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Miguel Porto Bertolo Adão Pereira

Miguel Porto Bertolo Adão Pereira

Post-Doc Researcher

Post-Doc Researcher
Member type
CIBIO-InBIO, Universidade de Évora, Casa Cordovil 2ª Andar, Rua Dr. Joaquim Henrique da Fonseca, 7000-890 Évora, Portugal
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My research is centered in the study of plant communities on different facets. During the PhD, I have focused mainly in studying the effects of management on mediterranean forest plant communities, in an attempt to understand, using simulation and optimization techniques, what could be the best management practices in reconciling human activity with plant biodiversity in human-dominated landscapes. However, my interests are broader, and include the understanding of how plant species traits influence community organization, how plant species diversity, in its different facets, is structured across spatial scales and what is the role of rare species in that structuring. 

Since 2014, I am carrying out my post-doc research under the main theme of metacommunity ecology. Here, I intend to thoroughly explore what can we expect to learn about metacommunity functioning, using just observational data as a basis. Using simulation models built upon theoretical metacommunity processes, I'm trying to understand to what extent can we infer underlying metacommunity processes in simulated communities by just running null models and comparing patterns with real data, which is the usual way to conduct metacommunity hypothesis testing with purely observational data. I will then apply this knowledge to uncover what metacommunity processes may be operating in a real dataset of plant communities in a fragmented landscape.

In parallel with these scientific interests, I'm concerned and interested in the conservation of rare plant communities and species, especially those under no protection, and for which very little is known.

Further, I am also involved in the development of biodiversity databases and web-based interactive ways to explore, present and share biodiversity data, keeping focus on exploring all the potential of a large and rigorous dataset.

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