Use of a morphology-based functional approach to model phytoplankton community succession in a shallow subtropical lake

Authors

  • ANGEL M. SEGURA,

    1. Universidad de la República, Facultad de Ciencias, IECA-Oceanografía y Ecología Marina and Functional Ecology of Aquatic Systems, Montevideo, Uruguay
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  • CARLA KRUK,

    1. Laboratory of Ethology, Ecology and Evolution, Instituto de Investigaciones Biológicas Clemente Estable and Functional Ecology of Aquatic Systems, Limnology, IECA, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay
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  • DANILO CALLIARI,

    1. Universidad de la República, Facultad de Ciencias, IECA-Oceanografía y Ecología Marina and Functional Ecology of Aquatic Systems, Montevideo, Uruguay
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  • HUGO FORT

    1. Universidad de la República, Instituto de Física, Montevideo, Uruguay
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Angel M. Segura, Facultad de Ciencias, IECA-Oceanografía y Ecología Marina, Iguá 4225, P.C.11400, Montevideo, Uruguay. E-mail: amsegura@fcien.edu.uy

Summary

1. We use morphology-based functional groups (MBFGs) of phytoplankton to predict ecological performance of groups with a mechanistic model. We evaluate MBFGs’ performance in relation to hypothetical examples of succession and compare it with empirical data for a shallow subtropical hypertrophic lake (Lake Rodó, Uruguay).

2. Our model predicts a trade-off between maximum growth rate (μmax) and competitive ability (Tilman’s R*) for most MBFGs. The model predicts that phytoplankton succession will proceed from groups with high surface/volume, high μmax and without specialised structures (opportunists) towards the dominance of large colonial or filamentous algae with lower μmax and specialised traits (gleaners). These predictions are generally congruent with empirical data for Lake Rodó.

3. The MBFG classification explained ecological performance of phytoplankton community dynamics while reducing system dimensionality. Despite species aggregation, MBFGs can resolve the occurrence of potentially noxious groups and thus is a promising tool for understanding and managing ponds and lakes.

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