Plant Diversity Hotspots in the Atlantic Coastal Forests of Brazil

被引:186
作者
Murray-Smith, Charlotte [1 ]
Brummitt, Neil A. [1 ]
Oliveira-Filho, Ary T. [2 ]
Bachman, Steven [1 ]
Moat, Justin [1 ]
Lughadha, Eimear M. Nic [1 ]
Lucas, Eve J. [1 ]
机构
[1] Royal Bot Gardens, Richmond TW9 3AE, Surrey, England
[2] Univ Fed Lavras, Dept Ciencias Florestais, BR-37200000 Lavras, MG, Brazil
关键词
biodiversity hotspots; complementarity; conservation assessments; endemism; Mata Atlantica; Myrtaceae; predictive modeling; complementariedad; endemismo; evaluaciones de conservacion; modelos predictivos; sitios de importancia para la biodiversidad; SPECIES DISTRIBUTIONS; BIODIVERSITY; CONSERVATION; MYRTACEAE; PATTERNS; EASTERN; CLIMATE; AFRICA; REGION; MAP;
D O I
10.1111/j.1523-1739.2008.01075.x
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Plant-diversity hotspots on a global scale are well established, but smaller local hotspots within these must be identified for effective conservation of plants at the global and local scales. We used the distributions of endemic and endemic-threatened species of Myrtaceae to indicate areas of plant diversity and conservation importance within the Atlantic coastal forests (Mata Atlantica) of Brazil. We applied 3 simple, inexpensive geographic information system (GIS) techniques to a herbarium specimen database: predictive species-distribution modeling (Maxent); complementarity analysis (DIVA-GIS); and mapping of herbarium specimen collection locations. We also considered collecting intensity, which is an inherent limitation of use of natural history records for biodiversity studies. Two separate areas of endemism were evident: the Serra do Mar mountain range from Parana to Rio de Janeiro and the coastal forests of northern Espirito Santo and southern Bahia. We identified 12 areas of approximately 35 km(2) each as priority areas for conservation. These areas had the highest species richness and were highly threatened by urban and agricultural expansion. Observed species occurrences, species occurrences predicted from the model, and results of our complementarity analysis were congruent in identifying those areas with the most endemic species. These areas were then prioritized for conservation importance by comparing ecological data for each.
引用
收藏
页码:151 / 163
页数:13
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