Introduction

The accelerating decline of large terrestrial mammals poses profound ecological consequences, as more species vanish from their historic ranges (Ceballos et al., 2005). Among these, megaherbivores such as elephants, rhinos, and gaur play a crucial role as ecological architects, profoundly impacting vegetation structure, nutrient cycling, predator-prey dynamics, and trophic interactions while affecting the composition and dynamics of ungulate communities (Owen-Smith, 1988; Fritz et al., 2002; Mondanaro et al., 2017). Their presence supports ecological balance, while their decline has been closely associated with significant disruptions in ecosystem processes, underscoring their role in maintaining environmental stability (Berzaghi et al., 2022). Despite their ecological significance, mega herbivores face threats from hunting, competition with livestock, and land-use change, including habitat loss, human encroachment, cultivation, and deforestation (Ripple et al., 2015). By the end of the 20th century, 84 of the 175 species of large herbivores were classified as Critically Endangered, Endangered or Vulnerable (IUCN 2002).

To combat these losses, reintroducing species to their historical ranges is an increasingly recognized conservation method that promotes population recovery and strengthens entire ecosystems (Godet et al., 2018; Novak et al., 2021; Fischer & Lindenmayer 2000; IUCN/SSC, 2013). Such reintroductions involve relocating animals bred in captivity or from existing wild populations to regions where they have gone extinct (Seddon et al., 2007). Some examples of successful conservation relocations from around the world include Père David’s deer (Elaphurus davidianus) in Poyang Lake National Wetland Park, China (Jiang et al., 2000), and the restoration of white rhinoceroses (Ceratotherium simum) in Kruger National Park (Cromsigt et al., 2014). The red-rumped agouti (Dasyprocta leporina) has been reintroduced in Rio de Janeiro, Brazil’s Tijuca National Park. European bison (Bison bonasus) are being brought back to Romania’s Vanatori Neamt Nature Park (Soorae et al., 2021). Successful relocations in India include tiger (Panthera tigris) in Sariska Tiger Reserve (Sankar et al., 2013) and gaur (Bos gaurus gaurus) in Bandhavgarh Tiger Reserve (BTR) and Sanjay Tiger Reserve (Nigam et al., 2019; 2024a).

However, not all reintroduction efforts succeed, often due to a lack of social and political considerations, including insufficient support for conservation goals from local communities (Griffith et al., 1989). Although wildlife relocations have become a frequently applied tool in conservation over the past 25 years, few studies evaluate the social dimensions that often influence their success (Seddon et al., 2016; Brichieri-Colombi et al., 2016).

Recognizing these challenges, contemporary conservation strategies increasingly incorporate social feasibility assessments to ensure that reintroduction plans align with community interests and mitigate potential conflicts (Seddon et al., 2016; Brichieri-Colombi et al., 2016).

Social feasibility refers to the extent to which local attitudes, values, and beliefs align with conservation objectives, and it relies on the community’s ability to recognize potential benefits while creating solutions for perceived and actual challenges (Reading & Kellert 1993; Redpath et al., 2013). Concerns such as fear of livestock depredation, economic loss, inadequate compensation for wildlife–related losses, personal safety, and restricted access to forest resources can build resistance to management and conservation initiatives (Allendorf et al., 2006; Talukdar & Gupta, 2014). Intangible factors, such as anxiety and perceived threats associated with the presence of large carnivores and megaherbivores, frequently contribute to negative public perceptions of species reintroduction efforts (Hiroyasu et al., 2019; LeFlore et al., 2020). However, when local communities recognize tangible benefits from forests and wildlife such as enhanced ecosystem services, improved livelihoods, increased biodiversity, and opportunities for ecotourism, their attitudes toward conservation initiatives, including species reintroductions, tend to become more favorable (Williams et al., 2002; Lindsey et al., 2005; Lamichhane et al., 2018; Sakurai et al., 2020).

Understanding social dynamics becomes especially important when reintroducing species that have declined in the past (Nigam et al. 2014; Nigam et al. 2024a & b). The gaur, one of India’s iconic megaherbivores, was historically present across diverse forested landscapes. Owing to habitat degradation, poaching, and changing land-use patterns, the species have become locally extinct in several areas, including Sanjay Tiger Reserve, Bandhavgarh Tiger Reserve, Kanger Valley National Park, and Thattekad Wildlife Sanctuary (Sankar et al., 2001). Historical records, such as the 19th-century Imperial Gazetteer of India (Hunter, 1886), mention that the forests of Nagarjuna Srisailam Tiger Reserve (NSTR) once harbored viable gaur populations. However, decades of habitat loss and hunting pressure have rendered the species locally extinct in NSTR.

This study was conducted as part of the Gaur Reintroduction Plan for NSTR aimed at reintroducing and restoring the gaur population within NSTR. We conducted a social feasibility assessment for the reintroduction of gaur to NSTR to answer two questions: (1) How do local communities perceive the reintroduction of gaur? (2) What factors shape community attitudes toward the reintroduction of gaur in NSTR?

Materials and Methods

Study Area
The study was conducted in Nagarjunasagar Srisailam Tiger Reserve, Andhra Pradesh, India (Figure 1). The reserve is known as India’s largest tiger reserve, spanning 3727.82 km² (NSTR – Tiger Conservation Plan, 2013-2023). The area is characterized by hilly terrain, plateaus, ridges and valleys that host tropical dry deciduous and tropical moist deciduous forests with bamboo and grasslands dispersed throughout (Qureshi et al., 2023; Yadav et al., 2023). The tiger reserve is hosted along the Nallamala range and fed by the river Krishna along the northern border for a 130 km stretch. The region receives an average annual rainfall of 676 mm, primarily from the southwest monsoon. Temperatures range from 16°C to 46°C, with perennial streams and springs such as Palanka Waterfall and Gundlakamma River (Yadav et al., 2023). Within the reserve, 3,285 people live across 10 villages in the core and 12 in the buffer, primarily belonging to Chenchu, Lambadi, Yadava, and Vaddera communities (NSTR – Tiger Conservation Plan, 2013-2023).

Social Survey
A questionnaire survey was conducted to assess social feasibility in NSTR. A structured questionnaire (Supplementary S1) was developed, drawing on the frameworks proposed by Vasudeva et al. (2021), Gillingham & Lee (2003), Bhattacherjee (2012), and Karanth & Ranganathan (2018). The survey included both closed and open-ended questions to evaluate knowledge levels and opinions on the reintroduction of the species in forests where the communities reside and are dependent. A reconnaissance survey was conducted from May to July 2024 to understand the socio-economic status, livestock practices, non-timber forest produce (NTFP) usage, and community perceptions toward wildlife conservation. The survey was conducted over 20 days across 22 villages situated within the core and buffer zones of NSTR. A total of 109 individuals, each from a different family, were interviewed; the survey represented approximately 3.3% of the resident population. The survey was conducted by two trained interviewers familiar with the local language and each interview lasted approximately 20–25 minutes. Participants were initially selected randomly within each village (Vodouhê et al., 2010; Hariohay et al., 2018; Karanth & Ranganathan, 2018), and the snowball sampling method was adopted to further recruit additional willing participants from the same village (Parker et al., 2019). Respondents were stratified into six categories: farmers, government employees, private sector employees, bamboo collectors/artisans, self-employed individuals, and unemployed individuals. This stratification aimed to capture diverse socio-economic perspectives within the region. Interviewers were proficient in both the local language and English, facilitating effective communication. Verbal consent was obtained from all participants, and no incentives were provided, adhering to the ethical standards.

Variables of perception
Variables such as age, gender, education, household size, and the economic status of communities living in proximity to forested areas have been identified as influencing forest use and, in turn, the relationship with the forest and wildlife. (Williams et al., 2002; Ericsson & Heberlein, 2003; Meadow et al., 2005; Ogra & Badola, 2008; Badola et al., 2012; Karanth & Ranganathan, 2018; Hiroyasu et al., 2019; Vasudeva et al., 2021). A total of 15 independent variables were selected and grouped into four broad categories that could help understand possible drivers influencing people’s behavior towards wildlife conservation and gaur reintroduction. The categories included (1) Socio-Economic Values, (2) Sense of Forest and Ecosystem Values, (3) Perception of Gaur Reintroduction, and (4) Losses and Fear from Wildlife (Table 1).

The values of response variables ranged from 0 to 1, 0 indicating the lowest level of well-being and 1 indicating the highest level. In addition to quantitative data, respondents were also asked for their opinions on whether the gaur should be reintroduced into the NSTR (Table 1). Their answers were grouped into three broad categories: ‘Approval’ or ‘Disapproval’, and ‘Unsure / No response’ for unclear responses, and instances where no response was given. Responses to the question of gaur reintroduction were categorized into three broad groups: positive, negative, and neutral. For logistic regression analysis, neutral or unclear responses were excluded so that the dependent variable represented clear approval (1) or disapproval (0) for gaur reintroduction in NSTR.

Figure 1. Relocation of gaur (Bos gaurus gaurus) is proposed in Nagarjunasagar Srisailam Tiger Reserve, Andhra Pradesh, India

Data Preparation
Initially, the dataset was examined to identify any missing or inconsistent data points. Descriptive statistics were computed for each variable to assess the distributional characteristics.

The analysis involved calculating descriptive statistics to understand patterns in livestock ownership, land use, forest resource use, and human-wildlife interactions. Categorical data, such as livestock types, land utilisation, and employment activities, were summarized using percentages and frequency counts (Creswell & Creswell, 2017). Reports of crop damage by specific wildlife species were quantified, and the proportion of affected households was calculated. Attitudes toward conservation and gaur reintroduction were analysed by grouping responses into categories supportive, neutral, or opposing and determining the percentage distribution. Additionally, qualitative responses about potential risks and benefits of reintroduction were reviewed to identify recurring themes through content analysis.

Table 1. Four broad categories of variables used in logistic regression models to predict the attitude of people towards gaur reintroduction in NSTR, prepared following Vasudeva et al. (2021)

Construction of Composite Indices
To reduce the complexity associated with multiple related variables and to represent broader socio-economic dimensions, composite indices were developed by aggregating conceptually related variables, a common practice in social feasibility studies (Dewi et al., 2005; Soman & Anitha, 2020; Vasudeva et al., 2021). Specifically, four indices were constructed: the Economic Status Index (ESI), Forest Dependency Index (FDI), Income Dependency Index (IDI), and Resource Dependency Index (RDI) (Table 1). The indices were calculated by combining related variables into cohesive categories following the approach described by Vasudeva et al. (2021). For the Forest Dependency Index (FDI), variables such as forest usage frequency, types of forest resources utilized, and the extent of reliance on forest resources were combined to provide a comprehensive measure of forest dependency. Similarly, the Income Dependency Index (IDI) incorporated variables such as income from forest-based activities, percentage of income derived from forest resources, bamboo cutting and artistry, and income stability to assess economic reliance on forest resources. For the Resource Dependency Index (RDI), factors such as access to forest resources, frequency of resource extraction (NTFP collection), and types of resources accessed were aggregated to quantify resource dependency. The selection and combination of variables into these indices were guided by their thematic relevance and conceptual similarity while maintaining internal consistency (Vasudeva et al., 2021).

Correlation Analysis
Following the development of composite indices and the normality assessment of data (Shapiro & Wilk 1965), Pearson’s correlation coefficients were calculated to determine the strength and direction of relationships between the independent variables (Supplementary 1). This step aimed to identify multicollinearity and potential redundant variables that could distort the logistic regression model. Variables exhibiting correlation coefficients above 0.7 were flagged for further scrutiny, and in cases of high multicollinearity, one of the correlated variables was excluded based on theoretical relevance and statistical significance. The use of composite indices also helped streamline highly related variables by representing multiple dimensions within a single explanatory variable, thereby reducing redundancy in the final model.

Chi-square test
A Chi-square test was conducted to determine the statistical significance of associations between independent variables (such as age, gender, education, occupation, resource dependency, experience of loss, and conservation experience) and the two dependent variables (awareness of gaur and attitudes toward its reintroduction), with a null hypothesis set to no significant relationship between knowledge about the gaur and perspectives on its reintroduction. The tests were performed at a 95% confidence level. The analysis was done in R Software version 4.2.4 (Posit Team, 2024).

Logistic Regression Analysis
The logistic regression model (Hosmer et al., 2013) was used using the GLM function in R Software version 4.2.4 (R Core Team, 2024). The binary logistic regression model was structured as follows:

logit(P) = ln(P/(1-P)) = β₀ + β₁X₁ + β₂X₂ + … + βiXi

where P represents the probability of a respondent being supportive of gaur reintroduction, Xi are the independent variables, and βi are the corresponding coefficients.

Model Selection
A binary logistic regression model was used to assess the factors influencing the support or opposition to the reintroduction of the gaur. In this model, the dependent variable was binary, with a response of either 1 (approval) or 0 (disapproval) regarding the reintroduction. The model used 15 independent variables (as described in Table 1), which included socio-economic, demographic, and resource dependency indicators, encompassing socio-demographic characteristics, resource dependency, and prior experiences of loss due to wildlife.

Four different logistic regression models were constructed using various combinations of predictor variables (Table 4). The Akaike Information Criterion (AIC) was used as the primary criterion for model selection (Akaike, 1974; Burnham & Anderson, 2004), to identify the most parsimonious model that effectively balances goodness-of-fit and model complexity. Models with lower AIC values were considered optimal, indicating a better trade-off between model fit and the number of predictors included.

A probability threshold of p < 0.05 was applied to classify variables as statistically significant (Fisher, 1925). Variables with p-values less than 0.05 were identified as significantly reliable predictors of support for reintroduction, whereas those with p-values equal to or greater than 0.05 were non-significant (Cohen, 1994).

Results

General Socio-demographic profile
Of the 109 respondents, 77.6% were male, and all belonged to rural, forest-dependent communities near NSTR. Major occupations included bamboo craftsmanship (31.19%), private sector jobs (20.75%), agriculture (21.1%), and firewood collection (13%). Most followed a non-vegetarian diet (95.2%) and lived in Pakka houses (67.92%). Educational attainment was low, with over half (54.13%) reporting no formal education. While 86.79% had access to LPG, many still relied partly or fully on firewood for cooking (Table 2).

Livestock ownership was low with 76.6% of households owning no livestock. Very small percentages owned cows, buffalo, or fowl. Around 65% utilized forest resources in timber, bamboo, and NTFP. Among the 21.69% of respondents who reported wildlife-related losses, 66.67% attributed the damage primarily to wild boar, occasionally in combination with chital or sambar, and at times with bears (33%) and leopards (18.51%). Awareness of conservation measures among respondents was high at 75%, with 83% believing that wildlife populations have increased since their childhood.

Although only one respondent had seen a gaur during childhood, many cited having knowledge of them second-hand. When queried about the prospect of reintroducing gaur into the area, 52% of respondents expressed that there were no fears, while 24.5% expressed concern, and the remainder were indifferent. Those who supported reintroducing gaur stated ecological benefits, while those opposed expressed fear of limited access to forests and crop damage (Table 3).

Overall Perception Towards Gaur Reintroduction in NSTR
Knowledge about gaur was significantly associated with age (χ² = 5.083, p = 0.04), occupation (χ² = 14.072, p = 0.002), and experiences of wildlife-induced losses (χ² = 12.744, p = 0.0003). These findings suggest that older individuals, certain occupational groups, and those who had experienced wildlife-related losses were more likely to have knowledge of the species. No significant associations were observed with gender, education, resource dependency, or perceived importance of forests (Table 3).

Support for gaur reintroduction was significantly associated with occupation (χ² = 17.995, p = 0.0004), resource dependency (χ² = 14.297, p = 0.0001), sense of forest importance (χ² = 9.803, p = 0.001), and wildlife loss experiences (χ² = 13.037, p = 0.0003). Respondents more dependent on forest resources and those who had experienced losses were less supportive of reintroduction. No significant associations were found with age, gender, or education, though gender and education showed weak significance (p = 0.063 and p = 0.064, respectively) (Table 3).

Factors Influencing Gaur Reintroduction in NSTR
Socio-Economic Values: Logistic regression analysis provided insights into socio-economic determinants influencing approval of gaur reintroduction. Age group 3 (31- 40 years) showed a negative relationship with approval (β = −0.5847, p < 0.05), suggesting individuals in this cohort were less likely to support the initiative. Larger family sizes positively correlated with approval (β = 0.3055, p < 0.05), potentially indicating greater openness among households with more members to environmental changes. Access to government schemes was a significant predictor of approval (β = 1.032, p < 0.05), pointing to the role of institutional support in shaping public opinion. Economic well-being, however, did not emerge as a significant variable (β = 1.2929, p > 0.05) (Table 4).

Forest and Ecosystem Values
The second model offered insights into how perceptions of forest and ecosystem services influenced approval of reintroduction. The intercept (β = 1.0362, p < 0.05) established a positive baseline. Interestingly, the sense of ecosystem services (FECO) negatively influenced approval (β = −3.4491, p < 0.05), suggesting that those who highly valued ecosystem services might fear introducing a large herbivore such as gaur. Neither income dependence (β = −0.6273, p > 0.05) nor resource dependence (β = 1.5315, p > 0.05) was statistically significant, suggesting these factors exerted less sway in shaping opinion within this model’s framework (Table 4).

Perception of Gaur Reintroduction
In this model, the intercept (β = 0.0274, p > 0.05) indicated neutrality in perception. Knowledge of the gaur (KOG) showed a marginally significant positive effect (β = 0.7936, p < 0.05), suggesting that greater awareness of the species might improve public opinion of its reintroduction, implying that informed populations are more likely to accept ecological interventions (Table 4).

Loss and Fear due to Wildlife
Loss and fear emerged as a significant variable in the fourth model. The intercept (β = 1.4378, p < 0.01) suggested strong baseline approval. However, fear of the gaur’s impact on human life (FOGR) significantly reduced approval likelihood (β = −2.5001, p < 0.01). Contrary to expectations, actual reported losses (β = −0.032, p > 0.05) did not significantly shape perceptions, indicating that perceived fear may outweigh evidence-based experiences in driving opinion (Table 4).

Table 2. Socio-demographic profile of the respondents of household survey in Nagarjunasagar Srisailam Tiger Reserve, Andhra Pradesh, India.

Table 3. Summary Statistics of χ² test to determine the relationship between the dependent variables ‘Support for gaur reintroduction in NSTR’ and ‘knowledge about the gaur’

Discussions

The interactions of socio-economic factors with conservation support have been recognized widely. Both Bennett et al. (2017) and Kansky & Knight (2014) found economic stability, household characteristics, and trust in government authorities to be likely influences on community attitudes towards wildlife reintroductions. In NSTR, this was clearly tangible, with larger households showing greater approval for gaur reintroduction. Households with larger household sizes may be positioned for greater income prospects and collective decision-making which could lead to a more favorable trajectory towards projects for conservation. Additionally, access to and use of government services and welfare schemes were reported as important determinants of support for conservation. Redpath et al. (2013) and Oldekop et al. (2016) observed that communities that have greater access to institutional resources will tend to have more favorable attitudes toward conservation. In NSTR, respondents who were more aware of government schemes expressed stronger approval for the reintroduction of gaur, which indicates that institutional facilitation, perceived or not, can alleviate some of the hesitation regarding likely losses associated with wildlife. This finding emphasizes integrating strategic conservation practice with broader socio-economic development efforts, similar to various conservation landscapes observed globally (Brooks et al., 2012).

Table 4. Significant variables predicting the attitude of local households towards gaur reintroduction in NSTR (Category vis models using logistic binomial regression)

Several respondents were clearly in favor of introducing the gaur to the forest, in part with an understanding of potential aesthetic and ecological value. Comments such as “It is good to have animals in the forest” and “A species should stay in its forest” convey a positive attitude concerning restoring the fauna in NSTR. Other comments addressed the aesthetic and ecological values of reintroduction, highlighting that it would “add aesthetic value to the forest” or that it could help with biodiversity restoration and increase tiger numbers. Clearly in sync with broader conservation efforts, these perspectives also hint at some segment of the community recognizing the ecological value of reintroducing gaur, which is consistent with earlier studies showing community support as an essential factor for reintroduction (Bennett et al., 2017). These kinds of perspectives are also relevant because they suggest potential ways to build community engagement and conservation awareness.

Another important influence on community thinking about gaur reintroduction was dependence on forests. Communities dependent on forest resources often view conservation projects as a threat to their resource-dependent way of life (Infield & Namara, 2001; Berkes, 2004). In NSTR, concerns regarding limited access to the forest and stricter rules emerged as clear themes, particularly that residents would not be able to continue to collect firewood, graze cattle, or collect medicinal plants after gaur reintroduction. Similarly, in the Satkosia Tiger Reserve, a tiger translocation program was impeded by concerns about restricted access and resource dependence, as residents feared they would lose access to limited forest resources, and they expressed concerns about resource extraction restrictions surmounting the wildlife reintroduction approval process (Vasudeva et al., 2021).

Redpath et al. (2015) pinpoint the importance of addressing perceived resource restrictions, as ongoing perceived losses of access to resources could lead to heightened negativity toward conservation and prompt considerations of alternative approaches to developing sustainable resource management and compensatory mechanisms.

Community perceptions of gaur reintroduction had a small positive relationship to public support, but this relationship was only marginally significant. This was similar to findings in Taiwan, where Best & Pei (2020) showed that communities with greater awareness of leopard cat conservation were more supportive, and that younger, educated individuals were particularly supportive. Although many respondents in NSTR had limited direct experience of gaur, previous generations of the family often passed on anecdotal information. Dickman (2010) notes that a lack of direct encounters with a species can create misconceptions, making targeted awareness campaigns necessary. Barua et al. (2013) and Madden & McQuinn (2014) state that the use of participatory meetings, workshops, and storytelling is a way to address these gaps in information and promote informed support for conservation actions.

Fear emerged as a significant deterrent to community support for gaur reintroduction, with concerns centered around crop damage and human-wildlife conflict. Such fears are not unfounded, as previous wildlife interactions in NSTR have left lasting impressions on residents, shaping their attitudes toward new conservation projects. In extended contexts, fear of large carnivores has been a dominant factor influencing support for conservation. Bombieri et al. (2023) documented that bold brown bears in human-modified landscapes often triggered heightened public fears, leading to conflict and retaliatory actions. Similarly, Jansson et al. (2024) observed that lions in multi-use landscapes in Tanzania strategically altered their movements to avoid human encounters, suggesting that minimizing the potential for conflict is a critical component of coexistence strategies. Interestingly, in NSTR, the perceived loss of resources, such as income or access to land, was less significant than the overarching sense of fear and uncertainty.

Kansky et al. (2014) argue that fear often carries more weight than anticipated financial losses in shaping community attitudes. Therefore, effective conflict mitigation strategies, such as early-warning systems, compensation schemes for livestock losses, and proactive community engagement, are essential for fostering acceptance (Bruskotter et al., 2014; Slagle et al., 2012).

The fear of crop destruction was a strong factor in NSTR, with respondents expressing their view as “gaur would cause major damage to the crops”. This fear resonates with existing literature showing that human-wildlife conflict, particularly in agricultural settings, is a significant barrier to reintroduction project adoption (Kansky & Knight, 2014; Redpath et al., 2013). The perception of economic costs was also talked about fairly frequently, with respondents suggesting that the reintroduction of gaur could create restrictions on forest use and provide fewer resources. Phrases like “It will reduce our visits to the forest for livelihood” demonstrated fear that economic stability would be impacted by more wildlife use in the area. Although it was also interesting to note that some people believed that gaur could also help support tourism development, saying “This can help to promote tourism” and “It will positively impact the forest and hence the economic activity”, thus suggesting a somewhat dual perception of economic impacts.

Taken together, these findings provide a comprehensive understanding of how socio-economic, perceptual, and emotive factors influence community attitudes toward the reintroduction of gaur in NSTR. Understanding social and economic realities that shape communities’ thinking about species conservation initiatives is important for the success of these initiatives and for ecological factors. This study in sync with other global and local studies, adds to greater depth and underscores the need for holistic approaches to wildlife reintroduction.  The assessment of social feasibility is not simply advantageous but necessary for the success of conservation translocation initiatives. The IUCN, in its Guidelines for Reintroductions and Other Conservation Translocations, highlights that feasibility assessments should not only focus on biological and ecological aspects of a translocation but also on social context, community engagement, and potential future conflicts (IUCN/SSC, 2013; Seddon et al., 2014; Brichieri-Colombi & Moehrenschlager, 2016). These guidelines recommend an integrated approach that weighs conservation-related benefits and the social cost and risks involved in conservation plans, regardless of how they are formulated (IUCN/SSC, 2013; Redpath et al., 2013; Bennett et al., 2017).

Despite clearly written guidelines, many translocation projects have neglected social aspects, leading to significant difficulties during implementation. A review of 550 projects indicated that social feasibility assessments were often incomplete or limited in scope, resulting in unresolved controversies and community opposition that ultimately led to ineffective project outcomes (Miller et al., 2014; Soorae, 2018; Teixeira et al., 2007). The lack of consideration for social aspects has been a recurring feature of many failed conservation activities, especially in projects involving larger mammals and human-wildlife conflict (Dickman, 2010; Kansky & Knight, 2014; Nyhus, 2016).The present findings therefore reinforce the importance of incorporating social feasibility assessments into conservation planning and demonstrate that community perceptions, institutional support, livelihood concerns, and conflict-related fears are integral components of successful wildlife reintroductions

Conclusion

The success of gaur reintroduction in NSTR does not completely rely on ecological approaches but also on socio-economic and perceptual dimensions. Park managers, along with conservation efforts, should focus on engaging and building trust with forest-dependent communities, ensuring socio-economic stability, implementing welfare programs, and raising awareness of human-wildlife interactions through outreach and mitigation. Further deploying targeted, participatory awareness campaigns, such as community meetings, workshops, and storytelling, to bridge information gaps within local communities and highlight the gaur’s aesthetic and ecological value.  Also, integrating tangible benefits may alleviate hesitation, build goodwill, and strengthen trust. Providing a holistic approach that combines ecologically restorative use with community development leads to constant dialogue and ownership by the specific community – this amalgamation will allow for conservation models that are not only ecologically sound, but socially inclusive and place-based. Ultimately, the success of these actions will depend on how well they engage with the needs and perceptions of local communities and how they incorporate long-term benefits for both communities and wildlife.

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Acknowledgement

This study was conducted as part of a collaborative initiative be­tween the Andhra Pradesh Forest Department and the Wildlife Institute of India, with the support of the Nagarjuna Sagar Sri­sailam Tiger Reserve (NSTR), Andhra Pradesh. The authors ex­tend their sincere appreciation to the NSTR field staff for their invaluable assistance during data collection. Special thanks are also extended to Ms. Shanti Priya Pandey, Ms. Tanya Gupta, Mr. V. Saibaba, Mr. Anurag Meena, and Mr. Y. V. Narsimha Rao for their significant contributions and support throughout the study.

CONFLICT OF INTEREST
Samrat Mondol, Bilal Habib & Parag Nigam hold editorial positions at the Journal of Wildlife Science. However, none of them participated in the peer review process of this article except as authors. The authors declare that they have no competing interests.

DATA AVAILABILITY
The study contains sensitive household information, including personal details, which cannot be publicly shared. All relevant data generated during the study are provided within the article and its supplementary materials. Please contact the corresponding author at nigamp@wii.gov.in to obtain access to the dataset and associated documentation.

AUTHORS’ CONTRIBUTION
RV: data collection, formal analysis, visualization, writing—original draft.
AKG: data collection and analysis.
SC: data collection.
HF: review and editing.
AN: formal analysis, methodology, and writing—review and editing.
SM: supervision, writing—review and editing.
BH: supervision, writing—review and editing.
AKN: funding acquisition, permission acquisition, resources.
BNNM: funding acquisition, resources.
VAG: permission acquisition, resources.
PN: funding acquisition, investigation, resources, supervision, and review and editing.

ETHICS STATEMENT
The authors confirm that all procedures involved in this study adhered to the ethical guidelines established by the relevant national and institutional bodies, including the IUCN/SSC (2013) reintroduction guidelines and the Wildlife Institute of India under the Ministry of Environment, Forest, and Climate Change, Government of India. The research also complied with the Code of Conduct for contributors to the Journal. The interview methodology followed the IUCN/SSC (2013) guidelines, and informed con­sent was obtained from all participants for the use of the data collected. Participant anonymity has been maintained to ensure confidentiality.

ORIGINALITY STATEMENT
We, the authors, confirm that the manuscript submitted is our original research work. The content is not derived from other studies or the published work of the authors. We confirm that all the sources have been properly acknowledged and cited. We also understand that the journal conducts plagiarism checks, and we agree to provide ad­ditional clarification if the index exceeds 15% as required during the peer-reviewed process.