Determinants of beta diversity: the relative importance of environmental and spatial processes in structuring phytoplankton communities in an Amazonian floodplain

• Autor(a) principal: Nogueira,Ina de Souza
• Data de Publicação: 2010
• Outros Autores: Nabout,João Carlos, Ibañez,Maria do Socorro Rodrigues, Bourgoin,Laurence Maurice
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Acta Limnologica Brasiliensia (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-975X2010000300001
• topic: metacommunity; partitioning variation; rainy season; Amazon

AIM: Beta diversity is defined as the change in species composition along environmental gradients, and in the present study, we investigated the influence of local (i.e., environmental) and regional (i.e., dispersal) factors in community structure. The aims of this study were to evaluate the beta diversity of phytoplankton communities in the Curuaí floodplain and to determine the relative importance of environmental and spatial processes in shaping phytoplankton community structure; METHOD: The phytoplankton communities were sampled in 16 lakes of the Curuaí floodplain (Amazon Basin) during high-water periods in 2002 and 2003. We used partial redundancy analysis (pRDA) to evaluate the pure effect of environmental (six variables) and spatial (spatial filter) variability on phytoplankton community composition; RESULTS: There were 156 taxa recorded in the two study years, including 122 algae species in 2002 and 66 algae species in 2003. The beta diversity that we measured (βSIM index) was 0.889 in 2002 and 0.789 in 2003. The partitioning variation demonstrated that the majority of variation in phytoplankton community structure was not significantly explained by pure environmental and pure spatial components. However, environmental variables presented a larger coefficient of determination than the spatial variable; CONCLUSION: Other factors than those we measured in this study, such as local variables (i.e., biotic interactions, hydrology, etc.) and stochastic events, affected the absence of significant results in our data. Therefore, we suggest that additional variables, such as biological interactions and other local factors, should be considered in this type of analysis to increase its explanatory power for understanding the variation of diversity in these communities.