After obtaining my PhD in mathematics in 1998, I realized I was more fascinated by ecology than pure mathematics. I thus decided to start post-doctoral studies with the late Prof. Ilkka Hanski in ecological modelling. An important part of my career development was the ERC Starting grant project “Spatial ecology: bringing mathematical theory and data together” (SPAECO; 2008-2013). I became a full professor in mathematical ecology in 2009. I was a member in a Finnish CoE (Centre of Excellence in Metapopulation Research) as a PI in 2006-2011, as the vice-director in 2012-2014, and as the director for 2015-2017. In 2018, I established a Research Centre for Ecological Change (REC) with four other PIs. REC consists currently of ca. 60 researchers, post-docs and PhD students. My own group Statistical Ecology Research Group (SERG) focuses on developing and applying novel statistical methods to make more out of ecological data. I have been the advisor for 18 post-doctoral associates, the main supervisor for 13 PhD students, and co-supervisor for 5 PhD students. In addition to my permanent position in Helsinki, I have a visiting professor position in a Norwegian CoE since 2014.

My research focuses on the development of statistical approaches that help making more out of ecological data. Below, I list my most important research lines. For each research line, with few key references for each of them.

Metapopulation biology. As a post doc with the late professor Ilkka Hanski, I started my career in ecology in metapopulation biology. We developed the concept of metapopulation capacity, describing how metapopulation persistence depends on landscape structure [1]. We extended classical metapopulation ecology to more general spatial dynamics [2] and to eco-evolutionary dynamics [3], and developed novel statistical methods to relate these theoretical approaches to data [4]

1. Hanski, I. and Ovaskainen, O. 2000. The metapopulation capacity of a fragmented landscape. Nature 404, 755-758.
2. Ovaskainen, O. and Hanski, I. 2004. From individual behaviour to metapopulation dynamics: unifying the patchy population and classic metapopulation models. American Naturalist, 164, 364-377.
3. Hanski, I., Mononen, T. and Ovaskainen, O. 2011. Eco-evolutionary metapopulation dynamics and the spatial scale of adaptation. American Naturalist 177, 29-43.
4. Harrison, P. J., Hanski, I. and Ovaskainen, O. 2011. Bayesian state-space modeling of metapopulation dynamics in the Glanville fritillary butterfly. Ecological Monographs 81, 581-598.

Movement ecology. Inspired by the need to better understand movements of individuals among habitat patches in metapopulations, I developed a statistical approach for fitting diffusion-advection-reaction models to spatial mark-recapture data [1], which approach I applied with collaborators to predict how modification in landscape structure influences movements [2]. Recently we extended these models to multi-species context to relate movements to species traits and phylogenies [4]. I have pplied novel tracking methodology to understand evolution of dispersal rate in fragmented landscapes [3].

1. Ovaskainen, O. 2004. Habitat-specific movement parameters estimated using mark–recapture data and a diffusion model. Ecology 85, 242-257.
2. Ovaskainen, O., et al. and Kuussaari, M. 2008. An empirical test of a diffusion model: predicting clouded apollo movements in a novel environment. American Naturalist 171, 610-619.
3. Ovaskainen, O., et al. and Hanski, I. 2008. Tracking butterfly movements with harmonic radar reveals an effect of population age on movement distance. PNAS 105, 19090-19095.
4. Ovaskainen, O., et al. and Morales, J. M. 2019. Joint species movement modelling: how do traits influence movements? Ecology 100, e02622.

Mathematical methods for individual-based models. In collaboration with Stephen Cornell and others, we developed mathematically exact methods for the analysis of individual-based models [1], and applied these methods to understand both ecological and evolutionary processes [e.g. 2]. Recently, we made a major mathematical breakthrough by extending the approach of [1] to a very general “meta-model” consisting of reactants, products and catalysts [3]. We applied special cases of this meta-model to derive insights in movement ecology, evolutionary ecology, and metapopulation ecology [3].

1. Ovaskainen, O. and Cornell, S. J. 2006. Space and stochasticity in population dynamics. PNAS, 103, 12781-12786.
2. North, A., Cornell, S. J. and Ovaskainen, O. 2011. Evolutionary responses of dispersal distance to landscape structure and habitat loss. Evolution 65, 1739-1751.
3. Cornell, S. J., et al. and Ovaskainen, O. A unified framework for analysis of individual-based models in ecology and beyond. Nature Communications, in press.

Population genetics and evolutionary biology. In collaboration with Juha Merilä and others, we developed mathematical and statistical approaches to resolve whether evolution has taken place by neutral drift or by adaptive processes [1,2]. Our approach avoids the theoretical pitfalls of the more traditional FST-QST test [1] and has better statistical power than it [2].

1. Ovaskainen, O., et al. and Merilä, J. 2011. A new method to uncover signatures of divergent and stabilizing selection in quantitative traits. Genetics189, 621-632.
2. Karhunen, M., et al. and Ovaskainen, O. 2013. driftsel: an R package for detecting signals of natural selection in quantitative traits. Molecular Ecology Resources 13, 746-754.

 Fungal community ecology. While I am primarily a theoretical and statistical ecologists, I have become increasingly interested in empirical research, especially in fungal ecology. I used fungal communities as a model system to understand the effects of habitat fragmentation [1], life-history variation [2] and dispersal [3], and to develop novel methods for conducting highly cost-efficient large-scale surveys [4].

1. Nordén, J., et al. and Ovaskainen, O. 2013. Specialist species of wood-inhabiting fungi struggle while generalists thrive in fragmented boreal forests. Journal of Ecology 101, 701-712.
2. Ovaskainen, O., et al. and Nordén, J. 2013. Combining high-throughput sequencing with fruit-body surveys reveals contrasting life-history strategies in fungi. The ISME Journal 7, 1696-1709.
3. Norros, V., et al. and Ovaskainen, O. 2014. Do small spores disperse further than large spores? Ecology 95, 1612-1621.
4. Abrego, N., et al. and Ovaskainen, O. 2018. Give me a sample of air and I will tell which species are found from your region – molecular identification of fungi from airborne spore samples. Molecular Ecology Ressources 18, 511-524.

Statistical community ecology. With Janne Soininen, we conceived the idea of modelling species niches through a hierarchical structure that allows borrowing information across species [1]. Developing the Hierarchical Modelling of Species Communities (HMSC) is currently my main line of research [e.g. 2,3,5]. HMSC integrates data on species occurrences, environmental covariates, species traits and phylogenies in a single encompassing model, and it can be applied e.g. to spatially explicit data [3,5] or to time-series data [2,5]. In a comprehensive comparison among single species and joint species distribution models, HMSC was found out to have by far the highest predictive power [4].

1. Ovaskainen, O. and Soininen, J. 2011. Making more out of sparse data: hierarchical modeling of species communities. Ecology 92, 289-295.
2. Ovaskainen, O., et al. and Abrego, N. 2017. How are species interactions structured in species rich communities? A new method for analysing time-series data. Proceedings of the Royal Society Series B, Biological Sciences 284, 20170768.
3. Ovaskainen, O., et al. and Abrego, N. 2017. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecology Letters 20, 561-576
4. Norberg, A. et al. and Ovaskainen, O. 2019. A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecological Monographs 89, e01370.
5. Ovaskainen, O. and Abrego, N. Joint Species Distribution Modelling – With Applications in R. Cambridge University Press, in press.
   

Probabilistic methods of taxonomic placement. DNA-based surveys generate massive amounts of data, but come with the challenge of how to translate sequences to species names. With Panu Somervuo and others, we developed a probabilistic method for taxonomic placement of DNA-barcoding data [1]. The key novelty of our method is that it models explicitly also unknown species, making it outperform alternative methods for taxa with incomplete reference databases, such as fungi [2]. I am also interested in other types of newly emerging data, such as autonomous audio recording, for which we developed methods that combine machine learning with manual annotation to enable automated species identification [3].

1. Somervuo, P., et al. and Ovaskainen, O. 2016. Unbiased probabilistic taxonomic classification for DNA barcoding. Bioinformatics 32, 2920-2927.
2. Abarenkov, K. et al. and Ovaskainen, O. 2018. PROTAX-fungi: a web-based tool for probabilistic taxonomic placement of fungal ITS sequences. New Phytologist 220, 517-525.
3. Ovaskainen, O., de Camargo, U. and Somervuo, P. 2018. Animal Sound Identifier (ASI): software for automated identification of vocal animals. Ecology Letters 21, 1244-1254.