Conceptual and Analytic Model for Advanced Evaluation of Protected Areas’ Global Evolutionary Trends: The Protected Areas' Trends Assessment and Adaptive Management on the Basis of Long-Term Conservation Objectives or PA-TAMCO Analytic Model

Elysée Ntiranyibagira

doi:doi:10.11648/j.ajese.20190301.12

| Peer-Reviewed

Conceptual and Analytic Model for Advanced Evaluation of Protected Areas’ Global Evolutionary Trends: The Protected Areas' Trends Assessment and Adaptive Management on the Basis of Long-Term Conservation Objectives or PA-TAMCO Analytic Model

Elysée Ntiranyibagira

Published in American Journal of Environmental Science and Engineering (Volume 3, Issue 1)

Received: 26 December 2018 Accepted: 15 January 2019 Published: 15 February 2019

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

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.

Published in	American Journal of Environmental Science and Engineering (Volume 3, Issue 1)
DOI	10.11648/j.ajese.20190301.12
Page(s)	8-16
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

PA-TAMCO Model, Protected Area, Adaptive Management, Trend Index, Evolutionary Trend, Ecosystem Intrinsic Stability, Ecosystem Weighted Stability, Ecosystem Differential Sensitivity

References

[1]	IPCC (2007). Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J. van der Linden and C. E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 976 pp.
[2]	Dudley N., Stolton S., Belokurov A. and al. (2010). Natural solutions: Protected areas helping people cope with climate change. Gland (Switzerland), Washington DC and New York (USA): IUCN-WCPA, TNC, UNDP, WCS. The World Bank, WWF, 130p.
[3]	McCarty J. P. (2001). Ecological consequences of recent climate change. Conservation Biology, 15: 320–331.
[4]	Hannah L. and Salm R. (2005). Protected areas management in a changing climate. In: Lovejoy T. E. and Hannah L. (eds.) Climate change and biodiversity. New Haven and London: Yale University press, 440p.
[5]	Hopkins J. J., Allison H. M., Walmsley C. A. and al. (2007). Conserving Biodiversity in a Changing Climate: guidance on building capacity to adapt, Department of Environment, Food and Rural Affairs, London, 32p.
[6]	Hocking M. and Phillips A. (1999). How well are we doing? Some thoughts on the effectiveness of protected areas. Parks, 9 (2): 5-14.
[7]	Hockings M., Stolton S., Leverington F. and al. (2006). Evaluating Effectiveness: A Framework for Assessing Management Effectiveness of ProtectedAreas, SecondEdition. N°14. UICN, Gland, Suisse. xiv + 105p.
[8]	Mackinnon J. K., Mackinnon G. C et Thorsell J. (1990). Aménagement et gestion des aires protégées tropicales, UICN, Suisse, 307p.
[9]	Chiffaut A. (2006). Guide méthodologique des plans de gestion deréserves naturelles. MEED/ATEN, Cahier technique N°79: 72p.
[10]	Hartley A., Nelson A., Mayaux P. and al. (2007). The Assessment of African Protected Areas. EUR 22780. A characterization of biodiversity value, ecosystems and threats, to inform the effective allocation of conservation funding. EN, Luxembourg: Office for Official Publications of the European Communities, 80p.
[11]	Deguignet M., Jufe-Bignoli D., Harrison J. et al. (2014). Liste des Nations Unies des Aires Protégées 2014. UNEP-WCMC: Cambridge, UK, 44p.
[12]	UICN (2012). Cadre et outils d’évaluation de l’efficacité de la gestion des aires protégées en Afrique de l’ouest et du centre. Programme Afrique Centre et Ouest, 21p.
[13]	Binot A. (2010). La Conservation de la Nature en Afrique Centrale. Entre Théories et Pratiques. Des Espaces Protégés à Géométrie Variable. Thèse de Doctorat, Université Paris 1 Panthéon-Sorbonne, 444p.
[14]	Colchester M. (2003). Nature sauvage, nature sauvée? Peuples autochtones, aires protégées et conservation de la biodiversité. Mouvement mondial pour les forêts tropicales, (WRM) et Fonds mondial pour la nature (WWF), 154p.
[15]	Neumann R. P. (1998). Imposing Wilderness: struggles over livelihood and nature preservation in Africa, Los Angeles and Berkeley: University of California Press, 268 pp.
[16]	Rossi G. (2000). Ingérence écologique. Environnement et développement rural du Nord au Sud. CNRS Editions Coll. Espaces et Milieux, Paris, 248p.
[17]	Besong J. B. and Wencélius F. L. (1992). Realistic strategies for conservation in the tropical moist forests of Africa: regional review. In Cleaver, C., Munasinghe, M., Dyson, M, Egli, N, Peuker, A. and Wencélius, F. (Eds.). Conservation of West and Central African Rainforests. The World Bank, Washington, D. C. pp. 21-31.
[18]	Sournia G. (1996). Les aires protégées d'Afrique francophone (Afrique centrale et occidentale). Hier, aujourd'hui, demain. Espaces à protéger ou espaces à partager ? Thèse de doctorat, Bordeaux, Université de Bordeaux III, 302p.
[19]	Ferraro P. J. and Kiss A. (2002). Getting what you paid for: direct payment an alternative investment for conserving biodiversity, Science n 268, November 29, 2002.
[20]	Binot A. et Joiris D. V. (2007). Règles d’accès et gestion des ressources pour les acteurs des périphéries d’aires protégées. VertigO - la revue électronique en sciences de l'environnement [En ligne], Hors-série 4\|novembre 2007, mis en ligne le 11 novembre 2007, URL: http://vertigo.revues.org/759; DOI: 10.4000/vertigo.759
[21]	Borrini-Feyerabend G., Dudley N., Jaeger T. and al. (2013). Governance of Protected Areas: From understanding to action. Best Practice Protected Area Guidelines, Series 20, Gland, Switzerland: IUCN, xvi, 124p
[22]	Ntiranyibagira E. (2017). Dynamiques d’occupation du sol, tendances évolutives globales et facteurs d’évolution des aires protégées. Etude diachronique du Parc national périurbain de la Rusizi (Burundi) de 1984 à 2015. Thèse de Doctorat Unique en Sciences de l’Environnement, Université Cheikh Anta Diop de Dakar (Sénégal), 340p.
[23]	Aubertin C. et Rodary E. (2008). Aires protégées, espaces durables ? IRD, 276p
[24]	James A. N (1999). Institutional constraints on protected area funding. Parks, 9(2): 15-26.
[25]	Howard P., Davenport T., Kigenyi F. and al. (2000). Protected area planning in the tropics: Uganda's national system of forest reserves. Conservation Biology, 14: 858-875.
[26]	Sambou B. (2004). Evaluation de l’état, de la dynamique et des tendances évolutives de la flore et de la végétation ligneuses dans les domaines soudanien et sub-guinéen au Sénégal. Thèse de Doctorat d’Etat en Sciences Naturelles. Université Cheikh Anta Diop de Dakar (Sénégal), 209p.
[27]	Noss R. F. (1996). Ecosystems as conservation targets. Trends in Ecology & Evolution, 11:351.
[28]	Cowling R. M., Knight A. T., Faith D. P. and al. (2004). Nature conservation requires more than a passion for species. Conservation Biology, 18:1674-1676.
[29]	Chape S., Harrison J., Spalding M. and Lysenko I. (2005). Measuring the extent and effectiveness of protected areas as an indicator for meeting global biodiversity targets. Philosophical Transactions of the Royal Society B 360: 443-455.
[30]	Fahrig L. (2003). Effects of habitat fragmentation on biodiversity. Annual Review of Ecology, Evolution and Systematics, 34: 487-515.
[31]	Henle K., Lindenmayer D. B., Margules C. R. and al. (2004). Species survival in fragmented landscapes: Where are we now? Biodiversity and Conservation, 13: 1-8.
[32]	Clerici N., Bodini A., Eva H. and al. (2007). Increased Isolation of Two Biosphere Reserves and Surrounding Protected Areas (WAP: W-Arly-Pendjari, Ecological Complex, West Africa). Journal of Nature Conservation, 15: 26-40.
[33]	Jeremy B., Youssoufou S., Yahaya S. and al. (2007). Identification of ecological indicators for monitoring ecosystem health in the trans-boundary W Regional Park: A pilot study. Biological Conservation, 238: 73-88.
[34]	Tilman D., May R. M., Lehman C. L. and al. (1994). Habitat destruction and the extinction debt. Nature, 371:65-66.
[35]	Terborgh, J. and van Schaik C. P. (1997). Minimizing species loss: the imperative of protection. In Kramer R., van Schaik C. and Johnson J. éds. Last stand: protected areas and the defense of tropical biodiversity, University of Oxford Press, Oxford, United Kingdom, 15-35.
[36]	Rodríguez J. P., Balch J. K. and Rodríguez-Clark K. M. (2007). Assessing extinction risk in the absence of species-level data: quantitative criteria for terrestrial ecosystems. Biodiversity and Conservation, 16: 183-209.
[37]	Eva H. D., Brink A. B. and Simonetti D. (2006). Monitoring land cover dynamics in Sub-Saharan Africa. A pilot study using Earth observing satellite data from 1975 and 2000. JRC Scientific and Technical Reports, 40p.
[38]	Stott A. and Haines-Young R. (1996). Linking land cover, intensity of use and botanical diversity in an accounting framework in the UK.
[39]	Alun J. and Clark J. (1997). Driving forces behind European land use change: an overview. Claude Resource Paper n° 1.
[40]	Lambin E. F., Turner B. L., Geist H. and al. (2001). The Causes of Land-Use and Land-Cover Change: Moving beyond the Myths. Global Environmental Change, 11: 261-269.
[41]	Rodríguez J. P., Rodríguez-Clark K. M., Baillie J. E. M. et al. (2011). Elaboration des Critères de l’UICN pour la Liste Rouge des Ecosystèmes Menacés. Conservation Biology, Volume25, 11p.
[42]	Farina A. (2000). Landscape ecology in action. Kluwer Academic Publishers, Dordrecht. The Netherlands, 267p.
[43]	Davidson C. (1998). Issues in measuring landscape fragmentation. Wildlife Society Bulletin, 26: 32-37.
[44]	Burel F. et Baudry J. (2003). Ecologie du paysage. Concepts, méthodes et applications. Paris: France, Tec & Doc. 359 pp.
[45]	Lockwood M., Worboys L. G. and Kothari A. (2006). Managing Protected Areas: A Global Guide. London Earthscan, IUCN, xxx, 802 pp.
[46]	UICN (1994). Lignes directrices pour les catégories de gestion des aires protégées. CPNAP, CMSC, 102 p.
[47]	Di Gregorio A. and Jansen L. J. M. (1997). A new concept for a Land Cover Classification System. Earth observation and evolution classification. Compte rendu de la conférence des 13-16 octobre 1997. Alexandrie, Égypte, 10 p.
[48]	CEC (2001). Manuel des concepts relatifs aux systèmes d’information sur l’occupation et l’utilisation des sols. Luxembourg, Office des Publications officielles des Communautés européennes. Thème 5: Agriculture et Pêche, Edition 2000, 96p.
[49]	Robin M. (2002). Télédétection, des satellites au SIG. Une analyse complète du processus de création d’un type essentiel d’information géographique. Nathan Université. ISBN: 2 -0919-1224-7, 318p.
[50]	Barima S. S. Y., Barbier N., Bamba I. et al. (2009). Dynamique paysagère en milieu de transition forêt-savane ivoirienne. Bois et forêt des tropiques, 63 (299) : 15-25.
[51]	Baulies X. I. and Szejwach G. (ed.) (1997). Survey of needs, gaps and priorities on data for land use and land cover change research. LUCC Data requirements workshop, Barcelone, November, 11-14, 1997, LUCC report series 3.
[52]	ITTO (2002). “Reintegrate secondary forests into the landscape”. ITTO Tropical Forest Update 10/4/2002.
[53]	Locke H. and Dearden P. (2005). Rethinking protected area categories and the new paradigm. Environmental Conservation, 32 (1): 1-10.
[54]	Girard, M. C et Girard C. (1999). Traitement des données de télédétection. Paris, Ed. Dunod, ISBN: 2 -1000-4185-1,529p.
[55]	Mas J. F. (2000). Une revue des méthodes et des techniques de télédétection du changement. Canadian Journal of Remote Sensing, 26 (4): 349-362.
[56]	Galicia L. and Garcia-Romero A. (2007). Land use and land cover change in Highland temperate forests in the Izta-Popo national park, central Mexico. Mountain Research and Development, 27 (1): 48-57.
[57]	Tabopda W. G. and Fotsing J. M. (2010). Quantification de l’évolution du couvert végétal dans la réserve forestière de Laf-Madjam au nord du Cameroun par télédétection satellitale. Sécheresse, 21 (3): 169-178.
[58]	Ulbricht K. A. and Heckenford W. D. (1998). Satellite images for recognition of landscape and land uses changes. Journal of Photogrammetry and Remote Sensing, vol.53: 235-243
[59]	Mayaux P., Eva H. D., Palumbo I. et al. (2007). Apport des techniques spatiales pour la gestion des aires protégées en Afrique de l’Ouest. In : Fournier A., Sinsin B., Mensah G. A. (éds). Quelles aires protégées pour l’Afrique de l’Ouest ? Conservation de la biodiversité et développement. Paris, IRD, coll. Colloques et séminaires, p.321-328.
[60]	Hargis C. D., Bissonette J. A. and David J. L. (1997). Understanding measures of landscape pattern. In: Wildlife and landscape ecology (eds. Bissonette J. A.), pp. 231-261. Springer, Berlin Heidelberg, New York.
[61]	Jaeger J. A. G. (2000). Landscape division, splitting index, and effective mesh size: new measures of landscape fragmentation. Landscape Ecology, 15:115–130.
[62]	Fortin M. J. (2002). Spatial analysis in ecology: statistical and landscape scale issues. Ecoscience, 9: iii-v.
[63]	Bogaert J., Ceulemans R. and Salvador-Van Eysenrode D. (2004). Decision tree algorithm for detection of spatial processes in landscape transformation. Environment Management, 33(1): 62-73.
[64]	Hansson L. and Angelstam P. (1991). Landscape ecology as a theoretical basis for nature conservation. Landscape Ecology, 5 (4): 191-201.
[65]	Forman R. T. T. (1995a). Land mosaics—the ecology of landscapes and regions. Cambridge University Press, Cambridge, 632p.
[66]	Collinge S. K. and Forman R. T. T. (1998). A conceptual model of land conversion processes: predictions and evidence from a micro landscape experiment with grassland insects. Oikos, 82: 66–84.
[67]	McGarigal K. and Marks B. J. (1995). Fragstats: Spatial Pattern Analysis Program for Quantifying Structure. Department of Agriculture, Pacific Northwest Research Station General Technical Report PNW-GTR-351. Oregon, USA.
[68]	Oloukoi J., Mama V. J. et Agbo F. B. (2006). Modélisation de la dynamique d’occupation des terres dans le département des Collines au Bénin. Télédétection, 6 (4): 305-323.

Cite This Article

Plain Text BibTeX RIS

APA Style

Elysée Ntiranyibagira. (2019). Conceptual and Analytic Model for Advanced Evaluation of Protected Areas’ Global Evolutionary Trends: The Protected Areas' Trends Assessment and Adaptive Management on the Basis of Long-Term Conservation Objectives or PA-TAMCO Analytic Model. American Journal of Environmental Science and Engineering, 3(1), 8-16. https://doi.org/10.11648/j.ajese.20190301.12

Copy | Download

ACS Style

Elysée Ntiranyibagira. Conceptual and Analytic Model for Advanced Evaluation of Protected Areas’ Global Evolutionary Trends: The Protected Areas' Trends Assessment and Adaptive Management on the Basis of Long-Term Conservation Objectives or PA-TAMCO Analytic Model. Am. J. Environ. Sci. Eng. 2019, 3(1), 8-16. doi: 10.11648/j.ajese.20190301.12

Copy | Download

AMA Style

Elysée Ntiranyibagira. Conceptual and Analytic Model for Advanced Evaluation of Protected Areas’ Global Evolutionary Trends: The Protected Areas' Trends Assessment and Adaptive Management on the Basis of Long-Term Conservation Objectives or PA-TAMCO Analytic Model. Am J Environ Sci Eng. 2019;3(1):8-16. doi: 10.11648/j.ajese.20190301.12

Copy | Download

@article{10.11648/j.ajese.20190301.12,
  author = {Elysée Ntiranyibagira},
  title = {Conceptual and Analytic Model for Advanced Evaluation of Protected Areas’ Global Evolutionary Trends: The Protected Areas' Trends Assessment and Adaptive Management on the Basis of Long-Term Conservation Objectives or PA-TAMCO Analytic Model},
  journal = {American Journal of Environmental Science and Engineering},
  volume = {3},
  number = {1},
  pages = {8-16},
  doi = {10.11648/j.ajese.20190301.12},
  url = {https://doi.org/10.11648/j.ajese.20190301.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajese.20190301.12},
  abstract = {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” (Si), “weighted stability” (S w) and “relative expansion rate” (Re). 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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Conceptual and Analytic Model for Advanced Evaluation of Protected Areas’ Global Evolutionary Trends: The Protected Areas' Trends Assessment and Adaptive Management on the Basis of Long-Term Conservation Objectives or PA-TAMCO Analytic Model
AU  - Elysée Ntiranyibagira
Y1  - 2019/02/15
PY  - 2019
N1  - https://doi.org/10.11648/j.ajese.20190301.12
DO  - 10.11648/j.ajese.20190301.12
T2  - American Journal of Environmental Science and Engineering
JF  - American Journal of Environmental Science and Engineering
JO  - American Journal of Environmental Science and Engineering
SP  - 8
EP  - 16
PB  - Science Publishing Group
SN  - 2578-7993
UR  - https://doi.org/10.11648/j.ajese.20190301.12
AB  - 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” (Si), “weighted stability” (S w) and “relative expansion rate” (Re). 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.
VL  - 3
IS  - 1
ER  -

Copy | Download

Author Information

Elysée Ntiranyibagira

Integrated Center of Environmental Research, Training and Studies for Development in Africa (ICE-RTSDA), Kigali, Rwanda

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Elysée Ntiranyibagira. (2019). Conceptual and Analytic Model for Advanced Evaluation of Protected Areas’ Global Evolutionary Trends: The Protected Areas' Trends Assessment and Adaptive Management on the Basis of Long-Term Conservation Objectives or PA-TAMCO Analytic Model. American Journal of Environmental Science and Engineering, 3(1), 8-16. https://doi.org/10.11648/j.ajese.20190301.12

Copy | Download

ACS Style

Elysée Ntiranyibagira. Conceptual and Analytic Model for Advanced Evaluation of Protected Areas’ Global Evolutionary Trends: The Protected Areas' Trends Assessment and Adaptive Management on the Basis of Long-Term Conservation Objectives or PA-TAMCO Analytic Model. Am. J. Environ. Sci. Eng. 2019, 3(1), 8-16. doi: 10.11648/j.ajese.20190301.12

Copy | Download

AMA Style

Elysée Ntiranyibagira. Conceptual and Analytic Model for Advanced Evaluation of Protected Areas’ Global Evolutionary Trends: The Protected Areas' Trends Assessment and Adaptive Management on the Basis of Long-Term Conservation Objectives or PA-TAMCO Analytic Model. Am J Environ Sci Eng. 2019;3(1):8-16. doi: 10.11648/j.ajese.20190301.12

Copy | Download

@article{10.11648/j.ajese.20190301.12,
  author = {Elysée Ntiranyibagira},
  title = {Conceptual and Analytic Model for Advanced Evaluation of Protected Areas’ Global Evolutionary Trends: The Protected Areas' Trends Assessment and Adaptive Management on the Basis of Long-Term Conservation Objectives or PA-TAMCO Analytic Model},
  journal = {American Journal of Environmental Science and Engineering},
  volume = {3},
  number = {1},
  pages = {8-16},
  doi = {10.11648/j.ajese.20190301.12},
  url = {https://doi.org/10.11648/j.ajese.20190301.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajese.20190301.12},
  abstract = {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” (Si), “weighted stability” (S w) and “relative expansion rate” (Re). 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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Conceptual and Analytic Model for Advanced Evaluation of Protected Areas’ Global Evolutionary Trends: The Protected Areas' Trends Assessment and Adaptive Management on the Basis of Long-Term Conservation Objectives or PA-TAMCO Analytic Model
AU  - Elysée Ntiranyibagira
Y1  - 2019/02/15
PY  - 2019
N1  - https://doi.org/10.11648/j.ajese.20190301.12
DO  - 10.11648/j.ajese.20190301.12
T2  - American Journal of Environmental Science and Engineering
JF  - American Journal of Environmental Science and Engineering
JO  - American Journal of Environmental Science and Engineering
SP  - 8
EP  - 16
PB  - Science Publishing Group
SN  - 2578-7993
UR  - https://doi.org/10.11648/j.ajese.20190301.12
AB  - 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” (Si), “weighted stability” (S w) and “relative expansion rate” (Re). 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.
VL  - 3
IS  - 1
ER  -

Copy | Download