Nature representation in South American protected areas country contrasts and conservation priorities
Background: South America faces strong environmental transformations due to agriculture and infrastructure expansion and due to demographic growth, demanding immediate action to preserve natural assets by means of the deployment of protected areas. Currently, 7.1% of the (sub)continent is under stri...
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| Otros Autores: | , , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Materias: | |
| Acceso en línea: | http://ri.agro.uba.ar/files/download/articulo/2019baldi.pdf LINK AL EDITOR |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
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| 024 | |a 10.7717/peerj.7155 | ||
| 040 | |a AR-BaUFA |c AR-BaUFA | ||
| 245 | 1 | 0 | |a Nature representation in South American protected areas |b country contrasts and conservation priorities |
| 520 | |a Background: South America faces strong environmental transformations due to agriculture and infrastructure expansion and due to demographic growth, demanding immediate action to preserve natural assets by means of the deployment of protected areas. Currently, 7.1% of the (sub)continent is under strict conservation categories (I to IV, IUCN), but the spatial distribution of these 1.3 x 106 km2 is poorly understood. We evaluate protected area representativeness, map conservation priorities and assess demographic, productive or geopolitical causes of the existing protection spatial patterns using a random forest method. Methods: We characterized representativeness by two dimensions: the extent and the equality of protection. The first refers to the fraction of a territory under protection, while the second refers to the spatial distribution of this protection along natural conditions. We characterized natural conditions by 113 biogeographical units (specifically, ecoregions) and a series of limited and significant climatic, topographic and edaphic traits. We analyzed representativeness every ten years since 1960 at national and continental levels. In the physical approach, histograms allowed us to map the degree of conservation priorities. Finally, we ranked the importance of different productive or geopolitical variables driving the observed distributions with a random forest technique. Results: Representativeness was variable across countries in spite of its priority in conservation agendas. Brazil, Peru and Argentina underrepresented a significant fraction of their natural diversity, while Bolivia and Venezuela protected their natural diversity equitably under extensive conservation networks. As protected networks increased their extent, so did their equality across countries and within them through time. Mapping revealed as top continental priorities southern temperate, subhumid and fertile lowland environments, and other country - specific needs (e.g., hot, humid plains of Venezuela). Protection extent was generally driven by a low population density and isolation, while other variables - like distance to frontiers, were relevant only locally (e.g., in Argentina). Discussion: Our description of the spatial distribution can help societies and governments to improve the allocation of conservation efforts, being top continental priorities the southern temperate, subhumid and fertile lowland environments. We identify the main limitations that future conservation efforts will face, as protection was generally driven by the opportunities provided by low population density and isolation. From a methodological perspective, the complementary physical approach reveals new properties of protection and provides tools to explore nature representativeness at different spatial, temporal and conceptual levels, complementing the traditional ones based on biodiversity or biogeographical attributes. | ||
| 653 | |a PROTECTED AREAS | ||
| 653 | |a PROTECTION EQUALITY | ||
| 653 | |a PROTECTION EXTENT | ||
| 653 | |a NATURE REPRESENTATION | ||
| 700 | 1 | |9 26877 |a Baldi, Germán |u Universidad Nacional de San Luis. Instituto de Matemática Aplicada San Luis. San Luis, Argentina. |u CONICET - San Luis, San Luis, Argentina. | |
| 700 | 1 | |a Schauman, Santiago |u Universidad Nacional de San Luis. Instituto de Matemática Aplicada San Luis. San Luis, Argentina. |u CONICET - San Luis, San Luis, Argentina. |9 70083 | |
| 700 | 1 | |9 32541 |a Texeira, Marcos |u Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información. Buenos Aires, Argentina. |u Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina. |u CONICET – Universidad de Buenos Aires. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina. | |
| 700 | 1 | |a Marinaro, Sofía |u Universidad Nacional de Tucumán. Instituto de Ecología Regional. Tucumán. Argentina. |u CONICET - Horco Molle, Horco Molle, Tucumán |9 49454 | |
| 700 | 1 | |a Martin, Osvaldo A. |u Universidad Nacional de San Luis. Instituto de Matemática Aplicada San Luis. San Luis, Argentina. |u CONICET - San Luis, San Luis, Argentina. |9 67375 | |
| 700 | 1 | |a Gandini, Patricia |u Universidad Nacional de la Patagonia Austral. Instituto Ciencias del Ambiente, Sustentabilidad y Recursos Naturales. Santa Cruz, Argentina. |u CONICET, Puerto Deseado, Argentina. |9 70084 | |
| 700 | 1 | |a Jobbágy, Esteban G. |u Universidad Nacional de San Luis. Instituto de Matemática Aplicada San Luis. San Luis, Argentina. |u CONICET - San Luis, San Luis, Argentina. |9 7390 | |
| 773 | |t PeerJ |g Vol.7 (2019), e7155, 23 p., grafs., tbls., mapas | ||
| 856 | |f 2019baldi |i en internet |q application/pdf |u http://ri.agro.uba.ar/files/download/articulo/2019baldi.pdf |x ARTI202003 | ||
| 856 | |u https://peerj.com/ |z LINK AL EDITOR | ||
| 942 | |c ENLINEA | ||
| 942 | |c ARTICULO | ||
| 976 | |a AAG | ||