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Course:CONS200/2026WT2/Conservation technology and illegal poaching in Sub-Saharan Africa: An overview of its effectiveness

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Background of Illegal Poaching in Sub-Saharan Africa

Rhinoceros have ivory tusks - valuable black-market items in certain parts of the world.

Illegal poaching and wildlife trade in Sub-Saharan Africa has become one of the most serious environmental problems in the world that is pushing many species toward extinction. The region has the highest number of terrestrial mammals listed as vulnerable, endangered, or critically endangered by the IUCN[1]. In 2013, around 17,000 to 20,000 African elephants were illegally killed and around 1,000 rhinos were slaughtered[2]. Much of this poaching is driven by demand in Asian markets where rhino horns and ivory are sold in countries like Vietnam, Thailand, and China as medicine or status symbols[2]. Behind this demand, organized criminal networks move illegal goods from African ports in Kenya and Tanzania to Asian markets, largely through areas near roads, water sources, and the edges of protected areas where animals are easiest to access[1][2]. Overall, illegal poaching is a major global issue that is shaped by many factors. The need for improved spatial data, monitoring tools, and targeted conservation strategies is urgent.

Effective Anti-Poaching Conservation Methods

In Southern Africa, imposing trade laws as the sole anti-poaching measure can be inimical to conservation interests. This is because on their own, they can disrupt local livelihoods and security, as well as push markets underground[3].

Effective conservation with trade laws is achievable through strong enforcement, community incentives and social support frameworks. Community welfare, economic viability and natural conservation do not stand independently from each other; they are intricately linked. As such, effective approaches against poaching must be holistic.

In 2024, Hiller & ‘t Sas‐Rolfes carried out an investigation to identify and analyse methods currently used in Southern Africa, demonstrating high effectiveness in the following three methods[4]:

  1. Well-funded, intelligence-driven, proactive law enforcement.
  2. Community-based natural resource management programs.
  3. Reducing demand in consumer markets.

The following sections provide examples for each strategy.

1. Locally Grounded Enforcement in Zambia

Detection probability has been shown to be a more effective poaching deterrent than punishment severity[3]. As such, an awareness of where poachers are likely to operate, and why they would target that area, offers a valuable advantage for conservation efforts.

A herd of elephants gathered by the water.

In Zambia’s Lupande Game Management Area, dramatic increases in arrests and strong community support were achieved through the employment of well-paid village scouts. Manpower increased from 11 to 26 local village scouts from 1985 to 1987, and the area of effective law enforcement increased from 200 km2 to 400 km2 [5]. Consistent with this trend, the annual mortality from the illegal hunting of both elephants and black rhinos decreased by at least tenfold[5].

Intelligence-based enforcement leveraged local knowledge to anticipate poaching events, rather than simply respond to them. Resident villagers understand seasonal wildlife movements, have an expert tracking skillset, can recognise subtle ecological disturbances and identify where poachers may enter and exit the region from.

Hiller & ‘t Sas‐Rolfes[4] stress that funding and structure makes local support effective. Communities require reliable salaries, training, equipment and an administrative system. With these conditions, community trust builds and intelligence flow improves[5]. Therefore, proactive law enforcement has been made effective in Zambia through an integrated, locally grounded approach to conservation.

2. Community-based Natural Resource Management (CBNRM) in Namibia and Botswana

CBNRM leans on the collective stewardship of natural resources by the communities who directly depend on them. It recognises the interdependence of community and natural well-being in an integrated approach to conservation. Ownership is not merely symbolic; it ensures that resources are managed with future generations in mind[6]. Local communities possess unique insight into their ecosystem and have a shared reliance on the environment.

Khama Rhino Sanctuary

In Namibia, the LIFE program (Living in a Finite Environment) has successfully improved the social/ecological knowledge base for management of communal natural resources and mobilised communities into legally recognised bodies[7]. The program reduced poaching pressure, increased wild black rhino and elephant populations and created an invaluable conservation incentive for local communities[8]. This was achieved through the complete devolution of high-value wildlife resources to communities with strong governance structures in sparsely populated areas. As such, communities can see tangible benefits when they actively report poachers and support local patrols because the wildlife now has a far greater value for them alive than dead.

Botswana's Khama Rhino Sanctuary is a community-based wildlife project created in 1992 to assist the recovery of black and white rhinos. The reserve covers 8585 hectares of former cattle farmland and is located on historic ancestral lands of the Bangwato people[9]. The rhino breeding programme led to the successful relocation of 16 individuals from an initial founding population of 4 rhinos[10]. Anti-poaching patrols carried out by community rangers and the Botswana Defence Force guarantee a safe breeding ground, supported largely by tourism. Benefits flow to local communities in the form of sustainable use of natural resources, tourism revenue and stable employment opportunities. Additionally, the Environmental Education Centre in the reserve enables both locals and international visitors to learn about the importance of the project and how they are contributing to conservation of rhinos.

3. Reduction of demand in Asian markets

Hiller & ‘t Sas‐Rolfes highlight the importance of coupling law enforcement and trade bans with demand reduction[4]. Campaigns and legal restrictions in consumer markets can reduce ivory demand, shrink markets and lower prices. The 1989 ivory ban provided the groundwork for anti-ivory campaigns in Japan. A social stigma was introduced, making trading ivory ethically unacceptable. This effectively reduced Japanese demand and resulting in smaller ivory markets in sub-Saharan Africa[11].

In Hong Kong, a similar post-ban market decrease was recorded. The number of ivory craftsmen decreased from 600-1000 in 1988 to zero in 2002, and there were also no ivory workshops in 2002[11]. The price (kg-1) of 5-10 kg elephant tusk shifted from US$180 in 1988 to US$200-320 in 2002[11]. The rise in price was because of scarcity after the industry collapsed.

An elephant bathes in the river.

In 2015, Thailand restricted all legal ivory trade to tusks from registered captive Asian elephants only, completely banning the import, export and trade of African elephant ivory[12]. Law enforcement was enhanced through random testing of registered ivory to verify its Asian origin. Surveys found that the number of ivory products for sale in Bangkok decreased from 7421 in 2014 to 283 items in 2016[12]. Therefore, campaigns and legal restrictions have been shown to reduce ivory demand and lower prices. By decreasing demand in consumer markets, poaching pressure is reduced.


It is important to note that there is no ‘one-size-fits-all’ approach against poaching, and the effectiveness of a particular approach is dependent on socioeconomic, geographical and political factors.

Ineffective Anti-Poaching Conservation Methods

1. Large-scale Militarization

Large-scale militarization, which involves using (often violent) military tactics to protect wildlife, has been growing more common as a solution to poaching. Initially gaining traction in the 1970s, the militarization of conservation, also referred to as ‘green militarization’ has taken form in strategies such as fortress conservation, which defends protected areas using weapons and military personnel. While it is aimed to take a more forceful approach against poaching and other violent threats, it raises several ethical concerns in terms of the removal of indigenous people and increased violence.

1.1 Enforcement over Prevention

Green militarization often doesn’t address the root cause of poaching, because it focuses on enforcement rather than prevention. In many cases, the motives behind poaching are a result of socioeconomic factors, such as poverty and inequality, as well as wealth dynamics in demand countries that create a larger market[13] Without addressing these sources, the militarization approach will only continue to push illegal poaching activities into new areas rather than eliminating it altogether. The Non-Governmental Organizations (NGOs) that front the militarization measures often heavily criminalize poachers, creating a ‘hero and villain’ persona between rangers and poachers. Virunga National Park in eastern Democratic Republic of Congo (DRC) advertises the “Fallen Rangers Fund”, which promotes this characterization for donations.[13] The antagonization of poachers emphasizes the fight against them, rather than targeting the systemic cause. The idealization of the rangers also has negative effects, as it does not discuss the negative actions they take, such as forcibly removing indigenous communities from their land. As a result, the strained relationship between locals and law enforcement can reduce cooperation of conservational efforts.

1.2 Removal of Local Peoples

The militarization of anti-poaching approaches has often included the severe mistreatment or forceful removal of local people. Methods such as informant networks and home raiding by military personnel have been used to uncover information on potential poaching activities or organizations[13], causing disruption and raising fear in local communities. In other cases, locals will get forcefully displaced altogether, under argument by the NGOs and donors that nature cannot thrive under any human interaction [14]. This can lead to further conflict, such as in the Kahuzi-Biega National Park in eastern Democratic Republic of Congo (DRC), where the local Batwa people forcibly took back their ancestral forests in 2018. Originally forced out of their home in the 1970s, the Batwa people decided to rally against the militarized authorities after being denied compensation and formal and legal access to their ancestral lands[15]. In addition to increased violence and fatalities among both parties, the removal of the Batwa people led to the loss of their participation in the conservation initiatives. Local communities are often at the center of conservation efforts, so the violation of their rights leads to weakened civilian support and fosters resentment towards conservation actions.

1.3 Increased Violence

The increased violence that comes with fortress conservation has also been raised as an ethical concern. With the rise of militarization, rangers originally tasked with conservation responsibilities are now forced to participate in highly violent military tactics for which most have had little training or experience [13]. Kruger National Park in South Africa has seen an increase in Post-Traumatic Stress Disorder (PTSD) cases among its rangers[13], indicating a psychological toll associated with militarized conservational practices.

Policies permitting the use of lethal force against poachers have also raised concerns regarding the violation of human rights. During the 1970s and 1980s, several countries, such as Botswana, Uganda, Zimbabwe, Kenya, Tanzania, Central African Republic, and Malawi, have implemented “shoot-to-kill” policies that allowed law enforcers to fatally shoot poachers and bandits on site within protected areas [16]. This sparked controversy, raising the question as to whether biodiversity protection should take precedence over human life.

Emerging Conservation Methods

Two rhinos gathered together.

1. Satellite Technology

GNSS (Global Navigation Satellite System) is a system that utilizes wireless and positioning technology to track and monitor the movement patterns, food sources, habitats and breeding cycles of animal populations.

GNSS technology has been successful among South Africa’s tackle against illegal poaching and dehorning of its rhino population. Due to high demand for their prized horns, black rhinos have been an endangered species primarily due to illegal poaching, dwindling their populations down to a couple thousand. To protect the black rhino populations, the Black Rhino Ecology Project glued a semi-spherical GNSS device in place of the removed horn that allowed scientists to monitor rhino individuals within a population[17]. The GNSS battery lasts for 2-3 years without requiring intervention, providing scientists with tracking data that can help improve future management practices towards species recovery. Further, the GNSS trackers are incredibly useful for reserves as the tracking devices can follow the movement of individual rhinos, or rhinos in relation to other species and can detect foreign parties or irregularities such as a potential poaching vehicle[17].

In addition, GNSS technology has been used as a hybrid method to track endangered burrowing animals that were previously difficult to track. Pangolins are one of the most trafficked animals due to their valuable scales that are often used in Chinese medicine [17]. After rescuing pangolins from poachers, pangolins cannot be sustained in captivity but must go through a release program as their specific diet of ants and termites requires them to hunt. However, pangolins still need time to acclimate to their environments. GNSS technology is integrated into tracking collars to monitor nightly and defensive behaviors in the wild. Once pangolins have adjusted and established a home, the collars are removed – allowing the pangolins to roam freely [17].

2. SMART and AI

Spatial Monitoring and Report Tool (SMART) was a platform developed to improve conservation and area management by compiling wildlife ranger data to better inform area management of protected areas. Today, SMART is the most widely used conservation technology used for community and national conservation[17]. Several case studies show its impact across different regions. In Uganda’s Queen Elizabeth National Park, adopting SMART led to a 250% increase in the detection of illegal activities without adding more ranger resources[18]. In Belize's Glover's Reef Marine Reserve, SMART contributed to a 55% decline in fisheries infractions[18]. In Russia, SMART was used across tiger sites in the Russian Far East, where tiger numbers increased by 61% between 2011 and 2014[18].

Additionally, Harvard Engineering and Applied Science lab students have developed an AI machine learning software tool called Protection Assistant for Wildlife Security (PAWS) which is used to predict potential poaching hotspots in protected areas. PAWS utilizes historical poaching data from SMART’s database and compiles the information, breaking down the protected area into 1 km2 squares and analyzing the probability of poaching activity[19]. As of 2020, PAWS has been implemented into over 800 protected areas and parks and  continues to evolve with additional data and is one of the most popular emerging conservation methods.

3. Drone Implementation

Recently, drones have grown in popularity within conservation. As a cost-effective and relatively simple resource, they provide good solutions to aerial surveys and identity confirmation challenges. Drones have also recently been introduced into anti-poaching solutions as a way to identify both endangered species and poachers. In comparison to current anti-poaching aerial surveys that use manned aircrafts, drones provide a safer, cheaper alternative that records more efficient and more accurate data[20]. As poachers often carry firearms and other weapons, drones are a safer option because they don’t have an on-board observer who is put in danger.

Drones can also use a variety of different sensors, such as the Red Green Blue color model (RGB), which analyzes visible light, and thermal infrared (TIR), which maps thermal energy sources. RGB models are suitable for midday surveys when the camera is able to pick up enough light to differentiate between objects, and the heat contrast is at a low, making the TIR models less accurate. TIR cameras are best at dawn and dusk, when the environment is cool enough that endothermic organisms such as human poachers and animals will differentiate against the background. The majority of poaching events occur at twilight, making TIR the best for detecting them[21]. In addition, drones provide pictures and videos that can be replayed, making them advantageous for more in-depth analysis and tracking[20]. They can follow a predefined flight path, eliminating the need for a pilot and allowing for autonomous navigation instead[20]. This capability is especially valuable in low-visibility conditions, such as at night or when operating beyond visual line of sight. Drones also provide a quieter alternative and can operate at a lower altitudes than manned aircraft, making them a more discrete option.

Several studies have also introduced Artificial Intelligence (AI)-based detection programs into drones to create automated detection. Bondi et al. (2018)[22] used an AI application called Systematic Poacher de-Tector (SPOT), which automatically detected poachers using drone footage in near real-time. Automated detection was found to be accurate, time efficient, and convenient. With almost instantaneous detection, action could be taken against poachers after their identification, proving to be efficient and valuable.

4. Open Source Platforms

Open source platforms such as EarthRanger provide a centralized and comprehensive database that reduces the barriers to conservation. EarthRanger tools are equipped with GNSS, allowing the information that supports well-informed decision making and increases situational awareness[17]. EarthRanger tools are accessible to the public and on mobile phones, meaning wildlife rangers and teams and even the public can be equipped with real-time information and crucial data for strategic judgement.

Open source platforms such as EarthRanger’s model can also create a sense of ownership and responsibility. EarthRanger’s ease of access allows community members to report poaching incidents, illegal logging and suspicious activity[23]. By increasing conservation transparency and lowering the barriers for conservation, communities are empowered to contribute to conservation leading to reduced wildlife trade and stewardship[23]. Unlike the majority of conservation research, EarthRanger’s technology is mainly based in the Southern Hemisphere – particularly in the southern regions of Africa. Not only is EarthRanger’s technology efficient, but the contribution towards area management is often neglected and balances out the lack of data in the Southern Hemisphere.

Limitations

1. Economic Poverty

The power imbalances, corruption and debt sub-Saharan countries owe towards imperial powers restrict economic growth, resulting in economic poverty. In 2014, Mozambique saw a radical spike in poverty and has only experienced a slight decrease since 2019. Sub-Saharan countries carry some of the most diverse wildlife and ecosystems, but lack the economic success to fund their conservational efforts. Participants of illegal poaching and underground trade are often incentivized into trafficking animals out of desperation in order to provide for their families. Economic incentives for participating in underground trade are a global problem. One rhino hunt earns a poacher above the annual income of a rural citizen in southern Africa, and tiger poachers in Southeast Asia can earn a small fortune from tiger pelts, bones and genitalia[24].

2. AI

The duality of AI in conservation to support informed decision making while potentially worsening climate change raises a crucial ethical dilemma: Would the benefits and convenience of using AI for conservation outweigh the environmental costs? While AI is commonly used in technology and engineering in new and upcoming conservation technology, AI overdependency could lead to a greater tradeoff causing significantly more damage to the environment. For example, in training Microsoft’s Azure, one training run generated 223,920 kg CO2, equivalent to the emissions of 49 cars in a year [25]. The effectiveness of AI in conservation is also held back by the lack of research across regions. For instance, Tanzania has been well-studied, but Central Africa lacks sufficient data [1]. AI models trained on limited or region-specific data may not work well in other areas. This can cause poor use of conservation resources in places that are less studied but still important for biodiversity. In assessing the environmental impacts of widely used, existing models, the drawbacks would far outweigh the benefits of using AI in conservation to support species population.

References

  1. ↑ 1.0 1.1 1.2 Duporge, I., Hodgetts, T., Wang, T., & Macdonald, D. W. (2020). The spatial distribution of illegal hunting of terrestrial mammals in Sub-Saharan Africa: a systematic map. Environmental Evidence, 9(1). https://doi.org/10.1186/s13750-020-00195-8
  2. ↑ 2.0 2.1 2.2 Merem, E. C., Twumasi, Y., Wesley, J., Isokpehi, P., Fageir, S., Crisler, M., Romorno, C., Hines, A., Ochai, G. S., Leggett, S., & Nwagboso, E. (2018). Appraising variations in climate change parameters along the lower West African region. Advances in Life Sciences, 8(1), 1–25. https://doi.org/10.5923/j.als.20180801.01
  3. ↑ 3.0 3.1 African Researchers Magazine. (2025, October 24). Impact of Wildlife Trade Restrictions on Iconic Species in Southern Africa. African Researchers Magazine. https://www.africanresearchers.org/impact-of-wildlife-trade-restrictions-on-iconic-species-in-southern-africa-law-enforcement-community-engagement-and-conservation-policy-insights/?
  4. ↑ 4.0 4.1 4.2 Hiller, C., & t Sas‐Rolfes, M. (2024). Systematic review of the impact of restrictive wildlife trade measures on conservation of iconic species in southern Africa. Conservation Biology. https://doi.org/10.1111/cobi.14262
  5. ↑ 5.0 5.1 5.2 Lewis, D., Kaweche, Gilson B, & Mwenya, A. (1990). Wildlife Conservation Outside Protected AreasLessons from an Experiment in Zambia. Conservation Biology, 4(2), 171–180. JSTOR. https://doi.org/10.2307/2385810
  6. ↑ Sustainability Directory. (2025, July 19). Community-Based Resource Management → Fundamentals. Sustainability Directory. https://sustainability-directory.com/fundamentals/community-based-resource-management-fundamentals/
  7. ↑ Jones, B. (1999). Evaluating Eden Series Community-based Natural Resource Management in Botswana and Namibia: an inventory and preliminary analysis of progress. https://www.iied.org/sites/default/files/pdfs/migrate/7799IIED.pdf
  8. ↑ DeGeorges, P., & Reilly, B. (2009). The Realities of Community Based Natural Resource Management and Biodiversity Conservation in Sub-Saharan Africa. Sustainability, 1(3), 734–788. https://doi.org/10.3390/su1030734
  9. ↑ Must See Spots. (2026). Khama Rhino Sanctuary: A Guide to Rhino Conservation in Botswana. Must See Spots. https://www.mustseespots.com/botswana/articles/khama-rhino-sanctuary-a-guide-to-rhino-conservation-in-botswana/#touch-the-sky
  10. ↑ Khama Rhino Sanctuary. (2019). Khama Rhino Sanctuary. Khamarhinosanctuary.org.bw. https://khamarhinosanctuary.org.bw/animals
  11. ↑ 11.0 11.1 11.2 Stiles, D. (2004, December). The Ivory trade and elephant conservation. ResearchGate; Cambridge University Press (CUP). https://www.researchgate.net/publication/231964536_The_Ivory_trade_and_elephant_conservation
  12. ↑ 12.0 12.1 Krishnasamy, K., Milliken, T., & Savini, C. (2016). In Transition: Bangkok’s ivory market - Wildlife Trade Report from TRAFFIC. Traffic.org. https://www.traffic.org/publications/reports/in-transition-bangkoks-ivory-market/
  13. ↑ 13.0 13.1 13.2 13.3 13.4 Duffy, R., Massé, F., Smidt, E., Marijnen, E., Büscher, B., Verweijen, J., Ramutsindela, M., Simlai, T., Joanny, L., & Lunstrum, E. (2019). Why we must question the militarisation of conservation. Biological Conservation, 232, 66–73. https://doi.org/10.1016/j.biocon.2019.01.013
  14. ↑ Igoe, J. (2002). [Review of Fortress Conservation: The Preservation of the Mkomazi Game Reserve, Tanzania, by D. Brockington]. The International Journal of African Historical Studies, 35(2/3), 594–596. https://doi.org/10.2307/3097688
  15. ↑ Simpson, F. O. L., & Pellegrini, L. (2023). Agency and structure in militarized conservation and armed mobilization: Evidence from eastern DRC’s Kahuzi-Biega National Park. Development and Change, 54(3), 601–640. https://doi.org/10.1111/dech.12764
  16. ↑ Neumann, R. P. (2004). Moral and discursive geographies in the war for biodiversity in Africa. Political Geography, 23(7), 813–837. https://doi.org/10.1016/j.polgeo.2004.05.011
  17. ↑ 17.0 17.1 17.2 17.3 17.4 17.5 Solito, G. u-blox. (2024). Tracking and monitoring endangered wildlife for conservation, digitally. u-blox. https://www.u-blox.com/en/blogs/stories/wildlife-tracking-digitally-cloud-based-positioning-technology Cite error: Invalid <ref> tag; name ":8" defined multiple times with different content
  18. ↑ 18.0 18.1 18.2 Impact. (n.d.). https://smartconservationtools.org/en-us/SMART-in-Practice/Impact
  19. ↑ Zewe, A. (2020). Preventing poaching. Harvard John A. Paulson School of Engineering and Applied Sciences. https://seas.harvard.edu/news/preventing-poaching
  20. ↑ 20.0 20.1 20.2 Hambrecht, L., Brown, R. P., Piel, A. K., & Wich, S. A. (2019). Detecting “poachers” with drones: Factors influencing the probability of detection with TIR and RGB imaging in miombo woodlands, Tanzania. Biological Conservation, 233, 109–117. https://doi.org/10.1016/j.biocon.2019.02.017
  21. ↑ Doull, K. E., Chalmers, C., Fergus, P., Longmore, S., Piel, A. K., & Wich, S. A. (2021). An Evaluation of the Factors Affecting ‘Poacher’ Detection with Drones and the Efficacy of Machine-Learning for Detection. Sensors, 21(12), 4074. https://doi.org/10.3390/s21124074
  22. ↑ Bondi, E., Fang, F., Hamilton, M., Kar, D., Dmello, D., Choi, J., Hannaford, R., Iyer, A., Joppa, L., Tambe, M., & Nevatia, R. (2018). SPOT Poachers in Action: Augmenting Conservation Drones With Automatic Detection in Near Real Time. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11414
  23. ↑ 23.0 23.1 Lynam A.J., Cronin D.T., Wich S.A., Steward J., Howe A., Kolla N., Markovina M., Torrico O., Reyes V., Sophalrachana K., Stevens X., Schmidt E. & Cox H. The rising tide of conservation technology: empowering the fight against poaching and unsustainable wildlife harvest. (2025) Frontiers in Ecology and Evolution (13) https://doi.org/10.3389/fevo.2025.1527976
  24. ↑ United Nations Office on Drugs and Crime. (n.d.). Incentives to participate in illegal wildlife, logging and fishing economies. UNODC Education for Justice (E4J) University Module Series. https://www.unodc.org/e4j/zh/wildlife-crime/module-5/key-issues/incentives-to-participate-in-illegal-wildlife--logging-and-fishing-economies.html
  25. ↑ Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2023). The AI gambit: Leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. AI & Society, 38(1), 283–307. https://doi.org/10.1007/s00146-021-01294-x


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