The role of current and historical factors on the patterns of fish beta diversity in agroecosystem streams

Abstract

The conversion of local and regionally heterogeneous natural systems to agriculture has been leading to a widespread local and regional habitat and fauna homogenization. However, community response to landscape changes is not always immediate (i.e., historical legacy in communities). Moreover, the way the landscape changes happened over time (dynamic of landscape changes) can also represent an important factor to the current community patterns. Despite that, these historical factors are still neglected in ecological studies. Thus, our aims are to investigate the effects of current (land use in the watershed and instream habitat) and historical (time since initial deforestation and dynamic of landscape changes) factors on (i) instream habitat structure, (ii) species-environment relationships and (iii) taxonomic and functional fish beta diversity in stream from agroecosystems. We selected fish assemblages from Alto Paraná (São Paulo state), Machado (Rondônia state) and Aripuanã-Juruena (Mato Grosso state) basins, which show different patterns time since deforestation (more recent versus old) and forest and agriculture changes over time. Furthermore, these basins have different current deforestation scenarios, from 100% to 0% of forest loss. We are going to calculate taxonomic and functional beta diversity, ordinations (RLQ and fourth-corner) and association between matrices to analyze the relationship between historical and current factors and taxonomic and functional beta diversity. We believe that streams with older, more intense deforestation and landscapes more dynamics will show instream habitat homogenization, with a weak species-environment relationship and lower beta diversity due to the loss of habitat-specialist species with original traits. The results from this project will improve our comprehension of the structuring mechanism of the current patterns of fish assemblages from agroecosystem streams.