, Volume 27, Issue 6, pp 799-812
Date: 04 Mar 2012

How deforestation pattern in the Amazon influences vertebrate richness and community composition

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Abstract

The effects of habitat configuration on species persistence are predicted to be most apparent when remaining habitat cover is below 30%. We tested this prediction by comparing vertebrate communities in 21 landscapes located in the southern Amazonia, including 7 control landscapes (~100% of forest cover) and 14 fragmented landscapes (4 × 4 km). The fragmented landscapes retained similar proportions of forest (~25%), but had contrasting configurations, resulting from two different deforestation patterns: the “fish-bone pattern” common in small properties, and the large-property pattern generally used by large ranchers. Vertebrates were surveyed in all landscapes in February–July 2009 with interviews (n = 150). We found a significant difference in reported species richness among the fish-bone, large-property, and control areas (mean = 29.3, 38.8 and 43.5 respectively). Control areas and large-properties tended to have a higher number of specialist species (mean = 13.7, and 11.7, respectively), when compared with the fish-bone pattern (5.1). Vertebrate community composition in the control and large-properties was more similar to one another than to those of the fish-bone landscapes. The number of fragments was the main factor affecting the persistence of species, being negatively associated with specialist species richness. Species richness was also positively related with the size of the largest fragment structurally connected to the studied landscapes (i.e., a regional scale effect). Our results demonstrated that the large-property pattern, which results in less fragmented landscapes, can maintain a more diverse community of large vertebrates, including top predators, which are considered fundamental for maintaining ecosystem integrity. These results support the hypothesis that landscape configuration contributes to the persistence and/or extirpation of species.