Understanding variation in key functional traits across gradients in high diversity systems and the ecology of community changes along gradients in these systems is crucial in light of conservation and climate change. We examined inter- and intraspecific variation in leaf mass per area (LMA) of sun and shade leaves along a 3330-m elevation gradient in Peru, and in sun leaves across a forest-savanna vegetation gradient in Brazil. We also compared LMA variance ratios (T-statistics metrics) to null models to explore internal (i.e., abiotic) and environmental filtering on community structure along the gradients. Community- weighted LMA increased with decreasing forest cover in Brazil, likely due to increased light availability and water stress, and increased with elevation in Peru, consistent with the leaf economic spectrum strategy expected in colder, less productive environments. A very high species turnover was observed along both environmental gradients, and consequently, the first source of variation in LMA was species turnover. Variation in LMA at the genus or family levels was greater in Peru than in Brazil. Using dominant trees to examine possible filters on community assembly, we found that in Brazil, internal filtering was strongest in the forest, while environmental filtering was observed in the dry savanna. In Peru, internal filtering was observed along 80% of the gradient, perhaps due to variation in taxa or interspecific competition. Environmental filtering was observed at cloud zone edges and in lowlands, possibly due to water and nutrient availability, respectively. These results related to variation in LMA indicate that biodiversity in species rich tropical assemblages may be structured by differential niche-based processes. In the future, specific mechanisms generating these patterns of variation in leaf functional traits across tropical environmental gradients should be explored.

Made available in DSpace on 2014-06-12T18:29:15Z (GMT). No. of bitstreams: 2 arquivo4279_1.pdf: 1161561 bytes, checksum: 80aee0b17f88de11dd7d0999ad1594a1 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2008 / Esta tese de doutorado trata da realização de inferências no modelo de regressão linear sob heteroscedasticidade de forma desconhecida. No primeiro capítulo, nós desenvolvemos estimadores intervalares que são robustos à presença de heteroscedasticidade. Esses estimadores são baseados em estimadores consistentes de matrizes de covariâncias propostos na literatura, bem como em esquemas bootstrap. A evidência numérica favorece o estimador intervalar HC4. O Capítulo 2 desenvolve uma seqüência corrigida por viés de estimadores de matrizes de covariâncias sob heteroscedasticidade de forma desconhecida a partir de estimador proposto por Qian eWang (2001). Nós mostramos que o estimador de Qian-Wang pode ser generalizado em uma classe mais ampla de estimadores consistentes para matrizes de covariâncias e que nossos resultados podem ser facilmente estendidos a esta classe de estimadores. Finalmente, no Capítulo 3 nós usamos métodos de integração numérica para calcular as distribuições nulas exatas de diferentes estatísticas de testes quasi-t, sob a suposição de que os erros são normalmente distribuídos. Os resultados favorecem o teste HC4