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Department of Geosciences and Geography
Physical Geography

Miska Luoto

Science highlights

On this page, I will present highlights of the recent research projects. Requests for the materials should be addressed to

Soil moisture’s underestimated role in climate change impact modelling in low-energy systems
Global Change Biology 19, 2965–2975 (2013)
doi: 10.1111/gcb.12286
le Roux, P.C., Aalto, J., & Luoto, M.

Shifts in precipitation regimes are an inherent component of climate change, but in low-energy systems are often assumed to be less important than changes in temperature. Because soil moisture is the hydrological variable most proximally linked to plant performance during the growing season in arctic-alpine habitats, it may offer the most useful perspective on the influence of changes in precipitation on vegetation. Here we quantify the influence of soil moisture for multiple vegetation properties at fine spatial scales, to determine the potential importance of soil moisture under changing climatic conditions. A fine-scale data set, comprising vascular species cover and field-quantified ecologically relevant environmental parameters, was analysed to determine the influence of soil moisture relative to other key abiotic predictors. Soil moisture was strongly related to community composition, species richness and the occurrence patterns of individual species, having a similar or greater influence than soil temperature, pH and solar radiation. Soil moisture varied considerably over short distances, and this fine-scale heterogeneity may contribute to offsetting the ecological impacts of changes in precipitation for species not limited to extreme soil moisture conditions. In conclusion, soil moisture is a key driver of vegetation properties, both at the species and community level, even in this low-energy system. Soil moisture conditions represent an important mechanism through which changing climatic conditions impact vegetation, and advancing our predictive capability will therefore require a better understanding of how soil moisture mediates the effects of climate change on biota.

Horizontal, but not vertical, biotic interactions affect fine-scale plant distribution patterns in a low energy system
Ecology 94, 671–682 (2013)
le Roux, P.C., Lenoir, J., Pellissier, L., Wisz, M.S. & Luoto, M.

Studies of species range determinants have traditionally focused on abiotic variables (typically climatic conditions), and therefore the recent explicit consideration of biotic interactions represents an important advance in the field. While these studies clearly support the role of biotic interactions in shaping species distributions, most examine only the influence of a single species and/or a single interaction, failing to account for species being subject to multiple concurrent interactions. By fitting species distribution models (SDMs), we examine the influence of multiple vertical (i.e. grazing, trampling and manuring by mammalian herbivores) and horizontal (i.e. competition and facilitation; estimated from the cover of dominant plant species) inter-specific interactions on the occurrence and cover of 41 alpine tundra plant species. Adding plant-plant interactions to baseline SDMs (using five field-quantified abiotic variables) significantly improve models' predictive power for independent data, while herbivore-related variables had only a weak influence. Overall, abiotic variables had the strongest individual contributions to the distribution of alpine tundra plants, with the importance of horizontal interaction variables exceeding that of vertical interaction variables. These results were consistent across three modelling techniques, for both species occurrence and cover, demonstrating the pattern to be robust. Thus, the explicit consideration of multiple biotic interactions reveals that plant-plant interactions exert control over the fine-scale distribution of vascular species that is comparable to abiotic drivers and considerably stronger than herbivores in this low energy system.

Stochastic species distributions are driven by organism size
Ecology 94, 660–670. (2013)
Soininen, J., Korhonen, J. & Luoto, M.

The strength of environmental drivers and biotic interactions are expected to show large variability across organism groups. We tested two ideas related to the degree of ecological determinism vs. stochasticity using a large dataset comprising bacterio-, phyto- and zooplankton. We expected that 1) there are predictable, size-driven differences in the degree to which planktonic taxa respond to different drivers such as water chemistry, biotic interactions and climatic variables, and 2) species distribution models show lowest predictive performance for the smallest taxa due to the stochastic distributions of microbes. Generalised linear models (GLMs), generalized additive models (GAMs), and generalized boosted methods (GBMs) were constructed for 84 species to model their occurrence as a function of eight predictors. Predictive performance was measured as the area under the curve (AUC) of the receiver-operating characteristic plot and true skill statistic (TSS) using independent model evaluation data. We found that the model performances were typically remarkably low for all planktonic groups. The proportion of satisfactory models (AUC > 0.7) was lowest for bacteria (11.1% of the models), followed by phyto- (24.2%) and zooplankton (38.1%). The occurrences of taxa within all planktonic groups were related to climatic variables to a certain degree but bacteria showed the strongest associations with the climatic variables. Moreover, zooplankton occurrences were more related to biotic variables than the occurrences of smaller taxa while phytoplankton occurrences were more related to water chemistry. We conclude that the occurrences of planktonic taxa are highly unpredictable and that stochasticity in occurrences is negatively related to the organism size perhaps due to efficient dispersal and fast population dynamics among the smallest taxa.

Biotic interactions affect the elevational ranges of high-latitude plant species
Ecography 35, 1048–1056. (2012)
le Roux, P.C., Virtanen, R., Heikkinen, R.K. & Luoto, M.

Ecological theory suggests that positive plant – plant interactions can extend species distributions into areas that would otherwise be unfavourable. However, few studies have tested this hypothesis, and none have explicitly examined the associated prediction that inter-specifi c interactions between plants may broaden species altitudinal distributions. Here we test this prediction, using fi ne-scale species distribution data for 156 bryophytes, lichens and vascular plants spanning a 900 m elevational gradient in north-western Finland and Norway, analysed with a niche modelling approach. Species altitudinal ranges of all three groups of plants were more accurately predicted when including the cover of any of the 24 most wide-spread and abundant species (‘dominants’) than when using abiotic variables alone, emphasizing the importance of including relevant biotic predictors in species distribution models. Half of the models showed that species had very low probabilities of occurrence under high cover of dominants, suggesting a strong negative impact of dominant species. Similarly, for species that are predicted to occur irrespective of dominant species cover, 62% of models showed narrower species altitudinal distributions when occurring under high dominant cover, with contractions of species ’ lower and upper elevational limits being common. Nonetheless, high cover of dominant species was associated with upslope range extension in 43 species, and a net range expansion in nearly 10% of all models. Species distributional responses to dominants were only weakly related to species traits, with larger range contractions associated with arctic-alpine dominants. Therefore, dominant species appear to exert a strong infl uence on the elevational distribution of other species in high latitude environments.

See my current research projects funded by the Academy of Finland: 1) Impacts of climate change on Arctic vegetation and biodiversity (part of the FICCA research program) and 2) Remote sensing and GIS in biodiversity modelling.