Introduction

Study Area & Methodology

Result & Discussion

Conclusion

References

Study Area

The Western Ghats (WG), also known as "Sahyadri" is a region of high biodiversity along the west coast of India is about 45-65 million years old. It originates from the south of Tapti River near Gujarat and Maharashtra border, extends up to Kanyakumari, at the southernmost tip of the Indian Peninsula. WG covers the parts of Gujarat, Maharashtra, Goa, Karnataka, Kerala, and Tamil Nadu with an area of 1, 64000 sq. km. hills of the Northern Western Ghats (NWG) are lower, with an average elevation of 1, 220 m, than hills of Southern Western Ghats (SWG). Palghat Gap which lies at the Tamil Nadu/Kerala border between Nilgiri Hills and Annamalai Hills, is the only which interrupts the massive mountain chain of WG's. Nilgiri Hills ("Blue Mountains") in Southern WG's is the meeting point of the Western Ghats with the Eastern Ghats.
WG constitutes a watershed of perennial rivers, which drain 40% of the Indian sub-continent, and provide hydrological services that sustain food security. The landscape of northern WG has drier climatic conditions and the evergreen forest part of WG's is less disturbed, and therefore, many original Gondwana relics, Himalayan relics and other trans migrants are reported from these forest areas.
WG's exhibits a high degree of endemism. The region is one of the eight "Hottest Biodiversity Hotspots". World Wide Fund for Nature (WWF) designated 200 ecoregions in which SWG's moist forests and the rivers and the streams fall under Critically Endangered Category. Similarly, WG is identified as one of the important areas of freshwater biodiversity by World Conservation Monitoring Centre (WCMC).
Floral and Faunal Diversity of WG
The flora of WG comprises about 12,000 species ranging from unicellular cyanobacteria to angiosperms. In this spectrum, the flowering plants constitute about 27% of Indian flora with 4500+ species, of which about 1,500 species are endemic (Gadgil et al., 2011) . Plants such as Aristolochia indica, Arundinella metzii, Canthium parviflorum, Smithia hirsuta, Flacourtia Montana, Geissaspis cristata, Crotalaria lutescens. Rhynchospora wightiana, Trees such as Artocarpus heterophyllus, Artocarpus hirsute, Caryota urens, Garcinia indica, Holigarna arnotiana, Hopea ponga, Hydnocarpus laurifolia, Ixora brachiata, Lagerstroemia microcarapa, Litsea laevigata, Mammea suriga, Memecylon talbotianum, Myristica malabarica, Polyalthia fragrans, Vateria indica etc., are endemic to the Western Ghats. The WG exhibits rich and diverse fauna, including birds, fishes, reptiles, mammals, and amphibians among vertebrates. It was reported that around 500 species of endemic fishes, amphibians, reptiles, birds, and mammals exist in WG (Gadgil et al., 2011). Among mammals, Chiroptera is the largest order (50 species). Largest bird diversity of WG are found in evergreen, and moist deciduous, mid-altitude forests and 16 species are reported to be endemic to this region. Among Amphibian species, Rhachophoridae is the most diverse family and is also known as "the old world tree frogs", and 159 species are endemic to this region. Snakes are the majority of reptiles found in WG, and 61% (124 species) are endemic to this region. Out of all the faunal species found in WG, 200 species come under the globally threatened category.
East and West flowing rivers of Western Ghats
The west flowing rivers consists of small river basins lying to the South of the Krishna Basin (except Cauvery Basin), and drains into the Arabian Sea. The basin covered WG parts of Goa, Maharashtra, Kerala, Karnataka, and Tamil Nadu states and is located in the South-West corner of Peninsular India. There are 31 small river basins in these regions: Bhogeshwari, Netravathi, Aghanashini, Periyar, Pamba, Manimala, etc. They originate from the high mountains of WG and have steeply high banks, which rarely cause any flood or overflow.
East flowing rivers consist of many small basins of Peninsular India lying to the South of the Krishna basin (except Cauvery), and drains into the Bay of Bengal. The basin covers areas of Andhra Pradesh, Tamil Nadu, Puducherry, and Karnataka.
Climate
Altitude gradients and distance from the equator have influenced WG's climatic conditions. The average annual temperature is around 15 ^o֯C and the mean temperature ranges from 20 ^oC to 24 ^o֯C, from south to north. Elevations of 1,500 m and above in the north and 2,000 m and above in the south have a more temperate climate. 3,000-4,000 mm. is the average rainfall in this region. Maximum rainfall is in July-August. From Wayanad to the Mahabaleshwar region, annual rainfall exceeds 5000 mm over the entire region. WG regions have low elevation but still, receive high rainfall.
Northern Western Ghats
The spatial extent of Northern Western Ghats (NWG) is 65, 000 sq. km, extending from Gujarat to Goa (Figure 2) accounts for 33.75% of the total extent of Western Ghats (WG). There are five districts covering parts of NWG of Gujarat (NWGG), namely Dangs, Valsad, Surat, Tapi, and Navsari. In NWG of Maharashtra (NWGM) there are 12 districts covering parts of NWGM, namely; Ahmednagar, Dhule, Kolhapur, Nandurbar, Nashik, Thane, Pune, Raigarh, Ratnagiri, Sangli, Satara, and Sindhudurg. NWGM has many ecological characteristics, and rocky plateaus are one of them. NWG of Maharashtra represents biogeographic Zone 5 with two provinces, i.e., 5A (WG Malabar Plains) and 5B (Western Ghats Mountains). There are total 67 sacred groves within the NWG of Maharashtra. NWGM has seven wildlife sanctuaries in an area of 1593.54 sq. km. Sahyadri Tiger Reserve, with an area of 732.52 sq. km, including parts of Koyna and Chandoli WLS, is the only tiger reserve in this region. The corridor between Radhanagari and Chandoli forests sustains 20 tigers. Eight major rivers come out of this region. The total forest area of NWGM is 18,966 sq. km (FSI, 2017), including 67 sacred groves. Four Environmentally Sensitive Zones are there within NWGM. Two districts of Goa are part of NWG (NWGG), i.e., North Goa and South Goa.

Figure 2. Western Ghats (North and South) with district boundaries.
Population Density
Population density is related to persons present over a geographical area. Population density changes represent employment opportunities, educational facilities, industrial development, economic development, social environment, health and recreation, political, social institutes of education, and the exercise of residential preferences (Barakade, 2011) . Population data for NWG was collected from the Census of India, 2011, portal (http://censusindia.gov.in).
District wise population density estimation:
Table 1: Population Density of Northern Western Ghat Districts

Northern Western Ghats	⦁ Gujarat
	Highest Population density (person per sq km): Bansda Taluk Bansda Village (Highest): 2811 Sadad Devi (Lowest): 40
	Highest Population density (person per sq km): Mandvi Taluk Jamankuva Bar Village (Highest): 3228 Rundha Village (Lowest): 0 Lowest Population Density: Umarpada Taluk
	Highest Population density (person per sq km): Vyara Taluk Varjakhan Village (Highest): 925 Barmada Village (Lowest): 0 Lowest Population Density: Songadh Taluk

Districts	Comments
	Highest Population density (person per sq km): Taluk Satpura Village (Highest): 3719 Wawanda and Jamanpada Village (Lowest): 0 Lowest Population Density: Songadh Taluk
	Highest Population density (person per sq km): Kaprada Taluk Nana Pondha Village (Highest): 1065 Piproni Village (Lowest): 51 Lowest Population Density: Dharampur Taluk
Northern Western Ghats	⦁ Maharashtra
Districts	Comments
	Highest Population density (person per sq km): Shirampur Taluk Shrirampur Village (Highest): 4989 Kanhegaon Village (Lowest): 40 Lowest Population Density: Nagar Taluk
	Highest Population density (person per sq km): Sakri Taluk Bodakikhadi Village (Highest): 1353 Raikot Village (Lowest): 13 Lowest Population Density: Dhule Taluk
	Highest Population density (person per sq km): Karvir Taluk Kgadewadi Village (Highest): 4223 Yevati Village (Lowest): 0 Lowest Population Density: Bavda Taluk
	Highest Population density (person per sq km): Nawapur Taluk Bhadwad Village (Highest): 8446 Adalse, Umarvihir Village (Lowest): 0 Lowest Population Density: Nandurbar Taluk
	Nashik Highest Population density (person per sq km): Sinnar Taluk Sangavi Village (Highest): 8384 Bhojapur Village (Lowest): 0 Lowest Population Density: Baglan Taluk
	Highest Population density (person per sq km): Baramati Taluk Kutwalwadi Village (Highest): 5283 Hol Villagec(Lowest): 0 Lowest Population Density: Baramati Taluk
	Highest Population density (person per sq km): Mangaon Taluk Chinchali Village (Highest): 459 Aba Devi Village (Lowest): 35 Lowest Population Density: Roha Taluk
	Highest Population density (person per sq km): Ratnagiri Taluk Ganeshgule Village (Highest): 6876 Dhopatwadi (Lowest): 0 Lowest Population Density: Guhagar Taluk
	Highest Population density (person per sq km): Walwa Taluk Macchindra Gad Village (Highest): 3519 Ahirwadi village (Lowest): 67 Lowest Population Density: Khanapur Taluk
	Highest Population density (person per sq km): Karad Taluk Malkapur Village (Highest): 3572 British Shirwade village (Lowest): 67 Lowest Population Density: Jaoli Taluk
	Highest Population density (person per sq km): Malwan Taluk Nawabag Village (Highest): 1443 Chipi village (Lowest): 69 Lowest Population Density: Dodamarg Taluk
	Highest Population density (person per sq km): Murbad Taluk Deogaon Village (Highest): 8834 Gorakhgad village (Lowest): 53 Lowest Population Density: Shahapur Taluk
Northern Western Ghats	⦁ Goa
	Highest Population density (person per sq km): Bicholim Taluk Velguem Village (Highest): 704 Surla village (Lowest):203 Lowest Population Density: Satari Taluk
	Highest Population density (person per sq km):Quepem Taluk Xelvona Village (Highest): 761 Mangal village (Lowest):26 Lowest Population Density: Sanguem Taluk

Remote sensing data acquisition for LULC Analysis
Remote sensing data (LANDSAT) were downloaded from United States Geological Survey (USGS) archive (http://earthexplorer.usgs.gov/ ). Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) imageries of 30 m resolution were acquired in the pre-monsoon season between 1 January 2018 to 31 March 2018 and cloud-free were chosen for LU analyses.
Preprocessing
The Landsat 8 OLI data obtained for all districts of NWG for the time 2018 in 7 layers (TIFF format), were preprocessed by extracting bands, stacked in GRASS 7.4, and generated false-color composite (FCC) images.
Image Classification
A supervised classifier based on the Gaussian maximum likelihood algorithm (Lillesand et al., 2008; Bharath, and Ramachandra, 2021) is used LU classification of NWG into ten categories - semi-evergreen, moist deciduous, dry deciduous, scrubland, waterbody, horticulture, plantation, cropland, open area, and built-up. Accuracy of the classified image is assessed through producer accuracy, user accuracy, overall accuracy, and kappa statistics.
Forest fragmentation implementation
fragmentation analysis has been done (Ritters et al., 2000; Ramachandra et al., 2016) by computing Pf and Pff values using a 5×5 moving window (Figure 3) with the classified land use data of forest category.
Pf=Proportion of number of forest pixels/Total number of non-forested pixels in the window
Pff=Proportion of number of forest pixel pairs/Total number of adjacent pairs of at least one forest pair

Figure 3. KERNEL (5×5) for computation of Pf and Pff
Table 2. Forest fragmentation categories and their description

S.No.	Fragmentation Categories	Description
1.	Interior Forest	(Pf = 1), All of the pixels surrounding the center pixel are forest
2.	Perforated Forest	(Pf > 0.6 and Pf - Pff > 0), Most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the inside edge of a forest patch
3.	Patch Forest	(Pf < 0.4), Pixel is part of a forest patch on a non-forest background
4.	Edge Forest	(Pf > 0.6 and Pf - Pff < 0), Most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the outside edge of forest
5.	Transitional Forest	(0.4 < Pf < 0.6), About half of the cells in the surrounding area are forested and the center forest pixel may appear to be part of a patch, edge, or perforation depending on the local forest pattern
6.	Undetermined Forest	(Pf>0.6 and Pf=Pf) most of the pixels in the surrounding area are forested, but this center forest pixel could not be classified as being either perforated or edge

Human-Animal Conflict
After performing supervised classification and assessment of forest fragmentation for each district of NWG, human-animal conflict locations were compiled from field and literature. Details of the human-animal conflict were collected from published literature in peer-reviewed journals (sciencedirect.com), online portals (google scholar), previously published literature, newspaper, etc. Details include major conflict animals, economic loss to humans, details of compensation for crop and livestock depredation, and also the villages and forest reserves with the recurring instances of conflicts. Figure 4 elucidates the framework adopted in the study to investigate human-animal conflicts
Figure 4. Framework used