Conservation Insights for the Elusive Madras Hedgehog (Paraechinus nudiventris): Prioritizing Regions in Tamil Nadu, India

Conservation Insights for the Elusive Madras Hedgehog (Paraechinus nudiventris): Prioritizing Regions in Tamil Nadu, India | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 September 2025 V1 Latest version Share on Conservation Insights for the Elusive Madras Hedgehog (Paraechinus nudiventris): Prioritizing Regions in Tamil Nadu, India Authors : Kumar Brawin 0000-0002-5940-008X [email protected] , Thekke Thumbath Shameer , Abinesh Muthaiyan 0009-0007-7977-0567 , and Sophie Lund Rasmussen Authors Info & Affiliations https://doi.org/10.22541/au.175682179.96679499/v1 569 views 189 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Anthropogenic pressures are increasingly threatening many species, especially elusive ones with limited baseline data. The Madras hedgehog, a nocturnal species in the urbanized landscapes of Tamil Nadu, India, exemplifies this issue. Understanding its distribution and conservation status is critical for informing effective protection strategies. This study aimed to map the distribution of the Madras hedgehog and identify ecological factors influencing its habitat. Conducted from June 2016 to March 2023 in Tamil Nadu, we used spotlight surveys, local questionnaire surveys, fieldwork across diverse habitats, and a review of historical literature. These methods helped map hedgehog presence and created species distribution models for the region. Our findings show that approximately 7,737 km² of Tamil Nadu is suitable for the Madras hedgehog. Key ecological factors affecting its distribution were identified as ”mean annual temperature” and ”isothermality.” The most suitable habitats are near forests, at lower elevations, close to water bodies, and away from urban and agricultural areas. The species showed a preference for moderate temperature zones, with habitat suitability declining in warmer regions. These insights are vital for understanding the hedgehog’s habitat needs and guiding conservation strategies. Incorporating our ecological findings into urban planning and landscape management can enhance conservation efforts, particularly for nocturnal species in urban environments. By establishing wildlife corridors and promoting green infrastructure, we can mitigate habitat fragmentation and support the survival of the Madras hedgehog and similar species. Conservation Insights for the Elusive Madras Hedgehog ( Paraechinus nudiventris ): Prioritizing Regions in Tamil Nadu, India Brawin Kumar 1 , Thekke Thumbath Shameer 2 , Abinesh Muthaiyan 1,3 , Sophie Lund Rasmussen 4,5,6 1. Hedgehog Conservation Alliance, Swamithoppu, Kanyakumari, Tamil Nadu, India- 629 704. 2. Advanced Institute for Wildlife Conservation, Tamil Nadu Forest Department, Government of Tamil Nadu, Chennai, Tamil Nadu, India- 600 048. 3. Department of Ecology and Environmental Sciences, School of Life Sciences, Pondicherry University, Puducherry, India- 605 014. 4. Wildlife Conservation Research Unit, The Recanati-Kaplan Centre, Department of Biology, University of Oxford, Abingdon, UK. 5. Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark. 6. Linacre College, Oxford, UK. Brawin Kumar: 0000-0002-5940-008X Thekke Thumbath Shameer: 0000-0002-2306-1821 Abinesh Muthaiyan: 0009-0007-7977-0567 Sophie Lund Rasmussen: 0000-0002-2975-678X Corresponding Author: [email protected] Abstract 1. Anthropogenic pressures are escalating extinction risks for many species, particularly elusive ones that lack sufficient baseline data. The Madras hedgehog, a nocturnal species found in the urbanized landscapes of Tamil Nadu, India, exemplifies this challenge, highlighting the urgent need for comprehensive studies on its distribution and conservation status. 2. The primary objective of this study was to map the distribution of the Madras hedgehog and identify the ecological factors influencing its habitat. This research aims to fill critical knowledge gaps regarding this species, thereby informing conservation strategies. 3. Conducted from June 2016 to March 2023 in Tamil Nadu, our study employed a combination of spotlight surveys, questionnaire surveys with local residents, fieldwork in various habitats, and a review of historical literature. These methods facilitated the mapping of hedgehog presence across different landscapes and provided data to inform species distribution models predicting the distribution of the Madras hedgehog in Tamil Nadu. 4. Our findings revealed that approximately 7,737 km² of Tamil Nadu is highly suitable for the Madras hedgehog. The analysis identified ”mean annual temperature” and ”isothermality” as significant factors affecting its distribution. Suitable habitats for hedgehogs were primarily located near forested areas at lower elevations, close to water bodies, and away from urban and agricultural developments. Habitat suitability declined in regions with higher mean temperatures, indicating a preference for moderate temperature zones. This information is crucial as it not only enhances our understanding of the species’ habitat preferences but also serves as a foundation for developing targeted conservation efforts. 5. By integrating our empirical ecological data into landscape management and urban planning frameworks, we can enhance conservation initiatives’ effectiveness by targeting exclusively hedgehogs and other nocturnal mammals in urban environments. This comprehensive understanding of the habitat requirements and ecological interactions of the Madras hedgehog enables the formulation of targeted interventions, such as establishing wildlife corridors and implementing green infrastructure, to mitigate habitat fragmentation and promote urban biodiversity by prioritizing ecological integrity, ensuring the persistence of the Madras hedgehog and other at-risk species in environments altered by human activity. Keywords conservation strategy, Madras hedgehog, Paraechinus nudiventris, urbanized landscapes. Introduction The escalating human population growth and the consequent stress on natural resources have significantly amplified concerns regarding biodiversity conservation (Sala et al 2000). Numerous animal species now confront the looming threat of extinction due to human-induced stressors and habitat fragmentation (Pimm et al 1995). To address these challenges, it is crucial to target species facing extinction risks through meticulous planning and tailored conservation action plans. Understanding a species’ fundamental distribution is a cornerstone in evaluating its conservation status and devising effective conservation strategies (Solberg et al 2006). The ability to predict species distribution is a pivotal aspect with far-reaching implications in ecology, evolutionary studies, and conservation science (Guisan and Zimmermann 2000; Elith et al 2006; Shameer et al 2023). Despite substantial research efforts, a comprehensive and detailed large-scale overview of various mammal taxa distribution still needs to be discovered, significantly impeding our ability to formulate and implement effective conservation initiatives (Rondinini et al 2011). The challenge in conservation lies in preserving ecosystem integrity and functionality amid growing threats to biodiversity. Mapping species distribution may prove complicated, especially when the species is rare or elusive. A good example of this is hedgehogs, which are solitary and nocturnal. They are categorized within Eulipotyphla and the family Erinaceidae (specifically the subfamily Erinaceinae). They are recognized for their dense covering of tiny spines that envelop their entire body, except for their face, legs, underbelly, as well as their and distinctive pig-like snout. The Madras hedgehog ( Paraechinus nudiventris , commonly known as the bare-bellied hedgehog), hereafter referred to as “hedgehog”, is native to Southern Indian states like Tamil Nadu, Kerala, and Andhra Pradesh. Despite its current classification as “Least Concern” on the IUCN Red List of Threatened Species (Chakraborty et al 2017), the populations of this species are dwindling due to trade, habitat loss, medicinal exploitation, and hunting pressures (Kumar and Iyer 2013; Kumar and Nijman 2016; Kumar et al 2018a). The hedgehog predominantly inhabits the south-central and southern regions of Tamil Nadu, primarily within grasslands and dry open landscapes (Saravanan et al 2016; Kumar and Nijman 2016; Kumar et al 2018a). Despite this, uncertainties persist regarding its exact geographical range and ecological niche. An updated range map is urgently needed to guide conservation strategies and identify areas of priority to the conservation efforts targeted at this declining species. Species Distribution Models (SDMs) have emerged as reliable methods for providing crucial information, particularly for lesser-known and threatened species (Raman et al 2022; Shameer et al, 2021, 2022). SDMs offer insights into current ranges and potential shifts, presenting a proactive approach to address the impacts of climate change on species distributions (Lutz et al 2010; Raman et al 2020; Shameer et al 2022). Among these models, the ensemble modelling method proposed by Araujo and New (2007) has gained popularity for its capacity to provide accurate predictions. This method enhances the precision and resilience of SDMs by employing diverse algorithms, predictors, or modelling methods. Combining multiple models through techniques like weighted averaging mitigates overfitting, addresses model uncertainty, and captures the intricate relationship between species and their environment. The significance of these models in conservation planning is substantial The models’ outcomes help to design effective conservation networks to safeguard biodiversity in natural habitats (Cabeza et al 2004; Williams and Araujo 2000). Many aspects of Madras hedgehog ecology have yet to be described, as the corpus of published research on the species is currently very limited (Kumar and Iyer 2013; Marimuthu and Asokan 2014; Kumar and Nijman 2016; Kumar et al 2018a; Kumar et al 2018b; Kumar et al 2020; Kumar and Devi 2020; Zeng et al 2022). A previous attempt to predict the distribution of the species in southern India (Kumar et al 2019a), used data from secondary records. The intention of this study was to provide more accurate models using comprehensive ground surveys in tandem with secondary records. Existing records indicate the species’ presence within the urbanized landscapes of Tamil Nadu (Kumar et al 2024). Urgent efforts are necessary to assess and map the potential distribution areas of this species alongside the formulation of targeted conservation plans. The rapid proliferation of developmental projects in urban habitats poses a significant threat to the hedgehogs, amplifying the need for immediate action to safeguard their existence. Accordingly, we conducted an comprehensive study across various locations in Tamil Nadu with the purpose of assessing the distribution and conservation threats of the Madras hedgehog in Tamil Nadu and suggesting a long-term conservation plan for this vulnerable species. Furthermore, we applied the information gathered through our fieldwork to design distribution models predicting suitable habitats for this species in Tamil Nadu. 1. Methods 2. Questionnaire Surveys The assessment of the presence or absence of hedgehogs in Tamil Nadu was made through questionnaire surveys. We conducted questionnaire surveys from 2016 to 2023 to assess the hedgehog distribution in Tamil Nadu, which involved selecting diverse potential villages in the Tenkasi, Tuticorin, Tirunelveli, Sivagangai, Ramanathapuram, Kanyakumari, Tiruppur, Erode and Coimbatore districts to gather first-hand information. Structured and semi-structured questionnaires (supplementary material 1) were designed, addressing aspects such as observed behaviour, habitat preferences, past sightings, optimal sighting times, seasonal variations, traditional uses of hedgehog body parts, addressing concerns about hunting, ethical treatment and potential threats, including habitat loss. Local communities were actively engaged, and interviews were conducted with residents, farmers, and individuals possessing local ecological knowledge. Geographic coordinates of hedgehog sightings were recorded using Garmin etrex10 GPS for accurate mapping. The collected data were then analyzed to identify distribution patterns and regions with notable changes in hedgehog presence in Tamil Nadu (Fig. 1). 3. Data collection from newspapers The use of newspapers as a platform for documenting and collecting details of wildlife rescue operations has been widely observed. Newspapers effectively serve as a means to disseminate information about rescue operations to a wider audience in Tamil Nadu. Collecting information about the endemic hedgehog in newspapers is an effective strategy, as the newspapers serve as a platform to provide authentic up-to-date insights on rescue operations and raising awareness, especially on the ecological importance of the hedgehogs across districts. We supplemented our questionnaire dataset with records from newspaper data published between 2000 and 2023. This data was systematically gathered from districts including Tiruppur, Coimbatore, Erode, The Nilgiris, Salem, Dindigul, and Virudhunagar, thereby adding data from new districts to our distribution records. The newspapers selected for data extraction were influential local publications, specifically Dhinakaran, Malai Murasu, Dina Thanthi, and The Hindu. We curated our dataset and ensured the infusion of up-to-date data until 2023. 4. Direct visits In congruence, we conducted field visits to locations where the species had previously been spotted, such as Uppar dam (GPS: 10.770, 77.417) and Vijayapuram (GPS: 11.0947, 77.4091) in Tiruppur; Idaiyapatti (GPS: 9.9397, 78.2798) in Madurai; Theri Kaadu (GPS: 8.5292, 78.0048) in Tuticorin; Kalangal (GPS: 10.9964, 77.1381) in Coimbatore and Chennimalai (GPS: 11.160417, 77.600811) in Erode. These field visits provided insights into the behaviour and habitat preferences of the hedgehogs. Triggered by local reports of hedgehog roadkill incidents, we carried out field visits to collect specimens (road killed/dead) for future taxonomic analysis, thereby contributing crucial biological data to delineate the distribution and ecological data. These specimens serve as an ecological indicator, shedding light on the species’ presence in specific habitats. The field visits were tactically designed to transcend mere specimen collection, incorporating a dynamic ecological approach that engaged local communities. The community engagement involved detailed conversation sessions to extract qualitative information on observed hedgehog behaviour, ecological interactions and potential threats. During these field visits, we recorded ecological details of the specific locations inhabited by the hedgehogs to provide background information on the habitats for further use in our models. 2.4. Study area FIGURE 1 Map showing the occurrence records of hedgehogs across the study area plotted over the 30 m Shuttle Radar Topography Mission Elevation raster. Modelling approaches: All models and data analyses of this research were performed in RStudio Version 2024.04.0 Environmental variables We carefully selected 12 predictor variables for modelling based on the ecology of the hedgehog. The 12 predictor variables were Bio 1 (Annual Mean Temperature), Bio 3 (Isothermality), Bio 12 (Annual Precipitation), Elevation, Terrain Ruggedness Index (TRI), distance to forest cover, cropland, built-up area, road, water, human modification index (HMI) and Normalized Difference Vegetation Index (NDVI). To ensure consistency and account for the limitations in our data, we focused on annual mean variables sourced from WorldClim (http://www.worldclim.org) (Hijmans et al 2005). These variables included Bio 1, Bio 3, and Bio 12 to capture essential ecological factors. For elevation data, we utilized the Shuttle Radar Topography Mission (SRTM) 30m digital elevation model (DEM) developed by Farr et al in 2007. We calculated the TRI using R packages raster (Hijmans 2012) and rgdal (Bivand et al 2023). Categorical data, such as forest cover, croplands, and built-up areas, were extracted in raster format from the ESA World Cover 2022 database, derived from modified Copernicus Sentinel data (2020) processed by the ESA WorldCover consortium. The layers, such as road and water, were extracted from the open-source database (https://www.hotosm.org/). We converted the layers into polygons and computed their Euclidean distances using the rgeos package (Bivand et al 2022). Additionally, we accessed the Normalized Difference Vegetation Index (NDVI) from the Bhuvan database (https://bhuvan-app3.nrsc.gov.in/data/download/index.php). Compiling monthly data from 2016 to 2023, we derived maximum NDVI values and transformed them into a single raster layer in QGIS (Yang et al 2011). The human Modification Index (Kennedy 2019) was downloaded from the Socioeconomic Data and Applications Center (sedan) website. To ensure consistency in our analysis, we rescaled all environmental variables to a uniform 1-kilometre resolution using the raster package within the R environment. We performed a Pearson correlation coefficient analysis, setting a threshold of 0.75 addressing potential multicollinearity issues among these variables. This analysis, which was performed using the ggplot2 package (Wickham 2016) and the ggcorrplot function, revealed no significant collinearity among the variables. Hence, we retained and utilized these variables in our modelling process. Ensemble modelling We expanded our dataset by generating pseudo-absence records and matching the number of presence records using the gRandom method in the SDM package (Naimi and Araujo 2016). Our modelling approach involved exploring nine candidate models accessible within the SDM (Naimi & Araouj 2016) package: Generalized Linear Models (GLM), Boosted Regression Trees (BRT), Random Forests (RF), Flexible Discriminant Analysis (FDA), Mixture Discriminant Analysis (MDA), Multivariate Adaptive Regression Splines (MARS), Generalized Additive Model (GAM), Recursive Partitioning and Regression Trees (RPRT), Maximum likelihood (Maxlike) and Support Vector Machines (SVM). These models were utilised to create an ensemble model that joined their predictive capabilities. Out of the initial 580 species occurrence records gathered through the fieldwork, we randomly allocated 30% to test the models’ accuracy, with the remaining 70% being allocated for training. This process was iterated five times to compute mean values for crucial evaluation metrics—sensitivity, specificity, True Skill Statistics (TSS), kappa, Area under the Curve (AUC), and correlation—assessing the models’ accuracy collectively. To ensure robust predictive accuracy and minimise variance, we employed the bootstrapping replication method, consistent with methodologies in prior studies (Ahmed et al 2021; Harrell et al 1996; Lima et al 2019), for running individual algorithms. Subsequently, we combined the outputs of independent algorithms using a weighted averaging approach, utilising TSS as the evaluation criterion and setting a threshold at maximum sensitivity + sensitivity. The ensemble output presents probabilities of the hedgehog’s habitat suitability from 0 to 1, with values closer to 1 indicating higher suitability. This output layer was exported to QGIS 3.30.1 to classify and map the hedgehogs’ habitat-suitability areas. We classified the habitat suitability into four levels: ”Unsuitable”, ”Less suitable”, ”Moderately suitable”, and ”Highly suitable” using QGIS’s symbology function. Total suitability areas were calculated by summing the ”Moderately suitable” and ”Highly suitable” categories. 3. Results The fieldwork encompassed various regions across the Tirunelveli and Thoothukudi districts, totalling 124 days of study at nine sites, including the Radhapurum dry zones, the Gangaikondan grasslands, urban areas of Tiruchendur, the Theri kaadu or Kuthirai Mozhi Theri (red sand dunes) of Tuticorin, the Tenkasi district, the Ambasamudram Taluk, the Nanguneri shrublands, the Ittamozi red sandy areas and areas surrounding the Ottapidaaram villages. In addition to this dataset, information on hedgehog distribution was provided through community interviews involving 855 respondents from 90 villages in the Tirunelveli and Thoothukudi districts. The ecological insights gathered through responses from questionnaire surveys and direct visits involving interaction with local communities who encounter the hedgehogs in various habitats provided data on their preferred habitat types and spatial distribution. The adaptability towards various urban environments showcases a preference for a diverse range of habitat types. The habitat selection includes abandoned areas like urban wastelands, open areas with grass cover, providing foraging opportunities; grasslands with thorny vegetation such as Acacia sp. and Prosopis juliflora cover; woodlands; hedgerows, urban landscapes; human settlements with a mix of infrastructure and green spaces; unused or temporarily abandoned agricultural lands, urban meadows; edges along urban rivers or water bodies; graveyards with diverse vegetation; shrubs in and around industrial zones; shrubs along railway tracks; educational institutions with landscaped grounds adjacent to forest; within or around vegetable markets; and vegetated strips along urban roads. This species has adapted to thrive in arid to semi-arid regions in both rural and urban landscapes, while occasionally dwelling near human settlements for foraging. These regions consist of heterogeneous vegetation dominated by thorny trees and bushes, contributing to the hedgehog’s habitat. The species’ ability to inhabit diverse habitats ranging from abandoned grasslands to human-encroached urban spaces highlights its ecological resilience and adaptability. 3.1 Data collection from newspapers According to the newspaper records till 2023, hedgehogs were present in the villages of Kannampalayam, Karumathampatti, Pongalur, Saravanampatti, Verkeralam in the Coimbatore district; Palani in the Dindugal district; Arachalur, Avinashi, Chennimalai, Gobichettipalayam, Kodumudi, Modakurichi in the Erode district; Sayalkudi, Iruveli, Thathagapatti in the Salem district; Coonur in the Nilgiris district; Andipalayam, Anumaarpalayam, Kangayam, Madathukulam, Mangalam, Mulanur, Palladam, Raakiyapalayam, Settiipalayam, Perumanallur, Udumalai Pettai, Uthukulli, Verapandi in the Tiruppur district; and Satur as well as Kovilpatti in the Viruthu Nagar district. 3.2 Model validation The Random Forests model was the best predictor model compared to the others (Table 1). It achieved the highest Area Under the Curve (AUC) value of 0.94, indicating solid performance. Additionally, it demonstrated a high Correct Classification Rate (CCR) of 0.87, a substantial Correlation Coefficient (COR) of 0.79, and a low Deviance of 0.65. The True Skill Statistic (TSS) and Kappa values are also notable at 0.75 and 0.74, respectively. BRT also performed well, with a competitive AUC of 0.89 and a CCR of 0.82. It showed a good balance between Sensitivity (0.82) and Specificity (0.82), with a threshold of 0.52. Other models, such as FDA, GAM, and MARS, also demonstrated reasonable performance but were outperformed by Random Forest. The Random Forest was the best predictor model based on the metrics provided for this analysis. TABLE 1 This table illustrates the performance of the nine different models tested. The Random Forests model (RF) performed best, providing information on Area Under the ROC Curve (AUC), Correlation (COR), Deviance Prevalence, True Skill Statistic (TSS), Kappa, Threshold, True Positive Rate (Sensitivity), True Negative Rate (Specificity), Normalized Mutual Information (NMI), Matthews Correlation Coefficient (Phi), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Correct Classification Rate (CCR) for each model tested. Model ID AUC COR Deviance Prevalence TSS Kappa Threshold Sensitivity Specificity NMI Phi PPV NPV CCR Prevalence brt 0.89 0.66 1.05 0.45 0.65 0.65 0.52 0.83 0.83 0.34 0.65 0.80 0.85 0.83 0.47 fda 0.82 0.55 1.07 0.45 0.47 0.47 0.57 0.73 0.74 0.17 0.47 0.70 0.77 0.73 0.48 gam 0.82 0.64 8.85 0.44 0.60 0.60 0.27 0.80 0.80 0.30 0.60 0.76 0.83 0.80 0.46 glm 0.81 0.54 1.07 0.45 0.47 0.47 0.53 0.73 0.74 0.17 0.47 0.70 0.77 0.74 0.48 mars 0.86 0.64 1.94 0.45 0.59 0.59 0.62 0.80 0.80 0.27 0.59 0.77 0.82 0.80 0.47 maxlike 0.76 0.48 2.34 0.45 0.39 0.38 0.74 0.69 0.70 0.13 0.38 0.65 0.73 0.69 0.48 mda 0.85 0.60 1.25 0.45 0.53 0.52 0.62 0.76 0.77 0.22 0.53 0.73 0.79 0.76 0.47 rf 0.95 0.79 0.66 0.45 0.75 0.75 0.56 0.88 0.88 0.46 0.75 0.86 0.89 0.88 0.47 rpart 0.81 0.56 1.44 0.46 0.52 0.52 0.48 0.75 0.77 0.21 0.52 0.74 0.78 0.76 0.46 Abbreviations of Model ID’s: BRT: Boosted Regression Trees, FDA: Flexible Discriminant Analysis, GAM: Generalized Additive Model, GLM: Generalized Linner Model, MARS: Multivariate Adaptive Regression Splines, MAXIKE: Maximum Likelihood Model, MDA: Mixture Discriminant Analysis, PF: Random Forest, RPART: Recursive Partitioning and Regression Trees. 3.3 Distribution and Predictors of Madras Hedgehog Habitat According to our model, the predicted species’ distribution is prominent in the southwest and northwest regions of Tamil Nadu. Districts like the Ramanathapuram, Sivagangai, Tirunelveli, Tenkasi, Kanyakumari, Dindigul, Tiruppur, Erode, Coimbatore, Hasanur and Sathyamangalam had a higher amount of suitable areas. The model predicted a total area of 7,737 km² as highly suitable and 39,456 km² as moderately suitable, constituting approximately 5.95% and 30.34% of Tamil Nadu’s total 130,058 km², respectively. The highly and moderately suitable areas account for 36.29% of the state’s total area. Bio 1, Bio 3, and DEM variables chiefly influenced the distribution model of the hedgehog species (Fig. 2). Suitable habitats for the hedgehogs were found predominantly near forested regions, distant from urban development and agricultural land, at lower elevations, away from human alterations, and in proximity to water bodies. However, suitability decreased notably in areas with higher mean temperature values (Bio 1), indicating that this species’ suitable niche lies in regions with moderate temperatures (Fig. 3). FIGURE 2 The relative variable importance of the various environmental variables used in the modelling: A. Distance to the water body; B. terrain ruggedness index; C. distance to road; D. Normalized Difference Vegetation Index; E. human modification index; F. distance to forest cover; G. digital elevation model; H. distance to cropland; I. distance to build-up area; J. Bio 3; K. Bio 12; L. Bio 1. FIGURE 3 The partial response curve of the environmental variables used to predict the distribution of hedgehogs. The Y-axis indicates the probability of the prediction, and the X-axis indicates the values of the variables. The insights gained from the distribution models informed the creation of the following distribution prediction map (FIGURE 4). FIGURE 4 The predicted Madras hedgehog distribution in Tamil Nadu based on the ensemble modelling output. Discussion Distribution in Western plains of Tamil Nadu Our collected distribution records show that the hedgehogs exhibit a notable preference for the unique ecosystem type called “Korangadu grasslands”, defined as a traditional pastureland farming system in western Tamil Nadu, India (Kumar et al 2018b), which also aligns with the predicted distribution (Fig. 4). These open expanses, characterised by semi-arid savannah and grasslands, are spread over 50,000 hectares across four dominant western districts in the Coimbatore, Erode, Tiruppur and Dindigul districts of Tamil Nadu (Vivekanandan 2007). In the western part of the region, the Korangadu grasslands in Coimbatore District were once a hotspot for P. nudiventris , as it was previously sighted in Kaaramadai, Thondamuthur, Kannampalayam, Pogalur, Kovilpalayam, Verkeralam, Saravanampatti, Pongalur, Kannampalayam, Karumathampatti and Chinnavedampatti areas. The Tiruppur district in Tamil Nadu exhibits diverse habitat types, encompassing arid grasslands, rural agricultural fields and wetlands, which provide a home for various species of fauna, including the hedgehog. The region is known for its rich floral diversity in rural areas, as evidenced by surveys conducted in Udumalpet Taluk (Radha et al 2020). Moreover, Tiruppur is home to the Nanjarayan Tank, designated as Tamil Nadu’s 17th bird sanctuary, providing a habitat for migratory birds. The wetlands of the Tiruppur district play a crucial role in supporting various ecosystems and avian species (Priya and Varunprasath 2018). According to the results of our questionnaire surveys, the hedgehog is present in the villages of Mulanur, Palladam, Pongupalayam, Verapandi, Udumalaipettai, Settipalayam, Andipalayam, Madathukulam, Anumaarpalayam, Raakiyapalayam, Mangalam, Kangayam, Uthukulli and Sinnaandipalayam within the Tiruppur district. It was also sighted closer to the villages adjoining the Nanjarayan Tanks, such as Sathasivam Nagar, Andipalayam, Veliayampudur, Chettiyarpalayam, Jeganathanagar, Ganapathipalayam and Madathukulam. Rapid urbanisation and the consequent decline in fallow land within the Tiruppur district might have significantly impacted hedgehog populations. The district has experienced rapid urbanization, leading to a vegetation loss of 38.76% and an increase in water body encroachment by 15.78%, which might result in reduced suitable habitats for the hedgehogs in the area (Prabu and Dar 2018). The decrease in fallow land by 17.52% due to rapid industrial expansion has fragmented and shrunk natural habitats, thereby posing a significant threat to resident hedgehog populations (Krishnaraaju et al 2021). The reduction in fallow land areas and the consequent rise in habitat fragmentation have isolated local hedgehog populations. This isolation exposes them to increased risks in the form of vulnerability to predation, scarcity of essential resources elevated rates of road accidents, and potentially inbreeding depression. As their habitats become increasingly fragmented, hedgehogs face greater challenges in finding suitable shelter, foraging grounds, and safe corridors for movement. It is described that the key factor contributing to the increase in so-called urban heat islands is leading to adverse effects on human health and the overall species in the environment (Ramasamy et al 2020). Our model prediction indicates that Chennimalai, as well as the Arachalur areas in the Erode district, have notable hotspots for P. nudiventris ; the predicted distribution extends further to a range of habitats such as the hillocks of Uthiyur, sub-urban landscapes inTiruppur and grasslands (Fig. 5c) and the plains of Dharapuram in the Tiruppur district to the foothills of Palani in the Dindigul district. FIGURE 5 a . P.nudiventris in Theri Kaadu, Tuticorin; b. Roadkill of juvenile hedgehog in Theri Kaadu, Tuticorin; c. In suburban landscapes of Tiruppur; d. Urban areas of Satankulam, Tirunelveli. Photo credit: Abinesh Muthaiyan Distribution in Central plains of Tamil Nadu Furthermore, the model indicated that Ralkal, Saptur, Chikkkimangalam and the Vellimalai forest of Idayapatti in the Madurai district, known for its thorny vegetation and wild shrubbery, offering a diverse ecosystem with ample food resources, are potentially also suitable habitats for the Madras hedgehog. The western regions of Madurai are characterized by a rocky highland ecosystem featuring dry, thorny forests that support rare Kadambam trees and various Acacia spp. in this distinctive habitat, which significantly enhances the biodiversity of the Madurai district as the ecological conditions in this area are particularly conducive to hedgehogs, which thrive in rocky terrains with semi-arid weather patterns. The interactions between these dry forest ecosystems and the diverse flora create critical niches that support the resilience and stability of hedgehog populations in this region. Our direct observations near Idayapatti support a thriving population of hedgehogs, as the dry deciduous forest in this area provides essential shelter and food resources, creating an optimal environment for hedgehogs to flourish (Gazzard et al 2022; Pettett 2015). According to (Kumar et al 2024), the species is widely distributed in the Southern regions such as arid regions in Satakulam, Tirunelveli (Fig. 5d), Tenkasi, Sivagangai, Ramanathapuram and Kanya Kumari. However, Tiruchendur, Maharajapuram, Therku Karunkulam, Chettikulam, Srivaikundam and Kuthirai Mozhi Theri (red sand dunes) had more sightings in the past than the present, as reported by local people over the years in the Tuticorin district. Theri Kaadu (red sand dunes) in the Tuticorin district has distinct dry arid landscapes with massive red sand deposits (Fig. 5a). In general, P. nudiventris and other small mammals thrive in areas with minimal anthropogenic impact (Kumar et al 2019a). However, expanding road networks in such unique landscapes often results in hedgehogs and other small mammals becoming roadkill victims (Fig. 5b). Based on data from our surveys, Idaikal, Gangaikondaan, Palayamkottai, Maanur, Thalayuthu, Marakaanam, Vadivalpuram, Uralvaimozhi, Chella Durai Nagar, Radhapuram, Nakkaneri, Sankar Nagar, Sanganapuram and Pathamadai (Kumar el al 2019b) in the Tirunelveli district acts as a notable hotspot where the hedgehog sightings were higher in the last decade in comparison with other areas. Moreover, the grasslands in the Tirunelveli and Tuticorin districts support a diverse array of flora and fauna, which includes some endemic species such as Sitana maruthamneithal and Indigofera kudiraimozhiensis (Deepak et al 2016, Selvakumari and Rajkumar 2014). The villages of Irumbur, Kalaikulam, Kannagipuram, Kannanore, Karunchuthi, M. Managudi, Manavaryan, Mangulam, Melathuraiyoor, Memmeli, Nagamugunthankudi, Nenjathoor, Panangudi, Perungarai, Pottagavayal, Pudur, Pullyankulam, Sakkarapani, Salayoor, Sathani, Sembar, Thadyamangalam, Tiruvallur, Thohavoor, Vilangulam, Peryakannanor in the Sivagangai district and Mayladi, Marungur, Amarathi vilai, Aral Kumarapuram, Kannanapuram, sozhapuram, Muppanthal, Kaaval kiranu, Perungaliyapuram, Thikkalam Malai, Raamanputhur, Naavalkaadu, Andoor and Kovil Puram in the Kanya Kumari district had a notable number of hedgehog sightings in the past, which is also fitting with the model predicting the species’ distribution, which indicates suitable habitats in this region. Conclusion Our research, combining actual distribution records and models predicting distribution, demonstrates an intricate relationship between the elusive P. nudiventris , commonly known as the Madras hedgehog, and the unique grassland ecosystems in Tamil Nadu. Our results highlight the critical importance of preserving these habitats for the benefit of the conservation of the hedgehog. The species demonstrates a notable affinity towards the Korangadu grasslands, which spread across Coimbatore, Erode, Karur, Tiruppur and Dindigul districts. Insights from previous studies showed that the hedgehog was abundant and known to inhabit in southern grassland patches of Sivagangai, Ramanathapuram, Kanya Kumari and Red Sand Dunes in Tirunelveli and Tuticorin. However, these grasslands face multifaceted threats, including urbanization, industrial expansion, and changes in land use, leading to habitat fragmentation and declining hedgehog populations. Recognizing the ecological significance of grasslands as keystone habitats for hedgehogs, efforts are imperative to mitigate anthropogenic impacts, ensure habitat connectivity, and implement precise mapping techniques for effective conservation and restoration strategies. Preserving the unique biodiversity of grasslands secures the hedgehog. It contributes to overall ecosystem resilience, emphasising the need for sustainable land management practices and acknowledging community rights in safeguarding these vital landscapes. Acknowledgements: We are particularly grateful to the Tamil Nadu Forest Department (Chennai), Tamil Nadu Biodiversity Board and the District Forest Officers granting us the necessary permissions to conduct our research at these critical sites as evidenced by the permissions they granted (proceedings number: WL5(A)31710/2021; permission no: 92/2022). We are also thankful for the administrative support provided by LIFE Trust India and the ACTIF, which has enabled us to bring this project to life. A special thanks to IDEA WILD for essential equipment support. We acknowledge the efforts of Mohammed Thanvir, Mohammed Shahidh and other volunteers who have worked tirelessly to conduct both field and social surveys in different locations in Tamil Nadu. We thank TAAL Tech India Private Limited, India for funding the project. Conflict of Interest: None Author Contributions: Brawin Kumar and Abinesh Muthaiyan conducted fieldwork and collected data. Thekke Thumbath Shameer performed the analysis and authored the first draft of the analysis section. Abinesh Muthaiyan then wrote the second draft of the manuscript; Sophie Lund Rasmussen reviewed the analysis for accuracy and made necessary changes. Abinesh Muthaiyan prepared all the images in the manuscript and Thekke Thumbath Shameer created the maps. All authors have reviewed and approved the final version of the manuscript. Ethical Conduct : Our research activities received approval from the Tamil Nadu State Forest Department and Tamil Nadu Biodiversity Board, and we obtained permission from local forest department authorities to conduct fieldwork and community interviews. Data Availability Statement: I confirm that the Data Availability Statement has been included in the main file of my submission. That access to all necessary data files is provided for editors and reviewers. Reference: Araujo, M. B., & New, M. (2007) Ensemble forecasting of species distributions. Trends in Ecology & Evolution 1:42–47. https://doi.org/10.1016/j.tree.2006.09.010 Ascensão, F., Barrientos, R., & D’Amico, M. (2021) Wildlife collisions put a dent in road safety. Science 374(6572):1208–1208. DOI: 10.1126/science.abm8468 Bijlmakers, J., Griffioen, J., & Karssenberg, D. (2023) Environmental drivers of spatio–temporal dynamics in floodplain vegetation: grasslands as habitat for megafauna in Bardia National Park (Nepal). Biogeosciences 20(6):1113–1144. https://doi.org/10.5194/bg-20-1113-2023 Biswas, B., & Ghose, R. K. (1970) Taxonomic notes on the Indian pale hedgehogs of the genus Paraechinus Trouessart , with descriptions of a new species and subspecies. Mammalia 34: 467–477. https://doi.org/10.1515/mamm.1970.34.3.467 Chakraborty, S., Srinivasulu, C., & Molur, S. (2017) Paraechinus nudiventris . The IUCN Red List of Threatened Species 2017:e.T39594A22326706. https://dx.doi.org/10.2305/IUCN.UK.2017-2.RLTS.T39594A22326706.en. Accessed on 09 May 2024 Cabeza, M., Araújo, M. B., Wilson, R. J., Thomas, C. D., R. Cowley, M. J., & Moilanen, A. (2004). Combining probabilities of occurrence with spatial reserve design. Journal of Applied Ecology , 41 (2), 252-262. https://doi.org/10.1111/j.0021-8901.2004.00905.x Deepak, V., Khandekar, A., Varma, S., & Chaitanya, R. (2016) Description of a new species of Sitana Cuvier , 1829 from southern India. Zootaxa 4139 (2):167–182 http://doi.org/10.11646/zootaxa.4139.2.2 Lutz, J. A., & Franklin, J. F. (2010). Climatic water deficit, tree species ranges, and climate change in Yosemite National Park. Journal of Biogeography, 37 (5), 936-950. https://doi.org/10.1111/j.1365-2699.2009.02268.x Frost, D. R., Wozencraft, W. C., & Hoffmann, R. S. (1991) Phylogenetic relationships of hedgehogs and gymnures (Mammalia: Insectivora: Erinaceidae). Smithsonian Contributions to Zoology , 518: 1–69 Elith, J., Leathwick, J. R., & Hastie T (2008) A working guide to boosted regression trees. Journal of Animal Ecology 77:802–813 https://doi.org/10.1111/j.1365-2656.2008.01390.x Gazzard, A., Yarnell, R. W., & Baker, P. J. (2022) Fine–scale habitat selection of a small mammalian urban adapter: the West European hedgehog ( Erinaceus europaeus ). Mammalian Biology 102(2):387–403 https://doi.org/10.1007/s42991-022-00251-5 Glista, D. J., DeVault, T. L., & DeWoody, J. A. (2009) A review of mitigation measures for reducing wildlife mortality on roadways. Landscape and Urban Planning 91 (1):1–7 https://doi.org/10.1016/j.landurbplan.2008.11.001 Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling , 135 (2–3), 147–186 https://doi.org/10.1016/S0304-3800(00)00354-9 Hutterer, R. (2005) Order Erinaceomorpha. In: D. E. Wilson and D. A. Reeder (eds), Mammal Species of the World 212–219 Johns Hopkins University Press, Baltimore, Maryland, USA IUCN (2017) The IUCN Red List of Threatened Species. Version 2017–2. Available at: www.iucnredlist.org. Accessed on 14 September 2017 Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch‐Mordo, S., & Kiesecker, J. (2019). Managing the middle: A shift in conservation priorities based on the global human modification gradient. Global change biology , 25 (3), 811-826. https://doi.org/10.1111/gcb.14549 Kohli, M., Borer, E. T., Kinkel, L., & Seabloom, E. W. (2019). Stability of grassland production is robust to changes in the consumer food web. Ecology Letters , 22 (4), 707-716. https://doi.org/10.1111/ele.13232 Krishnaraaju, G., & Elangovan, K. (2021, December). Evaluation of Spatial land use/land cover changes due to Urbanization and Industrialization in Tirupur city using Remote Sensing. In Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India . http://dx.doi.org/10.4108/eai.7-12-2021.2314680 Kumar, B., & P, Iyer., (2013). Pledge for Hedgehogs. Zoo’s Print Magazine 28 (7): 1–3 Kumar, B., & V, Nijman. (2016). Medicinal uses and trade of Madras hedgehogs Paraechinus nudiventris in Tamil Nadu, India. Traffic Bulletin . 28: 7–10 Kumar, B., Babu, S., & Kumara, H. N. (2019). Predicting the potential distribution of the lesser-known endemic Madras hedgehog Paraechinus nudiventris (Order: Eulipotyphla, Family: Erinaceidae) in southern India. Mammalia , 83 (5), 470-478. https://doi.org/10.1515/mammalia-2018-0101 Kumar, B., Togo, J., & Singh, R. (2019). The South Indian hedgehog Paraechinus nudiventris (Horsfield, 1851): review of distribution data, additional localities and comments on habitat and conservation. Mammalia , 83 (4), 399-409. https://doi.org/10.1515/mammalia-2018-0128 Kumar, B., Rasmussen, S. L., Thanvir, M., & Shahidh, M. (2024). Mapping the Distribution of the Endemic Madras Hedgehog Paraechinus nudiventris in Tamil Nadu, India. Conservation , 4 (4), 812-829. https://doi.org/10.3390/conservation4040048 Kumar, B., Talmale, S. S., & Molur, S. (2020, June). New data on morphometry and appendicular skeleton of South Indian hedgehog Paraechinus nudiventris Horsfield, 1851 (Mammalia: Eulipotyphla: Erinaceidae) from urban landscapes of Tamil Nadu, India. In Proceedings of the Zoological Society (Vol. 73, pp. 205-214). Springer India. https://doi.org/10.1007/s12595-020-00321-x Lalitha, M., Dharumarajan, S., Khandal, S., Koyal, A., Parvathy, S., Kalaiselvi, B., … & Hegde, R. (2022). Vertical distribution of soil organic and inorganic carbon under silvipastoral system in a dry semiarid agro-ecological region, Tamil Nadu, India. Range Management and Agroforestry , 43 (1), 57-65. Marimuthu, R., & Asokan, K. (2014) Bare–bellied or Madras Hedgehog, Paraechinus nudiventris , (Horsfield 1851) in Coimbatore, Tamil Nadu. Magazine of Zoo Outreach Organization 33 Boval, M., & Dixon, R. M. (2012). The importance of grasslands for animal production and other functions: a review on management and methodological progress in the tropics. Animal , 6 (5), 748-762. doi: 10.1017/S1751731112000304 Monge-Nájera, J. (2018). Road kills in tropical ecosystems: a review with recommendations for mitigation and for new research. Revista de biologia tropical , 66 (2), 722-738. http://dx.doi.org/10.15517/rbt.v66i2.33404 Molur, S., Srinivasulu, C., Srinivasulu, B., Walker, S., Nameer, P. O., & Ravikumar, L. (2005). Status of south Asian non-volant small mammals: conservation assessment and management plan (CAMP) workshop report. Zoo Outreach Organization/CBSG-South Asia, Coimbatore, India , 618 . Naimi, B., & Araújo, M. B. (2016). sdm: a reproducible and extensible R platform for species distribution modelling. Ecography , 39 (4), 368-375. https://doi.org/10.1111/ecog.01881 Pacifici, M., Santini, L., Di Marco, M., Baisero, D., Francucci, L., Marasini, G. G., … & Rondinini, C. (2013). Generation length for mammals. Nature Conservation , 5 , 89-94. DOI: 10.3897/natureconservation.5.5734 Pettett, C. (2015). Factors affecting hedgehog distribution and habitat selection in rural landscapes (Doctoral dissertation, University of Oxford). Pettit, N. E., & Naiman, R. J. (2007). Fire in the riparian zone: characteristics and ecological consequences. Ecosystems , 10 , 673-687. DOI: 10.1007/s10021-007-9048-5 Pimm, S. L., Russell, G. J., Gittleman, J. L., & Brooks, T. M. (1995). The future of biodiversity. Science , 269 (5222), 347-350. https://doi.org/10.1126/science.269.5222.347 Prabu, P., & Dar, M. A. (2018). Land-use/cover change in Coimbatore urban area (Tamil Nadu, India)—a remote sensing and GIS-based study. Environmental monitoring and assessment , 190 , 1-14. https://doi.org/10.1007/s10661-018-6807-z Priya, P. J., & Varunprasath, K. (2018). Survey of Wetlands in and around Tiruppur district, Tamil Nadu, India. Annual Research & Review in Biology , 27 (2), 1-13. https://doi.org/10.9734/ARRB/2018/41419 Radha, P., Nagaraj, R., Udhayavani, C., & Sivaranjani, K. (2020). A survey on the floral diversity of rural areas in Udumalpet Taluk, Tiruppur District, Tamil Nadu, India. Bangladesh Journal of Plant Taxonomy , 27 (1). DOI: 10.3329/bjpt.v27i1.47575 Rajkumar, R., Shaijumon, C. S., Gopakumar, B., & Gopalakrishnan, D. (2020). Extreme rainfall and drought events in Tamil Nadu, India. Climate Research , 80 (3), 175-188. https://doi.org/10.3354/cr01600 Rajkumar, R., & Elangovan, K. (2020). Impact of urbanisation on formation of urban heat island in Tirupur region using geospatial technique. IJMS 49 . 1593–1598. Raman, S., Shameer, T. T., Charles, B., & Sanil, R. (2020). Habitat suitability model of endangered Latidens salimalii and the probable consequences of global warming. Tropical ecology , 61 (4), 570-582. Raman, S., Shameer, T. T., Pooja, U., & Hughes, A. C. (2023). Identifying priority areas for bat conservation in the Western Ghats mountain range, peninsular India. Journal of Mammalogy , 104 (1), 49-61. https://doi.org/10.1093/jmammal/gyac060 Pellaton, R., Lellei-Kovács, E., & Báldi, A. (2022). Cultural ecosystem services in European grasslands: A systematic review of threats. Ambio , 51 (12), 2462-2477. Journal of Human Environmental Studies, 51(12):2462–77 DOI: 10.1007/s13280–022–01755–7 Rondinini, C., Di Marco, M., Chiozza, F., Santulli, G., Baisero, D., Visconti, P., … & Boitani, L. (2011). Global habitat suitability models of terrestrial mammals. Philosophical Transactions of the Royal Society B: Biological Sciences , 366 (1578), 2633-2641. https://doi.org/10.1098/rstb.2011.0113 Sala, O. E., Stuart Chapin, F. I. I. I., Armesto, J. J., Berlow, E., Bloomfield, J., Dirzo, R., … & Wall, D. H. (2000). Global biodiversity scenarios for the year 2100. Science , 287 (5459), 1770-1774. https://doi.org/10.1126/science.287.5459.1770 Saravanan, A., Mathivanan, M., & Ganesh, T. (2016). Madras hedgehogs in the Tirunelveli and Thoothukudi districts of Tamil Nadu. Small Mammal Mail , 8 (2). Selvakumari, R., & Rajakumar, T. J. S. (2014). Indigofera kudiraimozhiensis -a new species of Fabaceae from Kudiraimozhi Theri, Thoothukudi district, Tamil Nadu, India. Indian Journal of Forestry , 37 (3), 309-310 DOI: 10.1515/MAMMALIA–2018–0101 Shameer, T. T., Backer, S. J., Yogesh, J., Mujawar, A. N., Ali, S. Z., Raman, S., … & Sanil, R. (2023). Phenotypic variations, habitat suitability, and diel activity of the endemic brown palm civets. Geology, Ecology, and Landscapes , 7 (4), 311-322. https://doi.org/10.1080/24749508.2021.1971411 Shameer, T. T., Backer, S. J., Nandhini, S., Raman, S., Mujawar, A. N., Yogesh, J., … & Sanil, R. (2022). How do the sympatric forest mongooses coexist in the Western Ghats landscape? Insights from spatio-temporal approach. Community Ecology , 23 (2), 231-245. https://doi.org/10.1007/s42974-022-00064-4 Shameer, T. T., Mungi, N. A., Backer, S. J., Raman, S., Reddy, S. R., Easa, P. S., & Sanil, R. (2023). Distribution and conservation status of the endemic Nilgiri marten (Martes gwatkinsii). Mammalia , 87 (4), 360-366. https://doi.org/10.1515/mammalia-2021-0113 Solberg, E. J., Saether, B. E., Strand, O., & Loison, A. (1999). Dynamics of a harvested moose population in a variable environment. Journal of Animal Ecology , 68 (1), 186-204. https://doi.org/10.1046/j.1365-2656.1999.00275.x Saravanakumar, S., Shyju, K., Rakesh S. S., Shrirangasami S. R., Murugaragavan, R., Elangovan, R., & P. T. Ramesh. (2021) Impact of climate change on farming community and adaptation measures in semi-arid region of Tamil Nadu. IJCS 8 (6):2804-9 https://doi.org/10.22271/chemi.2020.v8.i6an.11206 Thomas, S., Mathew, L., Joseph, S. K., Mary, P. C., Varghese, D., Anto, A., … & Lawrence, V. Sanjayankumar. 2012. A Reconnaissance Study of Animal Diversity in Periyar Tiger Reserve, Kerala with special reference to Invertebrates. Science and Society , 10 , 173-184. Vivekanandan, P. (2007) Korangadu: A traditional pastureland farming system in the drylands of Tamil Nadu, South India. SEVA Foundation: Madurai, India Wennström, P. (2018) Perspectives on the pastoral landscape: Combining remote sensing observations and pastoralist perceptions in southern Tamil Nadu, India. Asia in Focus: A Nordic journal on Asia by early career researchers (5):15–27 Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org Williams, P. H., & Araújo, M. B. (2000). Using probability of persistence to identify important areas for biodiversity conservation. Proceedings of the Royal Society B: Biological Sciences , 267 (1456), 1959–1966. https://doi.org/10.1098/rspb.2000.1236 Yang, Y., Xu, J., Hong, Y., & Lv, G. (2011) The dynamic of vegetation coverage and its response to climate factors in Inner Mongolia, China. Stochastic Environmental Research and Risk Assessment, 26 :357–373 https://doi.org/10.1007/s00477-011-0481-9 Yarnell, R. W., & Pettett, C. E. (2020) Beneficial land management for hedgehogs ( Erinaceus europaeus ) in the United Kingdom. Animals. 10 (9) p.1566 https://doi.org/10.3390/ani10091566 Zeng, Y., He, K., Chen, X., Bai, W., Lin, H., Chen, J., … & Liu, Y. (2022). Museum specimens shedding light on the evolutionary history and hidden diversity of the hedgehog family Erinaceidae. DOI: https://doi.org/10.21203/rs.3.rs-2160585/v1 Supplementary files Annexture 1: | Coimbatore | Kovilpalayam, Perinayakanpalayam, Kalangal, Appanaikenpatti, Kannampalayam, Pollachi, Karumathampatti, Annur, Kaaramadai, Chinnavedampatti, Thondamuthur, Veliayampudur, Pappampatti, Perur | | Erode | Avinashi, Perundurai, Arachalur, Chennimalai, Gobichettipalayam, Nadu Palayam, Near Perundurai, Sathyamangalam, Talavadi, Vellode, Annanagar, Arulavadi, Bhayanapuram, Dottagajanur, Gandhinagar Kalani, Gettavadi, Iggalore, Kalmandipuram, Mallanguli, Marur, Panahahalli, Solagardoddi, Susaipuram, Tamilpuram, Thiganare | | Kanyakumari | Mayladi, Marungur, Amarathi Vilai, Aral Kumarapuram, Kannanaputhur, Sozhapuram, Muppanthal, Kaaval Kinaru, Perungaliyapuram, Thikkalam Malai, Raamanputhur, Naavalkaadu, Andoor, Koyil Puram | | Ramanathapuram | Kadalaadi, Kadugusanthai, Kothangulam, Kottayanthel, Meenangudi, Melaselvanoor, Narasingkootam, Paaduventhal, Punjai, Saayalkudi, Sathankudi, Vellangulam, Veppankulam | | Karur | Maavadi, Anna Nagar | | Madurai | Idayapatti, Vellimalai, Karupayur, T.Kallupatti | | Nagercoil | Radhapuram, Maharajapuram, Chettikulam, Maruthuval Malai | | Sivagangai | Vilakulam, Peryakannanor, Irumbur, Kalaikulam, Kannagipuram, Kannanore, Karunchuthi, M. Manangudi, Manavaryan, Mangulam, Melathuraiyoor, Memmeli, Nagamugunthankudi, Nenjathoor, Panangudi, Perungarai, Pottagavayal, Pudur, Pullyankulam, Sakkarapani, Salayoor, Sathani, Sembar, Thachanendal, Thadyamangalam, Thiruvallur, Thohavoor, Vilangulam, Viyalanoor | | Tenkasi | Five Falls, Mulli Malai, Pudupatti, Kandapatti, Pa. Elanthakulam, Poolangulam, Subramaniyapuram, Karumbanur, Ayyanarkulam, Sivalingapuram, Nettur, Mylapuram, Malayankulam, Thuppakudi, Vellikulam, Ramanadhi Dam, Sambuathi Aaru, Aavoodaiyanoor, Mathalambaarai, Anavankudiyuruppu, Arunachalapuram, Kulayaneri, Aanaikulam, Ammaiyapuram, Senkottai | | Tuticorin | Melapathur, Thopur, Valayadi, Vallikuruchi, Vannavilai, Vellikovil, Therikaadu, Sinnamani Nagar, Vaagaikulam, Harbour Quarters, Puthur, Tharavaikulam, Perur Road, Radhapuram, Srivaikundam area, Kuthirai mozhi Theri, Tiruchendur, Kudankulam, Therku Karunkulam, Uralvaimozhi, Kovilpatti, Nanguneri, Chinna Mani Nagar, Vallanadu Black Buck Sanctuary, Elluvilai, Idayanvilai, Nazarath, Kanchanvilai, Kayamozhi, Maranthalai, Mookuperi, Nazerath, Poochikaadu, Purayur, Thailapuram, Thaivilai, Therikudiyirupu | | Tiruppur | Mulanur, Palladam, Udumalaipettai, Tiruppur-Perumanallur, Verapandi, Settipalayam, Udumalai Pettai, Andipalayam, Madathukulam, Anumaarpalayam, Raakiyapalayam, Mangalam, Kangayam, Uthukulli, Sinnaandipalayam, Uppar Dam, Dharapuram - Poolavadi Rd, Athimarathupalayam, Chinnamolarapatti, Gethalrev, Kalingikattuputhur, Nadu Palayam, Panarathu Palayam, Ponnalipalayam, Rangapalayam, Thasampatti, Thayampalayam, Therpathai, Thimanayakkampalayam, Thondamuthur Pathur, Uppar Dam, Vannapatti. | Tirunelveli | Palayamkottai, Idaikal, Gangaikondaan, Kadaya Nallur, Kalugumalai, Karunkulam, Keelapavoor, Cheranmadevi, Kottaankulam, Melaneelithanallur, Nagalapuram, Ambai, Maanur village, Reddiyarpatti village, Radhapuram Taluk, Thalayuthu, Sankarancoil, Seydunganallur, Sivanthipatti village, Kayatharu, Ukkirankottai, Mukkudal, Chella Durai Nagar, Kalakad Mundanthurai Tiger Reserve, Marakkanam, Pathamadai, Kalakad Mundanthurai Tiger Reserve, Pappankulam, Singampatti, Satankulam, Karayar. Supplementary Material File (figures.docx) Download 6.33 MB File (table.docx) Download 14.48 KB Information & Authors Information Version history V1 Version 1 02 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords ecological experiment ecosystem ecosystem ecology selection analysis terrestrial vertebrate Authors Affiliations Kumar Brawin 0000-0002-5940-008X [email protected] Hedgehog conservation alliance View all articles by this author Thekke Thumbath Shameer Advanced Institute for Wildlife Conservation View all articles by this author Abinesh Muthaiyan 0009-0007-7977-0567 Pondicherry University View all articles by this author Sophie Lund Rasmussen University of Oxford View all articles by this author Metrics & Citations Metrics Article Usage 569 views 189 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Kumar Brawin, Thekke Thumbath Shameer, Abinesh Muthaiyan, et al. Conservation Insights for the Elusive Madras Hedgehog (Paraechinus nudiventris): Prioritizing Regions in Tamil Nadu, India. Authorea . 02 September 2025. DOI: https://doi.org/10.22541/au.175682179.96679499/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175682179.96679499/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffebdd61a73300f',t:'MTc3OTQ4MzUzMw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();