Local richness and distribution of the lizard fauna in natural habitat mosaics of the Brazilian Cerrado

We investigate local lizard richness and distribution in central Brazilian Cerrado, harbouring one of the least studied herpetofaunas in the Neotropical region. Our results are based on standardized samplings at 10 localities, involving 2917 captures of 57 lizard species in 10 families. Local richness values exceeded most presented in earlier studies and varied from 13 to 28 species, with modal values between 19 and 28 species. Most of the Cerrado lizard fauna is composed of habitat-specialists with patchy distributions in the mosaic of grasslands, savannas and forests, resulting in habitat-structured lizard assemblages. Faunal overlap between open and forested habitats is limited, and forested and open areas may act as mutual barriers to lizard distribution. Habitat use is influenced by niche conservatism in deep lineages, with iguanians and gekkotans showing higher use of forested habitats, whereas autarchoglossans are richer and more abundant in open habitats. Contrary to trends observed in Cerrado birds and large mammals, lizard richness is significantly higher in open, interfluvial habitats that dominate the Cerrado landscape. Between-localities variation in lizard richness seems tied to geographical distance, landscape history and phylogenetic constraints, factors operating in other well-studied lizard faunas in open environments. Higher richness in dominant, open interfluvial habitats may be recurrent in Squamata and other small-bodied vertebrates, posing a threat to conservation as these habitats are most vulnerable to the fast, widespread and ongoing process of habitat destruction in central Brazil.

Importance of estimating matrix quality for modeling species distribution in complex tropical landscapes: a test with Atlantic forest small mammals

Information to guide decision making is especially urgent in human dominated landscapes in the tropics, where urban and agricultural frontiers are still expanding in an unplanned manner. Nevertheless, most studies that have investigated the influence of landscape structure on species distribution have not considered the heterogeneity of altered habitats of the matrix, which is usually high in human dominated landscapes. Using the distribution of small mammals in forest remnants and in the four main altered habitats in an Atlantic forest landscape, we investigated 1) how explanatory power of models describing species distribution in forest remnants varies between landscape structure variables that do or do not incorporate matrix quality and 2) the importance of spatial scale for analyzing the influence of landscape structure. We used standardized sampling in remnants and altered habitats to generate two indices of habitat quality, corresponding to the abundance and to the occurrence of small mammals. For each remnant, we calculated habitat quantity and connectivity in different spatial scales, considering or not the quality of surrounding habitats. The incorporation of matrix quality increased model explanatory power across all spatial scales for half the species that occurred in the matrix, but only when taking into account the distance between habitat patches (connectivity). These connectivity models were also less affected by spatial scale than habitat quantity models. The few consistent responses to the variation in spatial scales indicate that despite their small size, small mammals perceive landscape features at large spatial scales. Matrix quality index corresponding to species occurrence presented a better or similar performance compared to that of species abundance. Results indicate the importance of the matrix for the dynamics of fragmented landscapes and suggest that relatively simple indices can improve our understanding of species distribution, and could be applied in modeling, monitoring and managing complex tropical landscapes.

Comparing machine learning classifiers in potential distribution modelling

Species` potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species` potential distribution. (C) 2010 Elsevier Ltd. All rights reserved.