Environmental and Ecological Statistics group works broadly in statistics, environmental sciences and ecology. Our main focus is on developing statistical methods for analysing ecological and environmental data and to support environmental management and decision making. Statistical inference and uncertainty estimation are essential for these fields to ensure that appropriate conclusions and decisions can be reached from experiments and observations.
Statistics is a mathematical science with focus on analysis and interpretation of data. It is the traditional data science that is nowadays more important than ever. Statistics research provides tools to analyze data and extract information from it. It plays essential role in almost all fields of modern societies; including the modern change making technologies such as machine learning and artificial intelligence. Statistical methods are also key tools in solving global challenges related to environmental change and globalization.
Our research focuses on hierarchical Bayesian modeling and decision analysis. Specific research areas include spatial and spatio-temporal statistics, Gaussian processes, state-space models, Bayesian optimization, optimal design of observational studies and computational methods.
- Jarno Vanhatalo, Marcelo Hartmann and Lari Veneranta (in press). Additive multivariate Gaussian processes for joint species distribution modeling with heterogeneous data. Bayesian Analysis, [Link]
- Jarno Vanhatalo, Zitong Li and Mikko Sillanpää (2019). A Gaussian process model and Bayesian variable selection for mapping function-valued quantitative traits with incomplete phenotype data. Bioinformatics, 35(19):3684-3692.[Link]
- Marcelo Hartmann and Jarno Vanhatalo (2019). Laplace approximation and natural gradient for Gaussian process regression with heteroscedastic Student-t model. Statistics and Computing, 29:753-773 [Link]
- Sakari Kuikka, Jarno Vanhatalo, Henni Pulkkinen, Samu Mäntyniemi and Jukka Corander (2014). Experiences in Bayesian Inference in Baltic Salmon management. Statistical Science, 29(1):42-49. [Link]
- Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari (2013). GPstuff: Bayesian Modeling with Gaussian Processes. Journal of Machine Learning Research, 14:1175-1179. [Link]
Environmental sciences includes a broad range of scientific fields studying the environment and solutions to environmental challenges. Ecology studies the distribution and abundance of species, and their interactions with other species and the environment. Biodiversity and a clean environment have significant impact on our health and our economy. Hence, understanding how ecosystems and ecosystem processes respond to global megatrends and environmental changes is mandatory to understand the subsequent consequenses to, for example, food safety and human health.
Our research concentrates on ecological changes, species distribution modelling, population and fisheries management, uncertainty assessment and data fusion in environmental simulators as well as on environmental management and risk assessment.
- Jonne Kotta, Jarno Vanhatalo, et al. (2019). Integrating experimental and distribution data to predict future species patterns. Scientific Reports, 9(1): 1821 [Link]
- Jussi Mäkinen and Jarno Vanhatalo (2018). Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals. Diversity and Distributions [Link]
- Jarno Vanhatalo, Geoffrey R. Hosack and Hugh Sweatman (2017). Spatio-temporal modelling of crown-of-thorns starfish outbreaks on the Great Barrier Reef to inform control strategies. Journal of Applied Ecology, 54:188-197. [Link]
- Meri Kallasvuo, Jarno Vanhatalo and Lari Veneranta (2017). Modeling the spatial distribution of larval fish abundance provides essential information for management. Canadian Journal of Fisheries and Aquatic Sciences, 74:636-649. [Link]
- Maisa Nevalainen, Inari Helle and Jarno Vanhatalo (2017). Preparing for the unprecedented - towards quantitative oil risk assessment in the Arctic marine areas. Marine Pollution Bulletin, 114(1):90-101. [Link]