Combined land cover changes and habitat occupancy to understand

Eur J Wildl Res (2017) 63: 83 DOI 10.1007/s10344-017-1139-9

ORIGINAL ARTICLE

Combined land cover changes and habitat occupancy to understand corridor status of Laljhadi-Mohana wildlife corridor, Nepal Arjun Thapa 1,2,3 & Karan Bahadur Shah 4 & Chiranjibi Prasad Pokheral 5 & Rajan Paudel 5 & Dipendra Adhikari 2 & Prakash Bhattarai 2 & Nicolas James Cruz 6 & Achyut Aryal 7

Received: 24 January 2017 / Revised: 11 September 2017 / Accepted: 12 September 2017 / Published online: 26 September 2017 # Springer-Verlag GmbH Germany 2017

Abstract Corridor design is a centripetal conservation tool to facilitate movement between fragmented patches. Increases in anthropogenic activity have caused degradation in forest connectivity, influencing animal movement to a small degree. Laljhadi-Mohana wildlife corridor (LMWC), a corridor between Shuklaphanta National Park (Nepal) and Dudhwa National Park (India) created to be used by Panthera tigris and Elephas maximus in western Nepal, is under pressure of anthropogenic change. Using current knowledge, we analyzed land cover changes (LCC) of LMWC between 2002 and 2012. We used ERDAS IMAGINE 9.2 and Arc GIS 9.2 to process satellite images, and occupancy survey to assess status of corridor. We classified land cover into dense forest, sparse forest, cultivation, water bodies, grassland, expose surfaces, and sand bank as structural attributes of the corridor. Our analysis found

* Arjun Thapa [email protected]

1

Biodiversity and Environmental Management, NOMA Program, Central Department of Botany, Tribhuvan University, Kirtipur, Kathmandu, Nepal

2

Small Mammals Conservation and Research Foundation, PO Box 9020, Sundhara, Kathmandu, Nepal

3

Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

4

Natural History Museum, Tribhuvan University, Kathmandu, Nepal

5

National Trust for Nature Conservation, Khumaltar Lalitpur, Nepal

6

Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA, USA

7

Institute of Natural and Mathematical Sciences, Massey University, Palmerston North, New Zealand

dense forest area was reduced by 18.35% in a decade while cultivation and sparse forest increased by 10.15% and 8.89%, respectively. Illegal forest encroachment, resource extraction, grazing pressure, invasive species, and flood were major drivers of forest change. The null occupancy model estimated the highest detection probability of Elephas maximus (0.48 ± 0.08) and the lowest of Axis axis (0.20 ± 0.08). Incorporating site covariates improved occupancy estimates of Sus scrofa (0.82), Axis axis (0.76), Elephas maximus (0.76), Boselaphus tragocamelus (0.66), and Panthera pardus (0.55). Distance to cultivation was the most influential covariate, supported by the expansion of cultivated land in the corridor. LMWC is a functional wildlife corridor despite a decline in forest cover. This decline influenced the number and detection rates of large mammals, instigating crop raiding and conflict. Mitigation measures on LCC drivers, particularly forest encroachment, can improve the functional status of LMWC and raise detection rates of large mammals in future studies. Keywords Forest change . Functionality . Habitat use . Large mammals . Occupancy

Introduction Establishing protected areas can be an effective tool in biodiversity conservation; however, island populations inhabiting fragmented patches are under constant threat. Habitat fragmentation and loss are major threats to biodiversity conservation. The island biogeography theory (MacArthur and Wilson 1967) highlights the role and importance of fragmented or isolated areas. Corridor design and connectivity maintenance among fragmented resources are modern approaches implemented in

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heterogeneous landscape conservation. The structure and function of corridors are crucial for maintaining functional landscapes (Hess and Fischer 2001). Connectivity among habitat patches in a fragmented landscape is a key component of conservation, aiming to facilitate movement between fragmented resources (Taylor et al. 1993). Corridors allow wildlife to move between forage areas, water resources, and breeding grounds (Niemelä 1999; Okello and Wishitemi 2006). Furthermore, corridors’ land attributes play a crucial role in protecting both resident and migratory species. Understanding a corridor’s functionality is very important, as mammals are highly sensitive to land use changes (Randhir and Ekness 2009). Conserving heterogeneous landscape biodiversity, identifying and properly managing corridors, connectivity, bottlenecks, and trans-boundary corridors are recent popular conservation alternatives to increasing the number of protected areas. A highly ambitious landscape program, Terai Arc Landscape (TAL), aims to protect the remaining populations of tigers (Panthera tigris), Asian elephants (Elephas maximus), and greater one-horned rhinoceros (Rhinoceros unicornis) through protecting fragmented habitats and maintaining corridors between protected areas of southern Nepal and northern India (WWF 2003). To achieve conservation targets, the Nepalese Government proposed the following corridors within tiger habitat: Laljhadhi, Basanta, Khata, Madevpuri and Barandabhar corridor forests, and LaljhadiMohana biological corridor (LMBC) (WTLCP 2007). Among these, LMBC, which was designed as a potential corridor for ionic species such as tigers and Asian elephants, is under accelerating pressure from anthropogenic activities, causing forest connectivity degradation and resulting in decreased animal movement (WTLCP 2007; Aryal et al. 2012; Shrestha et al. 2014). LMBC was granted protected forest status under national law in 2011 (Shrestha et al. 2014). Adequate baseline data on habitat, faunal status, and both natural and anthropogenic changes in LMBC are poor. Therefore, this study was conducted to understand the current status of the corridor, land cover change, and the primary factors influencing the probability of detecting large mammals in corridor. By identifying drivers of change, we hoped to determine the important factors in effective conservation to maintain and improve corridor functionality. This study has elucidated the opportunities as well as challenges for protecting wildlife in the LMBC.

Eur J Wildl Res (2017) 63: 83

average minimum and maximum temperature of the area was 37 and 70 °C, respectively, and rainfall ranges from 3 to 572 mm, with nearly 80% of total rainfall during the monsoon season (June–September). The corridor, which is flat in topography, covers an area of 29,641.75 ha. The LMBC mainly consists of tropical and sub-tropical forest and is dominated by Shorea robusta. Grasslands, riverine forests, rivers, cultivated land, and human settlements are also found. The LMBC provides habitat for large charismatic species such as Asian elephants (Elephas maximus) and tigers (Panthera tigris), but also other common faunae such as common leopards (Panthera pardus), spotted deer (Axis axis), blue bull (Boselaphus tragocamelus), barking deer (Muntiacus muntjak), wild boar (Sus scrofa), golden jackal (Canis aureus), and sloth bear (Melursus ursinus) (Shrestha et al. 2014). The LMBC forest patches act as a transboundary wildlife corridor that connects Bardia National Park (BNP), Shuklaphanta National Park (ShNP), and Churiya forests in Nepal with Dudhwa National Park, Kishanpur Wildlife Sanctuary, and Katarniaghat Wildlife Sanctuary in India (Fig. 1; WTLCP 2007).

Methods Landcover classification Satellite images, sub-setting, pre-processing, and correction Landsat Enhanced Thematic Mapper (ETM+) satellite images (2002 and 2012) of the study area were used to classify land cover using ERDAS IMAGINE 9.2 and ArcGIS 9.2 (ArcGIS 9.2, ESRI, Redlands, CA, USA) (Fig. 2). Artifacts such as additive effects from atmospheric scattering were removed and first-order radiometric corrections were applied using the dark pixel subtraction technique (Lillesand and Kiefer 1994). Both images were registered geometrically in ERDAS IMAGINE using ground control points (GCPs) collected from topographic maps. Images were resampled by the nearest neighbor method using GCPs. Furthermore, digital enhancements such as level slicing, contrast stretching, spatial filtering, histogram equalization, edge enhancement, and resolution merging were performed using image enhancement tools/ options in the ERDAS IMAGINE software.

Methods and materials Classification and accuracy assessment Study area This study was conducted in the LMBC, which lies within the TAL in the Far-Western Region, Nepal (Fig. 1). The

Maps for each year (2002 and 2012) were constructed using the maximum likelihood classifier under supervised classification to detect the land cover change (Lillesand


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Fig. 1 Laljhadi-Mohana biological corridor (LMBC)

and Kiefer 1994; Lamichhane 2008). Land cover was classified into the following: (a) dense forest, (b) sparse forest, (c) cultivation, (d) water bodies, (e) grassland, (f) exposed surface, (g) sand bank (Table 1). The data collected from field surveys were used for the classification of the 2012 ETM+ satellite image whereas the 2002

Fig. 2 Landsat image classification process

ETM+ satellite image was classified based on the digital topographic map. Field reference data (N = 50 points) on land cover types were used to accurately assess land cover using the kappa index of agreement (KIA). Habitat occupancy survey Large mammal occupancy estimation was based on a single-season single-species model (Mackenzie et al. 2006). The LMBC was divided into 52 grids measuring 2.5 × 2.5 km2 using ArcGIS 9.2 (Fig. 3). Twenty-eight grids were randomly selected for occupancy surveys. Occupancy surveys were conducted during July and August of 2013, and focused on areas with a high probability of detection for large mammals within each grid (Karanth et al. 2011). The field survey team walked predetermined 2-km-long transects following high probability detection trails, roads, river, and streambeds searching for animals or animal signs (scats or dung, scrapes, pugmarks or prints, kills, urination sites, etc.). The detection histories were constructed for each grid, where B1^ indicated detection of the animal/animal sign and B0^ indicated non-detection. Three habitat covariates (Sal forest, mixed riverine forest, and cultivated land) were

83 Page 4 of 14 Table 1 Description of land cover classes adopted


Land cover classes

Description(s)

Dense forest

This class includes wood vegetation with canopy more than 10% (FAO 2000). There are different types of tree species within the forested area from topical to sub-tropical mixed hard wood forest with Sal (Shorea robusta) as dominant species. These different types of forest area were classified as a single-class forest because of the Landsat image (with spectral resolution of 30m × 30 m)

Sparse forest

This comprises of (sparsely distributed trees, bushes, deforested land) an area covered with shrub land and very spare tree (canopy cover > 10%) This covers agricultural land mass and human settlement

Cultivation Water bodies Grassland

This class consists of perennial rivers, lakes, and ponds (artificial/natural) This comprises of open land with grass inside or adjacent to forest. Also, it represents land having a ground story or vegetation cover in which grasses are dominant life forms

Exposed surface Sand bank

This consists of landside erode area, expose soil area, and other surfaces except open area in riverbank This consists of sand, river bank, and pebbles area

recorded. Tree felling (TF), wood cutting (WC), presence of people (PS), grazing (GR), forest fire (FR), and invasive species (IN) were recorded as disturbance factors. Other variables such as distance to water sources were recorded as DWA (distance to water > 0.5 km), DWB (distance to water 0.5–1 km), and DWC (distance to water < 1 km). Similarly, distance to nearest cultivation (agriculture and settlement) were classified into DCA (distance to cultivation > 1 km), DCU (distance to cultivation 1–3 km), and DCC (distance to cultivation < 3 km). Trail features (TR), tractor/cart trail (GTT), and human trail (HT) were recorded. Each covariate, when detected in the segment, was recorded as B1^ or B0.^

Fig. 3 Survey grids of LMBC

Data analysis Landsat image processing and land cover classification were performed using ERDAS IMAGE 9.2 and ArcMap 9.3. According to FAO (1995), annual rate of forest change (here rate of land cover change) was calculated using the formula " 1 # A2 n −1 100 Rate of Changeð%Þ ¼ A1 where A1, A2, and n represent base year data (2002 land cover), end time data (2012 land cover), and number of years (i.e., 10 years), respectively.


The detection histories were used to construct two key parameters: the probability that a grid was occupied by the

Score ¼

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species (ψ) and the detection probability estimation (p) using likelihood functions (Mackenzie et al. 2002).

Number of segments that contained covariates presence in a grid Total number segment in grid

Detection histories were constructed for each grid where B1^ indicated detection of animal/animal’s sign and B0^ indicated non-detection. Field data were analyzed using the Bcustom model^ of single-season analysis available in the program BPRESENCE^ ver. 6.2 (Hines 2006). Models were ranked by Akaike’s information criteria (AIC) value and all models whose ΔAIC < 2 were considered equivalent models (Burnham and Anderson 2002). The summed model weight of a particular covariate in the top models was used to infer the relative influence of each covariate. This was because a single ‘best’ model could not necessarily represent all of the variables that influenced the probability of occupancy or species detection (Bailey et al. 2004). A total of 20 spatial replicates were recorded in each grid, and further detection histories were truncated and scored into four spatial replicates to reduce imprecision in detection probability estimates (Kroll et al. 2010). As expected, estimates for the proportion of occupied sample units were greater than the proportion of sample units where species were detected in the field, and the magnitude of the difference was inversely related to the detection probability. A stepwise approach was utilized where detection probability (p) was modeled first followed by occupancy (ψ) modeling. A total of 15 models were produced to assess the influence of different covariates on detection probability: 1. ψ (.), p (.), the simplest model keeping the variability in occupancy and detection probabilities constant 2. ψ (.), p (DIST), occupancy kept constant with detection probability influenced by disturbance (six factors) 3. ψ (.), p (DCU), occupancy kept constant with detection probability influenced by distance to cultivation (settlements and agriculture) (three categories) 4. ψ (.), p (DWA), occupancy kept constant with detection probability influenced by distance to water (three categories) 5. ψ (.), p (HA), occupancy kept constant with detection probability influenced by habitat type (three types) 6. ψ (.), p (TR), occupancy kept constant with detection probability influenced by trail features (three types) 7. ψ (.), p (DIST + DCU), occupancy kept constant with detection probability influenced by disturbance (six factors) and distance to cultivation (three categories)

8. ψ (.), p (DIST + DWA), occupancy kept constant with detection probability influenced by disturbance (six factors) and distance to water (three categories) 9. ψ (.), p (DIST+ HA), occupancy kept constant with detection probability influenced by disturbance (sizx categories) and habitat type (three categories). 10. ψ (.), p (DIST + TR), occupancy kept constant with detection probability influenced by disturbance (six categories) and trail features (three categories) 11. ψ (.), p (DCU+ DWA), occupancy kept constant with detection probability influenced by distance to cultivation (three categories) and distance to water (three categories) 12. ψ (.), p (TR + DWA), occupancy kept constant with detection probability influenced by trail features (three categories) and distance to water (three categories) 13. ψ (.), p (DUC + HA), occupancy kept constant with detection probability influenced by distance to cultivation (three categories) and habitat type (three categories) 14. ψ (.), p (DIST + DCU + HA), occupancy kept constant with detection probability influenced by disturbance (six categories), distance to cultivation (three categories), and habitat type (three categories) 15. ψ (.), p (DIST + DUC + DWA), occupancy kept constant with detection probability influenced by disturbance (six categories), distance to cultivation (three categories), and distance to water (three categories) For occupancy modeling, it was hypothesized that all the covariates mentioned above would influence psi (ψ) and variables such as disturbance, habitat type, distance to water and cultivation, and trail features would influence p. For each species, occupancy was modeled by keeping covariates that influenced the probability of detection (≥ 2ΔAIC) as constant (p).

Results Status of land cover structure and change detection The average accuracy for the supervised maximum likelihood classification was 0.81, and average kappa index of agreement (KIA) was high (0.80). Both the producer and

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user’s accuracy were found to be 0.79 and 0.82, respectively. Analyzing the 2002 image, dense forest (54.85%) was the major land cover followed by cultivation (26.02%), sparse forest (26.09%), exposed surface (5.02%), water (4.2%), grassland (2.38%), and sand bank (1.44%) (Table 2, Fig. 4). Classification of the Landsat ETM (2012) showed that dense forest and cultivation occupied an equal percentage (36%) of the LMBC and were the primary land cover attributes. Other land cover types were sparse forest (15.09%), water (3%), grassland (2.35%), and sand bank (1.27%) (Table 2). Dense forest was primarily converted into cultivated land and sparse forest. Dense forest decreased by 18.53% while cultivation and sparse forest increased by 10.15 and 8.89%, respectively (Table 2).

Status of large mammals’ occupancy Five mammal species were recorded in surveying the grids (n = 28): Asian elephant (Elephas maximus), wild boar (Sus scrofa), chital (Axis axis), blue bull (Boselaphus tragocamelus), and common leopard (Panthera pardus). The most frequently detected signs of presence were dung/scats/pellets and pugmarks/hoofmarks. Additionally, there was direct observation of Asian elephants (N = 22 in a herd), chital (N = 10), and blue bull (N = 2) (Fig. 5). Most observations were recorded in the south, which is composed of more dense forest relative to other areas (Fig. 4). Asian elephants and blue bull were not recorded in the northern part of the corridor, which include Kishanpur area. Evidence of chital, wild boar, and common leopard were observed in the northern part of LMBC, which is contiguous with the Churiya forest and Shuklaphanta National Park. There were no observations of elephant sign in the north (Fig. 5). Although LMBC is designated as a migratory passage for Asian elephants and tigers, no tigers were detected during our study period.

Table 2 Land cover of LMBC (2002–2012) and changed in land cover attributes

Land cover attributes

Dense forest Cultivation Sparse forest Expose surface Water body Grassland Sand bank Total

Detection probability and occupancy estimation The ψ (.), p (.) models performed based on statistics ranked by the AIC value and were considered the best models with ΔAIC ≤ 2 (Table 3). Furthermore, detection probability and associated standard errors were averaged from these models (Table 4). The models with a precision less than 30 and a c- chat value close to one were shown in the statistical summary and identified as good models for occupancy estimation. Mean estimates of detection probability for the study area were highest for Asian elephants and lowest for chital (Table 3, Table 5 and Table 6). Once encounter rates were corrected by the previously mentioned detectability covariates, resultant mean estimates of type occupied in the study area ranged from 0.55 for common leopard to 0.82 for wild boar (Table 6). Results showed a substantial increase in the estimation of habitat use after correcting detectability through modeling. For example, the mean probability of occupancy for Asian elephants increased by 30% once detectability was incorporated into the estimates (Table 6). Covariates affecting occupancy also varied between species. These covariates were DIST, DCU, DWA, HA, and TR. Furthermore, the summed model weight varied among species with strong support for covariates DIST, DCU, and DWA (Table 6). Asian elephant occupancy was positively associated with cultivation (DCU) and negatively associated with distance to water, disturbance, and trail features. However, chital occupancy was negatively associated with distance to cultivation, habitat type, and disturbance. Only distance to cultivation was positively associated with leopard occupancy. Most covariates were positively associated with wild boar occupancy; however, it was not associated with habitat type or trail features. Blue bull occupancy was positively associated with distance to cultivation but negatively associated with disturbances (Table 4). Model fit greatly improved when p was modeled as a function of single or combination covariates: DCU (chital and leopard), DIST (blue bull), DWA and DCU (Asian elephant),

2002

2013

Change

Area (km2)

% cover

Area (km2)

% cover

Area (km2)

% area

195.45 92.72 22.09 17.91 14.50 8.49 5.14 356.30

54.86 26.02 6.20 5.03 4.07 2.38 1.44 100.00

129.44 128.90 53.76 20.62 10.69 8.36 4.53 356.30

36.33 36.18 15.09 5.79 3.00 2.35 1.27 100.00

66.02* 36.17 31.67 2.71 3.81* 0.13* 0.61*

18.53* 10.15 8.89 0.76 1.07* 0.04* 0.17*

*Indicates decreased in area


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Fig. 4 Classified land cover structures of LMBC in 2002 and 2012

and DIST, DCU, and DWA (wild boar) (Table 5). Occupancy was influenced by a combination of two or more covariates in generalist species like wild boar. Models having ΔAIC < 2 received the most support with AIC 77, 72, 69, 60, and 58% of total AIC weight for chital, wild boar, Asian elephant, blue bull, and common leopard, respectively. Thus, model averaging from these top models was undertaken to estimate occupancy, 95% CI, and model precision. Model averaging produced the estimated occupancy rates 0.82, 0.76, 0.76, 0.66, and 0.55 for wild boar, chital, elephant, blue bull, and common leopard, respectively. Among these species, the highest occupancy was estimated for wild boar and least for common leopard. These estimates were greater than naive estimates and models with a precision less than 30 (Table 6). Additionally, the summed model weight indicated good support for DCU (0.77 chital), DIST (0.73–wild boar; 0.54—elephant), DWA (0.73—wild boar), HA (0.49—wild boar), and least for TR (less than 0.20) (Table 3). Similarly, covariates influenced habitat type occupied by large mammals. Distance to water and disturbance were negatively (negative slope–β-coefficient) associated with elephant occupancy whereas all other covariates except trail features were positively associated with elephant occupancy (Table 3). All covariates were positively associated with wild boar

occupancy. Leopard occupancy was positively associated with distance to cultivation, distance to water, and distance to trail features. Leopard occupancy was negatively associated with disturbances. Blue bull occupancy was negatively associated with disturbances and distance to water, but was positively associated with distance to cultivation and habitat type (Table 3). More than 65% of the surveyed grids had occupancy rates greater than 0.60 for all species except for blue bull, which nearly 0.50.

Discussion Assessing corridor land cover structures Spatial information from images and extensive fieldwork enabled us to delineate the area into seven land cover types: dense forest, sparse forest, cultivation, water, grassland, exposed areas, and sand banks. The band combination of nearinfrared, red, and green was used to assess vegetation structure. The response of the near-infrared band to foliage content or leaf area index was useful in differentiating vegetation (Jensen, 1996). Various factors such as species composition,

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Fig. 5 Animal’s sign recorded in survey grids

vegetation strata, crown closure, crown geometry, stand density, soil moisture, hill slope, aspects, hydrological regime, and sun affected the spectral signatures recorded and made the classification more challenging (Treitz et al. 1992; Prince 1994) even with high-resolution imagery (Thapa 2011). Canopy overlap and multilayered vegetation structure made interpretation of the forest structure more challenging (Negendra 2001). Analysis of the 2002 image showed dense forest was the major land cover type in LMBC. Overall, forest area (dense and sparse) occupied more than 60% of the corridor in 2002. The corridor was considered a good connecting link between Nepalese (Shuklaphanta National Park) and Indian (Dudhwa National Park) protected areas, but it was not a conservation priority at the time. The district profile of Kanchanpur (DPK 2008) showed that about 45% of the area was under forest

cover. The corridor also connected with the Churiya forest in the northern part of corridor and maintained a good population of big cats (e.g., common leopard), the presence of which was supported by high detection rates as well as reports by locals regarding the killing of livestock. It was ascertained through temporal analysis that there has been a significant change in land use cover, particularly the conversion of dense forest into cultivated land (agriculture and settlement) and sparse forest (degraded forest). Dense forest decreased significantly (− 18%) and was converted into cultivated land and sparse forest in the southern region of the corridor as well as to exposed surface in northern region. The increase in exposed surface in LMBC may have also been attributed to increased soil erosion from deforestation in the Churiya. Furthermore, these results are in alignment with Pandit’s (2011) study that

Eur J Wildl Res (2017) 63: 83 Table 3

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Summary of the top model for factors affecting detection probability of five large mammals in LMBC

Model

p

SE

AIC

ΔAIC

AIC WT

Model likelihood

No. par.

Model precision

Elephant ψ (.), p (DCU + DWA)

0.42925

0.11215

106.48

0

0.1699

1

7

26.1

ψ (.), p (TR) ψ (.), p (.)

0.57371 0.56394

0.12009 0.02865

106.77 107.02

0.29 0.54

0.147 0.1297

0.865 0.7634

4 2

20.9 5.1

ψ (.), p (DWA) ψ (.), p (TR + DWA)

0.56340 0.59395

0.07955 0.12793

107.24 108.02

0.76 1.54

0.1162 0.0787

0.6839 0.463

4 7

14.1 21.5

ψ (.), p (DWA + DIST)

0.57131

0.14690

108.18

1.7

0.0726

0.4274

10

25.7

ψ (.), p (DCU + DIST) Chital

0.40890

0.13358

108.21

1.73

0.0715

0.4211

10

32.7

ψ (.), p (DIST + DCU + HA) ψ (.), p (DCU)

0.15624 0.17203

0.01817 0.03308

77.59 77.84

0 0.25

0.3398 0.2998

1 0.8825

13 4

11.6 19.2

ψ (.), p (DCU + HA)

0.15626

0.03133

78.49

0.9

0.2166

0.6376

7

20.1

Common leopard ψ (.), p (DCU) ψ (.), p (.) ψ (.), p (DCU + DWA)

0.24540 0.3675 77.03

0.05383

74.12

0

0.4129

1

4

21.9

0.1046 2.91

76.28 0.0964

2.16 0.2334

0.1402 7

0.3396

2

28.5

Wild boar ψ (.), p (DIST + DCU + DWA) ψ (.), p (DIST + DCU) ψ (.), p (DCU) Blue bull

0.31266 0.3017 0.28467

0.09243 0.077954 0.08614

89.51 89.53 92.71

0 0.02 3.2

0.4447 0.4403 0.0898

1 0.99 0.2019

13 10 4

29.6 25.8 30.3

ψ (.), p (DIST) ψ (.), p (DIST + DCU) ψ (.), p (DCU) ψ (.), p(.DIST + DCU)

0.24195 0.20810 0.42692 0.19018

0.04686 0.02882 0.10575 0.05751

77.1 77.22 79.76 80.5

0 0.12 2.66 3.4

0.3514 0.331 0.0929 0.0642

1 0.9418 0.2645 0.1827

7 10 4 10

19.4 13.9 24.8 30.2

ψ psi, p detection probability, SE standard error, Δ AIC relative difference in Akaike’s information criteria compared to top-ranked model, AIC WT AIC model weights, No. par. number of parameters in the model, (.) constant, DIST disturbances, HA habitat types, DWA distance to water, DCU distance to cultivation (settlement and agriculture), TR feature of trails

showed a considerable decrease in forest area between 1996 and 2010 in the Laljhadhi forest. These major changes occurred because of the high degree of anthropogenic activities and disturbances (e.g., forest encroachment, illegal timber collection, rampant road construction, overgrazing pressure, and flood victims). These activities may have been accelerated by a period of political instability from the Maoist rebellion, which resulted in weak law enforcement and consequently to natural resource exploitation, which may explain the change in the forested areas. About 133,968 ha of Terai forests was cleared for resettlement programs initiated by the Nepalese Government (Pradhan 2007). During the insurgency period, mild-hills people were forced to move into the Terai and urban areas to secure property and a better life. This resulted in further degradation of the lowland forests of the Terai. Moreover, results obtained through image processing revealed that there was considerable change in forest cover at a fast pace. Change analysis showed that these periods were under the influence of rampant human activities, potentially causing the rapid land cover change. Additionally, a settlement area near

the Doha river basin in Kishanpur VDC had shifted to the edge of forest due to heavy flooding in 2007. This event resulted in significant tree felling for building purposes and consequently put pressure on forest resources. Additionally, crop raiding and house damage by elephants were recorded. This may be the result of encroachment into wildlife habitat and an increase in new settlements on the edge of the forest. Similar studies from the Khata corridor show that there are not any significant changes in land use or land cover types from 1997 to 2011 (Nagarkoti 2012; Shrestha 2004). The Khata and Barandabhar corridor forests are functioning as good wildlife corridors in comparison to other corridors due to high conservation priorities focusing on endangered species such as tigers, greater one-horned rhinoceros, and Asian elephants. In addition, eradication of malaria in the Terai region after 1954 enabled the migration of people from the hill regions to the Terai (GoN 2007), spurring large amounts of land clearing for settlement and agriculture. Studies indicate rapid forest cover change around Ghodaghodi Lake (Khanal 2008), Lamahi bottleneck (Thakur 2012), and throughout plains of Terai with around 6838 ha forest being logged between

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Table 4 Covariates influencing detection of large mammals ranked by summed model weight, with average β-coefficient and associated standard errors

Elephant

Chital

Leopard

Wild boar

Blue bull

Covariates

∑ model wt.

β-coefficient

SE

DWA

0.437

− 0.472

0.502

DCU

0.241

3.102

0.058

TR DIST

0.225 0.144

− 0.616 − 10.88

0.968 0.88

HA DCU

NA 0.856

NA − 13.22

NA 0.326

HA

0.556

− 18.147

0.921

DIST

0.339

− 0.032

0.323

DWA

NA

NA

NA

TR

NA

NA

NA

DCU

0.634

1.440

0.8

DWA HA

0.196 0.096

NA NA

NA NA

TR DIST DIST DCU

0.096 0.053 0.885 0.885

NA NA 4.284 2.549

NA NA 0.324 0.505

DWA HA TR

0.885 NA NA

4.185 NA NA

0.733 NA NA

DIST DCU DWA HA TR

0.682 0.331 NA NA NA

− 15.0836 141.076 NA NA NA

0.37 0.050 NA NA NA

DIST disturbances, DWA distance to water, DCU distance to cultivation, HA habitats, TR trail features, NA not available

1990/1991 and 2000/2001 in Kailali (GoN/MFSC 2011). Similarly, the results of our study showed forest cover change at an alarming rate between 2001 and 2012 in the corridor. Similar forest cover changes have been reported in NagziraNavegaon corridor in central India (Yadav et al. 2012), Taveta district of Kenya (Syombua 2013), and Swat and Shangla Districts of Pakistan (WWF 2009). Fundamental drivers behind the forest changes were forest encroachment, flooding, cattle grazing pressure, invasive species, and increasing human population. Assessing the corridor’s functional status Detection probability, occupancy estimation, and influence of site covariates Based on direct and indirect sign, five mammal species were recorded in the corridor: Asian elephant, wild boar, chital, blue bull, and common leopard. All five mammals used the

southern part of the corridor more than the northern part. This may be due to the dense forest patches and agricultural areas in the forest fringe in the southern part of the corridor (Fig. 4). The agricultural area may provide food resources for animals such as Asian elephants, chital, wild boar, and blue bull. Elephants frequently raided crops and damaged houses in the southern region (Baise Bichawa, Shankarpur, and Raikawar Bichawa) but not in the northern region (Kishanpur). Furthermore, the northern landscape was highly fragmented while the southern part was highly aggregated. This may be due to the proximity to Dudhwa National Park, which could provide many resources to wildlife. The classified land cover showed a large dense forested area contiguous with highly productive agricultural lands. This indicates the presence of supportive habitats in the southern part of the corridor (Fig. 5). These results are in alignment with Rood et al. (2010), whose study indicated that elephants strongly prefer forests with a highly productive valley in Indonesia. Previous studies found that elephants prefer flat lowland forest habitats (Kinnaird et al. 2003; Hedge et al. 2005; Azad 2006; Pradhan and Wegge 2007). An east-west running highway may have influenced their study results. Moreover, these factors might be the cause of low elephant occupancy in the northern part of the study area. Additionally, according to local residents, Asian elephants have not visited the area for over decade. By applying modern occupancy modeling using PRESENCE, we were able to show that the mean estimates of detection probability for the entire study area ranged from 0.25 to 0.48. The highest detection was recorded for Asian elephants while the lowest detection was for chital. However, naive occupancy ranged between 0.54 and 0.33 and indicated that elephants had the highest occupancy. Also, our results suggested that there was a significant increase in occupancy with incorporating covariates and found that wild boar had the highest occupancy while common leopards had the lowest. Furthermore, it suggested that species-specific covariates could greatly influence occupancy estimations. Common leopard had a higher detection probability and a lower occupancy than chital. This may be due to their solitary, elusive nature and territorial behavior. In this study, common leopards were detected in the northern part of corridor and there was a single detection in the south. The fact that there were fewer selected survey grids in the north part than in south needs to be considered. This may be of the desire to select many grids in the heavily forested southern part. Traditional approaches to naive estimation always assumed perfect detection of species (e.g., p = 1.0) (Rota et al. 2009), but were found to underestimate the occupancy of ψ (.), p (.) by 20%. It was essential to incorporate variables into detection probability to produce more reliable occupancy estimates. This meant that there were strong covariates necessary to consider to assess occupancy. These were disturbances, distance to cultivation, distance to


Page 11 of 14 83

Table 5

Summary of model predicting occupancy (Est. ψ) and detection probability (Est. p) of large mammals in LMBC

Species

Model

AIC

Elephant ψ (DIST), p (DWA + DCU)

Chital

Blue bull

Model likelihood

No. par.

Est. ψ ± 1 SE

Est. p ± 1 SE

Est. cchat

105.21 0

0.3644 1

10

0.58870 ± 0.17150

0.4068 ± 0.08873 0.57

ψ (DIST), p (TR)

106.61 1.4

0.181

0.4966

10

0.83333 ± 0.11096

0.5722 ± 0.11065 0.58

ψ (.), p (.) ψ (DWA), p (DCU + DWA)

107.02 1.81 108.14 2.93

0.1474 0.4045 0.0842 0.2311

2 9

0.56394 ± 0.02866 0.58870 ± 0.02865

0.69 0.4582 ± 0.12885 0.56

ψ (TR), p (DCU + DWA)

109.06 3.85

0.0532 0.1459

9

0.72807 ± 0.17256

ψ (DCU), p (TR) ψ (DIST), p (DCU)

109.14 4.2 79.48 0

0.0446 0.1225 0.1924 1

7 9

0.55828 ± 0.16158 0.64282 ± 0.11850

0.5757 ± 0.12023 0.65 0.2309 ± 0.06266 0.99

0.45825 ± 0.1288

0.59

ψ (HA), p (DCU) ψ (TR), p (DCU)

79.57 0.09 80.28 0.8

0.1839 0.956 0.1289 0.6703

6 6

0.75750 ± 0.21233 0.80717 ± 0.17178

0.2013 ± 0.09615 1.02 0.2016 ± 0.07928 0.96

ψ (DCU), p (DIST + DCU + HA)

80.96 1.48

0.0918 0.4771

15

0.70833 ± 0.08333

0.2397 ± 0.08695 0.87

ψ (DWA), p (DIST + DCU + HA) ψ (DWA), p (DCU)

81.06 1.58 81.07 1.59

0.0873 0.4538 0.0869 0.4516

15 6

0.91667 ± 0.09452 0.77775 ± 0.19583

0.1602 ± 0.09613 0.69 0.1887 ± 0.08553 1.18

74.57 0 76.2 1.63

0.4076 1 0.1804 0.4426

6 6

0.67250 ± 010849 0.63667 ± 0.12412

0.3639 ± 0.09641 0.67 0.3720 ± 0.10895 0.70

76.28 1.71 77.48 2.91 87.28 0

0.1734 0.4253 0.0951 0.2334 0.3152 1

2 6 20

0.36750 ± 0.10460 0.87948 ± 0.18131 0.79167 ± 0.19023

0.4699 ± 0.07307 0.71 0.3447 ± 0.10014 0.71 0.4376 ± 0.12536 0.83

87.82 0.54

0.2406 0.7634

18

0.79167 ± 0.0.093

0.3160 ± 0.09231 0.80

88.42 1.14 89.46 2.18

0.1783 0.5655 0.106 0.3362

14 21

0.89612 ± 0.0790 0.75000 ± 0.1129

0.3119 ± 0.09120 0.80 0.4377 ± 0.10870 0.83

73.62 75.58 76.55 77.54

0.4395 0.1649 0.101 0.0619

9 11 9 12

0.675421 ± 0.11366 0.6497 ± 0.096163 0.783925 ± 0.090863 0.617983 ± 0.059083

Leopard ψ (HA), p (DCU) ψ (DWA), p (DCU)

Wild boar

ΔAIC AIC wt.

ψ (.), p (.) ψ (RT), p (DCU) ψ (HA + DIST), p (DIST + DCU + DWA) ψ (DIST), p (DIST + DCU + DWA) ψ (HA), p (DIST + DCU + DWA) ψ (DIST + DCU), p (DIST + DCU + DWA) ψ (DCU), p (DIST) ψ (HA + CU), p (DIST) ψ (TR), p (DIST) ψ (DCU + DWA), p (DIST)

0 1.96 2.93 3.92

1 0.3753 0.2311 0.1409

water, habitat type, and trail features. Moreover, our results suggested that large mammals occupied more than 50% of the survey grids. This reflects functional use of the corridor but does not address the population. Wild boar occupancy estimates using PRESENCE showed this species had highest occupancy (0.82 ± 0.12), reflecting a significant increase over the naive occupancy Table 6 Summary statistics of model-averaged estimates for each species, giving detection probability (p), naive estimate of habitat occupied (naïve), and modeled estimates of habitat occupied (ψ), along with standard errors for modeled estimates. All estimates are averaged over the entire study site Species

p (SE)

Naïve ψ

ψ (SE)

∑ model wt.

Elephant Chital Common leopard Wild boar Blue bull

0.48 (0.99) 0.20 (0.08) 0.40 (0.09) 0.35 (0.10) 0.25 (0.10)

0.5417 0.4167 0.333 0.501 0.375

0.76 (0.10) 0.76 (0.14) 0.55 (0.11) 0.82 (0.12) 0.66 (0.10)

0.69 0.77 0.58 0.73 0.60

0.2493792 ± 0.06 0.2522167 ± 0.04 0.2392417 ± 0.07 0.2504958 ± 0.10

0.81 0.88 0.79 0.78

estimation. Wild boar occupancy was positively associated with distance to cultivation (DCU), distance to water, disturbance, and habitat type, but was not associated with trail features. These relationships make sense ecologically as the closer animals are to cultivated lands, the more accessible food and water become. The spatial distribution of wild boar may be affected by forest fragmentation, despite their generalist nature and potential use of agricultural areas for feeding (Virgos 2002). Disturbance factors consist of the following: tree cutting and felling, human presence, grazing, fire, and invasive species. Tree felling provides good habitat to dig and forage while invasive species (such as Lantana camara and Eupatorium adenophorum, which were noted in field) could provide possible hiding areas from predators and humans. According to local people, crop damage is increasing due to wild boar conflict. This was particularly noted in the southern part of the corridor. Wild boar are highly adaptable and have the highest reproductive rate among ungulates, possibly influencing their high occupancy (Massei and Genov 2004).

83 Page 12 of 14

Asian elephant presence was detected in more than 75% of survey grids while the naive estimate was approximately 50%. Habitat occupancy of Asian elephants was negatively influenced by distance to water and was positively associated with distance to cultivation and disturbances. In the study area, agricultural areas connected with forests. As elephant detection was positively associated with distance to cultivation, elephant presence was likely concentrated at the forest edge. These results are in agreeance with Rood et al. (2010) yet are at odds with Kinnaird et al. (2003) who concluded that elephants avoid forest edges. In this study, cultivation was a combination of both agricultural lands and settlements. This positive relation may be due to the availability of nutritious food from agricultural areas. It also suggests that encroachment into elephant habitat is increasing as there was a positive relationship with disturbance, possibly increasing human-elephant conflict. Furthermore, damage to infrastructure and agricultural lands are frequent near the southern part of the corridor. Other studies showed that habitat alterations due to human activities greatly influence wildlife distribution which may be a threat to wild elephant populations. Although both the probability detection and naive occupancy estimate of the common leopard were low, there was an increase in estimated occupancy by incorporating site covariates. Only three covariates (distance to cultivation, distance to water, and trail features) were positively associated with leopard occupancy. These results indicate that leopards prefer habitat close to cultivated areas (settlement and agricultural). This might be due to poor prey base and a high dependency on livestock in study sites such as Kishanpur as supported by several studies (Seidensticker 1976; Sunquist and Sunquist 1989; Odden and Wegge 2005). In our study, common leopards occupied areas in the northern part of the corridor, in flat plains near the Churiya. These results contradict Ngopraset et al. (2007), who found that leopard activity was negatively correlated with distance from villages in Thailand, and agricultural land cover and livestock presence had a negative effect on common leopards in India (Henschel 2008). In the north, the Doha river separated the forest habitat. Leopard sign was recorded in fringe forests near the river basin, which may have caused the positive relationship with distance to water. This factor was contradicted by a study conducted in Chitwan National Park (Thapa 2011) which found that topography and distance to water do not significantly affect occupancy and were not considered to be limiting factors for leopards in Chitwan National Park. In addition, trial features were positively associated with leopard occupancy because large felids typically follow trails. These results along with those from Chitwan National Park (Thapa 2011) found that leopards typically use human trails rather than animal trails, dry riverbeds, or sand banks. The trail use by leopards differently can be attributed to the use of


main trails and streambeds by prey species and are landmarks that big cats frequently use to demarcate their territories (Smith et al. 1989; Ghoddousi et al. 2008). Several authors have reported that big cats prefer using roads or trails to travel through forests (Karanth and Nichols 2002; Carbone and Gittleman 2002). The blue bull occupancy was positively associated with distance to cultivated areas and negatively correlated with disturbances, but was positively associated with human disturbance in Shuklaphanta (Lamichhane 2017). The positive association might be due to seasonal food availability from cultivated land. Blue bull sign was recorded from Shankarpur site, where there was human activity in the forest after heavy flooding from the Doha river in 2007. Furthermore, there was a cut stump on the forest edge in that area. These observations might be plausible cause to suspect usages anthropogenic disturbance in that site.

Conclusion An integrated approach of remote sensing, geographical information system, and occupancy survey concluded LMBC is a functional corridor despite a significant change in forest cover that indicated decreasing wildlife habitat within a decade. In 2002, forest coverage was 58.85%, which was degraded into the sparse forest and cultivated lands, leaving only 36% forest cover in 2012. Forest encroachment, seasonal flooding, grazing pressure, invasive species, illegal resources extraction, and north–south human migration were the main drivers of land covering changes. Large mammals encountered in the field were mainly Asian elephants, common leopards, chital, wild boar, and blue bull, which were highly sensitive to habitat changes. Occupancy modeling with habitat covariates helps to understand a species’ functional status in terms of occupied habitat. Our results show that mean estimates of detection probability for the entire study area were lowest for chital and highest for elephants. However, the model was improved by incorporating habitat covariates and showed a substantial increase (< 20%) in habitat use by large mammals. Estimates of habitat occupied for the study area ranged from 0.55 for common leopard to 0.82 for wild boar. Habitat occupancy was influenced greatly by site covariates: distance to cultivation, distance to water, disturbance factors, and features of the trail. Among these, distance to cultivation was positively associated with habitat occupied by these mammals while disturbances were negatively associated. Generalist mammals such as wild boar were positively associated with all covariates and received the highest occupancy estimate score (0.88 ± 0.12). This corridor is functional, and although forest cover decreased over time, occupancy rates of large mammals in corridor remain high. The identified change drivers need to be addressed for effective conservation, and to maintain and


improve the functionality of the corridor. The study also shows that occupancy modeling with habitat covariates helps to understand a species’ functional status in terms of occupied habitat. Thus, the study has elucidated the opportunities as well as challenges for protecting corridor dwelling wildlife in the study area. Acknowledgements We thank Christian Gortázar, the editor, and one anonymous reviewer for valuable comments on the manuscript. The authors thank Norad’s Program for Master Studies for providing a scholarship to the first author, the University Grant Commission of Nepal for partial funding, Purnram Chaudhary and Shankar Choudhary for assistance during field work, and Professor Tej Bahadur Thapa for image processing and classification guidance.

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