Biotic interactions in driving biodiversity : Insights into spatial modelling

• Main Author: Mod, Heidi
• Other Authors: Callaway, Ragan, University of Helsinki, Faculty of Science, Department of Geosciences and Geography, Division of Biogeosciences, Helsingin yliopisto, matemaattis-luonnontieteellinen tiedekunta, geotieteiden ja maantieteen laitos, Helsingfors universitet, matematisk-naturvetenskapliga fakulteten, institutionen för geovetenskaper och geografi, Luoto, Miska, Heikkinen, Risto, Väre, Henry
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: Helsingin yliopisto 2016
• Subjects: maantiede; Norway; Northern Finland
• Online Access: http://hdl.handle.net/10138/161433

The effects of co-occurring species, namely biotic interactions, govern performance and assemblages of species along with abiotic factors. They can emerge as positive or negative, with the outcome and magnitude of their impact depending on species and environmental conditions. However, no general conception of the role of biotic interactions in functioning of ecosystems exists. Implementing correlative spatial modelling approaches, combined with extensive data on species and environmental factors, would complement the understanding of biotic interactions and biodiversity. Moreover, the modelling frameworks themselves, conventionally based on abiotic predictors only, could benefit from incorporating biotic interactions and their context-dependency. In this thesis, I study the influence of biotic interactions in ecosystems and examine whether their effects vary among species and environmental gradients (sensu stress gradient hypothesis = SGH), and consequently, across landscapes. Species traits are hypothesized to govern the species-specific outcomes, while the SGH postulates that the frequency of positive interactions is higher under harsh environmental conditions, whereas negative interactions dominate at benign and productive sites. The study applies correlative spatial models utilizing both regression models and machine-learning methods, and fine-scale (1 m2) data on vascular plant, bryophyte and lichen communities from Northern Finland and Norway (69°N, 21°E). In addition to conventional distribution models of individual species (SDM), also species richness, traits and fitness are modelled to capture the community-level impacts of biotic interactions. The underlying methodology is to incorporate biotic predictors into the abiotic-only models and to examine the impacts of biotic interactions and their dependency on species traits and environmental conditions. Cover values of the dominant species of the study area are used as proxies for the intensity of their impact on other species. The results show, firstly, ...