Geography meets ecology: developing proxies to understand variations of stream biodiversity
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
Linnanmaa, auditorium IT116
Topic of the dissertation
Geography meets ecology: developing proxies to understand variations of stream biodiversity
Doctoral candidate
Master of Science Olli-Matti Kärnä
Faculty and unit
University of Oulu Graduate School, Faculty of Science, Geography Research Unit
Subject of study
Geography
Opponent
Doctor of Philosophy Heikki Hämäläinen, University of Jyväskylä
Custos
Professor Jan Hjort, University of Oulu
Complementary insights to understand variations of stream biodiversity
The results of this doctoral thesis highlight that in addition to traditional environmental variables, biodiversity (i.e. biological variation) patterns in northern streams can also be explained by a set of novel geographical variables.
Biogeographers and ecologists must often rely on different proxy variables in exploring biodiversity-environment relationships. Biodiversity patterns in streams have been shown to be structured by direct physical properties of the local habitat and by proxy features on the catchment and regional scales, but a key problem has been the moderate explanatory power when using such traditional environmental variables in statistical modelling.
In this doctoral thesis, the goal was to study biodiversity patterns in northern streams by introducing and applying geographical variables focusing on environmental features (i.e. geodiversity) and dispersal (i.e. different geographical distances) along with traditional environmental variables. The data were analyzed by using modern statistical methods.
According to the results of this thesis, traditional environmental variables contributed most to the variation in stream biodiversity. However, geographical variables also shed light into understanding biodiversity-environment relationships. Physical distance measures describing dispersal routes showed a notable role in explaining community compositional variation between stream sites. Moreover, the results indicated that the geodiversity on local and catchment scales correlated with stream biodiversity.
According to the results, it can be proposed that the novel variables presented in this thesis provide new perspectives on the relationship between stream biodiversity and the environment. Conservation efforts for stream environments may also benefit from the identified cost-efficient geographical variables.
Biogeographers and ecologists must often rely on different proxy variables in exploring biodiversity-environment relationships. Biodiversity patterns in streams have been shown to be structured by direct physical properties of the local habitat and by proxy features on the catchment and regional scales, but a key problem has been the moderate explanatory power when using such traditional environmental variables in statistical modelling.
In this doctoral thesis, the goal was to study biodiversity patterns in northern streams by introducing and applying geographical variables focusing on environmental features (i.e. geodiversity) and dispersal (i.e. different geographical distances) along with traditional environmental variables. The data were analyzed by using modern statistical methods.
According to the results of this thesis, traditional environmental variables contributed most to the variation in stream biodiversity. However, geographical variables also shed light into understanding biodiversity-environment relationships. Physical distance measures describing dispersal routes showed a notable role in explaining community compositional variation between stream sites. Moreover, the results indicated that the geodiversity on local and catchment scales correlated with stream biodiversity.
According to the results, it can be proposed that the novel variables presented in this thesis provide new perspectives on the relationship between stream biodiversity and the environment. Conservation efforts for stream environments may also benefit from the identified cost-efficient geographical variables.
Last updated: 1.3.2023