/.
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[24]  Available online at https://earthexplorer.usgs.gov/.
In article      
 
[25]  Available online at https://livingatlas.arcgis.com/landcover/.
In article      
 
[26]  Philips S J, Anderson R P and Schapire R E. Maximum entropy modeling of species geographic distributions. Ecological Modelling. 2006: 190: 231-259.
In article      View Article
 
[27]  Anderson R P and Gonzalez I. Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent. Ecological Modelling. 2011. 222(15): 2796-2811.
In article      View Article
 
[28]  Panthi S and Pariyar P. Low M. Factors influencing the global distribution of the endangered Egyptian vulture. Scientific reports. 2021: 11: 21901.
In article      View Article  PubMed
 

Published with license by Science and Education Publishing, Copyright © 2022 Jacob Ngukir, Abprez Thungwon Kimsing, Talo Biju and Daniel Mize

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Cite this article:

Normal Style
Jacob Ngukir, Abprez Thungwon Kimsing, Talo Biju, Daniel Mize. Maxent Modelling for Predicting the Spatial Distribution and Habitat Suitability of Long-billed Vulture Gyps Indicus (Scopoli, 1786) in Arunachal Pradesh, India. Applied Ecology and Environmental Sciences. Vol. 10, No. 3, 2022, pp 147-153. http://pubs.sciepub.com/aees/10/3/10
MLA Style
Ngukir, Jacob, et al. "Maxent Modelling for Predicting the Spatial Distribution and Habitat Suitability of Long-billed Vulture Gyps Indicus (Scopoli, 1786) in Arunachal Pradesh, India." Applied Ecology and Environmental Sciences 10.3 (2022): 147-153.
APA Style
Ngukir, J. , Kimsing, A. T. , Biju, T. , & Mize, D. (2022). Maxent Modelling for Predicting the Spatial Distribution and Habitat Suitability of Long-billed Vulture Gyps Indicus (Scopoli, 1786) in Arunachal Pradesh, India. Applied Ecology and Environmental Sciences, 10(3), 147-153.
Chicago Style
Ngukir, Jacob, Abprez Thungwon Kimsing, Talo Biju, and Daniel Mize. "Maxent Modelling for Predicting the Spatial Distribution and Habitat Suitability of Long-billed Vulture Gyps Indicus (Scopoli, 1786) in Arunachal Pradesh, India." Applied Ecology and Environmental Sciences 10, no. 3 (2022): 147-153.
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  • Figure 6. Response curve showing how each environmental variable affects the Maxent prediction. The curves show the mean response of the 10 replicate Maxent runs (red) and and the mean +/- one standard deviation (blue, two shades for categorical variables)
[1]  Singh P, Recent birds’ record from Arunachal Pradesh. Forktail, 1994: 10: 65-104.
In article      
 
[2]  Mize D, Taba R, Chetry R and Payum T. Evaluation of the avian diversity survey in D’Ering Memorial wildlife sanctuary, Arunachal Pradesh. Journal of Bioresourses. 2014:1 (1): 4-10.
In article      
 
[3]  Annotated checklist of birds of Arunachal Pradesh 2013. Available online at http://www.delhibird.com/Checklists/arunachal%20pradesh.html.
In article      
 
[4]  Sen A K, and Mukhopadhyay S K. Avian fauna of Mouling National Park, Arunchal Pradesh, India. Current Science. 1999:76:1305-1308.
In article      
 
[5]  Myers N, Mittermier R, Mittermier C, Da Fronseca G and Kent J. Biodiversity hotspots for the conservation priorities. Nature. 2000:403: 853-858.
In article      View Article  PubMed
 
[6]  Fanklin J. Mapping Species Distributions, Spatial Inference and Prediction. Cambridge University Press, Cambridge. 2009.
In article      View Article
 
[7]  Guisan A, Edwards Jr T C and Hastie T J. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling. 2002:157: 89-100.
In article      View Article
 
[8]  Kadmon R, Farber O and Danin A. A systematic analysis of factors aff ecting the performance of climatic envelope models. Ecological Applications. 2003:13: 853-867.
In article      View Article
 
[9]  Thuiller W, Araújo M and Pearson R. Uncertainty in predictions of extinction risk. Nature. 2004: 430: 34.
In article      View Article  PubMed
 
[10]  Phillips S J, Anderson R P and Schapire R E. Maximum entropy modeling of species geographic distributions. Ecological Modelling. 2006: 190: 231-259.
In article      View Article
 
[11]  Pearson R G, Dawson T P and Lin C. 2004. Modeling species distributions in Britain: a hierarchical integration of climate and land-cover data. Ecography. 27: 285-298.
In article      View Article
 
[12]  Yang X, Kushwaha S, Saran S, Xu J and Roy P. Maxent modeling for predicting the potential distribution of medicinal plant. Justicia adhatoda L. in Lesser Himalayan foothills. Ecological Engineering. 2013: 51: 83-8.
In article      View Article
 
[13]  Remya K, Ramachandran A and Jayakumar S. Predicting the current and future suitable habitat distribution of Myristica dactyloides Gaertn using MaxEnt model in the Eastern Ghats, India. Ecological Engineering. 2015:9:184-188.
In article      View Article
 
[14]  Phillips S J and Dudik, M. Modeling of Species Distributions with MaxEnt: New Extensions and a Comprehensive Evaluation. Ecography. 2008: 31: 161-175.
In article      View Article
 
[15]  Sergio C, Figureueira R and Munt D D. Modelling bryophyte distribution based on ecological information for extent of occurrence assessment. Biological Conservation. 2007: 135(3): 341-351.
In article      View Article
 
[16]  Cory Merow C, Smith M J and Silander Jr J A. A practical guide to MaxEnt for modeling species' distributions: What it does, and why inputs and settings matter. Ecography. 2013: 36: 1058-1069.
In article      View Article
 
[17]  Mishra C, Madhusudan M D and Datta A. Mammals of the high altitudes of western Arunachal Pradesh, eastern Himalaya: an assessment of threats and conservation needs. Oryx. 2006: 40: 29-35.
In article      View Article
 
[18]  Sharma N and Shukla, S P. Geography and Development of Hill Areas: A Case Study of Arunachal Pradesh. Mittal Publications, New Delhi, 1992. Pp: 3-5.
In article      
 
[19]  Fuller M R and Mosher J A. Methods of detecting and counting raptors: A review. Studies in Avian Biology.198:6:235-246.
In article      
 
[20]  Bibby C J, Burgess N D, Hill, D A and Mustoe S H. Bird Census Techniques, 2nd edition. Academic Press, London 2000.
In article      
 
[21]  Keliang Z, Yao L, Meng J and Tao Z. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Science of The Total Environment. 2018: 634:1326-1334.
In article      View Article  PubMed
 
[22]  Available online at https://www.worldclim.org/.
In article      
 
[23]  Available online at https://lpdaac.usgs.gov/products/mod13a3v006/.
In article      
 
[24]  Available online at https://earthexplorer.usgs.gov/.
In article      
 
[25]  Available online at https://livingatlas.arcgis.com/landcover/.
In article      
 
[26]  Philips S J, Anderson R P and Schapire R E. Maximum entropy modeling of species geographic distributions. Ecological Modelling. 2006: 190: 231-259.
In article      View Article
 
[27]  Anderson R P and Gonzalez I. Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent. Ecological Modelling. 2011. 222(15): 2796-2811.
In article      View Article
 
[28]  Panthi S and Pariyar P. Low M. Factors influencing the global distribution of the endangered Egyptian vulture. Scientific reports. 2021: 11: 21901.
In article      View Article  PubMed