How many species are needed to ensure a functional restored ecosystem? An integrative analysis of restored forests in Brazil and Australia

Abstract

Today there's an unquestionable consensus that biodiversity loss decreases the ecosystems capacity of providing society's services by the biological communities efficiency loss in capturing essential resources, decomposing and recycling material. The relationship between biodiversity and function (BEF theory) has emerged as an important area of Ecology in the last few decades and has been the main scientific paradigm to the understanding of this problem. In the BEF perspective, a degraded ecosystem is the one that has suffered both reduction in its biodiversity and functioning, as restoration tries to recover this relationship by adding specific sets of species. Although there are syntheses on this subject in the literature, they are only based in herbaceous communities or in a broader range of terrestrial and aquatic ecosystems; with very few researches done in a manipulative context using forest species. In spite their importance for providing ecosystems services, to date we don't have empirical evidences on the format and magnitude of BEF relationship for the megabiodiverse ecosystems of tropical forests. Our objective with this project is to investigate the relationship between biodiversity (species richness and diversity and functional diversity) and functioning (biomass, productivity, nutrient stocks and others) in restored tropical forests, aiming at finding a minimum species or functional groups number which should be introduced to outcome in optimal functioning. The study will be undertaken using meta- analysis statistical tools to examine literature data. Published and unpublished literature related to the restoration of both Atlantic Forest in Brazil and the tropical and sub-tropical forests in Queensland State Eastern coast (latitude range from 0-30o S) will be analyzed and selected. Data from each study will be standardized to account for sample size differences, and the within and between site effects will be computed by appropriate statistic effect indexes. The format and magnitude of the BEF relationship will be studied by non-linear regression (random models) by fitting asymptotic functions. (AU)