INTERVENTION SUMMARY

The case study evaluated the use of SMART-based Law Enforcement Monitoring (LEM) as a tool to improve anti-poaching patrols across four protected areas (PAs) in the Russian Far East, critical for Amur tiger conservation. Poaching persisted because there was demand for tigers and their prey, opportunities to hunt them inside and outside the protected areas, and challenges in enforcement efficiency. The interventions focused on combating wildlife crime by implementing the SMART law enforcement monitoring system, increasing patrol effort and effectiveness, and reducing predictability of patrols. Patrol teams used GPS to record movements and paper forms to record violations, threats, and wildlife sightings. The collected data was also used to evaluate performance, set targets, and provide feedback. They also implemented a performance-based bonus system for members of the patrolling team to increase patrolling effort. As a result, over a four-year period, the project noted an increase in patrol effort and a partial reduction in threats. Poaching rates and firearm confiscations declined in some areas, but trends were inconsistent. Although they did not detect clear trends in ungulate numbers, tiger populations remained stable or increased, suggesting that poaching of tigers may be more limiting than prey depletion. The intervention was promising because SMART monitoring enabled standardized tracking of patrols and threats, supporting data-driven decision-making. It allowed achievement of the short-term goal of improving patrolling quality and reducing threats to wildlife. However, long-term conservation goals, such as increasing tiger numbers to 1 individual/100 km² at each study site, and prey numbers couldn’t be achieved. Ultimately, this study determined that further development of success indicators is necessary to properly evaluate and redirect anti-poaching efforts.

INTERVENTION DETAILS

What was the problem?

Direct poaching of tigers and poaching of tiger prey (ungulates) in Russia's far East province resulted in immediate risks to tiger populations, both through direct poaching and the depletion of prey critical for their survival. Poaching persisted because there was demand for tigers and their prey, opportunities to hunt them both inside and outside the protected areas, and challenges in enforcement efficiency.

What was the Intervention and How was it Implemented?

Improving Law enforcement Monitoring and Performance: The interventions to protect tigers focused on increasing patrol effort and effectiveness by implementing the Spatial Monitoring and Reporting Tool (SMART). This involved using software based on GIS technology to facilitate the storage and analysis of spatial patrol monitoring data. They also implemented adaptive management strategies, including regular reviews, performance-based bonuses to boost morale, and capacity building for patrol teams based on the collected data. Additionally, biological monitoring of tigers and prey populations tracked conservation outcomes. (Increase the Risks - strengthen formal surveillance)

Was the Intervention Effective, Ineffective, or Promising?

The intervention was promising. Patrol efforts increased significantly: there were clear, consistent yearly increases in kilometers covered on foot patrols in three of the four areas, averaging 1.9 times greater in the final year compared to the first. Patrolling effectiveness was less consistently successful. While two sites showed small improvements in patrol coverage (fewer unvisited areas), the other two showed no clear trends. Moreover, while poaching rates appeared to decline in two sites (LLNP and LAZO), these changes were not always consistent. At the other two sites (SABZ and ZOTI), these threats were "completely or practically absent". The program did not detect clear trends in ungulate numbers (prey) after the 3-4-year period of LEM implementation. The minimum number of tigers photographed per year (a simpler indicator used due to data limitations) remained stable or increased at all four sites since LEM implementation.

How do We Know?

The success of the interventions was driven by several key elements working together. The implementation of SMART provided standardized tools for tracking patrol activities and threats, enabling data-driven decisions. Adaptive management allowed for regular reviews of patrol performance and adjustments to strategies, addressing weaknesses and improving effectiveness over time. Patrol teams increased their efforts, covering more hours and distances with expanded spatial and temporal coverage, reducing opportunities for poachers. Performance-based bonuses boosted morale and motivated patrol teams to engage more actively. Patrol coverage increased over the years. However, the patrolling effectives was inconsistent. It allowed partially reaching the short-term goal of reducing threats to wildlife. However, log-term conservation goals, such as increasing tiger numbers to 1 individual/100 km² at each study site and prey numbers, couldn't be achieved. Ultimately, this study determined that further development of success indicators is necessary to properly evaluate and redirect anti-poaching efforts.

Were Conservation Outcomes Measured?

Yes. The study monitored patrol effort, recorded threats, prey abundance, and tiger population changes over four years. Tiger populations remained stable or increased, indicating some success, while prey populations showed mixed results with limited immediate improvement.

ASSESSMENT

The SMART-based Law Enforcement Monitoring (LEM) system increased patrol effort (kilometers covered, time on patrol, and area coverage), and led to improved detection and prevention of illegal activities.

The mechanism of change was primarily deterrence through increased patrol presence and unpredictability. By increasing patrol effort, reducing spatial and temporal predictability of patrols, and incentivizing ranger performance (via bonuses), the program aimed to discourage poachers from entering or operating in the reserves. The assumption was that a better patrol effort leads to fewer poaching incidents, more prey and, as a result, more tigers. Another mechanism was adaptive management feedback loops, where quarterly reviews of patrol data informed ongoing adjustments to patrol strategies.

Key contextual factors included:

  • Site size and terrain (larger, more remote sites like SABZ had lower patrol coverage).
  • Staff morale and leadership support (staff motivation was low at some sites like SABZ until leadership changed).
  • Seasonal conditions (e.g., winter snow helped track intruders).
  • Infrastructure access (some sites had better road access, influencing patrol strategies).
  • Legal and institutional context (all reserves operated under federal laws but had variable institutional support for LEM from higher government levels).

Implementation involved rolling out SMART LEM across four sites, with standardized patrol data collection (effort, violations, wildlife observations), regular data entry, analysis, and quarterly feedback meetings. Patrol teams received training, equipment, and financial incentives tied to verified performance. The system was embedded within an adaptive management cycle. Challenges included inconsistent patrol coverage, varying spatial unpredictability, staff fatigue, and differing management engagement across sites. Some areas had stronger implementation (LLNP, LAZO) while others lagged (SABZ, ZOTI initially).

The paper did not conduct a detailed cost-benefit analysis, but it did mention key costs such as that funding was provided for patrol vehicle fuel, maintenance, and staff performance bonuses (salaries were low (~$330/month), so bonuses were necessary for motivation). There was discussion of long-term sustainability concerns, especially regarding continued external funding for bonuses and patrol resources. The authors recommended only using bonus systems where long-term funding could be assured.

SCP COLUMNS

Strengthen formal surveillance

ADDITIONAL DETAILS

Citation

Hoette (2016)

Year Range

2011-2014

Country

Russia

Landscape

Forest

Target Species

Tiger

Problem type

Poaching

Source: Hoette, M. H., Kolodin, I. A., Bereznuk, S. L., Slaght, J. C., Kerley, L. L., Soutyrina, S. V., ... & Miquelle, D. G. (2016). Indicators of success for smart law enforcement in protected areas: A case study for Russian Amur tiger (Panthera tigris altaica) reserves. Integrative Zoology, 11(1), 2-15.