Agrupamento taxonômico ou funcional do fitoplâncton: qual o melhor descritor das condições ambientais?

• Ano de defesa: 2018
• Autor(a) principal: Arthur Cupertino Serpa dos Santos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais
• Palavras-chave em Português: Ecologia; Classificação; Morfologia (Biologia); Fitoplâncton
• Link de acesso: https://hdl.handle.net/1843/36026

To better identify the connection between environmental variables and phytoplankton communities, we can study nearly distributed lenthic systems, which are thus affected by the same climatic conditions. In these studies, the community structure is generally simplified by classifications based on the organism’s functions, physiology or morphological traits. We aimed to compare the efficiency of assemblages based on taxonomy and the morphology based functional groups (MBFG) classification to explain variations in phytoplankton communities under different environmental conditions. Our hypotheses are: 1) spatial variations in environmental conditions are more important than temporal variations; 2) MBFG’s are more efficient to explain the difference between systems. Physical and chemical variables were different among the six reservoirs studied, resulting in a pattern of similarity that was reflected in the structure of phytoplankton community, for both taxonomical and morphological groups. Differences among reservoirs were mainly determined by nutrients concentrations, reflected in the trophic state index. Even though seasonality moderately homogenized the systems, both classifications distinguished the communities with similar explanatory power (16% for taxonomy and 17% for MBFG), and were influenced by the same environmental variables. However, the classification in MBFG was more efficient to distinguish systems, the influence of environmental conditions on phytoplankton composition can be better evaluated based on this classification. The morphological classification can be useful for long-term monitoring programs or studies that compare a great number of systems, due to its simplicity and easiness, compared to taxonomic classification.