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Biology subjects

Riihimaki, H.

Publications and source records attributed to Riihimaki, H..

2 recordsLinked to original sources

Woody plants constructing tundra soils

In tundra, woody plants are expanding towards higher latitudes and altitudes due to increasingly favourable climatic conditions. Their expansion may also occur through increases in the coverage and height of the plants. These shifts may cascade further across the ecosystem, such as in the foundations of tundra: that is, in the soils. Yet, little is known about the effects woody plants have on local soil conditions. Here, we examined if the coverage and height of woody plants affect the growing-season soil moisture and temperature as well as soil organic carbon stocks. We carried out a field observation study in a dwarf shrub-dominated tundra and built a hierarchical model. We found that, after controlling for other possible factors influencing woody plants and soil conditions (namely, topography, snow, and the overall plant coverage), the coverage of woody plants inversely correlated with all three soil conditions. Yet, we found no link between the woody plant height to the soil variables. This indicates that woody plants affect local soil conditions in various ways, depending upon whether their expansion occurs though the growth of coverage or their height. Nevertheless, woody plants likely alter the very ground of the entire tundra system and feedback into the global climate system through the water, energy, and carbon cycles of tundra.

ecology↗

A topological data analysis based classification method for multiple measurement

BackgroundMachine learning models for repeated measurements are limited. Using topological data analysis (TDA), we present a classifier for repeated measurements which samples from the data space and builds a network graph based on the data topology. When applying this to two case studies, accuracy exceeds alternative models with additional benefits such as reporting data subsets with high purity along with feature values.\n\nResultsFor 300 examples of 3 tree species, the accuracy reached 80% after 30 datapoints, which was improved to 90% after increased sampling to 400 datapoints. Using data from 100 examples of each of 6 point processes, the classifier achieved 96.8% accuracy. In both datasets, the TDA classifier outperformed an alternative model.\n\nConclusionsThis algorithm and software can be beneficial for repeated measurement data common in biological sciences, as both an accurate classifier and a feature selection tool.

bioinformatics↗