the application areas including e.g. community ecology, population ecology, movement ecology, evolutionary ecology, and (meta)population biology. We develop both statistical and mathematical approaches, as well as their combinations in the context of e.g. Bayesian state-space approaches. Our main current projects relate to Hierarchical Modelling of Species Communities (HMSC) and PRObabilistic methods for TAXonomic assignment of DNA barcoding sequences (PROTAX).
At present, an estimated 80% of all species on Earth still await discovery. At the same time, we are losing biodiversity at an alarming rate. There is an urgent need to make sense of patterns and processes in biodiversity – while dealing with the unknown in an efficient way. Through LIFEPLAN, we aim to establish the current state of biodiversity across the globe, and to use our insights for generating accurate predictions of its future state under future scenarios. In LIFEPLAN, we thus characterize biological diversity through a worldwide sampling program, and develop the bioinformatic and statistical approaches needed to make the most out of these data. Together, we will generate the most ambitious, globally distributed and systematically collected data set to date on a broad range of taxonomical groups. Importantly, we will employ modern sampling methods that do not require taxonomical expertise from those collecting the data, and that will result in data that are directly comparable among different locations. We are now looking for collaborators, and hope that you will join our endeavor.
A key question in community ecology is to identify and quantify those processes that determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology and conservation biology, we have developed Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. HMSC belongs to the class of joint species distribution models, and it makes it possible to derive simultaneously species- and community level inference from data on species occurrences, environmental covariates, species traits, and phylogenetic relationships. HMSC applies to a wide variety of study designs, including hierarchical data, spatial data, temporal data, and spatio-temporal data. For more details, see Ovaskainen et al. (2017) and our software pages.
Ovaskainen, O., Tikhonov, G., Norberg, A., Blanchet, F. G., Duan, L., Dunson, D., Roslin, T. 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
A crucial step in the use of DNA markers for biodiversity surveys is the assignment of Linnaean taxonomies (species, genus, etc.) to sequence reads. This allows the use of all the information known based on the taxonomic names. Taxonomic placement of DNA barcoding sequences is inherently probabilistic because DNA sequences contain errors, because there is natural variation among sequences within a species, and because reference databases are incomplete and can have false annotations. The PROTAX method that we have developed allows one to account for all of these uncertainties in a statistically rigorous manner. This allows researchers not only to obtain the most likely taxonomic annotations of the DNA barcoding sequences, but also to know to what extent such annotations can be trusted. For more details, see Somervuo et al. (2017) and our software pages.
In addition to DNA barcoding data, also other new types of data, such as those arising from autonomous audio recording, come with the need of new approaches of automated species classification methods. To this end, we have developed the Animal Sound Identifier (ASI), which allows one to identify animal sounds directly from field recordings without the need for pre-defined template libraries. For more details, see Ovaskainen et al. (2018).
Somervuo, P., Yu, D., Xu, C., Ji, Y., Hultman, J., Wirta, H. and Ovaskainen, O. 2017. Quantifying uncertainty of taxonomic placement in DNA barcoding and metabarcoding. Methods in Ecology and Evolution 8, 398-407.
Ovaskainen, O., Camargo, U. and Somervuo, P 2018. Animal Sound Identifier (ASI): software for automated identification of vocal animals. Ecology Letters, in press.