In Africa, the important agro-pastoral activity and poverty in rural areas lead to strong anthropogenic pressures on protected areas and to their quick degradation. Therefore the efficient conservation and sustainable exploitation of protected areas require adaptive and dynamic management that integrates peripheral interactions with regard to their changing spatial and temporal dimensions. They call for the deployment of appropriate management indicators capable of translating all the issues raised into concrete and practical terms. To this end, a new conceptual and analytical approach to assess pressure indicators is needed to take into account the spatio-temporal oscillation or mobility of the area of socio-economic dependence that must henceforth provide the basis for sustainable management in the context of adaptation to climate change. The study responds to this concern through rigorous conceptualization, characterization and validation of original peripheral pressure indicators focused on a global and dynamic socio-economic framework. The method used consisted of an interpretative analysis of theoretical bibliographic data, measurements and field observations using GPS, ArcGIS 10.1 and Envi 4.5 and semi-structured interviews for the characterization of defined pressure indicators and their field validation. The five pressure indicators designed and applied on the basis of the criteria of direct dependence on protected areas are the coefficient of asymmetry (Kc), the periphery (Ψ), the dependent population (Dπ), the distance-access time (DAT) and the field daily working time (FDWT). The approach and pressure indicators were successfully applied to the Rusizi National Park (Burundi) for the period 1984-2015. The results showed that the park has a coefficient of asymmetry of 2.64 which represents a three times higher level than its circular equivalent, a periphery of 13.23 km radius composed of 35 localities characterized by distance-access times comprise between 0 to 2 h 30 and field daily working times ranging from 7 to 11 hours. They revealed that nearly 70% of peripheral populations are concentrated within 6 km from the boundaries and have distance-access times of less than one hour. The peripheral dependence on Rusizi Park reaches 100% for woody resources, 97% for livestock products, 88% for agricultural resources and 83% for animal protein products. The modeling of potential pressures and field observations showed that peripheral localities are the more threatening that they are more dependent, more populated and closer to the park. As a consequence, the important anthropogenic pressures led to a very significant degradation of the park during the study period.
Protected areas and biodiversity are currently facing important degradation, especially in tropical regions. This evolution questions the management systems and calls for adaptive and sustainable management on the basis of regular assessments of global evolutionary trends and continuous adjustments of conservation objectives and management tools. Adaptive management is yet missing rigorous and integrated indicators for advanced evaluations for many protected areas which have never been assessed despite periodical updating of management goals and plans. The development of reliable, global and low cost methods for adaptive management is therefore a great concern for scientific and conservationist communities given the limitations of commonly used tools and recurrent problems of conservation funding. The PA-TAMCO Analytic Model was designed to promote adaptive actions and management considering spatialized, categorized and aggregated changes from advanced global evaluations. It is an innovative approach and tool for protected areas' global evolutionary trends with reference to conservation objectives. Theoretically, the Model is based on land cover concepts and land cover analysis recognized as the most practical approach to assess ecosystem units, with reference to vegetation cover, natural processes and theoretical spatial changes. Basically, it relies on four key indicators and tools: (1) Trend Index, (2) Evolutionary Trend, (3) Evolutionary Trend's Decision Tree Algorithm and (4) Trend Index and Evolutionary Trend's Classification Grid. Technically, it is based on Remote Sensing data processing; land cover mapping and land cover change analysis using appropriated Remote Sensing and GIS Softwares. The spatial indices and processes responsible for recorded evolutionary trends are determined using landscape ecology tools. In the field of conservation, positive processes are respectively positive and negative when they affect vegetation classes and anthropogenic classes and vice-versa, for negative ones. The input data for the computation of evolution indicators and spatial processes are derived from raw export results of the classifications of Remote Sensing data to GIS software. The sensitivity and resilience of specific ecosystems units to external stresses are measured by three indicators that are "intrinsic stability" (S i), "weighted stability" (S w) and "relative expansion rate" (R e). These indicators are essential for rational management of strategic ecosystems like savannah, water bodies and wetlands in animal sanctuaries and wildlife parks. The implementation of the Model starts with the knowledge of management category, conservation objectives and desired evolutions. The validation process relies on semi-structured interviews involving technical staff and oldest rangers. The model was successfully applied to the Rusizi National Park (Burundi) from 1984 and 2015.
In Africa, protected areas are facing hudge illegal exploitations and accelerated degradation. Illegal exploitations are interesting indicators of local socio-economic needs and hostility of populations to conservation activities. The study aimed to develop a specific method for the analysis of illegal exploitations and the promotion of successful participatory management. Basically, the Multicriteria method used to determine the impact and the gravity of illegal exploitations relies on three criteria based on offenses themselves and affected resources. The method combines statistical analysis of management data using ANOVA and χ² tests, field observations and semi-structured interviews for validation. For the tested Rusizi national Park, the findings showed that the number of supervised exploitations increased from 1988 to 2015 while the number of supervised operators is limited and highly fluctuating between resources and periods. The public integration ratio is 8 0 / 000 and corresponds to 61 supervised operators of which 84% are involved in vegetal resources exploitations. In total, 10 illegal exploitations whose impact values range from 1 to 20 and belong to very high and high impact classes were reported. Average, 651 cases of which 71% cover direct cuts of vegetation were reported annually. Statistically, the most damaging illegal exploitations are made of tree and vegetation cuts, cattle grazing and fishing. Illegal exploitations are seasonal and more important in dry season than in rainy season. They are more important in Delta sector than in Palmeraie sector. The shift from gracious exploitations to lucrative operations, over-taxation of supervised exploitations, low ratio of public integration, political conflicts and unarmed protection contributed to increase and strengthen significantly illegal exploitations. Ultimately, the results revealed the limits of participatory management on illegal exploitations. Consequently, the success of participatory management in Rusizi national Park requires strategic and concerted development projects, more responsive regulatory measures and relevant partnerships with peripheral village.
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