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Research Article

Identifying suitable habitat and corridors for Indian Grey Wolf ( Canis lupus pallipes ) in Chotta Nagpur Plateau and Lower Gangetic Planes: A species with differential management needs

Contributed equally to this work with: Lalit Kumar Sharma, Tanoy Mukherjee

Roles Conceptualization, Formal analysis, Software, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Zoological Survey of India, Prani Vigyan Bhawan, New Alipore, Kolkata, West Bengal, India

Roles Conceptualization, Formal analysis, Methodology, Writing – original draft

Roles Data curation, Investigation, Validation

Roles Funding acquisition, Project administration, Supervision, Writing – review & editing

• Lalit Kumar Sharma, 
• Tanoy Mukherjee, 
• Phakir Chandra Saren, 
• Kailash Chandra

• Published: April 10, 2019
• https://doi.org/10.1371/journal.pone.0215019
• Reader Comments

Different Biogeographic provinces and environmental factors are known to influence the dispersibility of long-ranging carnivores over the landscape. However, lack of empirical data on long-ranging carnivores may lead to erroneous decisions in formulating management plans. The Indian Grey wolf ( Canis lupus pallipes ) is known to be distributed in the vast areas of the Indian subcontinent. However, the actual population estimates are available only for Gujarat, Karnataka, Rajasthan and Bihar. Whereas, its distribution, population and habitat ecology is poorly known from the eastern region. Hence, this article aimed to evaluate the habitat suitability along with landscape connectivity for the species over the two major biogeographic provinces of India, i.e., Lower Gangetic Plains (7b) and Chhota Nagpur Plateau (6b). The present model with significantly higher Area under the curve (AUC) value of 0.981, indicates its accuracy in predicting the suitable habitats and identifying biological corridors by using environmental, topological and anthropogenic variables. Precipitation of the driest quarter and the precipitation of seasonality were the two best performing variables in our model, capable of explaining about 26% and 22.4% variation in the data respectively. Out of the total area i.e. 4,16,665 Km 2 , about 18,237 Km 2 (4.37%) was found to be highly suitable area and about 3,16,803 Km 2 (76.03%) areas as least suitable. The corridor analysis indicated that the habitat connectivity was highest in the border line area of the two biotic provinces located in the south-eastern zone via districts of Purba Singhbhum and Paschim Singhbhum of Jharkhand state and Bankura and West Midnapore districts of West Bengal state. Among the Protected Areas (PAs), natural corridors exist connecting the Simlipal National Park (NP)-Satkosia Wildlife Santuray (WLS), Dalma ranges of Chotta Nagpur plateau along with Badrama WLS, Khulasuni WLS and Debrigarh WLS. Differential management through landscape level planning may be helpful in securing the future of the species in the landscape.

Citation: Sharma LK, Mukherjee T, Saren PC, Chandra K (2019) Identifying suitable habitat and corridors for Indian Grey Wolf ( Canis lupus pallipes ) in Chotta Nagpur Plateau and Lower Gangetic Planes: A species with differential management needs. PLoS ONE 14(4): e0215019. https://doi.org/10.1371/journal.pone.0215019

Editor: Govindhaswamy Umapathy, Centre for Cellular and Molecular Biology, INDIA

Received: November 12, 2018; Accepted: March 25, 2019; Published: April 10, 2019

Copyright: © 2019 Sharma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

India is home to two subspecies of the wolf, i.e. Tibetan Wolf ( Canis lupus chanco , Gray, 1863), and Indian Grey Wolf ( Canis lupus pallipes Sykes, 1831) [ 1 , 2 ]. The Tibetan Wolf is distributed in the Himalayan landscape in the elevation range of 3000–4000 m with sub-alpine and alpine conditions. On the contrary, the Indian Grey Wolf is one of the top carnivores in the much of the plans and peninsular region of the country with the varied type of habitats with warm and dry conditions, it occupies grassland, scrublands of semi-arid regions and agro-forestry landscape [ 3 , 4 , 5 ]. It has been stated that the primary factor behind the establishment of its niche in semi-arid and arid conditions is evolution during the dry period of the Pleistocene [ 3 ]. Among the two sub-species, the Indian Grey Wolf is more abundant and presently distributed in isolated grassland ecosystems of Rajasthan in West to West Bengal in East, and from Haryana in North to Karnataka in southern region of the country [ 5 ]. Whereas, the Tibetian Wolf is relatively less in number with very confined distribution in the relatively narrow niche in the higher himalayas.

Both the sub-species are losing their range due to a number of threats predominantly increasing incidences of retaliatory killing to reduce human-wildlife conflict [ 3 , 5 , 6 ]. In India, it has been provided highest level of protection by listing the species under the Schedule-I species as per the Indian Wildlife (Protection), Act, (1972). For curbing its illegal trade, the species is listed in Appendix-I of Conservation on International Trade in Endangered Species of Wild Fauna and Flora (CITES), and as per IUCN, the species is classified as least concern considering its wide spread populations of the subspecies globally [ 7 ].

In India, studies on Indian grey wolf were largely focused in its western and southern range, but the information on its current distribution and population status in its eastern range have not been evaluated except a short survey from West Bengal [ 8 ]. The population of Indian grey wolf have been estimated between 190 and 270 in Gujarat and 253 and 350 in Rajasthan [ 9 ]. An estimated 53–85 wolves in 1517 km 2 area from Solapur, Maharashtra [ 10 ]. A relatively recent study from Karnataka estimates a population of 555 wolfs spreading across 123,330 Km 2 [ 11 ]. A population of 2000–3000 wolves is present in the Indian peninsula which seems to be a more realistic population estimate [ 12 ].

In the present scenario, most of the large carnivores are experiencing threats and possing managerial challenges due to habitat loss and climate change. As a matter of the fact that the large carnivores require large areas with abundant prey species. But managing such conditions have become a daunting task to the forest managers in the current situation. Both of the sub-species of wolf are known for their involvement in conflicts with humans [ 6 , 13 , 14 ]. In India, conversion of forested land to other land use type and expansion of agriculture into marginalized areas resulting in a reduction of its habitat and prey species [ 15 , 16 , 17 ]. A number of studies are available indicating that the loss of habitat of species is a major factor behind increasing human-carnivore conflicts [ 14 , 18 , 19 , 20 ]. The carnivore such as wolf is relatively an opportunistic feeder, and its diet is composed of a variety of species [ 21 ]. In agroforestry landscapes, their diet is dominated by domestic species indicating their involvement in livestock depredation. Moreover, in some landscapes due to loss of wild prey or poor abundance prey, the wolves are thriving on domestic species [ 10 , 18 ]. Hence, in such landscapes, human-wolf conflict is becoming serious threat for its long-term survival.

The populist approach of conservation and management planning through Protected Area (PA) network is not enough for sustaining the viable population of large ranging species in India and elsewhere. Therefore, it warrants the policy planners to develop and adopt a landscape approach in conservation planning, so that consented investments can be made to secure the future of these species and associated ecosystems. For the conservation and management of long-ranging species such as wolf, a better understanding of their distribution and population status is a prerequisite [ 4 , 11 ]. Effective measures can only be adopted after mapping the species range and habitat assessment. A number of evidences are available where habitat fragmentation and loss of movement corridors resulted in local extirpation of species and loss of genetic vigour among the species populations [ 15 , 16 , 17 , 19 , 22 , 23 , 24 , 25 ]. Therefore, enhanced knowledge about the biological corridors is imperative for the management planning and sustaining the ecosystems on long-term basis.

Thus, the present study has been conducted to assess the current distribution range and also to map the potential biological corridors of the species in its eastern range of which Chota-Nagpur Plateau (6B) and Lower Gangetic Plans (7B) biotic provinces representing two bio-geographic zones of the country [ 26 ].

The present study was conducted in two biogeographic provinces namely Chotta Nagpur plateau (CNP) (6b) and Lower Gangetic planes (LGP) (7b) covering much of the Indian Grey Wolf eastern range. These two biotic provinces are part of the Deccan peninsula and Gangetic planes bio-geographic zones respectively, and their classification in based on climatic conduction, soil as well as uniqueness in biodiversity [ 26 ] ( Fig 1 ). The LGP cover most of the Bihar, whole of the West Bengal (excluding the Purulia district and the mountain-ous parts of Darjeeling district), eastern region of Orissa and north-eastern portion of Jharkhand States of India. Whereas, the CNP forms the north-eastern edge of the Indian peninsula and the entire plateau can be subdivided into several small plateaux or sub plateaux. It embraces the districts of four states: Bihar, West Bengal, Madhya Pradesh, and Orissa. The study landscape together (CNP and LGP) holds a large network of Protected Areas viz ., six National Parks and 36 wildlife sanctuaries which in totality account for about 3.47% (14,476.61 Km 2 ) of the total area of Chotta Nagpur Plateau and Lower Gangetic Plains [ 26 ]. The entire landscape is almost featureless plain except for few mountainous ranges of Malda-West Dinajpur tract, Chotanagpur plateau, and duars of Jalpaiguri ( S2 Fig ). The mean temperature ranges from 23–38 0 C with average annual rainfall of 100–150 cm. The vegetation is broadly characterised by dry deciduous forests, tropical and subtropical dry broadleaf forests [ 27 ]. The dominated land use type in both the biotic province is agriculture, constituting about 62.10% and 79.23% in CNP and LGP respectively followed by settlements, orchards and water bodies. Increased agricultural and other anthropogenic pressure results in fragmentation and increased disturbance in both the provinces. Recent trends in disturbance profiles also indicating impact of anthropogenic pressure, around 29.11% in CNP and 32.77% in LGP of the total vegetated areas are categorised to be in highest disturbance state [ 28 ]. Moreover among the vegetated areas the highly fragmented forest area have increased to about 3.33% and 8.07% in CNP and LGP respectively [ 28 ]. The other most prominent large mammalian species present in the study landscape includes viz ., Panthera tigris , Elephas maximus , Tetracerus quadricornis , Antilope cervicapra , Cuon alpinus and Melursus ursinus .

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Materials and methods

Ethical statement.

Since this study did not involve animal handling and use of biological samples. Therefore, ethical approval was not required. Research permission was taken from Principal Chief Conservator of Forest of West Bengal state of India.

Study design and data collection

We have used both primary as well as secondary source data for mapping the habitat suitability as well as identifying biological corridors for the species. The primary data of species observations (physical locations) was collected during 2015–2016 under the programme on status assessment of Indian Grey Wolf in West Bengal, Jharkhand and adjoining areas of Zoological Survey of India, Kolkata (ZSI). Whereas, the secondary data was extracted from the historic records of ZSI and Global Biodiversity Information Facility (GBIF) database ( www.gbif.org ). A combination of primary as well as secondary data was used to generate species presence information throughout the range of grey wolf in CNP and LGP. For collecting the primary data, field surveys were conducted after dividing the study landscape into 10 X 10 km grids, a line transect of 2–5 km was travelled in selected grids with grey wolf habitats ( Fig 2 ). A total of n = 31 primary wolf location grids along with n = 21 secondary wolf location were visited for recording direct as well as indirect observations (scat, pug marks, denning sites, livestock depredation). A total of n = 126 presence records were gathered during the study period from primary as well as secondary data. For all species presence location information such as GPS location, habitat type, distance to water, distance to the road was recorded [ 9 , 10 ]. Opportunistic night survey s were also conducted from 1800hrs to 2200hrs in areas where the local communities reported wolves.

The red colour grids are those where presence was recorded in primary data, and blue grids presence was based on secondary records.

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Additionally, the opinions of experienced field staff (n = 11) served for more than two decades in forest and wildlife department was also gathered concerning with presence and absence of the species ( S6 Fig ).

Identifying suitable habitats in CNP and LGP

For identifying suitable habitats for wolfs in the study landscape, we have attempted the ensemble approach implemented in VisTrails pipeline of SAHM package [ 29 ]. In the ensemble approach we have combining the five different models namely, Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Random Forest (RF), Multivariate Adaptive Regression Splines (MARS) and Max-entropy for computing the ensemble probabilities. But the results of different modelling algorithms were of similar nature and also all models resulted with AUC < 0.9. Further, for the ensemble approach all model where selected where cut off value for enabling was 0.9. But we suspected an over prediction of the suitability, considering the historical distribution and habitat ecology of the species. Moreover, the over predictions of the ensemble model were influencing our circuit model and making it more ecologically irrelevant, since we were dependent on the suitability output for creating the conductance surface. Therefore, considering the issue related to the over prediction and for making the output more ecologically relevant we drooped the ensemble approach and used the Maxent model in the present study. Ultimately for sake of giving the most accurate and realistic result for the landscape level planning we adopted the maximum entropy based modelling using the software MaxEnt version 3.3.3k [ 30 , 31 ]. The MaxEnt program provides the probability of occurrence of a given species, ranked from 0 (least likely occurrence) to 1 (most likely occurrence) [ 32 ]. The modelling has been executed following subsampling technique with 100 imitations and Receiver Operating Characteristics curve (ROC). The Area under the curve (AUC), has been calculated using 10,000 random background points as pseudo-absences [ 33 ]. As a matter of fact selection of the key environmental variables is decisive in determining the habitat niche of a species [ 34 , 35 , 36 ]. Hence, we selected those variables which are fund to be important for the study species [ 37 ]. We started with a total of 19 bioclimatic variables (BIO1 to BIO19) along with topographic (elevation, slope and aspect) and linear features (distance to the road, distance of river) and classified forest cover maps ( S3 Table ). The bioclimatic variables were downloaded from the worldclim database ( http://www.worldclim.org/bioclim ), ArcGIS 10.6 software (ESRI 2018) was used for calculating the Euclidian distance from river and road and for generating topographic variables (slope, aspect and elevation) using Advanced Spaceborne Thermal Emission & Reflection Radiometer (ASTER). The Landsat 8 data was used for generating the forest cover raster which was further classified into four forest density covers viz., dense forest, moderate dense forest, open forest, scrubs and non-forest types [ 38 ]. The Maximum Likelihood Algorithm was used to detect the forest cover classes and the accuracy assessment of the classified image with error matrix was both generated in ArcGIS 10.6. Overall accuracy, user and producer accuracy along with the kappa coefficient were then derived from the error matrices. All the variables were re-sampled at 1km resolution and were converted to ascii (raster) format using ArcGIS 10.6 (ESRI®, CA, USA) Spatial Analyst Extension [ 39 ]. The spatial multi-collinearity among the variable was tested using the ENM tool Version 1.3 and the variables with Pearson Correlation Coefficients (r) more than 0.8 were dropped from the analysis [ 40 ]. Finally, 12 spatially independent predictors were used for identifying the suitable habitats of wolf in the landscape ( Table 1 ). The model accuracy was assessed by using the Jackknife test in which all the variables were considered independently to measure their relative and absolute contribution to the model. Further, for evaluation of the model, 70% of the species presence sites were used as training data and the remaining 30% was for testing the statistical significance [ 41 ]. The threshold value based on the AUC of the ROC ranges from 0 to 1, the AUC score of 1 indicates perfect prediction, with zero omission. However, the values equal to 0.5 indicates random prediction, while AUC values 0.8< AUC<1 were treated as good; 0.7<AUC<0.8 as fair and AUC less than 0.7 poor prediction [ 42 ]. The resulting habitat Suitability classes were categorized into four classes’ viz., least suitable, low suitable, moderately suitable and high suitability having the omission ranges from, 0 to 0.060, 0.061 to 0.20, 0.21 to 0.40 and 0.41 to above, respectively for the species in the study landscape.

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The relation between mean suitability, range suitability and sum of suitability score were obtained for all PAs of the area. These values were derived by calculating the zonal statistics for all 42 protected areas extracted from the model output.

Landscape connectivity and Corridors in CNP and LGP

The Circuitscape software (version 4.0) was used for understanding the connectivity among the habitats of the wolf in the study landscape, which is based on the circuit theory and has been applied in a number of studies aimed at mapping intuitive ecological connections between the habitat patches [ 43 , 44 , 45 , 46 , 47 , 48 ]. We established the connections among the habitat by assessing the conductance values of the raster surface. The higher value of conductance indicates greater movements among the suitable habitats [ 44 ]. We used the suitability score of the landscape to develop connectivity model for the species [ 37 , 49 ]. The most accepted methods for connectivity modelling, are based on graph theory, comprises habitat patches and habitat links connecting the patches [ 50 ], followed by another approach i.e. Least Cost Method (LCM), which helps to identify the least resistance path between two points across a cost surface, but LCM have limitations path and actual distance travelled by the species [ 51 , 52 ]. In contrast to the LCM method circuitscape does not assume that animal drive according to preceding information of the surroundings, but is based on random walks [ 53 ]. Therefore, we utilized circuit theory approach as it predicts multiple paths of current flow between different habitat nodes [ 50 , 54 ]. The Circuitscape requires focal nodes which represents the points between which the connectivity is going to be modelled along with the habitat map reflecting the permeability of each cell, which usually referred to as resistance or conductance value. This conductance value is required for current flow. In the present study, we have evaluated the pair wise electrical resistance value by running the current flow between individual pairs of nodes [ 53 ]. We use habitat suitability model output as conductance layer and 22 nodes to run the pairwise connectivity model [ 55 , 56 , 57 ]. Selected nodes were having confirmed wolf presence from the survey data and are well spread throughout different habitat types found in the biotic provinces. We have not used all nodes for running the connectivity model so that complexity can be minimised. The resulting current density map shows cumulative loaded of current flowing through the nodes as a whole, which further characterizes the critical connective areas between nodes. The higher concentrations of current between nodes reveals routes by which animals more likely to move. Locations, where current flow is high or there is no alternate route for current flow is depicted in the model, acts as pinch points or a bottleneck to movement of the species. Such areas are of high conservation priorities and loss of which may have profoundly impact the landscape connectivity for the species.

A total of n = 126 spatially independent presence locations of the Indian Grey Wolf were recorded during two field surveys carried out in the year 2015–16. Out of which n = 32 presence locations were collected from the questionnaire surveys and secondary sources, i.e., old records of Zoological Survey of India, Kolkata, interview forest staff and GBIF database. A total of 360 ground truth points were used for accuracy assessment equally divided in to forest cover classes. The overall accuracy and the Kappa coefficient of the classified forest cover image was found to be 88.61% and 86.30% respectively where SE of kappa was 0.020 ( S1 Table ) ( S1 Fig ).

The model predicted suitable habitats of the wolf in Bankura, Purulia, Midnapore districts of West Bengal, Janjgir Champa, Raigarh, Singhbhum districts of Jharkhand and Sonepur and Angul districts of Orissa states. Further, the present model predicted that out of 42 PAs in the study landscape only 5 PAs such as Dalma WLS, Debrigarh WLS, Bhimbandh WLS, Satkosia and Simlipal Tiger reserve possess suitable habitat of the species in the landscape. However, much of the species suitable habitat exists outside the PA network ( Fig 3 ).

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The receiving operating characteristic curve (ROC) value of the present model was 0.981 with a standard deviation of 0.007 ( S3 Fig ), indicating the importance of selected variables in predicting the suitable habitat of Canis lupus pallipes in the study landscape. Among all predictors, the precipitation driest quarter and precipitation of seasonality (Coefficient of Variation) were the two best performing variables and were capable of explaining about 26% and 22.4% variation in the data respectively ( S4 Fig ). The linear predictors such as distance to road and distance to river/stream were found to be less useful and accounting only for 1% and 1.1% of explained variations in the model ( S4 Fig ). The response curves of Annual Mean Temperature, Mean Temperature of Coldest Quarter and Minimum Temperature of Coldest Month shows a positive relationship with the logistic output probabilities. However, Precipitation of Wettest Month, Precipitation of Seasonality (Coefficient of Variation), Precipitation of Driest Quarter and elevation were negatively correlated with the logistic probability ( S5 Fig ).

The Jackknife test indicates that the regularized training gain for study species in the present model showed the highest gain when the annual mean temperature was used in isolation for running the model. Whereas, the training sample gain was lowest after omitting the Precipitation of Driest Quarter from the model, indicating its imperativeness in identifying the suitable habitat of the study species ( Fig 4 ).

Blue bar = Shows importance of each variables in explaining the variation in the data when used separately. Green bar = loss in total model gain when the particular variable was dropped, signifies the presence of unique information necessary for explaining the model. Red bar = total model gain.

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Out of the total area of the study landscape (4,16,665 Km 2 ), about 18,237 Km 2 (4.37%) is classified as highly suitable area, followed by 22,801 km 2 (5.47%) under moderate suitable 5,88,24 km 2 (14.11%) in low suitable and about 3,16,803 km 2 (76.03%) areas as least suitable for wolf ( Fig 3 ). The model also identified that out of 42 protected areas under the CNP and LGP landscape, Satkoshia Tiger Reserve, Simlipal NP, Dalma WLS, Bhiambandh WLS, Nagi Dam WLS and Koderma WLS are the few which possess suitable habitat for the species. Considering the mean value of the suitability score Dalma WLS score the highest of about 0.166 followed by Nagi Dam WLS with 0.119 and Satkosia, Bhimbandh and Debrigarh scoring about ~00.5. Interestingly while summing all the suitability scores among PAs; was highest for Dalma WLS (50.07), followed by Bhimbandh WLS (50), Satkosia WLS and Simlipal NP resulted with a value of 39.75 and 14.17 respectively. The highest suitability score produced by the present model is 0.91 but when considering the max suitability scores under the PAs highest score was only about 0.575 in Dalma WLS followed by Bhimbandh WLS, Satkosia WLS and Simlipal NP scored maximum suitability score 0.398, 0.245 and 0.132 respectively. This indicates that most of the very high suitable areas in the study landscape falls under the non-protected and territorial ranges ( Fig 5 ) ( S2 Table ).

Values were derived from the zonal statistics calculation for all 42 protected areas extracted from the model output. X axis = Protected area, Y axis = Suitability range score for each PAs. Color ramp depicts the sum value of suitability score size of circle represents mean suitability values obtained by respective PAs.

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Potential corridors in CNP and LGP

The model indicates that much of the suitable habitats across the study landscape have biological connectivity. However, the cumulative current flow was highest in the zone which borders the two biotic provinces in the south-eastern side via districts of Purba Singhbhum and Paschim Singhbhum of Jharkhand and Bankura and West Midnapore districts of West Bengal. The present model also suggests that much of the connectivity exists in unprotected or territorial forest ranges in the landscape. However, two significant corridors have been identified which connects both biotic provinces, i.e., a corridor in the northern part of Chotta Nagpur Plateau via Bhimband and Koderma Range and other corridor is in the eastern face of the Chotta Nagpur plateau via districts of Bankura and West Mednipore ( Fig 6 ). Among the PAs, a biological corridor between Simlipal NP-Satkosia WLS may also exist which is connecting the south Bengal with the Dalma range of Chotta Nagpur plateau. However, the model also indicates relatively weak connectivity may also exist between Koderma WLS, Khulasuni WLS and Debrigarh WLS.

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The Indian grey wolf is one of the top carnivore species distributed in the open grasslands of peninsular India. Till date, much of the studies on the species have been conducted in its western and southern ranges, whereas, no reliable information is available in its eastern range except a short study by [ 8 ]. Furthermore, no attempt has been made to map its eastern range which is pro-vital for its conservation and management planning. The Indian grey wolf is threatened throughout its range due to its involvement in livestock depredation. The large tract of semi-arid eco-region is largely rainfed where the agriculture is mostly animal dependent and the economy of the local communities is based on animal husbandry. The increase in livestock depredation incidences by wolves is leading to the development of antagonistic behaviour among the locals towards the species which will be detrimental for its long-term survival [ 20 , 58 ]. Hence, through the present model, we identified and mapped the habitats suitable for the species and much of the highly suitable habitats are falling in areas compost of dry scrub vegetation, open forest and agroforestry landscape [ 38 ]. The higher AUC value of 0.981 indicates that the selected variables in the present model were very good predictors of mapping suitable habitat of the species ( S3 Fig ). The negative association of the precipitation of the driest quarter and seasonality indicates that the suitable habitats for the species are located in areas with relatively drier conditions with the low amount of precipitation. The present results corroborate with the findings of the other studies highlighting that the wolf is a top carnivore reported to be distributed in areas with hot and semi-dry environmental conditions [ 3 , 4 , 5 , 10 ].

The results indicate that out of the total area (14,476.61 Km 2 ) under PA network, only 1,332 Km 2 area was found to be suitable, suggesting that most of the suitable areas of the species were outside the PA areas of the landscape, which is one of the vital reasons for increasing human-wolf conflict. The wolf thrives well in Non-PA areas with relatively poor natural prey base [ 5 , 11 ]. Moreover, previous it has been documented that the wolf population in Gujarat and Rajasthan states of India are surviving on livestock because of poor availability of wild prey species [ 5 , 9 , 59 ]. In the present scenario based on the interviewed villagers and experienced forest guard the population of wolves is on decrease due to retaliatory killings, illegal hunting, and habitat degradation.

The forest biodiversity conservation and management agencies in India have adopted several conservation majors, but most of them have been applied in PAs with focus on other large charismatic mammals such as tiger, leopard and elephants. However, the wolf which occupies an area which is mainly outside the PA network needs a differential management strategy. The earlier researchers have also suggested conservation, and management strategies for Indian Grey wolf which may be applicable throughout the most of its distribution ranges across India [ 4 , 9 , 10 ]. Its hardiness and great dispersal ability make this species to survive in agroforestry as well as in degraded habitats. It is a species which has evolved to thrive in dry and resources poor landscape hence demands strategies through which the natural composition of the landscapes can be mentioned without altering the structural configuration of the habitats. The activities such as habitat improvement through plantation or alternation in the landscape configuration may not be useful or may results in creating stress to the species [ 11 ].

Moreover, the prevailing concept of concentrated management in PAs includes Wildlife Sanctuaries, and National Park may not suffice the long-term conservation and management of the wolf population in India. The conservation strategies for wolf should not be restricted to some small patches of vast landscape, instead focus should be given on protecting the natural composit of its habitat which will promote the smooth functioning of the biological corridors and connectivity between the habitat patches. We suggest that the two identified corridors connecting Chotta Nagpur Plateau and Lower Gangetic plans are via Bhimband and Koderma Range and other from Bankura and West Mednipore will be vital for the long-term viability of the wolf populations may be prioritized for management interventions ( Fig 6 ). The wolves are great dispersers and know to travel long distance for which they negotiate human disturbance. However, land use change and other anthropogenic disturbances can lead to negative impacts and also pose mortality risk to the species. At fine scale the habitat and landscape utilization of the species may get influence with habitat characters and anthropogenic disturbance [ 37 , 60 ]. The non-PA forestry landscape management documents should have effective treatments focusing on wolves. In Indian forest management system, PAs are managed with focus on flagship faunal species whereas the non-PAs or the territorial forests are managed under the working plans. These working plans are focused on production forestry where treatment remedies are provided for enhancing the productivity of the forests. Hence, considering the fact that wolves live on both non-PAs and PAs there is a need to have differential management needs.

Differential management needs of Indian Grey wolf ( Canis lupus pallipes) in Chotta Nagpur Plateau and Lower Gangetic Plans

Although the two biotic provinces Chotta Nagpur Plateau and Lower Gangetic Plans represents two bio-geographic zones, i.e. Gangatic plans and Deccan Plateau but the border areas of these two provinces possess noteworthy commonality in terms of faunal species composition, topography, and climate as well as forest types. The present study has highlighted that the border area of the two provinces provides habitat which is supporting the remnant population of wolves in this landscape. The study could be also able to map the possible biological corridors through which the species may be using as movement corridors and utilizing the habitats.

In India the PAs and non-PA areas are managed with different aims and objectives, which are sometimes not complementary, and the species such as wolf which inhabit in the composit of both the types of areas in a landscape suffers. Moreover, while far-reaching the scopes, the implementation of the existing mechanism are exceedingly slow and not as productive as expected. Hence, urgent steps are needed to adopt landscape-level management planning for the long-term viability of wolf populations in the region. The working plans of the territorial forests in such areas should be developed with adequate conservation and management remedies for the species.

The Indian National Working Plan Code 2014 (NWPC 2014) for forest management is primarily based on the scientifically collected data relating to the growth, biodiversity, crop composition, forest biomass and their management strategies [ 61 ] but the efforts to link it with wildlife management objectives and practices have yet to come to desired levels. The NWPC 2014 should have a component which dealing with population management of flagship species such as wolf by retaining the original composition and the structure of the forested habitats through uneven and selection type forests. Since, earlier studies have established that wolf occupies areas with relatively open canopy forest patches and the management activity such as afforestation or changing the natural structure and composition of the forests may negatively impact the species [ 10 ]. Hence, we strongly propose changes in the National Working Plan Code 2014 (NWPC 2014) guidelines so that the wildlife management components could be given enough space in the document. Further, we also suggest the participation of local community in management planning should take centre stage so that the biological functionality of the identified wolf corridors in the landscape can be mentained for the long-term genetic viability of wolves. The capacity enhancement and mass awareness creation among the local communities will lead to minimize wolf-human conflict and also it will be helpful in developing compassionate attitude among the locals towards the species in the region.

Further, considering the fact that the species is patchy in distribution and non-availability of quality fine-scale data on habitat ecology of the species we suggest a long-term study covering the entire distribution range in the region. Moreover, we strongly recommend landscape genetic study on the species to understand the biological functionality of the corridors identified in the present study for the long-term conservation and management of the species.

Supporting information

S1 fig. forest cover map of cnp and lgp..

https://doi.org/10.1371/journal.pone.0215019.s001

S2 Fig. Elevation map of CNP and LGP.

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S3 Fig. The average training ROC for the replicate runs is 0.981, and the standard deviation is 0.007.

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S4 Fig. Percentage contribution and permutation importance of selected variables.

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S5 Fig. Response curves of the important variables for habitat suitability of gray wolf.

Curves shows how logistic prediction of the model changes with the selected variables. Keeping all other variables at their average sample value.

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S6 Fig. 100% staked bar chart of open-ended semi-structured questionnaire survey.

Representing the relative percentage of multiple questionnaire survey series along with the no. of respective respondent. Black bar = indicates the respondents aggress with the statement, Grey bar = indicates the respondents who doesn’t aggress with the statement.

https://doi.org/10.1371/journal.pone.0215019.s006

S1 Table. Accuracy assessment table for forest cover classification of CNP and LGP.

https://doi.org/10.1371/journal.pone.0215019.s007

S2 Table. Zonal statistics table for protected areas in CNP and LGP.

https://doi.org/10.1371/journal.pone.0215019.s008

S3 Table. List of all 23 variables for habitat suitability modelling for Indian Grey Wolf ( Canis lupus pallipes ) in the study landscapes (Cotta Nagpur Plateau and Lower Gangetic Plain).

https://doi.org/10.1371/journal.pone.0215019.s009

Acknowledgments

Authors are thankful to Shri K.C Gopi, Additional Director and Dr G. Maheshwaran, Head of the Office, Dr Basudev Tripathy, O/C Technical Section, Dr Mukesh Thakur, Scientist-C at Zoological Survey of India, Kolkata for encouragement and support. We highly appreciate two reviewers of improving the content of the manuscript specially Dr. Mohammad Kaboli for giving valuable inputs.

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• Published: 31 March 2021

Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method

• Sougata Sadhukhan 1 ,
• Holly Root-Gutteridge 2 , 3 &
• Bilal Habib 1  

Scientific Reports volume  11 , Article number:  7309 ( 2021 ) Cite this article

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• Animal behaviour
• Behavioural ecology
• Conservation biology
• Ecological modelling
• Environmental sciences
• Population dynamics

Previous studies have posited the use of acoustics-based surveys to monitor population size and estimate their density. However, decreasing the bias in population estimations, such as by using Capture–Mark–Recapture, requires the identification of individuals using supervised classification methods, especially for sparsely populated species like the wolf which may otherwise be counted repeatedly. The cryptic behaviour of Indian wolf ( Canis lupus pallipes ) poses serious challenges to survey efforts, and thus, there is no reliable estimate of their population despite a prominent role in the ecosystem. Like other wolves, Indian wolves produce howls that can be detected over distances of more than 6 km, making them ideal candidates for acoustic surveys. Here, we explore the use of a supervised classifier to identify unknown individuals. We trained a supervised Agglomerative Nesting hierarchical clustering (AGNES) model using 49 howls from five Indian wolves and achieved 98% individual identification accuracy. We tested our model’s predictive power using 20 novel howls from a further four individuals (test dataset) and resulted in 75% accuracy in classifying howls to individuals. The model can reduce bias in population estimations using Capture-Mark-Recapture and track individual wolves non-invasively by their howls. This has potential for studies of wolves’ territory use, pack composition, and reproductive behaviour. Our method can potentially be adapted for other species with individually distinctive vocalisations, representing an advanced tool for individual-level monitoring.

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Introduction.

Accurate population estimates are a critical part of wildlife biology, conservation and inform management strategies 1 . Informed management decisions rely on accurate estimates which can be hard to achieve but are critical as the conservation status of any species is dependent on its population size, which is inversely correlated with extinction risk 2 . Therefore, it is of the foremost importance to have a statistically robust population estimation technique. However, widely used population estimation methods such as camera trapping and sighting-based distance sampling fall short in analysing population trends of certain elusive species or species living in extensive home ranges 3 , 4 . Many of these species are vocally active, which inspired scientists to study the effectiveness of an acoustics-based population abundance model for these species 5 , 6 , 7 , 8 . Acoustic monitoring has long been used to monitor the presence of aquatic animals, amphibians, insects, and birds 9 , 10 , 11 , 12 , 13 . The critical advantages of acoustic monitoring are that it can be operative at day and night 14 and detect visually cryptic species or those spread over large home ranges 7 , 15 , 16 . Like camera traps, passive acoustics devices can operate throughout the day for weeks without intervention, and this perpetual data can be analysed easily with the advancement of methodologies for automating the process 17 . Recordings from these devices can be used in calculating a wide range of metrics including acoustic indices 18 , 19 , animal diversity 19 , 20 , animal localisation 21 , 22 , 23 , and density 24 , 25 estimation. This density estimation is mostly obtained through Spatially Explicit Capture-Recapture (SECR) that relies on multiple recording stations for Capture-Mark-Recapture (CMR), and instead of ‘recapture’ with time, it considers ‘redetection’ in different points in space 24 , 25 , 26 . This methodology is applied to individuals that are not identifiable from their calls 25 , 27 . The conventional CMR model requires identification at the individual level 27 , 28 , but it provides a robust population estimation 28 and much additional information such as home-range, survival rate, animal movement pattern, and population viability analysis 29 . However, the difficulty of successfully identifying unknown individuals from their calls has prevented its use for more species, though new techniques are being developed for some species, including the use of unsupervised classifiers to cluster calls 30 . Here, we explore the potential of identifying individuals through supervised classification from their vocal features to potentially improve identification to the point where CMR surveys are possible for an elusive and wide-ranging species.

Like other grey wolf subspecies, Indian grey wolves are known for their long-ranging communication via howls 31 . Howling is a social communication process, vital for the overall behaviour of many canid species 32 . It has evolved in wolves to communicate with other group members over a long distance as well as to demarcate their vast territories 33 . Due to its high amplitude and low frequency, a howl can travel for six kilometres or more 34 , 35 , 36 . Hence, an acoustics survey using howling may be more advantageous than a visual survey or other methods, such as snow-tracking 22 , 23 , 35 , 37 . As vocalisations of wolves were found to be highly variable within and among individuals 31 , 38 , the howl is a useful tool to identify individuals 39 , 40 , 41 ; thus, wolves are ideal candidates for acoustic monitoring techniques.

Previously the ‘ Howlbox ’, a self-contained active acoustics-monitoring device that broadcasts howls and records the responses automatically, was tested for its capability to detect wolves 42 , 43 . This device was unsuccessful in surveying wolves due to low detection rate as, instead of howling back, the wolves visited the device site without howling, and various technical failures 42 . A few studies using passive acoustic devices show the potentiality of successful localisation and monitoring of the grey wolf 23 , 44 . However, these only allowed for presence to be detected and stopped short of individual identification. In contrast, the identification of wolves from their distinctive howls will open an opportunity for more conventional CMR methods 45 , and this will improve population estimation without bias and help to measure other ecological variables, such as site occupancy and home-range. With the ability to identify individual wolves from howl recordings, information on population sizes, dispersal patterns, pack composition and the presence of pups could be obtained. These would be used to develop conservation management strategies and to examine population trends with howl surveys conducted over multiple years. Therefore, our study aimed to record howls from Indian wolves ( Canis lupus pallipes ) and test the feasibility of identifying unknown individuals from their howls alone using a supervised classification method.

Study species

Indian wolf, subspecies of the grey wolf is among the keystone species found in the Central Indian landscape 46 and reside in arid grasslands, floodplains, and the buffer of dense forests 46 , 47 , 48 , 49 . The Indian wolf plays a significant ecological role in controlling ungulate populations in the human-dominated landscapes 50 , 51 . The population status of Indian wolves is entirely unknown 52 . It is known that Indian wolves face a major threat from humans as their habitat is increasingly used by humans, and human-wildlife conflict is increasing 53 . Therefore, time is a critical factor to their conservation. The major challenges for population estimation of the wolf are its vast home range of ~ 230 km 2 48 and that they actively avoid camera traps because of camera sound, light, and odour emission 54 . Since implementing standard population monitoring tools in these landscapes is a tremendous challenge, monitoring their population through howls can be an essential technique. The average fundamental frequency and duration of Indian wolf howls are 422 Hz and 5.21 s, respectively 55 . Due to its low-frequency range and longer duration, it can be heard from an extended distance like howls of other subspecies 23 , 35 , 36 .

The study was conducted on captive individuals of Jaipur Zoo and free-ranging, wild wolves of Maharashtra, India.

Jaipur Zoo is situated at the heart of Jaipur City, Rajasthan, India. Since Jaipur is one of the major tourist destination and capital of Rajasthan, the anthropogenic noise is reasonably high in and around the zoo. All the wolves (n = 10) in Jaipur zoo were offspring of captive-bred individuals except one adult male recently captured from a wild population of Rajasthan.

The data of free-ranging wild wolves were collected from six districts of Maharashtra. Pune, Ahmednagar, Solapur and Osmanabad (Fig.  1 ) districts fall under the semi-arid drought-prone area of the Deccan peninsula Biogeographic Zone (Zone 6) 56 . The dominant habitat type in our sampling areas was Deccan thorn scrub forests 57 . The terrain is gently undulating with mild slopes and flat-topped hillocks with intermittent shallow valleys, which forms the primary drainage channels. Grassland area is distributed in fragmented patches, creating a mosaic of grazing land, agricultural land and human settlements. Striped hyenas ( Hyaena hyaena ), golden jackals ( Canis aureus indicus ), and Indian leopards ( Panthera pardus fusca ) are the co-predators in this landscape 48 , 58 . Wild prey include blackbucks ( Antilope cervicapra ), chinkaras ( Gazella bennettii ) and wild pigs ( Sus scrofa cristatus ); but a significant part of their diet is domestic livestock 48 , 50 , 59 .

Map showing howling recording locations of the free-ranging wolf in six districts of Maharashtra. Yellow round bullets indicate the survey locations and Red triangular bullets represent the howling recording sites.

In Maharashtra, Nagpur and Gondia districts come under the central Deccan Plateau with Tropical dry deciduous broadleaf forests 56 , 57 . Due to moderate to high rainfall, vegetation is dense in most of the areas. Our sampling areas were mostly packed with open forest and modest density forest. The terrain is generally flat. Nagpur division is surrounded by Many National parks and Sanctuaries. Wolves are primarily found in the buffer areas of these protected areas. Co-predators in those stretches are tigers ( Panthera tigris tigris ), Indian leopards, sloth bears ( Melursus ursinus ), striped hyenas, dholes ( Cuon alpinus ), and golden jackals. Prey species are sambar ( Rusa unicolor ), nilgai ( Boselaphus tragocamelus ), chital ( Axis axis ), chousingha ( Tetracerus quadricornis ), and wild pigs.

Data collection

The howls from the Indian wolves were recorded from November 2015 to July 2016. The howls were recorded during the systematic howling surveys accompanied by the opportunistic and spontaneous recordings of captive and free-ranging wolf howls. Howling surveys were done in the early morning (from 4:30 am onwards) and early evening hours (up to 7:45 pm) [time varies depending on sunrise and sunset]. The survey protocol was adapted from Harrington and Mech 60 . Each howling session consisted of five trials with three-minute intervals. A series of 50-s-long pre-recorded solo howls (from an individual in Jaipur Zoo) was played three times with increasing amplitude; the session was followed by a 50-s-long chorus howl (from three individuals in Jaipur Zoo) in the order of mid and highest amplitude level of the speaker respectively. A 40-W JBL Xtreme speaker (Harman International Industries, 2014) was used for the playbacks. If howling responses were recorded, the playback session was terminated and repeated after 15 to 20 min. All howls were recorded in a single microphone setup, using a Blue Yeti Pro USB Condenser Microphone (Blue Microphone, 2011) attached with Zoom H4N Handheld Audio Recorder (Zoom Corporation, 2009) with a sampling rate of 44.1 kHz and 16-bit depth.

Ethical approval

The study on captive wolves in zoos was done with the permission of the Director of Jaipur Zoo and the Forest Department of Rajasthan, India [Letter no- 3(04)-II/CCFWL/2013/4586–87; Dated 30th Oct 2015]. The survey of free-ranging wolves of Maharashtra was performed with the consent of the Principal Chief Conservator of Maharashtra Forest Department [Letter no- 22(8)/WL/CR-947(14–15)/1052/2015–16; Dated- 6th Aug 2015]. No animal was harmed during the study, and the standard non-invasive protocol of howling survey was maintained. All the data collection were approved by the Animal Ethics committee of Wildlife Institute of India, Dehradun, India.

Feature extraction

The howls were sorted, and spectrograms were generated using a Discrete Fourier Transform (DFT) algorithm in Raven Pro 1.5 software 61 . Discrete Fourier Transform (DFT) algorithm transforms the same length sequence of equally spaced sample points (N, where N is a prime number) with circular convolution being implemented on the points 62 . All the spectrograms were produced using Hann windows  at the rate of 1800 samples on 35.2 Hz 3 dB filter (Fig.  2 ). Only recordings with low levels of background noise and without any overlapping sounds, where the howls were clearly visible as contours, were selected for further analysis. Spectral images were digitised using Web Plot Digitizer Software 63 . Thirteen different features (Table 1 ) were measured from the digitised value by using Microsoft Excel. The details methodology is represented in Fig.  3 .

Spectrogram of Gangewadi Wolf howl (160203-001_Gangewadi2_A5) showing how different variables were measured.

The pictorial representation of methodology for identifying unknown Indian wolves by their howls.

One hundred and thirty-three howls that were longer than 5-s were used for further analysis, with more than ten individual wolves included. The 5 s cut off were chosen to avoid social squeak calls that are very similar to howl but shorter ( \({\overline{\text{x}}}\)  = 3.87 s) and high-frequency variable calls, described by Sadhukhan et al. 55 . Also, the longer howls might contain more identification features than the shorter howls do. Principal Component Analysis (PCA) was conducted on measured parameters of 133 howls to reduce the dimension and emphasise the variation between each howl. Out of 133 howls, only 69 howls were identified to an individual. The 69 howls were from nine wolves with known identities: three were captive wolves and six wild, free-ranging wolves, which were identified from their visual features when they were howling in front of the observer and thus howls could be attributed to them individually. The data was further subdivided into training and test datasets. Forty-nine howls from five individuals (2 captives; 3 wild) were used as the training data, and 20 howls from four different individuals (1 captive, 3 wild) as test data to ensure the validity of the method (Table 2 ). Since the known wolf howls were used test data never used in building model, it provides ‘unbiased sense of model effectiveness’ 64 .

Discriminant function analysis

Linear discriminant function analysis (DFA) was performed with 49 howls from five individuals (training data) using seven PCA values that contributed more than 5% variation (Table 3 ) [The cut off value was chosen from scree plot, See Supp. Material 1 : PCA.pdf]. The objective of DFA was to construct the linear combination of independent principal component variables (PC1–PC7) that will discriminate howls of different individuals. The howls were plotted with discriminant functions at two-dimensional space followed by the group prediction (Fig.  4 ).

Figure showing a two-dimensional plot of discriminant function analysis using LD1 (Linear Discriminant) and LD2. Each colour represents each wolf. 100% accuracy was achieved in identifying 49 howls from five Indian wolves.

Hierarchical clustering

To test the success rate of identifying different individuals from their howls with Linear Discriminant (LD) score, an Agglomerative Nesting hierarchical clustering (AGNES) was executed on 49 howls (training data) that were used in DFA. AGNES initially considers each howl as a different cluster and use a ‘ bottom-up ’ algorithm to join different clusters based on the similarities 65 . The analysis was performed in R using ‘ agnes ’ function in the package ‘dendextend’ and ‘ manhattan ’ metric was used to build the cluster 66 . The same analysis was performed on the test data to determine the accuracy of identifying unknown individuals and estimating the number of wolves from their howls. While the test data contained howls from known individuals, the wolves’ identities were not included in the model. The variables of these 20 howls were calculated from the equation of DFA of 49 howls for cluster analysis.

Dimensions reduction to emphasis on variation among howls

Seven Principal Components (PC) that explained more than five percent of the variance (Table 3 ) each were generated from 13 scalar variables (Table 1 ). These seven PCs together explained 94.8% variance among different howls (Fig.  5 ). SD of the fundamental frequency (f 0 ), Frequency (f 0 ) range, Maximum f 0 and the number of abrupt change (> 25 Hz) were the most important contributing factors for building PC1 which contributed 41.2% explaining the variable (Fig.  5 ).

The spider web bubble plot is describing how the Simple Scalar Variables (SSV) are ultimately contributing to two LD functions through PC values. The bubble size of each SSV represents the contribution for building each PC function. The blue line represents LD 1, and Orange represents LD2. Since PC1 and PC2 contribute 85% for LD1, the most important SSVs are Stdv f 0 , Min f 0 , Max f 0 and Mean f 0 . Similarly Duration, Abrupt changes, Co-fv contribute the most in building the LD2 function via PC4 and PC5. LD1 was best defined by the different fundamental frequency factors, while LD2 was best defined through the shape of the frequency contour. Therefore, the critical factors for individuality were encoded in X and Y variables.

Building discriminant function to emphasis on howl variation among different individuals

The objective of DFA was to build an equation that discriminates the howls of different individuals. The LD score also highlights the variation among howls from different individuals. DFA achieved 100% accuracy in identifying five individuals from 49 howls (Fig.  4 ). As the first two Linear Discriminants (LD1 and LD2) were responsible for 96.2% of the variance to differentiate between howls of different individuals (LD1 = 87.57% and LD2 = 8.63%), we calculated LD1 and LD2 for rest of the howls using the same function (equation) from last DFA. PC1 and PC2 contributed 85% in building LD1; PC4 and PC5 are the most crucial factor (65%) for LD2 function (Fig.  5 ).

Identifying individuals from their howls in testing dataset

First, we tested AGNES on the training dataset (49 howls from 5 individuals) and found 48 howls (~ 97.9% accuracy) were identified correctly at 2.2 clustering scale (Fig.  6 ). When the same analysis was performed on 20 howls of four different individuals to test the accuracy for the non-training dataset, 15 out of 20 howls from (75% accuracy) four individuals were identified correctly at 2.2 clustering scale (Fig.  7 ; Table 4 ). Two howls from wolf BMT.A were misclassified to wolves BMT.SA2 and CG2.A2; Three howls from wolf NU.A were misclassified to wolves BMT.SA2 (1 howl) and CG2.A2 (2 howls) (Fig.  7 ; Table 4 ).

Hierarchical Clustering of 49 howls from five individuals. These 49 howls were used in training the data. 48 howls were identified correctly with the accuracy of 97.9%. The wrongly identified howl is marked in red.

Hierarchical Clustering of 20 howls from four Indian wolves. None of the 20 howls was used in training the data. 15 howls were identified correctly with the accuracy of 75%, and all the four individuals were identified correctly as different clusters. The correctly identified howls are marked in black, and the five wrongly identified howls are marked in red.

Here, we presented a new approach to train the classification model, which can identify individuals from their howls and determine the number of wolves present in a certain number of howls, allowing for fine-scale population surveys. In this study, we built an identification model with known training data which was verified with novel test data. The testing data included howls from the known individuals of both captive and wild Indian wolves but independent from the training dataset so that we can cross-check the identification accuracy without bias. The key finding of our study was 97.9% wolf howls were identified correctly from training data, whereas the accuracy of the model on the testing data was 75%. Moreover, we were able to identify four individuals accurately from the testing dataset. The primary significance of this study is that it can be replicated for any other wolf sub-species with a set of a known wolf howls. This study increases the feasibility of wolf pack census using a howling survey 35 , 60 . Since wolves may actively avoid camera traps 54 and photo-identification of wolf requires arduous effort 3 , 67 , identifying wolves from their howls is a big step towards population estimation using CMR.

Although CMR associated with camera trapping provides population estimation without bias for an identifiable animal like a tiger 68 , camera trapping has several limitations for non-identifiable and long-ranging species like the wolf 3 . Other non-invasive methods like DNA-based CMR resulted in biased population estimation due to the animals’ non-uniform scent-marking patterns 59 , 69 . However, acoustics based surveys allow vast area sampling with limited resources as compared to camera trapping and other non-invasive methods 3 . Furthermore, our field observations of wolves have shown that the whole pack typically howls during choruses and that all individuals are acoustically active.

For population size estimation through an acoustics-based survey, a combination of CMR and Distance Sampling is required to reduce bias and heterogeneity in detection probability 27 , 70 . Identifying individual wolves from their howls close this gap of implementing the CMR technique for the population assessment of this elusive and challenging to track species 7 , 25 , 27 . While a few studies have established that howls carry individuality information 38 and known howls can be distinguished from each other 39 , 45 , 71 , no study has been successful before in identifying unknown individuals from a set of howls. Furthermore, attempts to count the number of individuals present in a recording have been limited by difficulties in minimising confidence intervals 18 , 72 . There are two ways to identify individual wolves or packs—supervised clustering and unsupervised clustering. While supervised clustering requires a set of known training data and cluster validation is straightforward, unsupervised clustering requires ground-truthing before it can be used to monitor populations at a survey level and does not allow individual level CMR or tracking 30 .

Although DNA-based identification from faecal sampling is more accurate in identifying individuals than our result, it has drawbacks, such as biased population estimation and the increased cost and effort required to collect and analyse the faeces 59 , 69 . Nevertheless, the acoustics-based identification model requires further work to increase its accuracy, though we believe that the successful implementation of this method as a CMR-based supervised population estimation model is already possible.

Wolves mostly live in packs that habitually howl together, and it is challenging to identify the specific wolf that is howling, particularly in choruses. If included and incorrectly attributed to a particular wolf, these howls could lead to erroneous predictions by the model. Therefore, this limited our potential data set to those howls which were conclusively attributed to a known individual, and we dropped many howls, especially the chorus howls, from the analysis to avoid misleading the model. However, larger training datasets from different wolf populations might increase the efficacy of the identification model and verification with more wolf howls conceding better reliability as found for Southwestern Willow Flycatcher 73 . Thus, our result of 75% may represent a baseline, not a limit, on the accuracy we could achieve. The inclusion of multiple series of howls from every individual would give a more precise result. However, since none of the free-ranging wolves was radio-collared or marked, this was not possible for the wild wolves. Studying howls of collared wolves would help in adding multiple howl sequences from many free-ranging wolves in the training data and may fill this research gap.

This study revealed that the number of wolves present in the recordings could be determined from their howls and the individuality information is sufficient for supervised population estimation through CMR techniques 7 , 25 , 27 , 30 . Therefore, wolves recorded in one location can be acoustically recaptured at another location, and we can identify them individually. Since our model is exclusively built on fundamental frequency, changes in terrain or vegetation should not affect the accuracy of the model. The information gained from recapturing wolves across different locations would help in deriving territoriality (home-range) information, and this information is crucial for spatially explicit individual-based point process models. This is a clear advancement for developing howling playback surveys as a wolf pack census method. Regular population monitoring will help towards conserving and saving this cryptic species before its population falls beyond a recovery level. Furthermore, since wolf howls can be detected across distances of more than 6 km, identifying wolves from their howls also opens up a new opportunity for non-invasive tracking of this species across large landscapes.

Guidelines to implement the methodology on the field

We used this methodology to identify individual Indian wolf howl. However, one can use this methodology to identify species, sub-species or individual from their calls. This requires a set of calls to make up the training dataset and a set of calls to make up the testing dataset. We recommend some precautions and step by step guidelines for adapting this method.

Before the data collection, one should be cautious about choosing the recorder and data collection methodology. Although we are not definite about the impact of multi-recorder setup in identification accuracy, we recommend using a single microphone set up to keep consistency, especially for individual identification as differences in sensitivity and recording parameters can influence acoustic integrity [See 45 ].

The multiple groups in the training dataset should be carefully selected to represent distinct group member calls with high confidence (e.g. species/sub-species/individuals), as a single incorrectly identified call in the training dataset can lead the model to erroneous results.

The selection of appropriate spectral features is important. While many species encode their identity in the same features, some encoding is species-specific. We tested a wide range of software which fell short in feature extraction for overlapping calls or where background noise was present. The feature description is only as reliable as the extraction. Here, we used web-plot digitiser software for spectrogram digitisation. We recommend the use of any semi-automated graph digitiser tool for noisy or overlapping spectral data.

The training data should contain only known groups (multi-species/multiple sub-species/multiple individuals). Each training group should have at least three to five calls and recordings from multiple sessions will increase the accuracy of the model as the animals may have higher intra-individual variation across days than within them. Thus, the higher the intra-individual or intra-group variation, the greater the number of vocalisations and individuals that should be included in the training dataset to make a robust model for the testing dataset.

Even though one can choose an unknown dataset as test data, we recommend using a known dataset when originally validating the model. Using multiple test datasets will increase the model’s confidence.

We recommend using multiple small batches as test data (50–100 sample of calls) instead of large data to avoid confusion in cluster groups that may represent other variation in the calls.

To allow study replication, we have made our data and codes available in the Supplementary Materials. While the data needs to be replaced for each study, the system of analysis and classification should be robust and replicable.

Our study reached substantial accuracy in identifying wolves from their howls. Since the methodology was validated using known wolf data and was found to be reasonably reliable, unknown howls can also be classified. This opens up a new opportunity for population estimation and tracking of wolves through howling surveys. Although we analysed our data with Indian wolf howls, the procedure is replicable for other subspecies that have a set of known howls from different individuals and could potentially be applied to other species with individually distinctive vocalisations. This would refine and improve both population estimates and the ability to monitor individuals in situ, with global implications for conservation and ecology.

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Acknowledgements

We want to thank the authority and staffs of the Jaipur Zoo and Maharashtra Forest Department for permitting to conduct this research. We sincerely acknowledge the funding agencies, the Department of Science and Technology, the Govt of India (No. EMR/2015/000036) and the Forest Department of Maharashtra (No. 1852). We appreciate all our field personals (Daut Shaikh, Shivkumar More, Sarang Mhamane) and wildlife enthusiast groups (Pune Wolfgang, Mihir Godbole, Vineet Arora, R. V. Kasar, Rajesh Pardeshi, Sawan Behkar and others) from Maharashtra who helped in local information gathering and various logistic arrangement during data collection. The first author is thankful for the effort of Shivam Shrotriya assisting during the initial analysis. The authors are grateful to Dr Arik Kershenbaum, foe his consistent support. We are delighted for having continuous support from the Wildlife Institute of India, Dehradun and our lab members.

This research was funded by Maharashtra Forest Department ( http://mahaforest.gov.in ) (Grant No. 1852) and Department of Science and Technology, Govt of India ( http://www.dst.gov.in/ ) (Grant No. EMR/2015/000036). BH was the principal investigator of both the project, and SS was the researcher in those projects. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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B.H. conceptualised the study. S.S. collected all the data and did data extraction, analysis and writing the manuscript. H.R.G. and B.H. both supervised in data interpretation along with the manuscript writing. B.H. played a sole role in funding acquisition. All authors contributed critically to the drafts and gave final approval for publication.

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Supplementary information 1., supplementary information 2., supplementary information 3., supplementary information 4., appendices: supplementary materials.

All the data and R code require to recreate the analysis are hosted in https://github.com/bhlabwii/wolf_howlID platform. Raw sound files are available on request to the corresponding author. Compiled reports from R Scripts can be found in following supporting material:

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Sadhukhan, S., Root-Gutteridge, H. & Habib, B. Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method. Sci Rep 11 , 7309 (2021). https://doi.org/10.1038/s41598-021-86718-w

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Genetic diversity, structure, and demographic histories of unique and ancient wolf lineages in India

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• Yellapu Srinivas   ORCID: orcid.org/0000-0002-5412-4717 1 &
• Yadvendradev Jhala   ORCID: orcid.org/0000-0003-3276-1384 1  

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Assessing genetic diversity, population connectivity, demographic patterns, and phylogeographic relationships is vital for understanding the evolutionary history of species and thus aid in conservation management decisions. Indian wolves (currently, Canis lupus pallipes and Canis lupus chanco ) are considered ancient, unique and divergent lineages among grey wolves, yet their population genetics are poorly understood. To void this knowledge gap, we collected samples from Indian peninsular ( n  = 77) and Himalayan wolves ( n  = 24) and used a combination of maternal (mtDNA CR and Cyt b ) and bi-parental (nuclear microsatellites) markers to estimate levels of genetic diversity, examine the patterns of genetic structuring between them and within their distribution range, and assess their demographic histories. Both the wolf populations showed moderate levels of genetic variability, comparable to other grey wolves. Low levels of genetic differentiation were observed within both the Indian peninsular and Himalayan wolves indicating high levels of gene flow within their populations. On the other hand, high levels of genetic differentiation were observed between the two wolves indicating absence of gene flow. Molecular analysis highlighted the uniqueness of both the Indian wolves which was further supported by the presence of unique haplotypes indicating no admixture between them. Demographic analysis using both mtDNA and microsatellites revealed decline in population sizes of both the wolf lineages and both have undergone bottlenecks. Estimates of past effective population size revealed recent population declines of both lineages of Indian wolves at around 25–50 generations corresponding to about 100–200 years ago. Our results further support the designation of both lineages of Indian wolves as two distinct species Canis pallipes and Canis himalayensis and suggest increasing conservation efforts to save the unique and ancient wolf species from extinction.

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Acknowledgements

The authors would like to thank the officials of state forest departments and zoological parks for the logistic support with sample collection. We specifically thank the sample providers without whom this study would not have been possible. Special thanks to Dhruv Jain and Soham Seal from Wildlife Institute of India for their help with GIS map and Images beautification. Our sincere thanks to the editor and an anonymous reviewer for constructive comments to improve the manuscript.

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YVJ and YS conceptualised the study and collected samples. YS performed the experiments, analysed the data and wrote the manuscript. YVJ supervised the study, reviewed the drafts and finalised the manuscript.

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10592_2023_1553_MOESM1_ESM.jpg

Supplementary file1 (JPG 395 KB). Supplementary Figure 1: Bayesian phylogenetic relationships of the Indian peninsular and Himalayan wolves to Holarctic wolves and other Canis species based on Cyt b DNA sequences with GenBank accession numbers.

10592_2023_1553_MOESM2_ESM.tiff

Supplementary file2 (TIFF 78 KB) Supplementary Figure 2: Mismatch distributions of pairwise differences of CR haplotypes for the Indian peninsular wolves (A) and Himalayan wolves (B). Depicted are observed (dashed lines) and expected (solid lines) frequencies.

10592_2023_1553_MOESM3_ESM.tiff

Supplementary file3 (TIFF 2808 KB) Supplementary Figure 3: Population genetic structure of Indian peninsular wolves using 25 nuclear microsatellites implemented in STRUCTURE v 2.3 with no prior location information. Plot of STRUCTURE Harvester showing ΔK peaking at K=4 (A) and log-likelihood change in probability (B). Summary bar plot of STRUCTURE runs at K=2, 4, and 6 (C) showing population assignments for each individual. The populations 1, 2, 3, 4, 5 and 6 represents Gujarat, Rajasthan, Uttar Pradesh, Bihar, Maharashtra, and Karnataka respectively.

10592_2023_1553_MOESM4_ESM.tiff

Supplementary file4 (TIFF 742 KB) Supplementary Figure 4: Population genetic structure of Himalayan wolves obtained using 25 nuclear microsatellites implemented in STRUCTURE v 2.3 with no prior location model. Plot of STRUCTURE Harvester based on ΔK showing K=2 (A) and log-likelihood change in probability (B). Summary bar plot of STRUCTURE runs at K = 2, 3, and 4 (C) showing population assignments for each individual. The sampling localities 1, 2, and 3 represents Ladakh, Himachal Pradesh and North Sikkim respectively.

Supplementary file5 (DOCX 45 KB)

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Srinivas, Y., Jhala, Y. Genetic diversity, structure, and demographic histories of unique and ancient wolf lineages in India. Conserv Genet 25 , 33–48 (2024). https://doi.org/10.1007/s10592-023-01553-y

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ORIGINAL RESEARCH article

Distribution, status, and conservation of the indian peninsular wolf.

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Corrigendum: Distribution, status, and conservation of the Indian peninsular wolf

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• 1 Wildlife Institute of India, Dehradun, India
• 2 Department of Wildlife Sciences, Aligarh Muslim University, Aligarh, India

An understanding of the distribution range and status of a species is paramount for its conservation. We used photo captures from 26,838 camera traps deployed over 121,337 km 2 along with data from radio-telemetry, published, and authenticated wolf sightings to infer wolf locations. A total of 3,324 presence locations were obtained and after accounting for spatial redundancy 574 locations were used for modeling in maximum entropy framework (MaxEnt) with ecologically relevant covariates to infer potentially occupied habitats. Relationships of wolf occurrence with eco-geographical variables were interpreted based on response curves. Wolves avoided dense wet forests, human disturbances beyond a threshold, arid deserts, and areas with high top-carnivore density, but occurred in semi-arid scrub, grassland, open forests systems with moderate winter temperatures. The potential habitat that can support wolf occupancy was 364,425 km 2 with the largest wolf habitat available in western India (Saurashtra-Kachchh-Thar landscape 102,837 km 2 ). Wolf habitats across all landscapes were connected with no barriers to dispersal. Breeding packs likely occurred in ≈89,000 km 2 . Using an average territory size of 188 (SE 23) km 2 , India could potentially hold 423–540 wolf packs. With an average adult pack size of 3 (SE 0.24), and a wolf density < 1 per 100 km 2 in occupied but non-breeding habitats, a wolf population of 3,170 (SE range 2,568–3,847) adults was estimated. The states of Madhya Pradesh, Rajasthan, Gujarat, and Maharashtra were major strongholds for the species. Within forested landscapes, wolves tended to avoid top-carnivores but were more sympatric with leopards and dhole compared to tigers and lions. This ancient wolf lineage is threatened by habitat loss to development, hybridization with dogs, fast-traffic roads, diseases, and severe persecution by pastoralists. Their status is as precarious as that of the tiger, yet focused conservation efforts are lacking. Breeding habitat patches within each landscape identified in this study should be made safe from human persecution and free of feral dogs so as to permit packs to breed and successfully recruit individuals to ensure wolf persistence in the larger landscape for the long term.

Introduction

Reliable information on the status, that is the distribution, population size, extent, and habitat contiguity between populations, are essential for the management of any endangered species ( Sousa-Silva et al., 2014 ). This basic information is not available for many species, and conservation management is often based on educated guesses that can have direr consequences ( Blake and Hedges, 2004 ) and is especially relevant for threatened species that occur outside of protected areas ( Maron et al., 2018 ; Simmonds and Watson, 2019 ). Carnivores, due to their wide-ranging behavior, low density, and elusive nature, are one of the most difficult taxa to study ( Garshelis, 1992 ). The status of many carnivores was assessed from indices, such as pug-marks for tigers and lions ( Wynter-Blyth and Dharmakumarsinhji, 1949 ; Choudhary, 1970 ), simulated howls for wolves ( Harrington and Mech, 1982 ), and golden jackals ( Graf and Hatlauf, 2021 ), questionnaire surveys, and interactions with the local community ( Jhala and Giles, 1991 , Karanth et al., 2009 ). In the absence of any better approach, the information generated by these methods was often used for policy decisions and management actions. However, now with the advent of cost-effective modern technologies, such as camera traps and radio-telemetry, and analytical approaches, i.e., species distribution models ( Sousa-Silva et al., 2014 ), better insights on species distribution and abundance and their determining factors are possible.

Indian peninsular wolves ( Canis lupus pallipes ) are an ancient lineage of wolves endemic to the Indian sub-continent ( Sharma L. K. et al., 2004 ; Hennelly et al., 2021 ). They are considered endangered and are listed on Schedule 1 of the Wildlife Protection Act (1972) . Several attempts have been made to evaluate their status locally ( Jhala and Giles, 1991 ; Kumar and Rahmani, 1997 ; Singh and Kumara, 2006 ) and at the country scale ( Shahi, 1982 ; Jhala, 2003 ; Karanth et al., 2009 ; Srivathsa et al., 2020 ). Earlier range maps and population estimates were based on ground surveys, information from local pastoralists, and knowledge of wolf ecology and their habitat ( Shahi, 1982 ; Jhala and Giles, 1991 ; Kumar and Rahmani, 1997 ; Kumar, 1998 ; Kumar and Rahmani, 2000 ; Jethva and Jhala, 2004 ; Singh and Kumara, 2006 ; Kumar and Rahmani, 2008 ; Agarwala et al., 2010 ). Karanth et al. (2009) used expert knowledge, while Srivathsa et al. (2020) used a combination of data from field surveys, citizen science, and authenticated reports, while both studies used occupancy framework with eco-geographical and human footprint covariates to model wolf distribution across India.

In this study, we used data generated from the largest camera trap survey to date covering 121,337 km 2 ( Jhala et al., 2020 ) in combination with wolf locations obtained from radio-telemetry and authenticated records as presence data to model species distribution. We subsequently estimate population size based on territory size and pack size estimates in occupied and breeding habitats. We evaluate wolf distribution and relative abundance with respect to other large competing carnivores and identify wolf stronghold populations that should be targeted for conservation to ensure wolf persistence in the larger landscape for the long term.

Materials and Methods

The geographical extent of our study covered the entire range of Indian wolves within India. We modeled wolf distribution using the maximum entropy approach in maximum entropy framework (MaxEnt; version 3.4.1, Phillips et al., 2006 ) that uses machine learning from occurrence locations of the target species and background points along with ecologically relevant spatial environmental variables to develop statistical relationships ( Elith et al., 2011 ). These relationships are then used to predict species occurrence across modeled space ( Elith et al., 2011 ). We used a combination of methods to infer wolf presence locations. These were (a) extensive coverage of forested habitats across 20 Indian states by camera traps carried out by State Forest Department personnel and research biologists of the Wildlife Institute of India ( Jhala et al., 2020 ). Camera traps with heat and motion detectors were deployed at 26,838 locations in 2018–2019 to cover a forested area of 121,337 km 2 ( Figure 1 ). All photo captures of wildlife were geotagged and subsequently segregated into species. Camera trap locations that recorded wolf captures were used for modeling wolf distribution. (b) Since Indian peninsular wolves were known to use agro-pastoral landscapes (outside of forest habitats; Jhala, 1993 ) and since these areas were not camera trapped, we obtained records of wolf presence from Shahi (1982) , Jhala (1993 , 2003 , 2007) , Jhala and Sharma (1997) , Kumar and Rahmani (1997) , Jethva (2003) , Habib (2007) , Lokhande and Bajaru (2013) , Saren et al. (2019) , Ghaskadbi et al. (2021) , Mahajan and Khandal (2021) , Maurya et al. (2021) , Sadhukhan et al. (2021) , Sharma (2021) , and Trivedi et al. (2021) , and from radio-telemetry ( Jhala, 2007 ) and geotagged records from Jhala Y.V. et al. (2021) to augment the camera trap data.

Figure 1. Wolf distribution modeled in MaxEnt using presence locations with eco-geographical variables of human modification, climate, habitat, competing carnivores, and prey. Map inset shows region within India where wolf distribution was modeled.

Since many of the radio-telemetry-based locations and other locations were clumped, we picked only one location for approximately every 5 km 2 . This reduced the spatial redundancy of information in location data and we were left with 571 locations that were used for model building. Based on knowledge of wolf ecology and behavior ( Mech, 1970 ; Jhala, 1993 ; Mech and Boitani, 2007 ), we hypothesized a priori that Peninsular Indian wolves would occur in semi-arid grasslands, scrub, and open forests with high ambient temperatures, would avoid areas of high human density but occur in rural areas with livestock husbandry, and would avoid areas having a high density of competing carnivores. The eco-geographical variables used in MaxEnt were as follows: (a) habitat characteristics (land use land cover, Normalized Difference Vegetation Index (NDVI), elevation, and ruggedness; (b) climatic factors (temperatures of coldest and hottest months, rainfall, and aridity); (c) human footprint indices (distance to night light, distance to roads, road density, and human modification index; (d) prey indices as livestock density, goat and sheep density, and cattle density, and (e) top-carnivore density (tiger and lion density across their range of occurrence) ( Supplementary Table 1 ). Linear, quadratic, and product features available in MaxEnt were used in combination with representative variables from each of the above-mentioned eco-geographical variable categories. The models were assessed based on area under the curve (AUC) of receiver operator curves (ROC), specificity and sensitivity of the models, and testing the model classification accuracy on 30% of the data that were not used for model building ( Jiménez-Valverde, 2011 ). Best models were selected on the basis of model fit and parsimonious use of relevant ecological covariates that made ecological sense based on our a priori expectations ( Supplementary Table 1 ). We used clog-log analysis ( Phillips et al., 2017 ) to determine the probability value beyond which pixels had high wolf occurrence classification and below which wolves were likely absent to determine the area occupied by wolves. We also determined the pixel probabilities for 16 known breeding packs from 14 different areas spread across India and used one SD on the mean pixel values to address uncertainty in the cutoff values to determine occupied and breeding habitats.

Wolves are known to be territorial where neighboring territory areas overlap minimally ( Jhala, 2003 ; Habib, 2007 ). Since 100% Minimum Convex Polygon territories of four wolf packs reported by Habib (2007) did not differ from 95% fixed kernel estimates of another eight radio-collared packs from three different sites ( Jhala, 2007 ) ( t -test, p = 0.9) we combined these estimates for our analysis to get better coverage of territory sizes from across India ( Supplementary Table 2 ). We removed isolated wolf occurrence habitat patches that were <100 km 2 from further analysis as these would be too small to harbor wolves. We used data from 35 wolf packs for estimating adult pack size ( Supplementary Table 3 ) to estimate the potential wolf population within areas of breeding habitat. Occupied areas outside of breeding habitats would hold dispersing individuals, old ousted pack members, and sub-adults biding their time to join packs or form their own packs ( Packard and Mech, 1980 ). For areas that were above the MaxEnt clog-log probability value of occurrence but below the threshold of breeding packs, we used a conservative estimate of wolf density of less than one wolf per 100 km 2 (range between 0.75 and 0.5 wolves per 100 km 2 ).

To get a better understanding of species interactions within forested habitats, we computed relative abundance index (RAI, Carbone et al., 2001 ) as the number of photo captures per 100 trap days of wolves, dhole, leopards, and tigers and averaged these for all camera traps in 25 km 2 grids. We plotted wolf RAI against dhole RAI, leopard density, and tiger density from Jhala et al. (2020) and Jhala Y.V. et al. (2021) and inspected scatterplots, fitted models, and tested for linear correlations to better understand species interactions.

We obtained 34,858,623 photographs of wildlife from which 2,812 were of wolves from 313 camera locations. Published (34), other geo-tagged records (365), and radio-telemetry (2,612) contributed to a total of 3,324 wolf presence locations from across the range of the species in India ( Figure 1 ). The best MaxEnt model was a good fit with an AUC of 0.83 and performed well in classifying 30% of the test data ( Figure 2 ). Wolf occurrence was best explained by (1) climatic variables: (a) average rainfall, (b) average temperature of the coldest quarter; (2) habitat characteristics: (a) pre-monsoon NDVI, (c) land use and land cover; (3) Human Modification Index (maximum contribution to the model 40%); (4) prey availability in the form of livestock density; and (5) density of top-carnivores ( Figure 2 ). As per our a priori predictions, wolves were tolerant of higher temperatures ( Figure 2 and Supplementary Figure 1 ), they preferentially occurred at semi-arid sites that had lower rainfall, higher temperatures, lower values of canopy cover (NDVI), avoided high human densities but their occurrence coincided with moderate livestock densities. As expected, the response of wolves to top-carnivore density was a right-skewed bell-shaped function, with wolves occurring in areas of low top-carnivore densities but declining at high top-carnivore densities ( Figure 2 and Supplementary Figure 1 ).

Figure 2. Response curves of wolf occurrence with eco-geographical variables, their contributions, and model fit assessment obtained from 100 bootstrap runs of the best MaxEnt model. (A) Variation in the omission of model data and predicted area with increasing MaxEnt cumulative threshold values. (B) Receiver operating curve of test and training data. (D) Land use land cover classes were (1) arid scrub, (3) grassland, (4) agriculture, (5) settlement, (6) open, (8) water, (9) riparian, (10) evergreen open, (11) evergreen broadleaf, (12) deciduous broadleaf, (13) deciduous open, (14) mixed open, (15) evergreen broadleaf open, (16) deciduous broadleaf open, (17) scrub, and (18) coastal marsh. (E) Normalized Difference Vegetation Index (NDVI). (F) Carnivore density (density of tigers and lions) across their range in India.

Wolf territory size was estimated at 189 (SE 23) km 2 ( Supplementary Table 2 ). The total area above the threshold value obtained from clog-log analysis ( p = 0.47 SE 0.0094) that could potentially be occupied by wolves after removing isolated areas that were smaller than 100 km 2 was 364,425 km 2 in India. The largest potential for wolf occupancy was in the contiguous Saurashtra-Kachchh-Thar landscape (102,837 km 2 , Figure 3 ). Area suitable for breeding packs was estimated at 89,138 km 2 with the largest contiguous breeding habitats available in the Central Indian landscape (37,323 km 2 , Figure 3 ). Considering an average adult pack size of 3 (SE 0.24) adult wolves ( Supplementary Table 3 ) for breeding habitat and a density range from 0.75 to 0.5 wolves per 100 km 2 for occupied areas outside of the breeding habitat, the potential number of wolves in India was estimated at 3,170 (SE range 2,568–3,847). Besides the Saurashtra-Kachchh-Thar landscape, the other habitat patch that could potentially hold a population of > 150 wolves was Udanti Sitanadi-Indravati-Kawal-Tadoba ( Figure 3 ). Shivpuri-Mukundara-Gandhi Sagar, Satpura-Betul-Melghat, Bandhavgarh-Sanjay, and Panna-Nauradehi were other areas that support good wolf populations. Madhya Pradesh supported the largest wolf population followed by the states of Rajasthan, Gujarat, and Maharashtra ( Table 1 ).

Figure 3. Wolf-occupied landscapes and breeding habitats across India inferred from the MaxEnt models. MaxEnt, maximum entropy framework.

Table 1. State-wise estimated wolf population based on the MaxEnt model estimate of potential occupied, breeding habitat, average pack size of 3 (SE 0.24), and territory size of 188 (SE 23) km 2 .

Scatter plots of wolf RAI against dhole RAI, leopard, and tiger density categories in forested habitats ( Supplementary Figure 2 ) showed that wolf relative abundance declined with an increase in competing for carnivore relative and absolute abundances. Declines in wolf photo-capture rates were sharper and statistically significant with an increase in tigers compared to that of leopard and dhole.

Assessing the status of widespread, low density, and elusive species, such as the wolf, is a difficult task ( Kunkel et al., 2005 ). Shahi (1982) estimated the Indian wolf population at ≈800 individuals, while subsequent estimates were higher (2,000–3,000; Jhala, 2003 ) due to a better understanding of wolf distribution and ecology. The current assessment uses robust quantitative information of occurrence data (from large-scale geo-tagged camera trap, telemetry, and authenticated sightings) in combination with species distribution models with relevant eco-geographic covariates to evaluate wolf status. We use clog-log models with 100 bootstrap runs in MaxEnt ( Phillips et al., 2017 ) to determine the threshold probability below which wolf occurrence was unlikely, to determine wolf-occupied area. Estimates based on models are only as good as the data used to build these models; with an extensive coverage of wolf location data from across their range, from varied habitats, and eco-climatic conditions, we believe that our model predictions are good (as also shown by model evaluation statistics). However, we caution that due to the clog-log threshold used to determine wolf occupancy, there will be some areas where wolves may be present and our model threshold failed to predict them or predicated wolf occupancy in areas of known absence. We believe that at the country scale, these small errors would not matter, but at local scales where conservation measures need to be implemented, deviations from the truth would make a large difference. Therefore, the wolf habitat suitability map provided in this article should be used as a first cut and subsequent ground validation of the model results eventually used for conservation investments and management. The current distribution ( Figures 1 , 2 ) and population estimate ( Table 1 ) are similar to earlier estimates and validate Jhala (2003) with better information and formal model-based analysis. In the past two decades, wolf populations seem to have colonized new areas while losing out in some of their strongholds. Wolves have been recently recorded from several areas from where they had been exterminated or were not known to exist in the recent past [e.g., Rajaji Tiger Reserve ( Sharma, 2021 ), Bangladesh ( Muntasir et al., 2021 ), Indian Sundarbans ( Ghai, 2017 ), Valmiki Tiger Reserve ( Maurya et al., 2021 ), and Kaveri Wildlife Sanctuary ( Gubbi et al., 2020 )]. While wolves have declined from their stronghold of Kachchh and parts of Rajasthan primarily due to persecution, hybridization with dogs, and development of fast traffic roads. The easternmost limit of the Indian wolf was the Sundarban mangrove forest ( Ghai, 2017 ; Muntasir et al., 2021 ), there were no records of the Indian wolf from Assam and the North East States. No suitable occupied habitat was predicted in the states of Haryana and Punjab, possibly due to extensive and intensive agriculture, yet it is possible that wolves can also sporadically occur in these two states. It was believed that Indian peninsular wolves rarely used forested habitats ( Jhala, 2003 ), however, as evidenced from the extensive camera trap data, wolves have been recorded from several forested areas of India ( Figure 1 ). Notably, the tiger reserves of Mukundara, Kawal, Udanti Sitanadi, Melghat, Panna, Palamau, Bor, Kanha, Satpura, and Pench had a good number of wolf photo captures. Wolf photo captures from these tiger reserves were either from the buffer zone or from parts of the reserve that had relatively open canopied forests and scrubland habitats, and these parts had a relatively low density of tigers. Conserving a large carnivore outside of the realms of a protected area, especially when it has the propensity of predation on livestock, is a formidable task despite being protected by law ( Woodroffe et al., 2006 ). Protected areas targeting wolves as a focal species for conservation were few (e.g., Mahuadanr, Hazaribagh, Gandhi Sagar, and Nauradehi wildlife sanctuaries). Therefore, documenting breeding wolf populations in some well-protected areas of India heralds well for the long-term conservation of Indian wolves. Earlier estimates of wolves from Gujarat and Rajasthan ( Jhala and Giles, 1991 ) mapped their distribution and abundance based on extensive ground surveys and expert knowledge of local pastoral communities. These estimates were lower than the estimates reported herein. The MaxEnt-based analysis identifies habitats that meet the requirements for wolf occupancy based on the covariates used to build the model, human persecution can severely deplete wolf populations within suitable habitats as has been observed in Kachchh in recent times. Therefore, detailed ground surveys and radio-telemetry-based estimates of pack size, territory configurations, and sizes in selected sites are required to validate the population estimates obtained by model-based inference and for monitoring long-term population trends. Telemetry studies from mid-1990s to 2005 in the Bhal and Kachchh regions of Gujarat and Nashik ( Jhala, 2007 ) and Sholapur in Maharashtra ( Kumar and Rahmani, 1997 ; Habib, 2007 ; Habib et al., 2021 ) have shown that wolf populations were vulnerable to disease and persecution and fluctuated substantially ( Jhala, 2003 ). Unfortunately, no long-term telemetry-based studies are being implemented on the Indian wolves at specific sites to monitor population dynamics. Source populations of wolves within each of the identified landscapes need to be monitored continuously through radio-telemetry to keep the pulse of the population , i.e., ensure that these populations are not declining, and if declining, identify site-specific threats so as to address them in a timely manner. As long as these source populations are secure within each landscape, they will recruit wolves that will disperse and occupy the larger landscapes. Efforts to reintroduce wolves from captive-bred zoo populations should only be considered after appropriate rewilding, evaluation of their behavior, and skills of hunting wild prey. Such wolves (if habituated to humans) can become a major cause of human-wolf conflict ( Jhala and Sharma, 1997 ; Rajpurohit, 1999 ) and compromise the conservation of the entire species due to community backlash ( Treves et al., 2006 ).

Response curves of wolf occurrence to eco-geographical covariates were in consonance with our hypothesis conforming to their behavioral ecology. Besides climatic and habitat characteristics, top carnivore densities contributed (12.6%) to explaining wolf occurrence. It has long been speculated that Indian wolves have likely been out-competed by other large carnivores that dwell in forested habitats ( Jhala, 1993 ). The alternative hypothesis could be that Indian wolves evolved at a time when India was undergoing a dry spell ( Sharma D. K. et al., 2004 ; Hennelly et al., 2021 ) and adapted to open semi-arid habitats and therefore now avoid thick forests. Wolves often occurred in the buffer zones of protected areas, but were rarely seen within the core areas of PA’s that have high large carnivore densities even though habitats were suitable. For example, the habitats of Gir Protected Area and that of Ranthambore National Park were suitable for wolves (dry open canopied deciduous and thorn forests) and wolves occurred in the periphery of these reserves, but they were rarely seen in the core areas that have high lion and tiger densities, though these core areas abound in prey. While in protected areas, namely, Nauradehi, Gandhi Sagar, and Mukundara, that have similar habitats but do not have tigers or lions and dhole, wolves use most parts of these protected areas. These observations suggest that though Indian wolves may have specialized for open habitats, they were also likely limited by direct competition with other large carnivores. Since we had density estimates of only tigers and lions covering the full extent of these carnivores’ range across India, we could use these for modeling wolf occurrence in MaxEnt ( Figure 2 ). However, wolves were also likely limited by leopards and dhole. Leopards occur outside of forests as well ( Daniel, 1996 ), while dholes are primarily forest dwellers ( Johnsingh and Acharya, 2013 ) in tropical India. Since leopard, dhole, and wolf photo capture rates were available only from forested habitats, we restricted our analysis on their interactions to this habitat that was extensively camera trapped across India ( Jhala et al., 2020 ). Wolves tended to avoid all three competing large carnivores but were more tolerant of leopards and dhole compared to tigers ( Supplementary Figure 2 ).

The peninsular Indian wolf is an ancient lineage endemic to the Indian sub-continent ( Sharma L. K. et al., 2004 ; Hennelly et al., 2021 ), its status is precarious and with only ≈3,100 adult individuals their population is as big as that of the tiger in India ( Jhala Y. et al., 2021 ). Wolves are persecuted by pastoralists, threatened by diseases spread by dogs, and genetically swamped by a large feral dog population ( Jhala, 2003 ; Vanak and Gompper, 2009 ; Srivathsa et al., 2019 ). Conserving wolves is a more formidable task compared to tigers, since the majority of their population resides outside the realm of protected areas and there are currently no focused efforts for conserving the species. For successful recruitment, all that wolves require, within the larger occupied landscapes that include several types of land use and cover, are small patches (5–15 km 2 ) of safe habitat for denning and rendezvous sites between December to March ( Jhala, 2003 ). Besides the use of poison, the new multi-lane fast-traffic motorways being built through wolf habitats are a death knell for wolves and other threatened species and need careful mitigation to provide safe passage ( Dennehy et al., 2021 ). Ensuring that breeding habitats are well protected would enable wolves to continue to persist in the larger occupied landscape. This study provides the required information for focused efforts to target and assist in their long-term conservation.

Data Availability Statement

The data analyzed in this study is subject to the following licenses/restrictions: Data on pack size, territory size are included in the Supplementary Material , location data is provided in the figure. Since the precise location of Schedule 1 species under the Wildlife Protection Act is not possible to be provided in the public domain, therefore, wolf location data will be provided for genuine users based on reasonable requests to the corresponding author. Requests to access these datasets should be directed to corresponding author.

Ethics Statement

Ethical review and approval was not required for the animal study because the manuscript does not involve capture or handling of any animal and depends on secondary data that was generated with appropriate legal approvals as per the wildlife protection act.

Author Contributions

YJ conceived the study, collected field data, did the data analysis, and wrote the manuscript. SS conducted data analysis and wrote the manuscript. QQ and SK contributed field data. All authors reviewed and commented on the manuscript.

Extensive camera trap survey was funded by the National Tiger Conservation Authority and State Forest Departments as part of the National tiger status estimation exercise 2018.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We thank Indrajeet Ghorpade for providing wolf location data from northern Karnataka.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo.2022.814966/full#supplementary-material

Supplementary Figure 1 | Relationships of wolf occurrence with eco-geographical variables when all variables were considered together in the model and with variables that were considered in explaining wolf occurrence but not used in the final model.

Supplementary Figure 2 | Three-dimensional and two-dimensional scatter plots of wolf relative abundance index (RAI) against tiger (B,C) , leopard (A,B,D) , and dhole (A,E) . Two-dimensional scatter plots show intensity and 95% ellipses of data distribution. Wolf RAI was negatively correlated with all three large carnivores but was statistically significant ( p < 0.01) only for tigers.

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Keywords : Canis lupus pallipes , camera traps, radio telemetry, MaxEnt, home range, pack size, population estimate, wolf-large carnivore Interaction

Citation: Jhala Y, Saini S, Kumar S and Qureshi Q (2022) Distribution, Status, and Conservation of the Indian Peninsular Wolf. Front. Ecol. Evol. 10:814966. doi: 10.3389/fevo.2022.814966

Received: 14 November 2021; Accepted: 10 January 2022; Published: 04 March 2022.

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Copyright © 2022 Jhala, Saini, Kumar and Qureshi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yadvendradev Jhala, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Wolves play a crucial role in shaping ecological communities as an apex predator in the dry-open forests of semi-arid landscapes in India. Large scale habitat loss pertaining to human expansion and retaliatory killing by human caused severe decline in the wolf population across its range. The estimated wolf population size is close to 2000–3000 individuals in India; however, these estimates were decades old and the present status of the wolf in the semi-arid landscape is largely unknown. We assessed the distribution of wolves in Kailadevi Wildlife Sanctuary, Rajasthan using occupancy models and identified important factors associated with habitat-use by wolves. Occupancy modelling shifts the focus from individual animal to a site, while accounting for detection probability. To assess the habitat-use we used sign-based surveys that rely on data collected from adjacent sampling sites (replicates). The habitat-use was assessed across 672.82 km 2 surveying 48 grid cells, each measuring 14.44 km 2 . Estimated habitat-use Ѱ ( SD ) was found to be 0.82 (0.14). Our findings suggested that availability of agriculture land had the significant positive influence on the habitat-use of wolves. Other factors such as availability of water, scrubland, and wild prey (nilgai and chinkara) also had a positive effect on the habitat use of wolves, but it was not significant. Forest cover has a negative influence on the habitat use of wolves. This study is the first rigorous assessment of the Indian grey wolf habitat-use at the level of wildlife reserve with potential conservation value that can be applied to other areas in India.

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Article Contents

Introduction, results and discussion, materials and methods, supplementary material, acknowledgments, literature cited.

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Genome Sequencing of a Gray Wolf from Peninsular India Provides New Insights into the Evolution and Hybridization of Gray Wolves

Ming-Shan Wang, Mukesh Thakur, Yadvendradev Jhala contributed equally to this work.

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Ming-Shan Wang, Mukesh Thakur, Yadvendradev Jhala, Sheng Wang, Yellapu Srinivas, Shan-Shan Dai, Zheng-Xi Liu, Hong-Man Chen, Richard E Green, Klaus-Peter Koepfli, Beth Shapiro, Genome Sequencing of a Gray Wolf from Peninsular India Provides New Insights into the Evolution and Hybridization of Gray Wolves, Genome Biology and Evolution , Volume 14, Issue 2, February 2022, evac012, https://doi.org/10.1093/gbe/evac012

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The gray wolf ( Canis lupus ) is among the few large carnivores that survived the Late Pleistocene megafaunal extinctions. Thanks to their complex history of admixture and extensive geographic range, the number of gray wolf subspecies and their phylogenetic relationships remain poorly understood. Here, we perform whole-genome sequencing of a gray wolf collected from peninsular India that was phenotypically distinct from gray wolves outside India. Genomic analyses reveal that the Indian gray wolf is an evolutionarily distinct lineage that diverged from other extant gray wolf lineages ∼110 thousand years ago. Demographic analyses suggest that the Indian wolf population declined continuously decline since separating from other gray wolves and, today, has exceptionally low genetic diversity. We also find evidence for pervasive and mosaic gene flow between the Indian wolf and African canids including African wolf, Ethiopian wolf, and African wild dog despite their current geographical separation. Our results support the hypothesis that the Indian subcontinent was a Pleistocene refugium and center of diversification and further highlight the complex history of gene flow that characterized the evolution of gray wolves.

The gray wolf ( Canis lupus ) is one of the few megafaunal carnivores that survived the Late Pleistocene megafaunal extinctions. Despite extensive research on living and extinct gray wolves, the evolutionary history of this lineage remains unclear. Here, we sequence and analyze a draft genome of a gray wolf collected from peninsular India. We find that the Indian wolf lineage, which is both highly threatened and phenotypically distinct from other gray wolves, is the most deeply diverging lineage of extant gray wolves and, despite their physical isolation from other wolf lineages, has a long history of gene flow with other canid lineages.

The gray wolf ( Canis lupus ) first appears in the fossil records of Eurasia and North America some 500,000 years ago ( Nowak 1979 ) and later diversified into more than 37 named subspecies ( Wilson and Reeder 2005 ). Numerous morphological and genomic analyses of gray wolves have presented a complex and sometimes contradictory view of their evolutionary history ( Leonard et al. 2007 ; Sinding et al. 2018 ; Smeds et al. 2019 ; Loog et al. 2020 ). For example, analyses of mitochondrial DNA have revealed a lack of strong haplotype structure among populations across the Northern hemisphere ( Thalmann et al. 2013 ; Loog et al. 2020 ), whereas nuclear genomic analyses have identified distinct lineages in Eurasia and North America ( Fan et al. 2016 ; Gopalakrishnan et al. 2018 ). These studies have also revealed widespread admixture among domestic dogs, gray wolves, and other species in the genera Canis and Cuon ( Freedman et al. 2014 ; Fan et al. 2016 ; Gopalakrishnan et al. 2018 ; Pilot et al. 2019 ). This evolutionary history of dynamic long-distance dispersal, population replacements, and cross-species gene flow has complicated efforts to understand both how gray wolf populations are related to each other and the location, origin, and timing of dog domestication ( Koepfli et al. 2015 ; Perri et al. 2021 ).

Among the least studied populations of gray wolves are those that inhabit the Indian subcontinent. Early taxonomists described two species endemic to this region: the Himalayan wolf, Canis laniger ( Hodgson 1847 ), found in the highland regions of the Tibetan Plateau and eastern Kashmir, and the Indian wolf Canis pallipes ( Sykes 1831 ), distributed within the arid/semi-arid lowland plains of peninsular India. Since these first descriptions, Himalayan and Indian wolves have been reclassified as subspecies within the gray wolf complex, Canis lupus chanco and C. l. pallipes , respectively ( Allen 1938 ). The current range of C. l. pallipes extends from the eastern Mediterranean region of western Asia eastward to peninsular India, where several isolated populations are reported ( Nowak 1995 ; Jhala 2003 ).

Genetic studies using mitochondrial and nuclear markers have shown that the Himalayan wolf is distinct from other gray wolf populations ( Aggarwal et al. 2007 ; Ersmark et al. 2016 ; Werhahn et al. 2017 , 2018 ). Similarly, Indian wolves are morphologically, behaviorally, and genetically distinct from other wolf subspecies ( Aggarwal et al. 2003 ; Sharma et al. 2004 ; Aggarwal et al. 2007 ). Compared with other wolves, Indian wolves are smaller in size (18–22 kg) with less and relatively shorter hair that is light brown in color with black hair tips ( fig. 1A ). Indian wolves are also among the most threatened canid subspecies in the world, with an estimated population size of ∼2,000–3,000 individuals ( Jhala 2003 ).

Sampling location and mitochondrial phylogeny. ( A ) A photograph of an Indian wolf from peninsular India (provided by Y. Shah). ( B ) Map showing the distribution of samples used in this study. The red dot depicts the location where the Indian wolf IW01 ( supplementary fig. S1 , Supplementary Material online ) was sampled. ( C ) Maximum-likelihood tree estimated from mitochondrial genomes (15,462 bp). The Indian wolf (IW01) is a sister clade to domestic dogs and other gray wolves but inside the lineage of Tibetan wolf+Himalayan wolf. IDs in brackets are the GenBank accession numbers.

More recently, relationships among gray wolves have been analyzed using whole-genome sequences. In one study examining admixture among gray wolves and domestic dogs ( Fan et al. 2016 ), a wolf presumed to originate from India but lacking precise locality information (NCBI accession: SRS661487) clustered with wolves from Iran and Israel, which together were grouped within a larger cluster of gray wolves from Eurasia. This result was, however, at odds with earlier phylogenetic analyses based on mitochondrial sequences that suggested that Himalayan wolves and wolves from peninsular India are the earliest branchings and most divergent lineages among all gray wolf populations, with Indian wolves diverging from other lineages ∼270–400 thousand years ago (ka) ( Aggarwal et al. 2003 ; Sharma et al. 2004 ; Aggarwal et al. 2007 ). To resolve this inconsistency, additional analyses using wolf samples of unambiguous provenance are necessary, in particular as the complex history of admixture among canids can lead to discordance among individual gene trees (mitochondrial and nuclear) and the population/species tree ( Degnan and Rosenberg 2009 ; Toews and Brelsford 2012 ).

Here, we address this by generating and analyzing a high-coverage nuclear and mitochondrial genome from a male Indian wolf captured for a radio-telemetry study in Velavadar Blackbuck National Park, Gujarat State, in western India ( Jhala 2003 ), which we call IW01. IW01 had the morphological traits ( supplementary fig. S1 , Supplementary Material online ) and mitochondrial sequence of a typical Indian peninsular wolf ( Sharma et al. 2004 ). We analyze IW01 in conjunction with previously published genomic data from gray wolves sampled from Eurasia and North America ( supplementary table S1 , Supplementary Material online ), including SRS661487, the wolf mentioned above whose precise origins remain ambiguous. We find strong evidence that IW01, along with Himalayan/Tibetan wolves, comprise lineages that are basal to all other gray wolves in both mitochondrial and nuclear phylogenies. Reconstruction of demographic histories also reveals that IW01 has a distinct effective population size trajectory compared with other wolves. Finally, we uncover evidence of historical admixture between IW01 and several canid lineages from Africa despite their current geographical separation, as well as gene flow between the domestic dog + gray wolf clade and these African canids. Our analyses indicate, however, that despite this history of admixture, the Indian wolf lineage has been evolving in isolation from other gray wolf lineages for around 110 thousands years.

Genome Sequencing and Mitochondrial Phylogeny

We extracted genomic DNA from IW01 using a whole blood sample collected in 1995. We prepared four pair-end sequencing libraries from which we sequenced 93.5 G nucleotide bases. We mapped sequencing reads to the domestic dog CanFam3.1 reference genome assembly, which yielded a 30.7-fold coverage genome for IW01. In addition, we de novo assembled the mitochondrial genome from IW01 to 2,557-fold coverage. From this whole mitochondrial genome, we extracted the cytochrome b and 16S rRNA gene sequences, which we used to estimate a phylogeny including IW01 and previously published mitochondrial data from Indian and other gray wolves for which full mitochondrial genomes were unavailable. Maximum-likelihood trees based on these two genes place IW01 in a previously reported clade containing other wolves from peninsular India that, along with Himalayan/Tibetan wolves, is basal to Holarctic gray wolves and domestic dogs ( supplementary fig. S2 , Supplementary Material online ).

Using published raw read data, we also de novo assembled mitochondrial genomes of wolves putatively originating from India (SRS661487) and Iran (SRS661488), both of which lack precise locality information ( Fan et al. 2016 ). We aligned these to a data set of 36 previously published mitochondrial genomes representing different Eurasian and North American gray wolf populations, including one Tibetan wolf and one Himalayan wolf, domestic dogs, and other species belonging to the genera Canis , Cuon , and Lycaon . As with the single gene analyses, IW01 was basal to all Holarctic gray wolves but inside the clade containing the Himalayan and Tibetan wolves, and distant from the SRS661487 (India) and SRS661488 (Iran), which cluster within the clade comprising Holarctic wolves and domestic dogs ( fig. 1C ).

Phylogenetic Relationship between the Indian Wolf IW01 and Other Gray Wolves

We combined gene trees estimated from 5,000 randomly selected 20-kb regions across the nuclear genomes of IW01 and 18 other canids and reconstructed a species tree using ASTRAL-III ( Zhang et al. 2018 ). As observed previously ( Koepfli et al. 2015 ; Gopalakrishnan et al. 2018 ), the African wolf and golden jackal are basal to the coyote and gray wolf clades ( fig. 2A and B ), and the Ethiopian wolf is an outgroup to the golden jackal. Domestic dogs and East Asian gray wolves formed a clade sister to European gray wolves, but with low support ( fig. 2A and supplementary fig. S3A , Supplementary Material online ). Quartet frequencies of gene trees comprising domestic dog, East Asian wolf, and European wolf were similar ( supplementary fig. S3A , Supplementary Material online ). When IW01, SRS661487 (India), and SRS661488 (Iran) are included in the ASTRAL tree, these three lineages form a well-supported clade basal to North American and Eurasian wolves following the split of Himalayan and Tibetan wolves, the latter of which comprises the earliest diverging lineage in the gray wolf/domestic dog clade ( fig. 2A ). This result is inconsistent with the phylogenetic tree presented in Fan et al. (2016) , based on a supermatrix analysis of genome-wide SNP data that do not account for gene tree discordance. In Fan et al. (2016) , SRS661487 and SRS661488 fall in the clade with European wolves, as they do in our mitochondrial phylogeny ( fig. 1C ). When we estimated the ASTRAL tree excluding IW01, SRS661487, and SRS661488 cluster with European wolves ( supplementary fig. S4 , Supplementary Material online ) as in Fan et al. (2016) . When the ASTRAL tree includes IW01 but excludes SRS661487 and SRS661488, IW01 falls basal to all gray wolves and the domestic dog, including the Himalayan and Tibetan wolf clade, with strong support ( fig. 2C and D , panel 10). However, the placement of the Himalayan/Tibetan wolf clade has low support ( supplementary fig. S3B , Supplementary Material online ), suggesting that the phylogenetic relationship among IW01, Himalayan/Tibetan wolf, and the domestic dog + gray wolf clade is not well resolved, possibly due to incomplete sorting and/or gene flow among these lineages.

Phylogenetic analysis of Indian wolf IW01 and other canids based on nuclear genomes. ( A ) Consensus phylogenetic tree obtained using ASTRAL-III, estimated from 5,000 20-kb regions sampled across the nuclear genomes of the domestic dog, representative gray wolves, and other canid species. The blue-colored values at each node show the mean posterior probability of that node. White numbers with black squares denote branch numbers. As domestic dogs are a monophyletic group within the clade containing gray wolves from Eurasia ( Fan et al. 2016 ; Wang et al. 2016 ), we only chose one high-coverage domestic dog genome for this analysis. ( B ) Quartet frequencies of three possible topologies for branch 9 in ( A ). The format “15,16|6,8” indicates the quartet topology with branches 15 and 16 together on one side and branches 6 and 8 on the other side. Quartet topology frequencies for 16 branches in the underlying unrooted phylogeny are shown in supplementary figure S3A , Supplementary Material online . The red bar indicates the frequency of the topology shown in ( A ) and the other blue-colored bars represent frequencies of the two alternative topologies. The dotted line represents the one-third frequency cut-off of the true topology for each quartet ( Allman et al. 2011 ). ( C ) Phylogenetic tree estimated from the nuclear genome using ASTRAL-III but excluding the previously reported gray wolf genomes of SRS661487 (India) and SRS661488 (Iran). ( D ) The quartet frequencies of three possible topologies for branch 10 in ( C ). Quartet topology frequencies for 14 branches in the underlying unrooted phylogeny are shown in supplementary figure S3B , Supplementary Material online .

To further explore the placement of IW01, we aligned the high-coverage nuclear genomes from IW01, a Tibetan wolf, a Chinese wolf, and a dhole and divided the alignment into 250-kb, 500-kb, and 1-Mb nonoverlapping segments, and then estimated maximum-likelihood phylogenetic trees for each segment. The most commonly observed topology, which accounted for 48–57% of windows, placed IW01 as basal to the Tibetan wolf and the Chinese wolf ( supplementary fig. S5 , Supplementary Material online ).

Given that the most commonly observed topology placed IW01 as basal to Tibetan wolves, which previously estimated contained as much as 39% ancestry from a deeply divergent “ghost” lineage ( Wang et al. 2020 ), it is possible that all or some component of the ancestry of IW01 is also from this “ghost” lineage. To test this, we constructed a neighbor-joining tree using only genomic segments characterized as of “ghost” origin in Himalayan and Tibetan wolves ( Wang et al. 2020 ). Similar to the mitochondrial tree ( fig. 1C ), IW01 and Himalayan/Tibetan wolves formed two distinct clades in this analysis, with the latter clade basal to other gray wolves, including IW01, with high bootstrap support ( supplementary fig. S6 , Supplementary Material online ). These results suggest that IW01 is not the possible source of the “ghost” lineage ancestry. Instead, the “ghost” lineage is likely basal to IW01.

Finally, we modeled the genetic makeup and phylogenetic assignments of IW01 using admixture graphs. Because this analysis is based on genotype calls, we prioritized genomes with sequence coverage over 10-fold. In agreement with the above analyses, our data fit the graph models (no f4 outliers) in which IW01 is assigned to a lineage basal to Eurasian gray wolves and shows no signals of admixture with other gray wolf populations ( fig. 3 ). Our results also indicate Tibetan wolves have admixed ancestry that is perhaps derived from ancient hybridization between a lineage basal to IW01 and Eurasian gray wolves. Interestingly, this analysis suggests that the Mongolian wolf is also admixed ( fig. 3 ), with the majority of its ancestry coming from European wolves, and the remainder from a lineage connecting them to Himalayan/Tibetan wolves. Previous studies have suggested that the range of Himalayan/Tibetan wolves was probably expanded across much of Mongolia and Northwest China ( Sharma et al. 2004 ), although these wolves maintain different distributions and represent distinct genetic lineages today ( Werhahn et al. 2017 ).

Fitted admixture graphs (no f4 outliers) showing the genetic makeup for IW01, European wolves (represented here by the Spanish wolf), and two highland wolves (represented by the Mongolian wolf and Tibetan wolf). Dashed lines indicate inferred admixture events and the admixture proportions are reported next to the dashed lines. The likelihood is shown at the top of each graph. The first graph has the highest likelihood of support.

Gene Flow between Indian Wolf IW01 and Other Canids

The uncertainty of the phylogenetic placement of gray wolves SRS661487 (India) and SRS661488 (Iran), as well as previous reports of admixture among canid lineages ( Koepfli et al. 2015 ; Skoglund et al. 2015 ; Gopalakrishnan et al. 2018 ), suggest that one or more of the sampled Middle Eastern and Indian wolf lineages may have admixed ancestry. We explored genetic affinity and admixture between IW01 and other gray wolves using TreeMix ( Pickrell and Pritchard 2012 ) and D -statistics ( Green et al. 2010 ) by analyzing 32 nuclear genomes ( supplementary table S1 , Supplementary Material online ). Our results support IW01 as a diverged wolf lineage basal to other Eurasian gray wolves and that SRS661487 is closely related to Iranian and European gray wolves ( fig. 4 and supplementary figs. S4–S9 , Supplementary Material online ).

TreeMix tree graph allowing two migration edges. This configuration reveals IW01 is basal to other gray wolves and domestic dogs. Two admixture events are shown, one between the African wolf and Ethiopian wolf, and the other between IW01 and SRS661487 (India)+SRS661488 (Iran). Tree graphs and Treemix residuals inferred by allowing zero to five migration edges are shown in supplementary figures S7 and S8 , Supplementary Material online . The graph with two migrations has the lowest residual distance.

We also found evidence of gene flow between IW01 and the two wolves of suspect origin (SRS661487 [India] and SRS661488 [Iran]) ( figs. 4 and 5A and B ; supplementary figs. S7–S9 , Supplementary Material online ), as well as between IW01 and three more recently reported Iranian wolves ( supplementary fig. S10 , Supplementary Material online ) ( Amiri Ghanatsaman et al. 2020 ). Admixture among these lineages is expected, given the lack of reproductive barriers and any major geographic barriers separating these populations. We did not find evidence, however, of gene flow between IW01 and the Himalayan wolf. This is surprising, given the proximity of their ranges but consistent with previous findings based on mitochondrial sequences ( Sharma et al. 2004 ) and our phylogenetic results. It is possible that differences in local adaptation between highland wolves of the trans-Himalayan and Tibetan plateau ( Werhahn et al. 2018 ; Wang et al. 2020 ) versus lowland wolves of the semi-arid habitats in peninsular India, along with the small population sizes and fragmented habitat of Indian wolves may lessen chances for admixture between these lineages ( Owen et al. 2002 ; Blinkhorn and Petraglia 2017 ). However, given that our analyses are currently limited to a single Indian wolf sample of known origin, additional genomes from wolves sampled across peninsular India and the Himalayan region will be required to reveal the extent of gene flow among these lineages.

D-statistics testing the amount of allele sharing between IW01 and other canid species. ( A ) Schematic plot showing the topology used for calculating D -statistics. The calculation with | Z |≥3 was considered statistically significant. ( B ) D -statistics find no clear evidence of admixture between IW01 and Himalayan wolf. Most D values are positive, suggesting that IW01 shares more derived alleles with other gray wolves (H2) than with the Himalayan wolf. We note that D(IW01, H2; Himalayan wolf, Andean fox) calculated when H2 includes North American wolves showed significant negative values. However, this should not be taken to signify admixture between IW01 and the Himalayan wolf. Rather, this is likely due to North American wolves sharing ancestry from more divergent species like the coyote ( vonHoldt et al. 2016 ; Sinding et al. 2018 ). ( C ) D -statistics plot showing the amount of allele sharing between dhole and IW01 and other gray wolves. ( D ) D -statistics plot showing allele sharing between the African wolf and IW01. ( E ) D -statistics plot depicting allele sharing between the Ethiopian wolf and IW01. This test also finds evidence of admixture between the African wolf and the Ethiopian wolf. ( F ) D -statistics plot showing allele sharing between the African wild dog and IW01. ( G ) D -statistics plot showing the amount of allele sharing between the golden jackal and the ancestral clade of gray wolves. D -statistics were significantly positive when the domestic dog, East Asian wolves, and European wolves were in position H2, but became insignificant when North American wolves and Tibetan wolves were in the H2 position. These results suggest past gene flow between the Eurasian golden jackal and the ancestor of domestic dog and Eurasian gray wolves, supporting previous studies ( Gopalakrishnan et al. 2018 ; Chavez et al. 2019 ). This also suggests that the Eurasian golden jackal has no or less gene flow with IW01 compared with domestic dogs and Eurasian gray wolves, despite the overlapping distributions of the former two species.

Using D -statistics, we did not find any evidence of admixture between IW01 and the Asiatic dhole when the domestic dog, East Asian wolf, Croatian wolf, Spanish wolf, or North American wolf are in position H2 ( fig. 5C ). However, we detected significant gene flow between IW01 and Kenyan African wolf ( fig. 5D ), Ethiopian wolf ( fig. 5E ), and African wild dog ( fig. 5F ). This is consistent with the recent radiation of including Lycaon , Cuon , and Canis , which has been estimated at ∼1.72 Ma in models that include the possibility of gene flow among lineages ( Chavez et al. 2019 ). Such gene flow may have been mediated through an unknown, earlier diverging donor species ( Gopalakrishnan et al. 2018 ). We also found evidence of gene flow between IW01 and each of three recently reported northwestern African wolves (from Senegal, Morocco, and Algeria) ( Liu et al. 2018 ), although the proportion of shared ancestry varied among individuals sampled ( supplementary tables S2 and S3 , Supplementary Material online ). Moreover, past gene flow has been reported in other geographically distant canid species ( fig. 5E and F ; supplementary table S2 , Supplementary Material online ), such as between Ethiopian wolf and Eurasian gray wolves and golden jackals, and between Ethiopian wolf and lineage ancestral to northwestern and eastern African wolves ( Gopalakrishnan et al. 2018 ).

We constructed admixture graph models to further investigate admixture among IW01 and African canids. Because this analysis requires a specified graph topology for testing, it is challenging to implement this test with a large number of populations or species with histories involving complex admixture events. Following previous canid genomic studies ( Sinding et al. 2018 ), we simplified admixture graphs by beginning with a model that includes European wolf, Tibetan wolf, IW01, and Andean fox (as an out-group), and then adding African canid species to fit all possible f4-statistics ( Lipson 2020 ). In agreement with our D -statistics results, the fitted admixture graphs (no f4 outliers) indicated that IW01 had gene flow with the African wolf, Ethiopian wolf, and African wild dog ( fig. 6 and supplementary fig. S11 , Supplementary Material online ). We found more gene flow between IW01 and the African wolf and Ethiopian wolf than between IW01 and with the African wild dog. Because the admixture history among gray wolf and canid species is complex, these fitted graphs reflect a parsimonious summary of our data and may not reflect the complete admixture history for these lineages.

Admixture graph modeling the gene flow between IW01 and African canids. ( A ) Fitted model (no f4 outliers) showing the genetic makeup of the Ethiopian wolf. ( B ) Fitted model (no f4 outliers) showing that the African wild dog carries 1% ancestry from IW01, which supports the D -statistics analysis ( fig. 5F ). Admixture between IW01 and African wild dogs is also detected when African wolf or golden jackal are included ( supplementary fig. S11 , Supplementary Material online ). ( C ) Fitted model (no f4 outliers) showing admixture between IW01 and the African wolf and the Ethiopian wolf. This result is also supported by an alternative fitted model ( supplementary fig. S11 , Supplementary Material online ). Both support a previous conclusion that African wolves carry admixed ancestries ( Gopalakrishnan et al. 2018 ). Dashed lines indicate inferred admixture events and admixture proportions are reported beside the dashed lines. Because this analysis required genotype calls, we included only genomes with sequencing coverage >10-fold. As genome sequence coverage for the Himalayan wolf is 7-fold, we used the Tibetan wolf to represent the highland gray wolf for admixture graph construction.

Lastly, we applied PCAdmix ( Brisbin et al. 2012 ) to perform local ancestry inference for IW01, with African canids, Eurasian gray wolf, and domestic dog as source populations. Although this analysis has low power and resolution to infer small tracts reflecting anciently admixed ancestry, IW01 shared some potentially admixed tracts (posterior probabilities > 0.9) with each of the three African canid species, the African wolf, Ethiopian wolf, and African wild dog ( supplementary table S4 , Supplementary Material online ). The identified admixed tracts were short and few in number, indicative of ancient gene flow. IW01 shared the largest number and length of admixed blocks with the African wolf, followed by the Ethiopian wolf.

The above analyses support pervasive ancient gene flow between IW01 and African canids. Compared with the two wolves SRS661487 (India) and SRS661488 (Iran), IW01 shares less ancestry with African wolves and a comparable amount of ancestry with the Ethiopian wolf ( fig. 5D and E ). A possible explanation for this pattern is that gene flow between IW01 and African canids was mediated through Middle Eastern wolves. However, this model does not explain the shared ancestry between IW01 and African wild dogs ( figs. 5F and 6 ; supplementary fig. S11 , Supplementary Material online ).

Further, our results show that Iranian wolf genomes shared a large excess of genetic ancestry with IW01 ( fig. 4 and supplementary fig. S7 , Supplementary Material online ). This suggests that the lineage leading to IW01 may have been more widely distributed in the past, from the Indian subcontinent to the Arabian Peninsula ( Sharma et al. 2004 ), and overlapping in range and potentially hybridizing with Middle-Eastern gray wolves and African canid lineages in the past.

Our results support the hypothesis that the Sinai Peninsula and Southwest Levant are important hubs of canid evolution, where pervasive interspecific hybridization has been detected among gray wolves, African wolves, and Eurasian golden jackals ( Koepfli et al. 2015 ; Gopalakrishnan et al. 2018 ). Assemblages of Early Pleistocene mammalian fossils from the Pinjor Formation in India, including remains of at least two species of Canis , suggest paleobiogeographic linkages with African and Middle Eastern faunas ( Patnaik and Nanda 2010 ). The connections between the faunas of India and Africa are also supported by the vertebrate fossil records from Late Pleistocene deposits in Gujurat, which includes a Canis sp. that is larger and more robust than the present-day Indian wolf ( Costa 2017 ), and from other taxa, as Asiatic lions in India have experienced extensive gene flow with African lions ( de Manuel et al. 2020 ), and African leopards are known to have admixed with leopards from the Middle East (Palestine region) and Central Asia (Afghanistan) ( Paijmans et al. 2021 ). Our model is, of course, speculative, and additional data from both fossils and living animals will be helpful to understand the history of admixture among these canid lineages.

Intriguingly, D -statistics tests of allele sharing between IW01 and African canids revealed the Himalayan wolf as distinct from other wolf lineages ( fig. 5C–G ), leading us to hypothesize that the Himalayan wolf was less admixed. To test this, we computed D -statistics with the Himalayan wolf as H1, domestic dog and gray wolves as H2, and African wolf, Ethiopian wolf, African wild dog, or golden jackal as H3. All analyses resulted in significant positive D values ( Z > 3), suggesting that the domestic dog and gray wolves also shared excess derived alleles with African canids and golden jackals ( supplementary fig. S12 , Supplementary Material online ). This analysis provides support for the idea that present-day wolves and domestic dogs have admixed ancestries ( Fan et al. 2016 ; Frantz et al. 2016 ) and that the Himalayan wolf is relatively isolated (less or unadmixed with other canids) compared with other wolves ( fig. 5B ).

Demographic History and Divergence Time for the IW01 and Other Gray Wolves

To place the evolution of IW01 in a chronological context along with other gray wolves, we calculated relative cross-coalescence rates (CCR, the ratio between the cross- and the within-coalescence rates) for each pair of populations using the multiple sequentially Markovian coalescent (MSMC) model ( Schiffels and Durbin 2014 ), including genomes with a sequence coverage >20-fold. Using 50% CCR as a cutoff to estimate divergence time, these analyses suggest that IW01 diverged from domestic dogs and Chinese, Tibetan, European, and American wolves ∼110 ka ( fig. 7A ). This divergence date is much older than the previous estimates of ∼68–81 ka for divergence between the Tibetan wolf and domestic dog/East Asian gray wolves ( Wang et al. 2020 ) and supports our phylogenetic result that IW01 is basal to the Tibetan/Himalayan wolf and domestic dog + gray wolf clade ( figs. 2C and 4 ; supplementary figs. S5–S7 , Supplementary Material online ). This analysis also showed that IW01 split from SRS661487 (India) and SRS661488 (Iran) more recently, around 86 and 81 ka, respectively ( fig. 7A ), although these estimates will be impacted by the admixed ancestry of these three individuals ( fig. 4 ). We estimated that SRS661487 diverged from the domestic dog and Chinese wolf ∼68–85 ka and from European wolf ∼17 ka ( supplementary fig. S13 , Supplementary Material online ), and that SRS661487 separated from the Iranian wolf (SRS661488) ∼5.5 ka, consistent with these two samples clustering together in the phylogeny ( fig. 4 and supplementary figs. S6 and S7 , Supplementary Material online ). Therefore, SRS661487 likely represents a gray wolf that recently descended from Middle Eastern and European wolf lineages that then admixed with the IW01 lineage, whereas IW01 is a distinct and deeply diverged lineage.

Inferences of the time of divergence and demographic history of Indian wolf IW01 and other gray wolves. ( A ) MSMC estimation of splitting time for Indian wolves (IW01) from the domestic dog and other representative gray wolves. Gray-dashed vertical line indicates the estimated split time at ∼110 ka. ( B ) Results of PSMC analysis showing the demographic trajectories of seven representative gray wolves. For each sample, we performed 100 bootstrap replicates.

We used the pairwise sequentially Markovian coalescent (PSMC) model ( Li and Durbin 2011 ) to reconstruct historical patterns of effective population size over time for IW01 and other gray wolves with sequencing coverage ≥20-fold ( fig. 7B ). Generally, all gray wolves shared similar demographic trajectories up to ∼150 ka. Thereafter, IW01 and the Tibetan wolf diverged first around 110 ka and then experienced continuous contractions in population size. Generally consistent with MSMC and PSMC, we used Coal-HMM ( Mailund et al. 2011 ) and estimated that IW01 diverged from dogs and other gray wolves ∼130–140 ka ( supplementary fig. S14 , Supplementary Material online ). In contrast, SRS661487 shared a similar demographic trajectory with European, Iranian, and North American wolves whose population size expanded slightly between 100 and 50 ka, which was then followed by contraction ( fig. 7B ). These results corroborate that IW01 and SRS661487 represent two different gray wolf lineages.

To explore the recent history of the Indian wolf population, we examined nucleotide diversity and runs of homozygosity (ROH) for IW01 and compared this with the estimates from other gray wolves. Because such analyses are sensitive to genotyping errors, we focused on genomes with sequencing coverage ≥20-fold. IW01 had a nucleotide diversity of approximately 0.00104 ± 0.00098 (mean±SD), slightly higher than that of the Tibetan wolf, but lower than that estimated for European wolves, SRS661487, the Iranian wolf (SRS661488), the Mongolian wolf, and the North American wolf ( supplementary fig. S15 , Supplementary Material online ). IW01 had 11 blocks of ROH with a length >1 Mb, the longest of which was 1.57 Mb, whereas the Tibetan wolf had 48 blocks of ROH >1 Mb and 5 ROH >2 Mb ( supplementary fig. S16 , Supplementary Material online ). We found that 33% of the IW01 genome and 43% of the Tibetan wolf genome were homozygous, which was higher than that observed in other gray wolves except for the Chinese wolf ( supplementary fig. S16 , Supplementary Material online ). These results are consistent with the long-term small effective population sizes inferred in our PSMC analysis and with earlier ecological studies ( Aggarwal et al. 2003 , 2007 ; Sharma et al. 2004 ), and also suggest recent inbreeding.

Our results suggest that IW01 represents an evolutionarily distinct gray wolf lineage living in the semi-arid lowland region of the Indian subcontinent that diverged from other gray wolf populations ∼110 ka. IW01 shares ancestry with other gray wolves (SRS661487 and SRS661488) that fall within the geographic range described for C. l. pallipes . Consistent with our previous study, gray wolves from the Trans-Himalayan mountain range and Tibetan Plateau also carry deeply diverged ancestries ( Wang et al. 2020 ). The persistence of these ancient and diverged lineages in the Indian subcontinent may be due in part to the region’s unique topography and paleoenvironmental history. Similar patterns of locally divergent lineages have been observed in Trans-Himalayan red pandas ( Hu et al. 2020 ) and Chinese mountain cats ( Yu et al. 2021 ). Together, these findings point to the importance of the Indian subcontinent and Trans-Himalayan region as refugia during the Pleistocene ( Sharma et al. 2004 ; Costa 2017 ) that enabled the persistence of divergent lineages.

During the Pleistocene ice ages, the Indian subcontinent was dry and cold, and much of the Himalayan and Trans-Himalayan regions and southern Tibet ( Owen et al. 2002 ) were covered by ice. Regional unglaciated refugia persisted, however, within which small populations of gray wolves may have become isolated, leading to the evolution of distinct lineages ( Blinkhorn and Petraglia 2017 ). Our estimate of the timing of divergence between IW01 and other gray wolves coincides roughly with the end of the Last Interglacial period (Eemian), when warmer, wetter conditions occurred in the northern latitudes of Eurasia, whereas the Indian subcontinent and neighboring lower latitude regions experienced a cooler, drier climate ( Pedersen et al. 2017 ). These paleoclimatic differences, combined with geographic isolation, may have facilitated ecological and genetic divergence of the Indian wolf lineage.

Despite the relative isolation and small population size of Indian wolves today, we find that the IW01 lineage harbors evidence of a mosaic of past gene flow with the African wolf, Ethiopian wolf, African wild dog, and western Asian gray wolves. We also find that the Himalayan wolf shares significantly less admixed ancestry with modern-day African canids ( supplementary fig. S12 , Supplementary Material online ), which is consistent with its isolation and adaptation to the high-altitude arid environments of the Himalayan and Tibetan plateaus. It is possible that the distribution of gray wolves and African canids overlapped in the past, possibly in the Sinai Peninsula or Southwest Levant where several canid species are hypothesized to have hybridized ( Gopalakrishnan et al. 2018 ).

Our results present a scenario of pervasive gene flow between gray wolves and other canid species, adding to the growing evidence of the important role of interspecific hybridization in the evolution of canid species and populations specifically and the role of network-linked and reticulated evolution of species more generally. Although our study is based on a single sample of precisely known provenance, our analyses of IW01 bridge a data gap for gray wolves and provide an important resource for future studies. Additional sampling of Indian wolves from other regions of peninsular India, of other wolves from across the range of C. l. pallipes , and perhaps from ancient samples will be necessary to inform the conservation of this threatened and elusive gray wolf subspecies.

IW01: Origins and Sampling

The Indian wolf (IW01; fig. 1 and supplementary fig. S1 , Supplementary Material online ) sequenced for this study was captured in 1995 inside Velavadar Blackbuck National Park in Gujarat state, India (latitude = 22.0438°N, longitude = 72.0202°E), for a radio-telemetry-based ecological study of the species. The wolf was captured using a rubberized-jaw McBride foot-hold trap (Minnesota) and anesthetized using Telozol ( Kreeger et al. 1989 ). Whole blood was drawn from the brachial vein for DNA profiling and disease study. Permissions for capture and collaring were obtained from the Ministry of Environment and Forest, government of India, and from the Chief Wildlife Warden, Gujarat state. The whole blood sample was stored in alcohol at −20 °C until genomic DNA was extracted.

Genome Sequencing and Variant Calling

Four paired-end DNA sequencing libraries were prepared for IW01, resulting in a total of 311,789,040 paired-end 150-bp reads (corresponding to 93.5 Gb) generated by the M/s Xcelris Labs Ltd. Ahmedabad, Gujarat, India, using the Illumina HiSeq 2500 platform. We downloaded published genomic sequences from 30 other canid samples from the NCBI SRA (accession IDs are available in supplementary table S1 , Supplementary Material online ) including domestic dogs, African wild dog ( Lycaon pictus ), dhole (Cuon alpinus), coyote ( Canis latrans ), Eurasian golden jackal ( Canis aureus ), African wolf ( Canis lupaster ), Ethiopian wolf ( Canis simensis ), and Andean fox ( Lycalopex culpaeus ). We used Btrim ( Kong 2011 ) to remove low-quality bases. Because a highly contiguous chromosome-level reference genome assembly is not yet available for the gray wolf, we aligned the remaining reads to the domestic dog CanFam3.1 reference genome ( Lindblad-Toh et al. 2005 ) using the BWA-MEM algorithm ( Li 2014 ) with the settings “-t 4 –M.” We processed the bam alignment by coordinate sorting, marking duplicated reads, performed local realignment, and recalibrated base quality scores using the Picard (version 1.56; http://broadinstitute.github.io/picard/ , last accessed January 27, 2022) and GATK (version 3.7.0) packages ( McKenna et al. 2010 ). We called SNPs for all samples together using the UnifiedGenotyper function in GATK. To increase the reliability of the data, SNPs were further filtered as previously described ( Wang et al. 2020 ) using the VariantFiltration command in GATK with parameters: “QUAL < 40.0 MQ < 25.0 MQ0 ≥ 4 && ((MQ0/(1.0×DP)) > 0.1) cluster 3 -window 10.” Index, depth, and mapping statistics were computed using available tools in SAMtools v1.3.1 ( Li et al. 2009 ).

Mitochondrial Assembly and Phylogenetic Analysis

Because no complete mitochondrial genome is available in GenBank for the Indian wolf, we performed de novo assembly of the mitochondrial genome for IW01, SRS661487 (India), and SRS661488 (Iran) using NOVOPlasty v2.7.2 ( Dierckxsens et al. 2017 ) with a k-mer size of 31 based on whole-genome sequencing data. The domestic dog mitochondrial genome (GenBank accession: NC_002008.4 ) was used as a seed/reference sequence. We downloaded mitochondrial genomes for coyote, African dog, dhole, African wolf, and other gray wolves and domestic dogs from NCBI (GenBank accessions are shown in fig. 1C ) and included the Tibetan and Himalayan wolf sequences from a previous study ( Wang et al. 2020 ). A total of 39 mitogenomes were analyzed in this study. These sequences were aligned using MUSCLE v3.8.31 ( Edgar 2004 ) and the alignments were checked manually. After removing poorly aligned and control regions, an alignment file with a length of 15,462 bp was used for phylogenetic analysis. A maximum-likelihood tree was reconstructed using RAxML v8.2.12 ( Stamatakis 2014 ) with the GTR+G model of DNA substitution, and 1,000 bootstraps were run to assess node support.

We also downloaded previously reported mitochondrial cytochrome b and 16S rRNA sequences for Indian wolf, domestic dog, and other gray wolves from GenBank (accessions are shown in supplementary fig. S2 , Supplementary Material online ) and aligned and analyzed these data (554 bp for 16S rRNA and 332 bp for cytochrome b ) for phylogenetic analysis using the same methods described above.

Nuclear Phylogeny Construction

We constructed phylogenetic trees using nuclear genome sequences to explore the relationship of IW01 with other gray wolves and canid species. For each canid taxon, only one sample was used. Given that domestic dogs constitute a monophyletic clade ( Fan et al. 2016 ; Wang et al. 2016 ), we chose the high-coverage Dingo genome (31.3-fold; SRR7120191) to represent the domestic dog lineage. As a result, a total of 19 samples were used to construct phylogenetic trees ( fig. 2A and supplementary fig. S3 , Supplementary Material online ). We generated a consensus genome for each sample using ANGSD v0.931 ( Korneliussen et al. 2014 ) (-doFasta 1). Reads with a minimum mapping quality lower than 25 were discarded (-minMapQ 25). For genomes with an average sequencing depth of over or less than 10-fold, the minimum depth for each base was set to 4-fold (-setMinDepth 4) or to 3-fold (-setMinDepth 3), respectively. Additional filter parameters implemented were: -doCounts 1 -uniqueOnly 1 -nThreads 2. We selected 5,000 random regions with a length of 20 kb from across the genome of the domestic dog reference assembly and the other 18 canid taxa using the “random” function in BEDTools v2.28.0 ( Quinlan and Hall 2010 ) (-l 20,000 -n 5,000). Sequences for each region were retrieved using the “faidx” function of SAMtools v1.3.1 ( Li et al. 2009 ). For each region, a maximum-likelihood tree was constructed by RAxML v8.2.12 ( Stamatakis 2014 ) with 100 bootstrap replicates using the command: raxmlHPC-PTHREADS-SSE3 -x 12,345 -k -# 100 -p 321 -m GTRGAMMAI -T 4 -s myseq.fas -f a -n myseq.ml.tre. The 5,000 gene trees were then concatenated and used as input for ASTRAL-III v5.7.5 ( Zhang et al. 2018 ) to generate a species tree, using default parameters. We used DiscoVista ( Sayyari et al. 2018 ) to analyze the discordance frequencies between the ASTRAL species tree and the 5,000 gene trees.

We retrieved and concatenated genotypes for 31 samples (in VCF format) within regions containing the signal of diverged origin in high-altitude wolves (Himalayan and Tibetan wolves) ( Wang et al. 2020 ), and converted into .fas format files. A neighbor-joining tree was constructed using the mega-cc tool ( Kumar et al. 2012 ) in MEGA7 ( Kumar et al. 2016 ) and nodal support was evaluated with 1,000 bootstrap replicates. Lastly, following ( Wang et al. 2020 ), we split four high-coverage genomes from the Chinese wolf, IW01, Tibetan wolf, and dhole into 250-, 500-, and 1,000-kb windows across autosomes and constructed phylogenies for each window using TreeMix v1.13 ( Pickrell and Pritchard 2012 ) with dhole as the outgroup. The frequency of each topology was calculated using APE v5.5 ( Popescu et al. 2012 ).

PSMC Analysis

We used the PSMC model to infer historical demographic trajectories for the sampled gray wolves ( Li and Durbin 2011 ). We only analyzed genomes with coverage >20-fold to ensure the accurate calling of heterozygotes ( Nadachowska-Brzyska et al. 2016 ), although some studies used low coverage genomes with false negative rate corrections ( Kim et al. 2014 ; Hawkins et al. 2018 ). A diploid consensus sequence for each individual was generated using the “mpileup” command of the SAMtools package (v1.3.1) ( Li et al. 2009 ) with the option “-C50.” Variants with less than about 1/3 (“-d” option) or over two times (“-D” option) of average read depth were marked as missing and excluded from consensus sequence assignment. Sequences with consensus quality lower than 20 were also filtered out. The program “fq2psmcfa” from the PSMC package was used to convert the consensus sequences into 100-bp bin-input files for PSMC. We ran PSMC with parameters “-N25 -t15 -r5 -p 4 + 25×2 + 4 + 6.” A total of 100 bootstraps were analyzed for each sample. These PSMC estimates are scaled using a generation time ( g ) of 3 years and a mutation rate (µ) of 4e − 9 substitutions per site per generation as used previously ( Skoglund et al. 2015 ). This mutation rate was comparable to a recent estimation based on pedigree analysis ( Koch et al. 2019 ).

MSMC and Coal-HMM Inference of Splitting Time

We used the multiple sequential Markovian coalescent (MSMC2) model to infer the divergence time for the domestic dog and gray wolf population pairs ( Schiffels and Durbin 2014 ). Genotypes for all dogs and wolves were phased together using Beagle V.4.1 ( Browning and Browning 2016 ). The MSMC input files comprising four haplotypes (two individuals) were generated as suggested by the authors using available tools from the MSMC-tool package ( https://github.com/stschiff/msmc-tools , last accessed January 27, 2022). We ran MSMC for each pair of genomes using default settings and the time when the relative cross-coalescent rate was dropped to 50% as an approximate estimate of the splitting time ( Malaspinas et al. 2016 ). For each calculation, four haplotypes were analyzed, and estimations were scaled using a generation time ( g ) of 3 years and a mutation rate (µ) of 4e −9 substitutions per site per generation ( Skoglund et al. 2015 ). Similar to the PSMC analysis, we restricted this analysis to genomes with coverage >20-fold.

We also used Coal-HMM ( Mailund et al. 2011 ), a coalescent hidden Markov model-based approach, to measure the divergence time for the Indian wolf (IW01) and dog and other wolves. We performed estimation for each population pair using 1-Mb nonoverlapping sliding window segments across each chromosome. We filtered out windows with over 10% missing rate for such analysis. We also removed results for each segment where: 1) the recombination rate was lower 0.1 or over 10 cM/Mb, 2) the ancestral effective population size below 1,000 or above 1,000,000, and 3) the split time was below 1,000 years or above 1,000,000 years.

Nuclear Diversity and ROH Analysis

Nucleotide diversity (π) ( Nei and Li 1979 ) was calculated for each sample across the autosomes using VCFtools v0.1.13 ( Danecek et al. 2011 ) in 50-kb sliding windows with a step of 25 kb. ROH was calculated for each sample across the autosomes using the “roh” function in the BCFtools v1.4-7-g41827a3 ( Narasimhan et al. 2016 ) with default parameters.

TreeMix, ABBA-BABA, and AdmixtureGraph Analyses

To explore the phylogenetic relationships and admixture among gray wolves and other canid species, we also used TreeMix v1.13 ( Pickrell and Pritchard 2012 ) to construct maximum-likelihood tree graphs by allowing gene flow. TreeMix analysis was run for all variants located on autosomes using 1,000 variants per block (-k 1,000) and allowing zero to five migrations, with Andean fox used as the outgroup.

We used the ABBA-BABA test, also known as D -statistics ( Green et al. 2010 ) to detect the amount of allele sharing between gray wolf populations. This analysis is based on the topology (((H1, H2), H3), Outgroup) as shown in figure 5A . D = 0 suggests no gene flow between ingroup (H1 or H2) and H3; D > 0 suggests gene flow between H3 and H2; and D < 0 suggests gene flow between H3 and H1. We used the function “-doAbbababa 1” in ANGSD v0.931 ( Korneliussen et al. 2014 ) to perform this analysis with the additional settings “-doCounts 1 -minMapQ 25 -minQ 25 -uniqueOnly 1 -nThreads 6.”

To assess the genetic makeup and relationships among IW01, gray wolves, and three African canid species (African wolf, Ethiopian wolf, and African wild dog), we constructed admixture graph models using the qpGraph tool from AdmixTools package ( Patterson et al. 2012 ), the admixturegraph R package ( Leppala et al. 2017 ), and qpBrute ( Ni Leathlobhair et al. 2018 ; Liu et al. 2019 ). Because this analysis requires high-confidence genotype calls, we chose one sample with genome sequencing coverage over 10-fold from each population or species for constructing admixture graphs. To resolve the relationship between IW01, Himalayan/Tibetan wolves, and Eurasian gray wolves, we tested all possible graph models to fit all possible f4-statistics. The phylogenetic tree based on “ghost” admixed sequences and mitochondrial genomes from Himalayan or Tibetan wolves showed that the “ghost” lineage was basal to IW01. Therefore, we considered graphs in which Himalayan or Tibetan wolves were modeled as a product of admixture with one source from the lineage basal to IW01. To investigate the admixture between IW01 and African canids, we constructed admixture models starting with three populations (IW01, European wolf, and Himalayan or Tibetan wolf) and the fitted graph was then used as the base model in which we successively added each of the three African canid species.

Local Ancestry Inference

To identify potential admixed tracts along each chromosome in IW01, we performed local ancestry inference using PCAdmix ( Brisbin et al. 2012 ). We used phased genotypes as mentioned above as input, with IW01 designated as an admixed population and each of the African canid species, domestic dog, and Eurasian gray wolves as source populations. We performed two independent runs using 20 (default by the software) and 40 SNPs per window (“-w” parameter), respectively. The identified regions with posterior probabilities >0.9 were considered as potentially admixed.

Supplementary data are available at Genome Biology and Evolution online.

We thank the research communities for making their genomic data public, which makes this study possible. We also thank Y. Shah for the Indian wolf photos, and Robert Wayne for helpful discussions. This project was supported in part by DST-INSPIRE Faculty funding awarded to M.T. (04/2016/002246).

Author Contributions

M.T., Y.J., and B.S. conceived the idea and designed the research project. B.S., Y.J., K.-P.K., and R.E. G. supervised the analysis. M.-S.W., M.T., and S.W. performed the analysis with inputs from H.-M. C. and S.-S. D. Y.J., M.T., and Y.S. provided and coordinated genome sequencing of wolf sample IW01 within India. M.-S.W., M.T., and B.S. drafted the manuscript. B.S., Y.J., K.-P. K., M.-S.W., and M.T. revised the manuscript with input from all authors. Analysis of the Indian wolf sampled from western Gujarat, including sequencing and data analysis, was undertaken in India. Z.-X.L. submitted sequenced genome to NCBI. All authors read and improved the manuscript.

Data Availability

The genome sequencing raw reads were deposited in the NCBI-SRA database, under Bio-Project accession: PRJNA714797.

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• DOI: 10.3106/ms2021-0029
• Corpus ID: 244026559

Factors Influencing Habitat-Use of Indian Grey Wolf in the Semiarid Landscape of Western India

• P. Mahajan , D. Khandal , Kapil Chandrawal
• Published in Mammal Study 10 November 2021
• Environmental Science

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Preliminary status of the indian grey wolf in kailadevi wildlife sanctuary, rajasthan, india, spatial determinants of livestock depredation and human attitude toward wolves in kailadevi wildlife sanctuary, rajasthan, india, patterns of livestock depredation by carnivores: leopard panthera pardus (linnaeus, 1758) and grey wolf canis lupus (linnaeus, 1758) in and around mahuadanr wolf sanctuary, jharkhand, india, 80 references, den shifting by wolves in semi‐wild landscapes in the deccan plateau, maharashtra, india, refuge as major habitat driver for wolf presence in human‐modified landscapes, identifying suitable habitat and corridors for indian grey wolf (canis lupus pallipes) in chotta nagpur plateau and lower gangetic planes: a species with differential management needs, large herbivore populations outside protected areas in the human-dominated western ghats, india, human disturbance affects habitat use and behaviour of asiatic leopard panthera pardus in kaeng krachan national park, thailand, on a dhole trail: examining ecological and anthropogenic correlates of dhole habitat occupancy in the western ghats of india.

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Canid Biology and Conservation, 2021

The Indian grey wolf (Canis lupus pallipes) is the apex predator of the semi-arid landscapes of India. They have large home ranges and mostly thrive outside the protected areas, feeding on livestock to fulfil dietary needs, thus bringing them into direct conflict with humans, making it imperative to identify and conserve wolf-occupied areas. We used questionnaire surveys and field methods to estimate the number and status of wolves in Kailadevi Wildlife Sanctuary, Rajasthan. We estimated 19-45 wolves occurring at a density of 0.02-0.06 wolves/km 2 in 672.82 km 2 of Kailadevi Wildlife Sanctuary. The maximum number was estimated from the Nainyaki range. The presence of wolves was significantly positively related to the presence of sheep and goats. Due to low availability of natural prey in the study area, wolves depend on livestock, causing high economic loss to the resident people. Our study suggests that if strict conservation measures are taken, Kailadevi Wildlife Sanctuary holds the potential to act as a source population for the conservation of the Indian grey wolf in the larger landscape surrounding the study area. However, due to high anthropogenic pressure, the landscape is severely degraded and requires immediate attention to restore the existing scrubland for denning and rendezvous sites. Effective compensation schemes and awareness through outreach and education are required to reduce negative attitudes among the resident people and to prevent wolf persecution. Future research should make use of modern radio-telemetry techniques to better understand the ecology of the wolves in this landscape.

Biological Conservation, 2004

The Indian wolf (Canis lupus pallipes) is widely distributed in India, but is listed as data deficient in Gujarat. No proper study has been conducted on their current status, distribution, or threat assessment in the state. As per the current distribution, they are recorded from Northern and Central Gujarat. We present recent records of Indian wolves from Bharuch and Surat districts. We assume it to be southernmost distribution of the Indian wolf in Gujarat.

Abstract The Indian gray wolf Canis lupus pallipes is the major large carnivore in the plains of Karnataka, India. We carried out a study on its distribution and status from November 2001 to July 2004. We estimated 555 wolves occupying about 123 330 km 2 of the state. In the past 40 years, wolves have disappeared from the southern plateau from an area of about 31 801 km 2. Their distribution is now largely restricted to the north-eastern dry plains.

The Indian grey wolf is a crucial apex predator in India's semi-arid region, but their wide range and elusive behavior make population estimation challenging. Accurate population estimation is essential for effective management and conservation efforts. Our study focused on the bhal region of Gujarat and used local community information validated by ground surveys. Our estimates suggest a population of 64-88 wolves with a density of 0.021-0.029 wolf/km 2. However, despite the region's continued distribution, it faces numerous threats. To protect the Indian wolf, compensation schemes, community participation, and protection of traditional breeding areas are necessary.

Indian Forester, 2019

Thar desert is very diverse and have distinct geographical distribution, which is unique in terms of harboring different type of soil composition, land use, climatic variation, faunal and floral diversity. Most of human landscape areas are representative of rich biodiversity, with various micro ecosystems such as fellow land, agro-ecosystems, rocky ecosystem, open scrubs, and sacred grooves (Oran, Gauchers). Study area (26°31'06.1"N; 73°05'54.7"E) is located 30 km. north of Jodhpur city. The Indian Gray wolf population is surviving very well in and around Bhawad village. In the present study, the status of Indian Gray wolf in the human landscape, its ecology and behavioral activities were recorded. This study indicates that human landscape, human subsidy and traditional sacred grooves are playing very important role in the conservation and management of the Indian Gray wolf population as well as other wildlife in and around habitation.

Frontiers in Ecology and Evolution, 2022

An understanding of the distribution range and status of a species is paramount for its conservation. We used photo captures from 26,838 camera traps deployed over 121,337 km2 along with data from radio-telemetry, published, and authenticated wolf sightings to infer wolf locations. A total of 3,324 presence locations were obtained and after accounting for spatial redundancy 574 locations were used for modeling in maximum entropy framework (MaxEnt) with ecologically relevant covariates to infer potentially occupied habitats. Relationships of wolf occurrence with eco-geographical variables were interpreted based on response curves. Wolves avoided dense wet forests, human disturbances beyond a threshold, arid deserts, and areas with high top-carnivore density, but occurred in semi-arid scrub, grassland, open forests systems with moderate winter temperatures. The potential habitat that can support wolf occupancy was 364,425 km2 with the largest wolf habitat available in western India (S...

PLOS ONE, 2019

e-planet, 2019

The Indian grey wolf (Canis lupus pallipes) is a rare and lesser-known top predator in India. A rapid camera trapping survey was conducted to assess the large carnivores and their preys in the Sundargarh forest division, Odisha, India. Two individuals of Indian grey wolf were recorded during the survey offering the first photographic evidence of the Indian grey wolf outside protected areas of Odisha. This record increases knowledge on the distribution of the species. More extensive surveys are needed to understand the distribution and population dynamics of Indian grey wolf in the area. We provide photographic evidence of Indian grey wolves and highlight the importance of Odisha forest for species conservation.

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Global Ecology and Conservation, 2021

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