Our research spans from development of general and mathematically rigorous theories to statistical approaches linking the theories to data. We utilize both forward (from process to pattern) and inverse (from pattern to process) approaches to learn about the ecological, genetic, and evolutionary dynamics of populations and species inhabiting the heterogeneous and changing landscapes of the real world.
We have developed a general class of joint species distribution modes, called “Hierarchical Modelling of Species Communities” (HMSC), that make it possible to integrate data on species occurrence, environmental covariates, species traits, and phylogenetic relationships in a statistically efficient and ecologically informed way. The framework applies to many kinds of data, including hierarchical, spatially explicit, and temporally explicit study designs, and e.g. presence-absence data or abundance data. As special cases, HMSC includes single species distribution models and model-based ordinations.
We have developed methods for molecular species identification, more specifically probabilistic methods for taxonomic placement (PROTAX) of DNA barcoding sequences. What makes PROTAX different from the broad variety of other kinds of bioinformatics methods is that it is based on a statistically rigorous approach, allowing the unbiased assessment of species identification uncertainty.
This research project aims at putting together a large-scale and long-term database on Eurasian biodiversity. The ECN database shall cover the territory of former USSR and Finland, and include a description of the environmental change (e.g. in terms of forest structure and climatic conditions) that has taken place in the past 50-100 years, as well as the population dynamic and phenological responses of species of mammals, birds, insects and vascular plants.
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