Hierarchical Modelling of Species Communities (HMSC) is a model-based approach for analyzing community ecological data (Ovaskainen et a. 2017a). The obligatory data for HMSC-analyses includes a matrix of species occurrences or abundances and a matrix of environmental covariates. Additional and optional data include information about species traits and phylogenetic relationships, and information about the spatiotemporal context of the sampling design. HMSC partitions variation in species occurrences to components that relate to environmental filtering, species interactions, and random processes. HMSC yields inference both at species and community levels. It can be used to generate simulated communities under given environmental conditions, and thus its predictions can be compared to independent validation data, and it can be used for scenario simulations.
The development version is found on Github.
The old matlab and R versions of HMSC can be found here.
Forthcoming HMSC courses
Australia, Sydney. Two-day course 18-19 June 2020. This course will be organized just before the International Statistical Ecology Conference (ISEC) as a short course at UNSW Sydney. Teachers Otso Ovaskainen, Jari Oksanen, and possibly others. More information on this course coming soon. Note also the short course "Multivariate modelling in ecology and joint species distribution models" on Saturday 20th June and the 90 min HMSC tutorial on Monday 22nd June.
USA, Salt Lake City. One-day course 2nd August 2020. This course will be organized as a workshop of the Ecological Society of America (ESA) meeting. Teachers Otso Ovaskainen, Jari Oksanen, and possibly others. For more information, see https://www.esa.org/saltlake/
Europe, Finland, Helsinki, and online. Five-day-course 2-6 November. Teachers Otso Ovaskainen, Nerea Abrego, Jari Oksanen, and others: Hierarchical Modelling of Species Communities with the R-package Hmsc:
- Course Overview: This course is aimed for students and researchers who are interested in analysing data on community ecology in a way that allows placing their results in the context of modern theory. The course covers a comprehensive treatment of HMSC, including both the technical detail of the statistical methods, as well as the ecological interpretation of the results. With the help of worked out examples, the participants learn how to conduct and interpret statistical analyses in practice with the R-package Hmsc, providing a fast starting point for applying HMSC to their own data. The participants are encouraged to bring also their own data so that they can get hands-on support on HMSC-analyses of their own projects. Ideally, you will come back from the course with R-scripts that run the entire Hmsc pipeline from constructing and fitting the models to producing the result tables and figures, as well as draft texts for how the analyses were done (for the Material and Methods section for your manuscript) and what the results are (for the Results section of your manuscript).
- Course Outline:
- Introduction and motivation
- How does HMSC relate to ecological theory?
- The syntax and typical workflow of the R-package Hmsc
- Types of data that can be incorporated to Hmsc
- Types of questions that can be addressed by Hmsc
- Worked out case studies on plants, fungi and birds
- Break-out groups to analyse your own data
- How to describe the HMSC analyses in the “Material and Methods” section of your manuscript?
- How to report the results of the HMSC analyses in the “Results” section of your manuscript?
- Prerequisites: You will need to bring your own laptop to the workshop, with preferably both R and the R-package Hmsc (found on CRAN) installed. The course will closely follow the book (Ovaskainen & Abrego 2020), and thus we recommend that you familiarize yourself with the book prior to the course.
- Duration: 10.00 am to 4.00 pm all five days. Lunch break 12-13.
- The course is free of charge (no travel or accommodation provided)
- You can attend in person or follow online
- Register here. Participants will be prioritized according to registration order and relevance.
Core papers about HMSC
Ovaskainen, O., Tikhonov, G., Norberg, A., Blanchet, F. G., Duan, L., Dunson, D., Roslin, T. and Abrego, N. 2017a. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecology Letters 20, 561-576. https://doi.org/10.1111/ele.12757
Ovaskainen, O., Tikhonov, G., Dunson, D., Grøtan, V., Engen, S., Sæther, B.-E. and Abrego, N. 2017b. How are species interactions structured in species rich communities? A new method for analysing time-series data. Proceedings of the Royal Society B: Biological Sciences, 284, 20170768. https://doi.org/10.1098/rspb.2017.0768
Tikhonov, G., Abrego, N., Dunson, D. and Ovaskainen, O. 2017. Using joint species distribution models for evaluating how species-to-species associations depend on the environmental context. Methods in Ecology and Evolution 8, 443-452. https://doi.org/10.1111/2041-210X.12723
Tikhonov, G., Opedal, Ø. H., Abrego, N., Lehikoinen, A., de Jonge, M. M., Oksanen, J. and Ovaskainen, O. 2020a. Joint species distribution modelling with the R-package Hmsc. Methods in Ecology and Evolution, in early view, https://doi.org/10.1111/2041-210X.13345
Tikhonov, G., Duan, L., Abrego, N., Newell, G., White, M., Dunson, D. and Ovaskainen, O. 2020b. Computationally efficient joint species distribution modelling of big spatial data. Ecology, e02929. https://doi.org/10.1002/ecy.2929
Ovaskainen, O. and Abrego, N. 2020. Joint Species Distribution Modelling – With Applications in R. Cambridge University Press, in press.
Abrego, N., Norberg, A. and Ovaskainen, O. 2017. Measuring and predicting the influence of traits on the assembly processes of wood-inhabiting fungi. Journal of Ecology 105, 1070-1081. https://doi.org/10.1111/1365-2745.12722
Ovaskainen, O., Roy, D., Fox, R. and Anderson, B. 2016. Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods in Ecology and Evolution 7, 428-436. https://doi.org/10.1111/2041-210X.12502
Ovaskainen, O., Abrego, N., Halme, P. and Dunson, D. 2016. Using latent variable models to identify large networks of species-to-species associations at different spatial scales. Methods in Ecology and Evolution 7, 549-555. https://doi.org/10.1111/2041-210X.12501
A review of joint species distribution modelling:
Warton, D., Blanchet, G., O’Hara, R., Ovaskainen, O., Taskinen, S., Walker, S. and Hui, F. K. C. 2015. So many variables: joint modelling in community ecology. Trends in Ecology and Evolution 30, 766-779. https://doi.org/10.1016/j.tree.2015.09.007
How HMSC results link to the underlying community assembly processes:
Ovaskainen, O., Rybicki, J. and Abrego, N. 2019. What can observational data reveal about metacommunity processes? Ecography 42, 1877-1886. https://doi.org/10.1111/ecog.04444
The predictive performance of HMSC in comparison to other SDM methods:
Norberg, A., Abrego, N., Blanchet, F. G., Adler, F., Anderson, B., Anttila, J., Araújo, M., Dallas, T., Dunson, D., Elith, J., Foster, S., Fox, R., Franklin, J., Godsoe, W., Guisan, A., O'Hara, B., Hill, N., Holt, R. D., Hui, F., Husby, M., Kålås, J., Lehikoinen, A., Luoto, M., Mod, H., Newell, G., Renner, I., Roslin, T., Soininen, J., Thuiller, W., Vanhatalo, J., Warton, D., White, M., Zimmermann, N., Gravel, D. and Ovaskainen, O. 2019. A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecological Monographs 89, e01370. https://doi.org/10.1002/ecm.1370
Some empirically oriented papers applying HMSC:
Norberg, A., Halme, P., Kotiaho, J. S., Toivanen, T. and Ovaskainen, O. 2019. Experimentally induced community assembly of polypores reveals the importance of both environmental filtering and assembly history. Fungal Ecology 41, 137-146. https://doi.org/10.1016/j.funeco.2019.05.003
Minard, G., Tikhonov, G., Ovaskainen, O. and Saastamoinen, M. 2019. The microbiome of the Melitaea cinxia butterfly shows marked variation but is only little explained by the traits of the butterfly or its hostplant. Environmental Microbiology 21, 4253-4269. https://doi.org/10.1111/1462-2920.14786
Rocha, R., Ovaskainen, O., López-Baucells, A., Farneda, F. Z., Sampaio, E. M., Bobrowiec, P.E.D, Cabeza, M., Palmeirim, J. M. and Meyer, C. F. J. 2018. Secondary forest regeneration benefits old-growth specialist bats in a fragmented tropical landscape. Scientific Reports 8, 3819. https://doi.org/10.1038/s41598-018-21999-2
Häkkilä, M., Abrego, N., Ovaskainen, O. and Mönkkönen, M. 2018. Habitat quality is more important than matrix quality for bird communities in protected areas. Ecology and Evolution 8, 4019-4030. https://doi.org/10.1002/ece3.3923
Lammel, D. R., Barth, G., Ovaskainen, O., Cruz, L. M., Zanatta, J. A., Ryo, M., Souza, E. M. and Pedrosa, F. O. 2018. Direct and indirect effects of a pH gradient bring insights into the mechanisms driving prokaryotic community structures. Microbiome 6, 106. https://doi.org/10.1186/s40168-018-0482-8
Abrego, N., Dunson, D., Halme, P., Salcedo, I. and Ovaskainen, O. 2017. Wood-inhabiting fungi with tight associations with other species have declined as a response to forest management. Oikos 126, 269-275. https://doi.org/10.1111/oik.03674
Rocha, R., Ovaskainen, O., López-Baucells, A., Farneda, F. Z., Ferreira, D. F., Bobrowiec, P.E.D, Cabeza, M., Palmeirim, J. M. and Meyer, C. F. J. 2017. Design matters: an evaluation of the impact of small man-made forest clearings on tropical bats using a before-after-control-impact design. Forest Ecology and Management 401, 8-16. https://doi.org/10.1016/j.foreco.2017.06.053