DOI: 10.5061/dryad.cnp5hqcb1
title: Landscape-level habitat connectivity of large mammals in Chitwan Annapurna Landscape, Nepal

description: The populations of many species of large mammals occur in small isolated
 and fragmented habitat patches in the human-dominated landscape.
 Maintenance of habitat connectivity in fragmented landscapes is important
 for maintaining a healthy population of large mammals. This study
 evaluated the landscape patches and their linkages on two carnivores
 (leopard and Himalayan black bear) and seven prey species (northern red
 muntjac, chital, sambar, wild pig, Himalayan goral, rhesus macaque, and
 langur) between Chitwan National Park (CNP) and Annapurna Conservation
 Area (ACA) by using the least-cost path approach and the Linkage Mapper
 tool in ArcGIS. A total of 15 habitat patches (average area 26.67 ± 12.70
 km2) were identified that had more than 50% of the total studied mammals.
 A weak relation among the habitat patches was found for chital and sambar
 (Cost-weighted distance CWD: Euclidean distance EucD &gt;100), showed
 poor connectivity between the habitat patches, while the ratio of CWD and
 EucD was low (i.e., low least-cost path) between the majority of the
 patches for muntjac, wild pig and leopard hence had potential functional
 connectivity along the landscape. Similarly, a low least cost path between
 the habitat patches located in the mid-hills was observed for Himalayan
 goral and Himalayan black bears. Furthermore, the multi-species
 connectivity analysis identified the potential structural connectivity
 between the isolated populations and habitat patches. Therefore, these
 sites need to be considered connectivity hotspots and be prioritized for
 the conservation of large mammals in the landscape.
description: <strong>Occurrence points
 collection</strong> The study blocks were divided
 into four distinct blocks, namely A, B, C, and D considering landscape
 features, the main courses of rivers, and topographical features (Figure
 2C). Block A covers the BCF, part of CNP, and surrounding areas of BCF
 (Kabilas, Jugedi, Kerabari, Chaukidanda, Simaldhap) up to the Mahabharat
 range (it runs closely parallel to the Chure range and separates the Terai
 with the Hill region, i.e., mid-hill) of Chitwan district. Block B covers
 human-dominated mid-hill landscapes such as Devghat, Bandipur, Abu
 Khairani Rural Municipalities, and Vyas Municipality of Tanahun district.
 It follows the Seti and Trishuli River basins along with mid-hills. Block
 C covers the Bhimad Municipality, parts of Rishing Rural Municipality,
 Ghiring Rural Municipality, Magde Rural Municipality and Shuklagandaki
 Municipality of Tanahun District, and Rupa Rural Municipality of Kaski
 District along the Seti River basin. Block D covers Bharatpokhari,
 Nirmalpokhari, Pumdibhumdi, Panchase, Lumle, Ghandruk, Landruk, Deurali,
 and the Australian Camp area. This block harbors four types of forests:
 national forest, community forest, protected forest (Panchase), and
 conservation area (Annapurna). Transects were laid for
 the collection of presence points of selected species (nine species of
 mammals) in the landscape (Figure 1). The presence points of ungulates and
 primates were collected on the basis of direct sighting whereas, the
 presence points of the carnivores were collected on the basis of signs
 left by them (e.g., scats, scrapes, pugmarks, scent spray, etc.). Transect
 size and length were determined based on forest patch size. After
 identifying forest patches using a topo-base map (Esri, 2017), transects
 were overlaid, with patches selected based on diameter; patches less than
 2 km in diameter were excluded. Transects (150 out of 164) were
 systematically laid out according to patch size and accessibility in four
 blocks (31 in A, 35 in B, 38 in C, and 46 in D). Inaccessible areas (14
 transects) due to deep river gorges, steep mountains, and swampy lands
 were excluded. Transect lengths ranged from 1.18 to 7.84 km, with a
 minimum 500 m separation in regular forest patches, varying in scattered
 habitats like Mid Hills (Figure 2C, Table S1). We also collected the
 presence of those mammals opportunistically from other possible sites of
 the study area (e.g., croplands, river banks). These presence coordinates
 were recorded by using the Global Positioning System (GPS- Garmin eTrex
 10). The collected occurrence data were spatially filtered in 30 m by
 using the Spatially Rarify Occurrence Data tools of SDMtoolbox 2.0.0 in
 ArcGIS (Brown, 2020; Kaboodvandpour et al., 2021). The filtered data were
 converted into .CSV format for Maxent modelling (Table 1). The large
 mammals whose presence locations were less than 25, were removed from
 further analysis. <strong>Environmental
 variables</strong> To minimize the risk of
 over-fitting the model and develop the most parsimonious model, the
 environmental variables were selected based on field knowledge, experts’
 suggestions, and an extensive literature review of studied large mammals
 (Dickman & Marker, 2005; Mishra, 1982; Rather et al., 2020; Watts
 et al., 2019). The slope and terrain ruggedness index (TRI) were extracted
 by using the digital elevation model (DEM) in ArcGIS 10.8 (ESRI, 2019).
 The classified image from Landsat (acquisition date 2020-03-17) (Landsat
 8, Operational Land Imager (OLI)) was used for calculating the Euclidian
 distances to the nearest forest, grassland, water sources, developed area
 or human settlements, and cropland. We classified the images into eight
 different classes (Water sources, barren areas, grassland, riverine
 forest, Sal-dominated forest, mixed forest, cropland, and developed area)
 by using supervised classification based on the ground-truthing points
 (Adhikari et al., 2022). Among these classified eight classes, we merged
 riverine forest, Sal-dominated forest, and mixed forest as a single forest
 layer. We extracted water sources, grassland, forest, cropland, and
 developed areas from the available data and calculated Euclidian distances
 in ArcGIS 10.8 to be used as environmental variables for modeling. The
 Normalized Difference Vegetation Index (NDVI) is the most popular and used
 to quantify the greenness of the vegetation, and vegetation density and
 detect the changes in plant health using red and near infra-red bands of a
 remotely sensed image (Pettorelli et al., 2011; USGS, 2022; Yengoh et al.,
 2015), hence we selected NDVI as one environmental layer for mammals.
 Additionally, the modified Normalized Difference Water Index (MNDWI) is
 calculated by using the green and Short-wave Infrared (SWIR) bands and it
 enhances the features of open water. MNDWI also minimizes the features of
 developed areas that are correlated with open water in other indices (Xu
 & Guo, 2014; Xu, 2006). Furthermore, the Normalized Difference
 Built-up Index (NDBI) is a ratio that minimizes the effects of terrain
 brightness differences and atmospheric effects (Zha et al., 2003). Two
 spectral bands NIR and SWIR are used to enhance the build-up or developed
 area, thus differentiating built-up over the natural area. The values of
 each environmental variable were extracted at presence locations (Table
 1). For the layer of prey richness of leopard, the suitability map of
 preys was calibrated as 0 for absent and 1 for the present of the species
 based on mean equal test sensitivity and specificity logistic threshold.
 Then, these layers were   combined as a single layer. A
 total of 13 environmental variables were used for the modelling (Table 2).
 The variables were differed on the basis of nature of the mammals (Table
 2). The selected variable layers were converted into ASCII format with the
 same resolution, extent and projection system. The spatial resolution of
 30 m and UTM 45 N projected coordinate system was used for the
 modelling. <strong>Habitat suitability
 </strong><strong>model</strong><strong>s</strong> Maxent develops a model based on a series of features (environmental variables) (Phillips et al., 2006). Two types of data (occurrence data and environmental layers) were used for processing in the Maxent program (Phillips et al., 2006). The CSV file of the occurrence points in the samples menu and all selected variables layers in ASCII format in the environmental layers’ menu bar were loaded for analysis. The replicates and replicated run type were fixed 25 and subsample respectively. The Maxent model ran with 25 iterations and 1000 background points with 70 % of the points used as training data and 30 % points used as validation of the model. The output of the model was logistic. The performance of the model was evaluated based on AUC values of the receiver operator characteristic (ROC) plot analysis (Phillips, 2008; Phillips et al., 2006; Phillips & Dudík, 2008). The value of the predicted suitability ranges from 0 to 1. The logistic probability of suitability was further regrouped as 0 – 0.2 = unsuitable, 0.2 – 0.4 = moderately suitable, 0.4 – 0.6 = suitable, and 0.6 – 1 = highly suitable (Ansari & Ghoddousi, 2018; Kogo et al., 2019). All the spatial analysis and classification were performed in ArcGIS 10.8 (ESRI, 2019). We used these results of habitat suitability to identify the habitat patches of the species and preparation of resistance layer.  <strong>Landscape resistance </strong> The resistance or cost map was prepared using a raster habitat suitability map (Figure S1). Every cell on the map has a numeric value that indicates the cost that should be paid to pass through each cell (Bagli et al., 2011; Morovati et al., 2020). The cost map was developed by inverting the value of habitat suitability using the following formula (Almasieh et al., 2019; Morovati et al., 2020). Cost = 100 × (1 - habitat suitability)                                                                The lower cost is assigned to highly suitable areas whereas the highest cost is for the habitats with low suitability (Almasieh et al., 2019; Morovati et al., 2020). <strong>Identification of habitat patch</strong><strong> </strong> The continuous probability of occurrence was converted to binary predictions of presence and absence based on average equal sensitivity and specificity threshold. The predicted maps of all species were combined to identify the species richness of an area. The habitat patches were defined based on the number of species predicted in that area. About 50% of species’ present areas with 5000-pixel size were defined as the patch (Sahraoui et al., 2017). <strong>Modelling connectivity</strong><strong> </strong> The least-cost path (LCP) algorithms were used to identify a path or corridor (or linkage) between two geographical locations (Adriaensen et al., 2003; Unnithan Kumar & Cushman, 2022). The program Linkage Mapper 2.0.0. (McRae & Kavanagh, 2011) was used to identify the LCP for the movement of mammals from one patch to another. The Linkage Mapper identifies the closer patch, develops the networks between the patches, and calculates the least-cost distance and paths (McRae & Kavanagh, 2011). The lower cost-weighted distance is regarded as the strong corridor between two patches. The lower value of the least cost path (LCP) is regarded as the lower resistance for the movement of the animals i.e., it is highly suitable (Unnithan Kumar & Cushman, 2022). Two metrics were calculated to show the quality of each linkage. One is the ratio of cost-weighted distance (CWD) and Euclidean distance (EucD) that separate each pair of habitat patches. If the ratio of CWD and EucD is equivalent to 1, it is regarded as the highest possible quality linkage (Dutta et al., 2016). The second metric is the ratio of cost-weighted distance (CWD) and the length of the least-cost path (LCP). This provides the average resistance encountered along the optimal path between the habitat patches. The least-cost path of each species was identified and then, combined to find the single multi-species corridor between the patches using the raster calculator tools of ArcGIS. The Kernel density estimation method was used to identify the hotspots (Thakali et al., 2015) for the connection of isolated populations of mammals in the patches.
description: # Landscape-level habitat connectivity of large mammals in Chitwan
 Annapurna Landscape, Nepal Presence data for each species (Latitude and
 Longitude) in UTM. All files are CSV files. Please note that presence data
 was edited to reduce precision to within 1 kilometer to protect threatened
 species. #### Species: * Chital * Goral * Wild pig * Muntjac * Rhesus *
 Sambar * Himalayan black bear * Langur * Leopard