Introducing a Validation Technique for Corridor Modeling: A Comprehensive Evaluation of Predicted Wildlife Corridors in Modern Conservation Strategies

doi:10.21203/rs.3.rs-5005210/v1

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Introducing a Validation Technique for Corridor Modeling: A Comprehensive Evaluation of Predicted Wildlife Corridors in Modern Conservation Strategies

https://doi.org/10.21203/rs.3.rs-5005210/v1

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Effective biodiversity conservation strategies are paramount in addressing the persistent challenges of habitat fragmentation. This study investigates landscape connectivity for tigers across eight protected areas in Haryana, Uttarakhand, and Uttar Pradesh within the Terai Arc Landscape. Utilizing the least cost path methodology, the research identifies the most probable pathways connecting these protected areas. Additionally, circuit theory is employed to highlight crucial conservation areas, termed pinchpoints. The primary objective is to introduce a triangulation-based validation technique for predicted corridors, calculating the accuracy of predicted corridors between the eight protected areas. The results reveal several pinchpoints that require immediate action. The highest prediction accuracy is observed for the corridor between Rajaji National Park and Sonanadi Wildlife Sanctuary/Jim Corbett National Park, whereas the lowest accuracy is noted between Jim Corbett National Park and Kishanpur Wildlife Sanctuary. This research advances the precision and credibility of corridor modeling, offering significant contributions to wildlife conservation by elucidating landscape connectivity and presenting a novel validation technique. The findings provide practical implications for policymakers, conservation practitioners, and researchers, underscoring the need for rigor and validation in developing effective strategies to preserve and sustainably manage wildlife habitats.

Wildlife Biology

Corridors

Conservation

Validation

Least-cost

Circuit Theory

Circuitscape

In the face of persistent challenges associated with biodiversity conservation and the imperative to address habitat fragmentation impacts, the critical role of well-informed conservation strategies is paramount. Effective conservation strategies hinge upon a comprehensive understanding of wildlife movement patterns and the intricacies of the landscape. Animal species behaviour and their movement such as dispersal and migration act as one of the measures that ensures their survival in the landscape (Drielsma et al., 2007). United nations 2030 agenda for sustainable development identifies protected areas as a chief unit for wildlife and biodiversity conservation. Across globe, around 2, 02,000 protected areas have been legally designated covering 15% of the earth surface (Rahoof, 2019). In India, there are five kinds of protected areas namely, national parks, wildlife sanctuaries, conservation reserves, community reserves, marine protected areas. (wiienvis.nic.in). Details of protected areas of India are given in Table 1

Table 1

Protected areas of India as on December 2021

(Source: wiienvis.nic.in)
S.No.	Protected Area	Quantity	Total area	Coverage % of the country
1.	National Parks	106	44,372.42	1.35
2.	Wildlife Sanctuaries	564	1,22,509.33	3.73
3.	Conservation Reserves	99	4,726.24	0.14
4.	Community Reserves	218	1,445.71	0.04
	Total protected areas	987	1,73,053.69	5.26

5.26% of total geographical area of India is under protected area (WII,2021). The protected areas have played an important role in conservation of biodiversity. Since mid-20th century no mammal or bird is claimed to be extinct other than Cheetah (Acinonyx jubatus), pink headed duck (Rhodonessa caryophyllacea) (Divyabhanusinh, 1999; Ghosh-Harihar et al., 2019; MoEF&CC, 2020). Protected areas have played a great role in conservation of some endemic species of Indian subcontinent like one- horned rhinoceros (Rhinoceros unicornis) and Bengal tigers (Panthera tigris tigris) (Talukdar et al., 2008; Jhala et al., 2015; Ghosh-Harihar et al., 2019). However, conservation of species in between protected areas has been a serious cause of concern. The increase in human animal conflict around protected areas have been alarming. Bengal Tiger (Panthera tigris tigris) is one of the largest carnivores and a top predator belonging at the top of the food chain (Sabu et al., 2022). It is native to Indian sub-continent. Top carnivores are crucial for maintaining ecological integrity of a functional ecosystem. Tigers are one of the endangered species. In India tigers are distributed in almost all ten biogeographical zones including mountains, swamps, grasslands, dry and moist deciduous forests, evergreen forests (A. R. Joshi et al., 2016; B. Kumar et al., 2021; Sabu et al., 2022). Approximately, 70% of the world’s tiger population was present in India before 2000 (Nowell, 2000). Along with high population of tigers, India also embraces more than 60% of worldwide genetic variation in them (Mondol et al., 2009). Due to large scale poaching activities and habitat clearance population of tigers are confined to the small patches in the landscape. In 20th century tigers were hunted for commercial gains, chinese medicines and for showcasing bravery leading to decline of tiger population (Gittleman & Gompper, 2001; Sharma et al., 2010). Globally, 76 tiger conservation landscapes (TCL) are recognized which are habitats where tiger presence have been confirmed in last ten years. Six of the TCLs are situated in Indian subcontinent. The significant loss of tiger population resulted in adoption of ‘‘Tx2’’ objective at St. Petersburg, Russia, in 2010 which aims at doubling the population of the carnivore by 2022. Project Tiger by the Government of India is one of the most prominent steps towards this goal. Development of tiger reserves and protected areas were a pioneer step in this direction. However, it is crucial for wildlife sustenance to develop pathways for connecting protected areas together for movement activities like dispersal and migration (Majumder et al., 2012; New et al., 2008). Development of ecological corridors provide functional connectivity among habitat patches. Use of tools like graph theory and circuit theory is seen in wide range of studies for delineating ecological corridors. Movement of animals are crucial for maintaining gene flow and genetic variation. Connectivity among landscapes is vital for maintenance of ecological integrity. Movement of animals in the landscapes are largely for foraging, migration, dispersal or to evade predators. Connected landscapes helps in conservation and management of wildlife population. Natural and anthropogenic disturbances have resulted in the scattered and isolated population of animals. Various definitions are coined for landscape connectivity citing the structural (Brotons et al. 2003; Thies et al. 2003; Taylor et al. 1993; Tischendorf and Fahrig 2000; Schooley and Wiens 2003) as well as functional aspect (Taylor et al. 1993; With et al. 1997) of it. In former case, the connectivity is entirely based on landscape structure while in latter case, the connectivity is defined on the basis of behavioral responses of animals to landscape components (Kindlmann & Burel, 2008).

Circuit theory and graph theory is widely used in modeling ecological corridors. Graph theory based least cost paths are the routes where movement of animals incur lowest cost of transit (Adriaensen et al., 2003). They are single best pathways for movement derived from the resistance surfaces. Globally, many studies have used least cost method for delineating corridor paths. The study by LaRue & Nielsen (2008) identifies least cost pathways for movement of cougar (Puma concolor) in mid-western region of USA. Another study by Wierzchowski et al (2019) utilizes least cost method to derive a transport corridor for moose (Alces alces). A study by Wang et al (2009) identifies costs of various paths for movement of California tiger salamander (Ambystoma californiense) between grasslands and woods. Circuit theory, also uses resistance surface to delineate various probable paths for animal dispersal (Cushman et al., 2013; Dickson et al., 2020; McRae et al., 2008). Circuit theory is particularly useful when it is assumed that the moving individuals have limited knowledge of surrounding landscape (McClure et al., 2016; Maiorano et al., 2017; Keeley et al., 2017)

Validation is a crucial part of such practical studies. It helps in understanding whether the predicted structural corridor is functional corridor. Recent research has introduced statistical methods to substantiate the accuracy of projected wildlife corridors through on-ground datasets (Lalechère & Bergès, 2021). Many of these researches rely on location data, camera trap insights, GPS recordings, or telemetry information for verification purposes (Bond et al., 2017; Koen et al., 2014; Lalechère & Bergès, 2021). However, utilizing telemetry and GPS data is often hindered by their prohibitive costs. In a similar vein, Koen et al. (2014) used locations where animals have experienced road mortality to validate their connectivity model. Furthermore, several researchers have also cross-validated current density maps with radio telemetry data (Bond et al., 2017), which involves comparing the predicted movement pathways of animals with actual recorded movements. (Bond et al., 2017). There are various studies that use proximity analysis to validate the predicted corridors based on the distance between location of animal presence and the predicted path (Bond et al., 2017; Koen et al., 2014; Lalechère & Bergès, 2021). However, animal spotting outside of a protected area is often not recorded and there is very less data available outside of the protected area. In this study, a new corridor-based validation technique is proposed and applied on the corridors predicted using least cost path principle. This corridor assessment method is centered around translating real-world observations into a quantifiable measure of corridor accuracy. Triangulation is a fundamental geometric process used in various fields to create networks of triangles over a set of points. This method is crucial for transforming complex spatial data into a simplified, yet accurate, representation. Triangulation can handle a variety of data types, including those used in Geographic Information Systems (GIS), where it is often employed to model terrain, optimize sensor networks, and more.

The foundational work by Selvi, Oztug Bildirici, and Yerci (2010) discusses the triangulation method for area-line geometry-type changes in map generalization. Pradhan et al. (2007) utilize Delaunay triangulation for GIS terrain data compression, emphasizing its efficiency in handling complex terrain data. Argany et al. (2011) highlight a Voronoi-based approach in GIS for optimizing wireless sensor network coverage, demonstrating the versatility of triangulation in various applications. Further, Wu and Amaratunga (2003) explore wavelet triangulated irregular networks, while Zhang et al. (2018) enhance surface flow routing over drainage-constrained triangulated irregular networks. These studies collectively showcase the adaptability of triangulation in handling spatial data.

In the context of ecological studies, particularly in validating predicted animal corridors, triangulation plays a pivotal role. Animal movement data, typically in the form of geographic coordinates, can be used to create a triangulated network. This network provides a framework for analysing the spatial patterns of animal movement and assessing the accuracy of predicted corridors. Figure 1 explains the triangulation method used on two sets of three points forming points T1 and T2. A line connecting these points is termed as reference line. This process involves connecting the points in a manner that minimizes the total length of the lines while avoiding intersection, creating a Delaunay triangulation. This method ensures that the network is both efficient and accurate. Furthermore, this reference line can be compared with the least cost path predicted by calculating the perpendicular distances between them. The average of perpendicular distance can be taken as corridor score. The lower the value of the corridor score, higher chances are that it is a functional corridor. The novelty of this research lies in its innovative application of triangulation to validate predicted wildlife corridors using empirical animal movement data. This work creates a framework for evaluating the precision of corridor forecasts and analysing spatial trends by using geographic coordinates to create a triangulated network. The implementation of Delaunay triangulation guarantees a precise and effective network, hence augmenting the dependability of the generated reference line. Through perpendicular distance measurements, the analysis compares this reference line with the least cost approach and suggests a measurable corridor score. This approach fills a major gap in the present ecological research methodology by offering a reliable metric for assessing corridor functionality in addition to improving validation approaches (LaPoint et al., 2013).

This research specifically targets the investigation of landscape connectivity for tigers across eight protected areas spanning Haryana, Uttarakhand, and Uttar Pradesh states of India, within the geographically significant Terai Arc Landscape. The primary objective of this research is to introduce and propose a validation technique tailored for predicted corridors. Addressing a critical gap in modern-day corridor modeling techniques, the study recognizes the lack of a standardized validation approach. The method presented in this paper seeks to systematically assess and enhance the reliability of corridor predictions, thereby equipping conservationists with a tool for evidence-based decision-making that positively influences both animal populations and ecosystems

Study area

In this study, the connectivity analysis is carried out between 8 protected areas of India including protected areas located in Terai arc landscape. Terai arc landscape is stretched from the base of Himalayas from south-central part of Nepal to north western India. It has an area of 50,911 km² with 26,201 km² in India and 24,710 km² in Nepal. The study connects protected areas from Haryana, Uttarakhand and Uttar Pradesh together (Kalesar, Sonanadi, Kishanpur, Katarniyaghat, Corbett, Rajaji, Dudhwa, Simbalbara, Nandhaur). Since in this landscape tigers occupancy is highly found in large and continuous patches namely, Kalesar in Haryana to Kishenpur in Uttar Pradesh and Dudhwa tiger reserve (Jhala et al., 2008).

Table 2

Table showing area of protected areas
S. No	Protected Area	Area
1.	Kalesar National Park & wildlife Sanctuary	46.8 Km²
2.	Sonanadi Wildlife Sanctuary	301 Km²
3.	Corbett National Park/ Tiger reserve	521 Km²
4.	Dudhwa Tiger reserve (Kishenpur wildlife sanctuary, Katarniyaghat wildlife Sanctuary, Dhudhwa National Park)	490.3 Km²
5.	Rajaji National Park	390 Km²
6.	Simbalbara National Park	27.88 Km²
7.	Nandhaur Wildlife Sanctuary	269.95 Km²

a. Data used

In this study, land use land cover (LULC) type is used for quantifying habitat suitability of the study area. LULC is particularly useful here because it helps in understanding the distribution of prey species for carnivores and foraging habits of herbivores. The PAs are present in between human populated zones therefore an LULC analysis would provide good insights in habitat suitability analysis or developing resistance layer. The data is derived from country level biodiversity characterization dataset of the study area developed by Roy et al (2015). Vegetation type map of the study area along with anthropogenic variables like road networks and railways were also used to develop the habitat and resistance layer.

b. Methodology

The study can be broadly classified into two major stages namely habitat suitability analysis and connectivity analysis between the selected protected areas based on the habitat suitability analysis.

Tiger habitat variables

In India, tigers are spread across wide range of habitats. Tigers have unique ability to persist in wide range of varying habitat due to their high adaptability. Tigers are said to flourish in tropical forests having temperatures in the range of 20–29°C and also in woodlands of pine, birch and oak having temperatures up to -34°C. They are also found in dry, hot thorny forest of Rajasthan and also in mangrove swamps of Sundarbans. (Sunquist, 2010). Prey abundance and water availability are important factors affecting tiger habitat (A. R. Joshi et al., 2016). Anthropogenic factors such as settlements, roads and railways act as disturbances to the habitat of tigers in the landscape (Kanagaraj et al., 2011). In this study, both natural and anthropogenic variables were used in generating resistance surfaces of the study area.

Developing resistance layer/surface

Based on the variables, resistance was generated using Gnarly utilities toolbox version 0.1.0 of ArcMap 10.5. Each category of the variables was given resistance values based on expert opinion survey and published materials. Table 3 provides the details on assigned resistance values and justification for the same.

Table 3

Resistance (%) given to each of the variable used to calculate the resistance
S. No.		Class	Assigned resistance value (%)	Justification
1.	Vegetation type	Temperate Forest	0	(Kitchener & Dugmore, 2000) (Biswas & Sankar, 2002; Chundawat et al., 1999; Sunquist, 2010) (Biswas & Sankar, 2002; Chundawat et al., 1999; Sunquist, 2010) (Biswas & Sankar, 2002; Chundawat et al., 1999; Sunquist, 2010) (Biswas & Sankar, 2002; Chundawat et al., 1999; Sunquist, 2010) (Ramesh et al., 2013) (Nowak, 2013) (Jhala, Y. V., Gopal, R., & Qureshi, Q. (2008)(Sunarto et al., 2012)
		Moist deciduous Forest	0
		Sal mixed moist deciduous	0
		Teak mixed moist deciduous	0
		Dry deciduous Forest	0
		Thorn Forest	0
		Bamboo Mixed Forest	0
		Sal forest	0
		Lowland swamp forest	0
		Forest plantations	0
		Mixed plantation	0
		Degraded forest	30
		Woodland	2
		Tree savannah	100
		Grassland	100
	LULC	Scrub	2
		Agriculture	49
		Cold deserts	100
		Barren land	6
		River bed	0
		Water body	6
		Wetlands	0
		Settlement	100
		Snow	100
		Riverine area	0
	Roads	National Highway	100
		State Highway	100
		Major District Roads	70
		Other District Roads	50
		State Road	50
	Railways	Railway tracks	100

Connectivity analysis

Circuitscape 4.0 is used to execute the circuit theory approach. Circuitscape calculates connectivity between landscapes elements (e.g., patches of forest) based on resistance values assigned to different landscape elements. The resistance values reflect how difficult it is for species to move across the landscape. Circuitscape generates a map of electrical current flow that shows the connectivity between landscape elements. It utilizes a resistance or conductance raster as an input along with a focal node (features across where connectivity is to be modeled). In this study, Circuitscape is applied in pairwise mode which generates current density across all pairs of focal nodes. Here connectivity is assessed between 8 protected areas in the Terai Arc Landscape. Similarly, the connection between protected areas is also optimized using linkage mapper 7.0.0. Linkage mapper utilizes the least cost path principle to generate the least expensive pathway in terms of migration and dispersal of animals in between the protected areas. There are a few differences in analysis and result Circuitscape and least cost path

Validation

In this approach, the technique involves employing triangulation on coordinates of tiger sightings. The coordinates within the study area were obtained from the Global Biodiversity Information Facility (GBIF) database. Using trigonometric calculations, the coordinates of the new points are established, ensuring accuracy and precision (Jick, 1979). This corridor assessment method is centred around translating real-world observations into a quantifiable measure of corridor accuracy. By employing triangulation, the methodology efficiently captures the positional data of identified animals using known coordinates. Triangulation involves measuring angles from at least three known points to determine precise locations of observed animals (Babbie, 2010; Creswell, 2014). The subsequent step involves creating new triangulated points based on these known coordinates. Once these points are established, a validation line is drawn through them. This line serves as a reference axis, aiding in the evaluation of how accurately the projected path aligns with the triangulated line (Jick, 1979). By systematically assessing the deviation of the predicted path from this reference line at consistent intervals, the method effectively quantifies the degree of alignment between the projected corridor and the triangulation-based reference line.

In this study, protected areas were considered as the core areas and connectivity is assessed in between the protected areas. Eight protected areas, highly populated with the carnivores and frequent human animal conflict affected areas were chosen to address the issue of species movement in the landscape.

Predicting the movement patterns using least cost approach

As shown in Fig. 3, the least cost analysis predicted a single corridor between Kalesar National Park/Wildlife Sanctuary and Rajaji National Park. The majority of the trail consists of Sal and mixed damp and dry deciduous forests. The predicted route also passes by water features and the riverbed. Between Jim Corbett National Park, Sonanadi Wildlife Sanctuary, and Rajaji National Park, a single route is anticipated. Sal forests are common along the path, along with moist and dry deciduous forests. Tigers are expected to utilize dry deciduous scrub areas, such as those with Lantana, as ideal hiding places before attacking their prey. Notably, the estimated path does not pass through any agricultural areas. The route likely follows the riverbed and a few areas of degraded forest, continuing toward tree plantations and grasslands beside the rivers. Additionally, a direct pathway is expected between Jim Corbett National Park and Kishenpur Wildlife Sanctuary, as well as between Nandhaur Wildlife Sanctuary and Kishenpur. A pathway is also anticipated between Dudhwa National Park and Nandhaur Wildlife Sanctuary. These paths are expected to follow waterbodies, as this area is rather fragmented and has few forested sections. Similarly, wildlife movement is expected between Katarniyaghat Wildlife Sanctuary, Dudhwa National Park, and Kishenpur along waterbodies and swampy grasslands. It should be highlighted that the projected route between these three protected areas will pass through agricultural land, which may become a potential hotspot for conflicts between humans and wildlife.

Circuitscape offers a comprehensive perspective on landscape connectivity, in contrast to the least-cost path approach, which identifies the most efficient route between two specific points. In this study, corridors are delineated using current density map values ranging from 9.21 to 15.70. These current density values serve as indicators of the relative ease of tiger movement across the landscape. Higher current density values correspond to more favorable conditions for tiger movement, signifying areas with lower resistance to traversal. Conversely, lower current density values indicate less favorable conditions, reflecting higher resistance and greater challenges for tiger movement.

The Fig. 4 highlights several critical pinch points, where wildlife movement is concentrated, creating bottlenecks due to the lack of alternative routes. Specifically, two pinch points are identified between Kalesar National Park and Rajaji National Park, situated at the edges of these protected areas. Similarly, three pinch points exist between Rajaji National Park and Sonanadi Wildlife Sanctuary. A long stretch of pinch points, along with one smaller pinch point, is observed between Jim Corbett National Park and Nandhaur Wildlife Sanctuary. Dense stretches of pinch points are also found between Nandhaur Wildlife Sanctuary and Kishanpur Wildlife Sanctuary, Kishanpur Wildlife Sanctuary and Dudhwa National Park, and a smaller pinch point between Kishanpur Wildlife Sanctuary and Katarniaghat Wildlife Sanctuary. These narrow corridors are crucial for maintaining connectivity, particularly for tiger conservation, and necessitate focused efforts to sustain wildlife corridors in these interconnected habitats. Most of these pinch points are located in deciduous forests, grasslands, and near water bodies, highlighting the importance of these habitats in supporting wildlife movement and survival.

Validation of the predicted corridors

In the Fig. 5, the reference line is constructed using triangulated points derived from the location coordinates of observed animals. The figure vividly demonstrates a remarkable alignment between the reference line and the projected corridor lines. The predicted trajectory and the reference line observed between Kalesar National Park and Rajaji National Park exhibit a nearly parallel alignment with minimal separation. Conversely, the distance between Rajaji National Park and Sonanadi/Jim Corbett National Park is recorded as zero, indicating a precise concurrence between the two lines and thereby validating the accuracy of the prediction.

Notably, the alignment between the predicted path and the reference line experiences an incremental increase from Corbett to Nandhaur. However, alignment is reestablished, with the lines coinciding seamlessly from Nandhaur onward through the remaining protected areas. This nuanced analysis underscores the variable precision of corridor predictions along the specified route, providing valuable insights into the reliability of the modeling outcomes. The proximity between these lines is noteworthy, signifying a negligible distance between the observed paths and the reference line. This alignment strongly suggests that the animals are indeed utilizing the predicted paths. The close correspondence observed in the figure is indicative of the precision of the projected wildlife corridors. The average distance between the reference line and predicted paths between each protected area is shown in Table 3.

Table 3

Average distance between the reference line and the predicted corridors between protected areas
S. No	Protected Areas	Average distance
1.	Kalesar Wildlife Sanctuary to Rajaji National Park	22.43
2.	Rajaji National Park to Sonanadi Wildlife Sanctuary & Jim Corbett National Park	2.9
3.	Jim Corbett National Park to Nandhaur Wildlife Sanctuary	19.47
4.	Jim Corbett National Park to Kishanpur Wildlife Sanctuary	27.68
5.	Kishanpur Wildlife Sanctuary to Dudhwa National Park	13.81

Figure 6 illustrates the predicted corridor between Kalesar National Park and Rajaji National Park. The figure also shows a reference line, prepared using the triangulation method, to compare with the predicted corridor. It is evident from the figure that the reference line slightly deviates from the predicted corridor. While both lines generally follow a similar path, the deviations indicate some discrepancies between the triangulation-based reference and the predicted route. These variations might be due to differences in the data sources, the methods used for analysis, or natural landscape features that influence the precise path of the corridor. The average distance calculated between the reference line and predicted corridor is 22.43.

Figure 7 shows the predicted corridor between Rajaji National Park and Sonanadi Wildlife Sanctuary& Jim Corbett National Park. The corridor begins at Rajaji National Park, extends eastwards, passes through Sonanadi Wildlife Sanctuary, and finally connects with Jim Corbett National Park. The reference line closely follows the predicted corridor, indicating a significant overlap between the expected and predicted pathways. This alignment suggests that the corridor analysis accurately identifies the route connecting these critical wildlife habitats, enhancing connectivity and promoting biodiversity conservation in the region. The average distance calculated between the reference line and predicted corridor is 2.9.

Figure 8 illustrates the predicted corridor connecting Jim Corbett National Park, Nandhaur Wildlife Sanctuary, Kishanpur Wildlife Sanctuary, and Dudhwa National Park. The figure reveals that the predicted corridor splits into two distinct paths. This suggests that tigers are more likely to move directly between Corbett National Park and Kishanpur Wildlife Sanctuary, rather than stopping or passing through Nandhaur Wildlife Sanctuary. Furthermore, the figure shows a path connecting Nandhaur Wildlife Sanctuary with both Kishanpur Wildlife Sanctuary and Dudhwa National Park. The reference line deviates significantly from the predicted corridor, particularly between Corbett National Park and Kishanpur Wildlife Sanctuary. However, the predicted corridor and the reference line roughly coincide when linking Nandhaur Wildlife Sanctuary with Kishanpur Wildlife Sanctuary and Dudhwa National Park. The average distance calculated between the reference line and predicted corridor between Corbett and Kishanpur is 27.68 and The average distance calculated between the reference line and predited corridor between Corbett to Nandhaur is 19.47.

Figure 9 illustrates the predicted corridors connecting Kishanpur Wildlife Sanctuary, Dudhwa National Park, and Katarniyaghat Wildlife Sanctuary. The map indicates a predicted path extending from Kishanpur Wildlife Sanctuary towards both Dudhwa National Park and Katarniyaghat Wildlife Sanctuary. It is observed that the reference line (represented by the dotted line) deviates slightly from the predicted path, forming a zigzag pattern between Dudhwa and Kishanpur before terminating there. A separate corridor path is also predicted from Kishanpur wildlife Sanctuary to Katarniyaghat Wildlife Sanctuary

The reference line is not visible near the predicted corridors extending from Dudhwa and Kishanpur to Katarniyaghat. This absence is due to the lack of coordinate data for the tigers in those regions, preventing the depiction of a reference line in these areas. The predicted paths, however, still highlight the connectivity between these significant wildlife sanctuaries, facilitating the movement of tigers across these protected areas. The average distance calculated between the reference line and predicted corridor is 13.81.

This study extensively examines the pivotal and complex domain of animal connectivity, scrutinizing methodologies and factors integral to the prediction and evaluation of wildlife corridors. By employing least cost path and circuit theory, this research delineates the optimal trajectories among eight designated protected areas (Beier et al., 2008). Moreover, it highlights the identification of bottlenecks and pinch points, critical considerations from a conservation perspective. The outcomes of this study hold significant implications for policymakers and legislators, providing valuable insights for pinpointing these pinch points and facilitating informed decision-making in support of conservation initiatives at specific locations. This research also provides a pragmatic solution for evaluating the precision of projected wildlife corridors through a quantitative analysis of alignment with midpoint-based reference lines (McRae & Kavanagh, 2011). This approach stands as a valuable contribution to the field. The demonstrated effectiveness of this technique, as illustrated in the figures, underscores its potential as a robust tool for researchers and conservationists alike. The findings emphasize the importance of accurate predictions in understanding and facilitating animal movement within habitats. The close correspondence observed between predicted paths and reference lines suggests a high level of reliability in the projected corridors. Such precision is essential for informed decision-making processes related to habitat connectivity, as it provides a quantifiable measure of the pathways' utility for wildlife.

The validation methodology introduced in this study distinguishes itself from prevailing contemporary approaches in the field. Unlike many current studies that rely on the direct utilization of observed animal coordinates for corridor validation, this approach diverges significantly (Zeller et al., 2012). The inherent limitation of depending solely on observed coordinates lies in its susceptibility to bias, primarily stemming from the challenge of obtaining accurate location points for animals inhabiting dense forest areas. Notably, the available coordinates tend to be concentrated within protected regions and their immediate vicinity, potentially skewing the validation process (Sawyer et al., 2011). In response to this limitation, this study pioneers an innovative validation method that transcends the exclusive reliance on animal coordinates. Recognizing the difficulty of obtaining comprehensive location data in densely forested environments, our approach introduces a more nuanced and inclusive technique for validating predicted wildlife corridors. By circumventing the exclusive dependence on observed coordinates, our method strives to mitigate biases introduced by the spatial constraints inherent in dense forest habitats. This innovation in validation methodology is poised to contribute significantly to the field by addressing the practical challenges associated with obtaining comprehensive animal location data. By adopting a more inclusive and adaptable approach, our study not only enhances the reliability of corridor validation but also sets a precedent for future research endeavors grappling with similar spatial limitations in wildlife habitat studies.

Looking ahead, the integration of advanced technologies and interdisciplinary approaches will further refine our understanding of animal connectivity. Continued research and application of these methods will play a pivotal role in shaping effective conservation policies and practices, ensuring the resilience and sustainability of ecosystems for generations to come. In the broader context of global biodiversity conservation, our work contributes to the growing body of knowledge aimed at preserving the intricate web of relationships that define the natural world.

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The authors declare no competing interests.

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Introducing a Validation Technique for Corridor Modeling: A Comprehensive Evaluation of Predicted Wildlife Corridors in Modern Conservation Strategies

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Abstract

Figures

Introduction

Materials and Methods

Study area

a. Data used

b. Methodology

Tiger habitat variables

Developing resistance layer/surface

Connectivity analysis

Validation

Results and Discussion

Predicting the movement patterns using least cost approach

Validation of the predicted corridors

Conclusion

References

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