O poder preditivo dos agrupamentos funcionais fitoplanctônicos responde ao tipo de ambiente.

• Autor(a) principal: Zanco, Barbara Furrigo
• Data de Publicação: 2017
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da Universidade Estadual de Maringá (RI-UEM)
• Texto Completo: http://repositorio.uem.br:8080/jspui/handle/1/4876
• topic: Fitoplâncton de água doce; Comunidades, Ecologia de; Abordagem funcional; Rios; Lagos de inundação; Reservatórios; Funcional; Brasil.; Phytoplankton; Ecology; Community; Rivers; Flooplain lakes; Reservoirs; Functional; Brazil.; Ciências Biológicas; Ecologia

The search for more parsimonious predictors of biodiversity to simplify taxonomic approaches has been constant in ecology. The use of biodiversity measures based on functional characteristics of the species is a strategy widely used in environmental management and monitoring. Several studies have used functional groups to explain the dynamics of the phytoplankton community. We compared the predictability of the phytoplankton community from lakes, reservoirs and rivers, at the species level, taxonomic groups and four phytoplankton functional classification (Reynolds Functional Groups RFGF, Reynolds et al., 2002; Morpho-Functional Groups MFGF, Salmaso and Padisák, 2007; Morphological Based Functional Groups MBFGF, Kruk et al., 2010; Geometrical Forms GF, Stanca et al., 2013). We tested the hypotheses that (i) the predictability of the functional classifications depends on the type of environment assessed, (ii) the functional classifications with the highest number of groups will be more efficient predictors, (iii) the taxonomic approach have less predictive power than functional groups. We sampled 120 environments in dry period, between 1997 and 2015, including lakes, rivers and reservoirs, distributed in tropical and subtropical regions. As we expected, we registered higher predictability at the functional group level than at the species level, with the greatest predictability of functional groups in the lakes. The MBFGF showed better response than the others functional classifications probably because it is more suitable when used for large spatial scales, and because it considers different traits besides the shape of the species, as verified in GF. The high predictive power verified for the taxonomic classes indicates that general characteristics of high taxonomic levels can be used to explain the phytoplankton dynamics in different types of environments. Our study demonstrates that using the characteristics of the species is a better proxy than the species level to understand the ecological processes driving the assembly of the phytoplankton community, and it also could help to understand the relationship between the biodiversity and the ecosystem functioning.