Abstract

Research on wildlife crime has been steadily increasing within the fields of criminology and conservation science. However, the policing aspects of this issue remain under-explored. A robust, intelligence-led approach is deemed more proactive and effective in policing wildlife within and around Protected Areas (PAs). This study enriches the literature on wildlife policing by examining the intelligence-led conservation strategies employed by stakeholders in the Mudumalai Tiger Reserve (MTR), a PA located in the Western Ghats of Tamil Nadu, India. The study utilises a mixed-methods approach, collecting quantitative data from rangers and qualitative data from local community members. The findings suggest that there is significant scope for enhancing intelligence-led wildlife policing in MTR. A notable lack of training and technological implementation in intelligence-led conservation approaches among rangers was observed. Moreover, local community members were found to be largely supportive of the rangers’ efforts in intelligence-led wildlife policing. The detailed findings of this provides valuable insights for the management of MTR and other tiger reserves, enabling them to review and enhance their existing intelligence-led initiatives to ensure effective conservation.

Keywords: wildlife policing, intelligence-led conservation, wildlife crime, protected area, Mudumalai, tiger reserve, western ghats

Background

The severity of wildlife crimes, including poaching and the illegal wildlife trade, has been escalating in India (Rana & Kumar, 2023). Consequently, protected areas, such as tiger reserves, are particularly at risk due to their abundant distribution of wild fauna and flora (Jhala et al., 2021). This situation necessitates that the state forest departments, the custodians of wildlife protection, adopt intelligence-led conservation strategies at various levels to enhance the prevention and detection of wildlife crime. In response to this need, we conducted an exploratory study to understand the extent of intelligence-led conservation strategies against wildlife crime in the Mudumalai Tiger Reserve (MTR). This research is part of a PhD thesis examining wildlife crime prevention measures in the same reserve (Alagesan, 2022). This tiger reserve is a protected area in the Western Ghats of Tamil Nadu, India. The study involved two specific conservation stakeholders: forest officers/staff and local community members. This study aims to gain insights into the effectiveness of intelligence-led conservation strategies and identify areas for improvement.

Intelligence-led conservation strategies have been identified as potentially effective approaches to reduce and prevent crimes against wildlife (Moreto, 2015). Several intergovernmental organisations, including INTERPOL and UNODC, are promoting these strategies globally to address the problem of wildlife crime (INTERPOL, 2023; UNODC, 2019). On the other hand, the scholarship on understanding wildlife crime is more extensively found in disciplines such as conservation biology and conservation science rather than in mainstream criminology (Kaltenborn & Linnell, 2021; Skidmore, 2021). Although empirical research within criminology on wildlife crime is limited, researchers have begun to generate knowledge on wildlife crime and its prevention over the decades (Kurland et al., 2017; Moreto & Lemieux, 2015; Petrossian & van Uhm, 2023; Pires & Olah, 2022; Sollund & Runhovde, 2020; Sosnowski et al., 2021; Viollaz et al., 2022; Wilson & Boratto, 2020).

The literature reveals significant findings on the effectiveness of intelligence-led conservation strategies. For instance, leveraging local informants has proven effective, with patrols based on tip-offs significantly increasing detections of wildlife crimes (Linkie et al., 2015). Moreto and Matusiak (2017) further emphasised the crucial role of intelligence and information gathering in preventing and detecting wildlife crime in protected areas, concluding that fostering good social relationships with local communities is vital for gaining their trust and obtaining valuable information to protect wildlife.

Intelligence-led initiatives have successfully countered wildlife crime in Africa, Asia, and North America (Heinrich et al., 2020; Steiner, 2020). Informants play a crucial role in detecting poachers, as highlighted by game warden (Forsyth, 2008). The use of technology, such as federated databases and social network analysis, shows promise in disrupting transnational wildlife trafficking networks (Haas & Ferreira, 2015).

A study by Warchol and Kapla (2012) on policing conservation landscapes in South Africa underscored the importance of intelligence gathering in wildlife crime prevention. The study found that rangers, trained in intelligence gathering and report preparation, relied heavily on information from local communities, hotel managers, police, and customs for their operations. This underscores the critical role of local communities in intelligence-led conservation strategies.

The Ruvuma Elephant Project (REP) in Tanzania, initiated by the PAMS foundation, aimed to improve elephant conservation by controlling poaching and ensuring effective law enforcement. The project’s success was attributed to non-conventional anti-poaching strategies, including community involvement, intelligence-led operations, and joint patrols. These strategies led to a significant reduction in elephant poaching and an overall improvement in the situation, highlighting the effectiveness of non-conventional approaches and the importance of community participation in wildlife conservation efforts (Lotter & Clark, 2014).

Moreto (2015) conducted a study with the Uganda Wildlife Authority (UWA) to assess the capacity, implementation, and status of crime and intelligence analysis for preventing wildlife crimes. The study found that while the current environment was suitable for an intelligence-led approach, the lack of analysts and a comprehensive framework hindered crime intelligence within the UWA. In a subsequent study, Moreto et al. (2018) collected data from various stakeholders within five protected areas under the UWA to understand their views on the concept of intelligence, its role, and its effectiveness. The study concluded that an intelligence-led approach, including actionable intelligence and community involvement, effectively curbing wildlife crime.

A study conducted in the Bandipur Tiger Reserve (BTR), Karnataka, India, investigated elephant mortality due to various causes, including natural causes, poaching, and human-elephant conflict (Varma, 2000). The study emphasised the importance of developing intelligence-gathering capabilities among forest staff, from forest watchers to forest range officers, as a crucial measure to control poaching. Government efforts, particularly in India, highlight integrating intelligence-led approaches into wildlife crime prevention strategies (MoEFCC, 2019; NTCA, 2009). However, evidence of effectiveness remains limited, emphasising the need for further research and evaluation.

Overall, the literature underscores the importance of intelligence-led approaches in wildlife crime prevention, emphasising community involvement, professional integration, and technological advancements. Yet, gaps persist in understanding the full scope of its effectiveness, particularly in diverse geographical contexts like India.

Scope of the study

Evaluating wildlife crime on a global scale is difficult due to the diverse ways each country handles and safeguards its terrestrial wildlife, fish, trees, and other plant species. Additionally, wildlife, fisheries, and forestry regulations are constantly changing in response to new risks and national priorities. Since no global treaty defines wildlife crime, the term doesn’t have a universally agreed-upon definition. However, in a broad sense, wildlife crime is generally understood as the illegal harvesting and trading of wild animals and plants violating national laws (UNODC, 2020). For this study, wildlife crime is hunting1, picking or uprooting certain plants, unauthorised access to protected areas, as outlined in the Wild Life (Protection) Act, 19722. While there are other types of wildlife crimes, they are not considered in this research while interacting with conservation stakeholders. The study involved forest officers, staff, and local community members in the MTR, collectively known as conservation stakeholders.

Methods

Study Site

The MTR is located (11° 35’ 0” N lat, 76° 33’ 0” E long) in the Western Ghats of the Nilgiris, Tamil Nadu, India (see Figure 1).

Figure 1

Map of MTR

It lies at the tri-junction of the State of Kerala, Tamil Nadu, and Karnataka. It is adjacent to Wayanad Wildlife Sanctuary on the north-west and Bandipur Tiger Reserve on the north. The MTR is also a part of a large contiguous forest area of 5520km² Nilgiris Biosphere Reserve (Jhala et al., 2020; Ramesh et al., 2012; Silori & Mishra, 2001; Suresh & Sukumar, 2018; Verma et al., 2017). The conservation of tiger, the top carnivore, guarantees the well-being of our forested ecosystems, biodiversity, as well as water and climate security (Jhala et al., 2019). Therefore, in the year 2007, in the exercise of powers conferred under section 38V of the Wild Life (Protection) Act of 1972, the Government of Tamil Nadu has notified 321km² (i.e., the area of Mudumalai National Park and Mudumalai Wildlife Sanctuary) as critical tiger habitat (Government of Tamil Nadu, 2007). Subsequently, in 2012, an area of 367.59km² comprising Segur, Singara, and a part of the Nilgiris Eastern Slope (NES) ranges were declared as the buffer area of the MTR (Government of Tamil Nadu, 2012). Now, the MTR consists of 688.59km² (core 321km² and buffer 367.59km²). The core area is designated as Ooty Division, and the buffer area is Masinagudi Division.

Data Collection and Analysis of Quantitative Data

Data for the study were collected from the conservation stakeholders, namely, forest officers/staff and the local community members, between August 2019 and December 2019. During the study period, the population of forest officers/staff in MTR were 250. Therefore, the first author has decided to go for a census as the population is finite. Whereas during the fieldwork, it was understood that due to the work nature of forest officers/staff and various logistical constraints inside the forest, including the remote location and walk-through rugged terrains to reach several anti-poaching camps and the presence of wild animals, the conduct of census was not considered feasible. Moreover, after the commencement of fieldwork, the Nilgiris received heavy rainfall (Shaji, 2019). Due to this torrential downpour, most of the rivers were flooded, especially the Moyar River, which passes through MTR, causing severe logistical constraints to the researcher. Despite these challenges/constraints, the first author earnestly attempted to reach out to the maximum number of respondents in the population. However, the final sample size was 153. With the help of interview schedule, quantitative data on the intelligence-led conservation strategies undertaken by them were examined. Such collected data were processed using the IBM SPSS Statistics for Windows (version 21.0). The processed data were then subjected to descriptive and inferential statistical analysis.

Categorisation and Scoring of Dependent Variable

The methodology for assessing the effectiveness of intelligence-led strategies in preventing and detecting wildlife crime involves a series of questions and statements. These are evaluated on a five-point scale, ranging from poor to very good (see Table 1 for an example). Statements with a frequency such as “sometimes” and “rarely” are interpreted as every three and six months, respectively.

Responses are scored as follows: “Do not know” is assigned a score of 1; “Poor” is 2; “Fair” is 3; “Good” is 4; and “Very good” is 5. Forest officers/staff who report employing the best intelligence-led approach in MTR will receive a score of 5. Good practices are scored 4, fair practices 3, and poor practices 2. For statistical analysis, a score of 1 was given to the response “do not know”. The mean score of all items were then calculated for analysis. A higher mean score indicates a higher level of intelligence-led approach being used to prevent and detect wildlife crime in MTR.

Data Collection and Analysis of Qualitative Data

Based on the data provided by the forest department, there are seven villages in the core area and 26 villages in the buffer area. The core and buffer areas form the two strata of the local community. Four focus group discussions were conducted in each stratum, with eight focus group discussions. Each focus group discussion consisted of four to five individuals with equal participation of women. The participants for FGD were recruited based on the suggestions given by the village head or the forest group head in each village. The researcher arranged the focus group discussion in coordination with the village/forest group heads and when the participants were at their leisure. Through this, the researcher has avoided FGD affecting the local community’s routine, including agricultural practices and other works they carried out for their livelihood. This has also helped the researcher have a detailed conversation with the local community, and the participants also expressed their opinions unhesitant. Each FGD was audio-recorded and later transcribed to Tamil. From Tamil, an abridged English version was also prepared. The responses for each broader exploratory question posed to the local community were listed consecutively by the researcher to familiarise himself with the data. Later, manual open coding was carried out to generate themes to bring out similarities and differences of responses rendered by the local community members during eight FGDs. A few unique responses were also derived from the FGD data. A frame of reference with broader questions relating to wildlife conservation/crime prevention was already prepared. Using the deductive approach, the findings have been presented accordingly.

Results and discussion

The approach taken by forest officers/staff to prevent and detect wildlife crime led by intelligence was evaluated based on certain factors. These include training on intelligence-led policing, availability of informants, consolidation of information with computer assistance, a crime database, and a reward system for informants. Initially, some results related to the independent variable of the respondents will be discussed, followed by a discussion on the dependent variable.

The average age of the forest officers/staff is 35.05 years, with the most represented age groups being those aged 25-29 (19.6%) and 20-24 (17%). From this, it can be observed that the forest department at MTR has a relatively younger workforce. This could assist them in performing demanding tasks, including patrolling the tiger reserve. In terms of work experience in their current role, the average is 6.12 years, with the majority (61.4%) having less than five years of experience. When considering overall work experience in the forest department, the average is 10.95 years. The most common experience levels are between 6-10 years (16.3%) and 11-15 years (14.4%).

The majority of the respondents, accounting for 85%, have an education level ranging from primary school to higher secondary (12^th grade). Approximately 12% of them hold a degree, either at the undergraduate or postgraduate level. A small group, specifically five individuals, reported having no formal education.

Table 4 summarises the forest territory, the specific forest range, and the distribution of respondents based on their designations. Out of the total 153 respondents, 64 were from the core area of the Tiger Reserve (Ooty Division). When looking at the sample distribution based on designation, it is evident that the majority (105 out of 153) of the respondents held the position of Anti-Poaching Watchers (APWs).

Only 2% of respondents indicated that training on intelligence-led policing is either infrequently (1.3%) or occasionally (0.7%) conducted. A significant proportion of respondents (43.8%) are unaware of such training provided by the WCCB or state police, while 54.2% reported that they do not receive such training. It’s important to note that forest officers/staff, unlike their counterparts in the WCCB and state police, lack extensive training in policing work, including intelligence-led policing.

The table above also reveals that a substantial percentage (77.8%) of respondents have a sufficient informant network to gain intelligence on threats to wildlife in the MTR. Gathering intelligence from local communities is crucial in any protected area to prevent and detect wildlife crime. A robust informant network aids the tiger reserve authorities in effectively tackle wildlife threats. The importance of informants in sharing intelligence on wildlife threats is well-recognised by numerous authors worldwide who have conducted studies in protected areas. Some researchers (Moreto & Matusiak, 2017) have highlighted the significance of fostering social relationships with local community members for intelligence gathering. A few researchers (Gustafson et al., 2018) have also confirmed that the best counter-poaching intelligence network is the local informant network within communities. Despite infrastructural and resource constraints, game wardens perform relatively well with many informants, as Forsyth (2008) found. Thus, the availability of informants could significantly support the prevention and detection of wildlife crime in MTR.

The findings also indicate that 76.5% of respondents are unaware of any computer-aided system for consolidating wildlife crime information in MTR. Approximately 23% of respondents, including foresters and range officers, confirmed the absence of such a system. Despite MTR having sufficient informants to provide information/intelligence, there is no computerised method to consolidate this data, making manual or traditional methods impractical. Chandran et al. (2011) introduced the Wildlife Enforcement Monitoring System (WEMS) to facilitate the consolidation and free flow of information/intelligence. WEMS significantly enhances enforcement through improved information/intelligence sharing and inter-agency collaboration.

Furthermore, 95% of respondents were unaware that wildlife crime data had been updated in the Wildlife Crime Database Management System (WLCDBMS) of the WCCB. WLCDBMS is an online platform that allows real-time data analysis to devise effective strategies for preventing and detecting wildlife crime across India. The WCCB instructs all state/UT forest and police departments to update the WLCDBMS with real-time wildlife crime data. Some researchers (Haas & Ferreira, 2015) have suggested that a federated crime database with social network analysis can enable law enforcement agencies to adopt a more strategic approach. Given the transnational nature of wildlife trafficking, law enforcers in a specific jurisdiction often struggle to disrupt criminal networks due to the dispersion of information across multiple databases. In this context, a single federated database like WLCDBMS, accumulating information from various agencies, can address this issue (Alagesan, 2020). However, the study’s findings suggest that the potential of the WLCDBMS is not being fully exploited.

Findings on rewards from the secret service fund suggest that a majority (56.2%) of informants are not rewarded for their contributions. Notably, about 26% of respondents are unaware of such rewards, possibly due to the fund’s management by senior forest officers. Despite the fund’s intention to incentivise those providing actionable intelligence, rewards are not widely distributed. It’s worth mentioning that the Annual Plan of Operation (APO)5 of the MTR indicates full utilisation of the secret service fund, as seen in the 2018-19 financial year (MoEFCC, 2019). The study reveals that 55% of respondents confirmed that local community members are consistently given informal rewards for sharing intelligence on wildlife crime. These informal rewards often include ‘pocket money’ and occasional treats like tea, snacks, or meals. This practice of rewarding informants informally is common in protected areas across various countries. For instance, a study by Linkie et al. (2015) in Kerinci Seblat National Park, Sumatra, Indonesia, found that informants providing tips on suspicious activities or potential poacher movements received small rewards, such as mobile phone top-ups or money for cigarettes from wildlife law enforcement personnel.

The intelligence provided by local community members not only aids in preventing wildlife crime but also bolsters the evidence leading to convictions. However, a significant 83.7% of respondents are unaware of the provisions in the Wild Life (Protection) Act, 1972 (WLPA) that reward local community members for sharing intelligence that results in a wildlife crime conviction.

Difference Between the Forest officers/staff’s Education, Work Experience, Designation, Forest Range and Level of Intelligence-led Approach to Prevent and Detect Wildlife Crime

The existing research (Milda et al., 2020) has shown a correlation between wildlife crime prevention strategies in protected areas and certain independent factors like education and work experience. Furthermore, extensive conversations with high-ranking forest officers revealed that these prevention strategies may vary depending on the officers’ education, work experience, role, and the specific forest range in which they operate. As a result, we tested the following hypotheses.

H₀1: There is no significant difference between the education of the forest officers/staff and the level of intelligence-led approach to prevent and detect wildlife crime.

H₀2: There is no significant difference between the overall work experience of the forest officers/staff and the level of intelligence-led approach to prevent and detect wildlife crime.

H₀3: There is no significant difference between the work experience of the forest officers/staff in their current designation and the level of intelligence-led approach to prevent and detect wildlife crime.

H₀4: There is no significant difference between the designation of the forest officers/staff and the level of intelligence-led approach to prevent and detect wildlife crime.

H₀5: There is no significant difference between the forest range of the forest officers/staff and the level of intelligence-led approach to prevent and detect wildlife crime.

The one-way ANOVA results indicate a significant difference between the respondents’ education and the level of the intelligence-led approach to prevent and detect wildlife crime (p < .001). The mean score suggests that respondents with undergraduate (26.73) and postgraduate (26.14) degrees are more effective in implementing this approach. Consequently, the null hypothesis (H₀1) was rejected.

A significant difference between the respondents’ overall work experience and the effectiveness of the intelligence-led approach to prevent and detect wildlife crime (p < .001) was observed. The data suggests that forest officers/staff with more work experience, particularly those with over 20 years of experience (M = 23.87), are more effective in implementing this approach compared to those with 1-5 years of experience (M = 18.86). As a result, the null hypothesis (H₀2) is rejected, confirming a statistically significant relationship. Previous research (Henson et al., 2016) has shown that rangers with adequate frontline law enforcement experience are generally more successful in anti-poaching initiatives. Similarly, Koen (2017) found that a lack of experience could hinder a ranger’s ability to perform their duties effectively.

The results, with a p-value > .001, support the null hypothesis (H₀3), indicating no significant difference based on work experience in their current role. All groups, regardless of their work experience in the current role, scored relatively high, suggesting good or fairly good intelligence-led approach. The lack of difference could be attributed to the forest officers/staff having only worked in the MTR, or recent transfers to the MTR, or long-term service in the same role without promotion.

The null hypothesis (H₀4), stating no significant difference between the respondents’ roles and the effectiveness of the intelligence-led approach to prevent and detect wildlife crime, is rejected due to a p-value < .001. This suggests that foresters, Forest Range Officers (FROs), DCF, and APCCF have performed better in implementing this approach compared to other staff, as indicated by their higher mean scores. The discrepancy could be attributed to the varying levels of required training and education for different roles. For example, local community members within the MTR are appointed as APWs without specific educational qualifications or induction training. In contrast, roles like forest watcher, forest guard, forester, FRO, and ACF require induction training of 10 to 24 months and educational qualifications ranging from SSLC to an undergraduate degree in relevant fields.

The MTR encompasses eight distinct forest ranges spread over 688.59 km², each with unique characteristics such as staff strength, human activity, wildlife abundance, and terrain. The one-way ANOVA results showed a significant difference (p < .001), forest range of the respondents influenced their level of the intelligence-led approach to prevent and detect wildlife crime led to rejection of the null hypothesis (H₀5). Further analysis revealed that respondents from the Nilgiris Eastern Slope forest range scored higher, suggesting more effective intelligence-led approach compared to other ranges.

Difference Between the Respondents’ Forest Territory and the Level of Intelligence-led Approach to Prevent and Detect Wildlife Crime

The MTR region is divided into core and buffer zones. The core zone, also known as the critical tiger habitat, has stringent restrictions on human activity. The buffer zone serves as an extra protective layer for the core area, where human movement is allowed but monitored by forest personnel. Consequently, we examined the following hypothesis:

H₀6: There is no significant difference between the forest territory of the respondents and the level of intelligence-led approach to prevent and detect wildlife crime.

To test the above hypothesis, an independent samples t-test was employed. The results thus obtained are presented in the following table.

From Table 7, it is observed that the p value for the four variables is > .001. Therefore, it can be inferred that statistically, there is no difference between the respondents’ forest territory and the level of intelligence-led approach. Interestingly, despite there being several differences, such as in the terrain, forest density, abundance of wild flora and fauna, number of anti-poaching camps, strength of forest officers/staff, movement of local community members and tourists between the core and buffer areas of the tiger reserve, there is no difference in their wildlife crime prevention measures. The null hypothesis (H₀6) is therefore accepted.

Level of Intelligence-led Approach to Prevent and Detect Wildlife Crime in the MTR

Based on the scoring methodology described in the methods section, the intelligence-led approach to prevent and detect wildlife crime undertaken by the study respondents in MTR can be categorised as poor, fair, good and very good. These categorical ratings are in line with the scoring methodology developed by the NTCA and the WII to evaluate the performance of tiger reserves in India (Mathur et al., 2017; NTCA, 2020). The higher the mean score, the higher the level of an intelligence-led approach to preventing and detecting wildlife crime in MTR.

The results shown in Table 8 can also be interpreted through the lens of the categorical rating system proposed by Hockings et al. (2006) and Parrish et al. (2003). As described in Table 9, they argue that these straightforward categorical ratings serve as a recognised framework for evaluating the efficacy of protected area management. These ratings hold scientific credibility and are easily understood by managers of protected areas and authorities in wildlife management. Consequently, the intelligence-driven strategy to prevent and detect wildlife crime falls outside the acceptable parameters and necessitates significant intervention for enhancement.

Local Community’s Willingness to Share Information/Intelligence

All groups expressed willingness to share information/intelligence with forest staff regarding incidents like suspicious people movement, tree felling, and wildlife mortality. However, one group from the buffer area expressed dissatisfaction, stating that forest staff only value information about the death of tigers or elephants, not other animals.

The forest staff, including APWs, watchers, and guards, have shared their contact numbers with local community members for prompt alerts. Most APWs are on patrol while visiting their villages, during which people inform them about any threats to or from wildlife. However, one group from the core area expressed hesitation in sharing information due to unfriendly behaviour from forest staff and attempts to relocate them against their will. They stated:

“Previously we used to share information, but now people hesitate as the forest staff are not so friendly towards us. Also, they try to relocate us from our land against our will. Hence, if they help us to be here in our land, we will also help them by sharing information with them.”

Another group mentioned that:

“We inform the forest guard and forester over the phone. There are a few APWs from our village, so we inform them also. We will inform the ranger if we cannot contact the APWs, guard, or anyone else. Once, based on our information, poachers were arrested by the forest officers/staff.”

According to the APO for the MTR for the financial year 2020-21, a sum of INR one lakh (approximately USD 1,200) was set aside in a secret service fund. This fund is intended to reward informants and motivate forest staff to collect information/intelligence (MoEFCC, 2020). Despite this provision, none of the groups participating in the FGD were aware of the existence of the secret service fund. If the local community knew about the reward system and the secret service fund, it could encourage them to provide valuable information/intelligence to help prevent wildlife crime. Due to their sense of responsibility towards wildlife, the local community may willingly share information with forest officers. However, if a reward system is in place for sharing information, it could boost the community’s morale and encourage them to share more information.

According to the results of the FGD, the local community is unaware of any instances where someone was rewarded for sharing information with forest officers. Some groups mentioned that a few villagers receive informal rewards from forest officers, such as tea, snacks, lunch, and occasionally money. However, a group from the core area made the following statement:

“Forest staff do not reward all the villagers who give information. They have specific individuals known to them and only reward them if they provide any information.”

The Wild Life (Protection) Act of 1972 includes a provision for formally rewarding individuals who assist in detecting crimes or apprehending offenders. The reward can be either fifty per cent of the imposed fine or INR 10,000 (approximately USD 120), as determined by the State government. Despite this provision, none of the groups involved in the FGD were aware of this formal reward system for sharing intelligence. Interestingly, the study also discovered that a significant portion of the participating forest officers and staff were also unaware of this formal reward system for intelligence sharing.

The willingness to become an informant for forest officers requires trust and long-term commitment. Despite these prerequisites, it’s crucial to encourage more individuals to become informants, as wildlife crime prevention cannot solely rely on law enforcement initiatives. In the FGD, mixed responses were received regarding the willingness to become informants. Two out of four groups from the core area expressed unwillingness to become informants, with one group stating:

“If the forest department helps and allows us to do agriculture, we would be happy to be the informants.”

Another group felt informants were unnecessary in the core area due to restricted access. On the other hand, all four groups from the buffer area expressed willingness to become informants, with two groups already acting as such. One group expressed:

“Yes, we definitely would like to be informants for the forest staff. Wild animals are like our children, so we must always protect them. As there are too many tourist movements in our area, we have to help the forest department in all possible ways.”

The qualitative data suggests that most local community members are currently sharing information/intelligence with forest officers and are open to continuing this in the future. However, the analysis also reveals a lack of awareness among the majority of community members about the secret service fund and other formal reward systems. The FGD also highlighted the community’s emotional connection to the forest and wildlife, as demonstrated by their expressed readiness to serve as informants for forest officers.

Limitations of the Present Study and Scope for Future Research

The existing literature review indicates that there have been few scientific efforts in the past focused on intelligence-led wildlife crime prevention in India. More specifically, studies investigating wildlife crime prevention in protected areas have been rare. As a result, the researcher chose an exploratory research design for this study. This study can be seen as an effort to produce knowledge from the Global South. While both quantitative and qualitative data were gathered for this research, the findings may not be generalisable to other tiger reserves due to certain limitations. The data gathered from the forest officers/staff through the interview schedule was self-reported and could have been supplemented with secondary data. Regrettably, secondary data regarding the intelligence-led approach implemented by the forest officers/staff in protected areas are scarce. The study was limited to a single protected area in Tamil Nadu. Although the researcher had planned to collect data from one more protected area (i.e., Sathyamangalam Tiger Reserve), this could not be carried out as planned due to unforeseen circumstances, including the recent COVID-19 pandemic. Consequently, the scope of the analysis was reduced, meaning that the current research could not examine the differences and/or similarities between the two tiger reserves. The first author planned to conduct a complete census of the 250 forest officers/staff in the MTR. However, various obstacles, including the difficult terrain, the nature of the officers’ work, and severe flooding that made many anti-poaching camps unreachable, prevented the completion of the census. The situation was further complicated when the COVID-19 pandemic led to a government-imposed lockdown, severely limiting movement. As a result, the researcher was unable to include all the forest officers/staff in the data collection. Despite having a local community member to assist us in interacting with their people, one tribal community chose not to participate. Out of respect for their decision, the data collected does not fully represent all local community members in MTR.

The research methods used in this study can be applied to similar studies in other tiger reserves across India. Future studies should consider a larger sample size by including other stakeholders in protected areas such as police officers, jeep drivers, tour operators, and owners/workers of resorts, cottages, hotels, and meat shops, as well as tourists visiting the reserves.

Conclusion

This research examined the extent of intelligence-led conservation strategies implemented by stakeholders, specifically forest officers/staff and local community members. The study concludes that the overall level of an intelligence-led approach to prevent and detect wildlife crime in the Mudumalai Tiger Reserve (MTR) is currently inadequate. Significantly, the study found that the local community plays a crucial role in sharing intelligence/information, demonstrating their active involvement in wildlife conservation. The findings also suggest that forest officers/staff should receive training on intelligence-led policing to enhance their ability to detect, investigate, and apprehend wildlife crime offenders. This training could be facilitated with the expertise of WCCB and state police.

The study also emphasises the need for APWs to further their education and receive induction training to enhance their on-ground intelligence/information collection and reporting capabilities. Additionally, implementing a computer-aided system for consolidating wildlife crime information/intelligence, such as the Wildlife Enforcement Monitoring System, is deemed crucial for the MTR. The proper utilisation of available secret service funds and legal provisions to reward informants could motivate the local community to share vital intelligence that could help prevent and detect wildlife crime.

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