New sampling methods and statistical tools for biodiversity research: integrating animal movement ecology with population and community ecologyCollaborative research project (funded by Academy of Finland and FAPESP) for 1.1.2014-31.12.2017 (48 months).
Prof. Otso Ovaskainen (University of Helsinki, Finland)
Prof. Milton Cezar Ribeiro (UNESP, Brazil)
Finnish-side team:Dr. Jukka Siren and M.Sc. Ulisses Camargo (University of Helsinki, Finland).
Brazilian-side team:The research groups of Prof. Milton Cezar Ribeiro, Prof. Mauro Galetti, Prof. Marco Pizo, and Prof. Leonor Patricia Morelatto (UNESP, Brazil), and Prof. Danilo Boscolo (USP, Brazil).
Main collaborators:Prof. Goncalo Ferraz (UFRGS, Brazil), Prof. Marie Josee Fortin (Univ. of Toronto, Canada), Prof. Jean Paul Metzger (USP, Brazil), Prof. Katia Ferraz (ESALQ/USP, Brazil).
This research proposal develops a multidisciplinary approach to study tropical forest biodiversity in two Brazilian ecosystems: Amazon and Atlantic forests. The overall objective is to develop new methods of field research and statistical modelling that will allow improved mapping and monitoring of tropical diversity. The main novelty of this project is in the use of new sampling technologies combined with the development of novel theoretical and statistical frameworks for obtaining robust inference at the levels of individuals, populations and communities.
We will thus contribute to the integration of movement ecology to the fields of spatial population ecology and community ecology in general and in particular will produce new information on tropical biodiversity, including both basic and applied aspects.
Compared to the earlier activities in tropical forest biodiversity monitoring, this research plan shifts gears in three essential ways. First, we will take the full advantage of recent technical developments to significantly improve monitoring designs that will result in an unprecedented level of spatial and temporal replication. Second, we will merge the movement ecology paradigm with population and community based approaches to assess the responses of fauna to landscape gradients. Third, we will apply and further develop recent methods in Bayesian statistics to enable the processing of high-throughput biodiversity data in a reliable and robust manner.