I’m interested in broad range of topics related to forest ecosystems: how plants and ecosystems function, how environment influences ecosystem responses, and how the ecosystems can be managed sustainably.
I’m working as a post-doc at our department, and also as a researcher at the Finnish Forest Research Institute (Metla). I’m currently a visiting associate at Department of Biological Sciences, Macquarie University, Sydney.
Current and recent research:
– I’m currently developing of a simple model framework to estimate GPP and transpiration of forest ecosystems, which could be utilised at large spatial scales. Model will link with forest inventory and remote sensing data in order to provide these estimates at large spatial scales. This work is conducted within the Academy of Finland project Carbbal, which is a joint project between our department, Metla, VTT, and FMI.
– Climate change induced drought effects on forest growth and vulnerability (Climforisk, Life+ project 2011-2014). This large applied research project is just about to start. It will compile together several datasets ranging from remote sensing data to NFI plot measurement. These data will be further combined to ecosystem models and climate scenarios in order to estimate what could be the consequences of climate change induced changes in water cycle to forests and their pest/pathogen vulnerability. An important aspect of the project is to identify gaps of knowledge and data for preparing such estimates.
– Structural acclimation of tree structure to drought and CO2. How tree structure is influenced by drought and elevated CO2 is presently unclear. Trees’ response to climate change induced changes in water balance may include structural changes in their structure that balance some of the direct consequences of elevated CO2 on productivity. These acclimations could impair the direct use of existing models in estimating climate change response of vegetation.
– Tree mortality in unmanaged forests. Models of tree mortality are acutely needed to predict sustainable forest management, but there has been surprinsingly little quantitative data on the subject. Economic, biodiversity and carbon balance studies of forest ecosystems would benefit from better mortality models. I just recently prepared competition-driven models of spruce mortality, and together with my colleagues at Metla, will further incorporate them into forest growth model.
– Modelling of fungal community structure and diversity living on dead wood in response to their environment. We still lack data on fungal communities in what comes to species that are not visible to our eyes. Rich community of fungi inhabits dead wood, for example, although we may see few of them. I participate in research, which aims to solve some of these unknown unknowns by molecular methods, and eventually aims at incorporating diversity models into a forest management model. More information.
My previous research and PhD pertained to use of forest inventory data and models for assessing large-scale forest carbon balances. This theme introduced me to modelling of vegetation and soil processes, uncertainty analyses and statistical sampling protocols.
List of publications
1. Peltoniemi, Mikko and Mäkipää, Raisa (2010): Quantifying distance-independent tree competition for predicting Norway spruce mortality in unmanaged forests, Forest Ecology and Management, 261 (1), p.30, Jan 2011, doi:10.1016/j.foreco.2010.09.019
2. Rajala Tiina, Peltoniemi Mikko, Pennanen Taina, Mäkipää Raisa (2010): Relationship between wood-inhabiting fungi determined by molecular analysis (denaturing gradient gel electrophoresis) and quality of decaying log, Canadian Journal of Forest Research, in press.
3. Mäkipää, R., Häkkinen, M., Muukkonen, P. & Peltoniemi, M. 2008. The costs of monitoring changes in forest soil carbon stocks. Boreal Environment Research 13(supp.B): 120-130.
4. Mäkipää, R., Lehtonen, A. & Peltoniemi, M. 2008. Monitoring carbon stock changes in European forests using forest inventory data. In: Dolman, A.J., Freibauer, A. & Valentini, R. (eds.). The continental-scale greenhouse gas balance of Europe. Ecological Studies 203: 191-210.
5. Palosuo, T., Peltoniemi, M., Mikhailov, A., Komarov, A., Faubert, P., Thürig, E. & Lindner, M. 2008. Projecting effects of intensified biomass extraction with alternative modelling approaches. Forest Ecology and Management 255(5-6): 1423-1433.
6. Monni, S., Peltoniemi, M., Palosuo, T., Lehtonen, A., Mäkipää, R. & Savolainen, I. 2007. Uncertainty of forest carbon stock changes – implications to the total uncertainty of GHG inventory of Finland. Climatic Change 81: 391-413.
7. Peltoniemi, M. 2007. Country-scale carbon accounting of the vegetation and mineral soils of Finland. University of Helsinki, Faculty of Biosciences. Dissertationes Forestales 50. 46 p.
8. Peltoniemi, M., Heikkinen, J. & Mäkipää, R. 2007. Stratification of regional sampling by model-predicted changes of carbon stocks in forested mineral soils. Silva Fennica 41(3): 527-539.
9. Peltoniemi, M., Thürig, E., Ogle, S., Palosuo, T., Schrump, M., Wutzler, T., Butterbach-Bahl, K., Chertov, O., Komarov, A., Mikhailov, A., Gärdenäs, A., Perry, C., Liski, J., Smith, P. & Mäkipää, R. 2007. Models in country scale carbon accounting of forest soils. Silva Fennica 41(3): 575-602.
10. Liski, J., Lehtonen, A., Palosuo, T., Peltoniemi, M., Eggers, T., Muukkonen, P. & Mäkipää, R. 2006. Carbon accumulation in Finland’s forests 1922-2004 – an estimate obtained by combination of forest inventory data with modelling of biomass, litter and soil. Annals of Forest Science 63(7): 687-697.
11. Peltoniemi, M., Palousuo, T., Monni, S., Mäkipää, R. 2006. Factors affecting the uncertainty of sinks and stocks of carbon in Finnish forests soils and vegetation. Forest Ecology and Management 232(1-3): 75-85.
12. Liski, J., Palosuo, T., Peltoniemi, M. & Sievänen, R. 2005. Carbon and decomposition model Yasso for forest soils. Ecological Modelling 189(1-2): 168-182.
13. Peltoniemi, M., Mäkipää, R., Liski, J. Tamminen, P. 2004. Changes in soil carbon with stand age – an evaluation of a modelling method with empirical data. Global Change Biology 10(12): 2078-2091.