Is wildlife restricted to protected Areas?

Evaluating the ecological effectiveness of protected areas in Tanzania

A blog entry by Anna-Lena Mieke

“Effective ecological management aims to preserve or increase biodiversity by protecting endangered species, maintaining genetic diversity, and ensuring the survival of various plant and animal communities​.”

The Katavi–Rukwa Ecosystem, located in western Tanzania, is a rich and diverse landscape that includes various protected areas with varying degrees of conservation enforcement. This region is home to a wide variety of wildlife, including several endangered and vulnerable species.

A recent study conducted by Giliba et al. (2024) utilized density surface models (DSM) based on dung counts to evaluate the ecological effectiveness of these protected areas. The findings provides valuable Insights into the influence of different protection levels and environmental variables on wildlife distribution and providing essential guidance for conservation strategies1. It amins to evaluate the ecological effectiveness of various levels of protection within the Katavi-Rukwa Ecosystem. This ecosystem includes several protected areas such as the Katavi National Park, Rukwa Game Reserve, Lwafi Game Reserve, Mlele Game Controlled Area, Mpanda Line Forest Reserve, Msaginia Forest Reserve, and Nkamba Forest Reserve, each offering different levels of protection. These areas collectively provide critical habitats for species like elephants, giraffes, buffaloes, zebras, topis, and hartebeests, which are key indicators of ecosystem health1.

What are Density SurFace Models?

 

Density Surface Models (DSM) are a spatially explicit modeling approach used to estimate and visualize animal population densities across landscapes by incorporating detection probabilities and environmental variables. An indirect method to estimate wildlife population densities is “Dung counts”. It involves counting dung piles along transects to estimate animal presence and abundance. By combining dung count data with spatial environmental variables, the DMS provides detailed insights into factors influencing animal distribution and conservation effectiveness2. DSM corrects these dung counts with environmental data to predict dung density across the study area and generates a continuous, explicit map of animal distribution3.

Key Findings

 

The study found out that the level of protection has a notable impact. Dung densities were significantly higher in strictly protected areas like national parks and game reserves compared to less-strictly protected and unprotected areas1. This indicates that stricter protection measures are more effective in maintaining higher densities of large mammals4,5.

Proximity to cropland and houses had consistent negative effects on dung densities, suggesting that human activities and land use changes negatively impact wildlife distribution1,6,7. Other environmental factors such as elevation, slope, rivers, and vegetation index (EVI) also played significant roles in determining species-specific dung densities8.

The DSM revealed that large mammals are widely distributed in core protected areas while they are largely absent from unprotected areas. This underlines the crucial role of protected areas in supporting viable populations of large mammals9.

Implications for conservation

 

The findings highlight the importance of strict protection measures for conserving large mammal populations. National parks and game reserves, with higher levels of enforcement, are crucial for maintaining wildlife densities. However, the study also reveals the challenges posed by human activities and habitat fragmentation around protected areas. To ensure the long-term survival of these species, conservation efforts need to extend beyond the boundaries of protected areas10,11. Developing land-use plans that balance the needs of wildlife conservation and human development is important. This includes establishing buffer zones and wildlife corridors to reduce conflicts and promote habitat connectivity12​​. Involving local communities in conservation efforts through education, capacity building, and providing economic incentives for sustainable practices fosters positive attitudes towards wildlife and conservation initiatives13​​. Strengthening anti-poaching measures and increasing the enforcement of wildlife protection laws are essential to curb illegal hunting and resource extraction activities​​14. Ongoing research and monitoring of wildlife populations and habitat conditions are necessary to inform adaptive management strategies and ensure the long-term effectiveness of conservation efforts15​​.

The Katavi–Rukwa Ecosystem is an important conservation area facing numerous social and ecological challenges. Addressing these issues through integrated conservation strategies and community involvement is essential for preserving this unique and biodiverse landscape for future generations16.

References

  1. Giliba, R. A., Kiffner, C., Fust, P. & Loos, J. Using density surface models to assess the ecological effectiveness of a protected area network in Tanzania. Ecosphere 15, (2024).
  2. Miller, D. L., Burt, M. L., Rexstad, E. A. & Thomas, L. Spatial models for distance sampling data: Recent developments and future directions. Methods in Ecology and Evolution vol. 4 1001–1010 (2013).
  3. Marques, F. F. C. et al. Estimating Deer Abundance from Line Transect Surveys of Dung: Sika Deer in Southern Scotland. Journal of Applied Ecology vol. 38 (2001).
  4. Newmark, W. D. Isolation of African protected areas. Frontiers in Ecology and the Environment vol. 6 321–328 (2008).
  5. Newmark, W. D. Aislamiento de parques de Tanzania y la extincion local de mamiferos grandes. Conservation Biology 10, 1549–1556 (1996).
  6. Geldmann, J., Joppa, L. N. & Burgess, N. D. Mapping Change in Human Pressure Globally on Land and within Protected Areas. Conservation Biology 28, 1604–1616 (2014).
  7. Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. (2019).
  8. Huete, A. et al. Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices (2002).
  9. Caro, T. Densities of Mammals in Partially Protected Areas] the Katavi Ecosystem of Western Tanzania. (1999).
  10. Waltert, M., Meyer, B. & Kiffner, C. Habitat availability, hunting or poaching: What affects distribution and density of large mammals in western Tanzanian woodlands? Afr J Ecol 47, 737–746 (2009).
  11. Waltert, M. et al. Foot Surveys of Large Mammals in Woodlands of Western Tanzania. J Wildl Manage 72, 603–610 (2008).
  12. Van De Perre, F., Adriaensen, F., Songorwa, A. N. & Leirs, H. Locating Elephant Corridors between Saadani National Park and the Wami-Mbiki Wildlife Management Area, Tanzania. (2014).
  13. Caro, T. & Davenport, T. R. B. Wildlife and wildlife management in Tanzania. Conserv Biol 30, 716–723 (2016).
  14. Plumptre, A. J. et al. Efficiently targeting resources to deter illegal activities in protected areas. Journal of Applied Ecology 51, 714–725 (2014).
  15. Williams, B. K., Nichols, J. D. & Conroy, M. J. (Michael J. Analysis and Management of Animal Populations : Modeling, Estimation, and Decision Making. (Academic Press, 2002).
  16. IPBES. The Global Assessment Report. (2019).

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Image: Anna-Lena Mieke
Quote: Wang, S., Martin, P.A., Hao, Y. et al. A global synthesis of the effectiveness and ecological impacts of management interventions for Spartina species. Front. Environ. Sci. Eng. 17, 141 (2023).

Map: Giliba, R. A., Kiffner, C., Fust, P. & Loos, J. Using density surface models to assess the ecological effectiveness of a protected area network in Tanzania. Ecosphere 15, (2024).