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Ecological characteristics of nivicolous slime molds with respect to climate change
Leshchenko, Yuliia ; Koukol, Ondřej (advisor) ; Man, Matěj (referee)
This diploma thesis focuses on nivicolous myxomycetes, a group of amoeboid protists widely distributed in terrestrial ecosystems, particularly in alpine, subalpine, and arctic zones. The main objective is to distinguish whether subgroups of nivicolous myxomycetes have distinct spatial requirements and to identify the key environmental factors influencing their distribution. I employed species distribution modelling (SDM) using three methods (MaxEnt, BIOCLIM, and Random Forest) to predict the habitat suitability of these subgroups. Three taxon experts validated the model predictions. This diploma thesis also explores the potential impact of climate change on their distribution using future climate projection scenarios. The results suggest that SDMs may not fully capture the complexity of habitat requirements for nivicolous myxomycetes in the European part of the Palearctic, however key bioclimatic variables for the study group were distinguished: temperature seasonality, precipitation of the warmest quarter, the maximum temperature of the warmest month. The evaluation by experts reveals some limitations regarding the research design, such as the need for accurate species categorization and consideration of fine-scale environmental variations in the study region. Keywords: nivicolous myxomycetes,...
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Distribution modelling of mountain endemic species of the Balkans peninsula
Rataj, Jakub ; Smyčka, Jan (advisor) ; Man, Matěj (referee)
Endemism is a biogeographical phenomenon where a taxon is restricted to a certain area and does not occur elsewhere. The study of the ranges of such taxa may provide new insights into their evolutionary history or the history of the locality where they are currently found. In the European context, mountainous areas are more interesting from this point of view, because they are characterized by a higher degree of endemism than the adjacent lowlands. One method how to effectively describe the range of an endemic species is species distribution models, SDMs. Based on these models, we are able to quantify the relationships between species and environmental components or predict the occurrence of species to new spatial and temporal locations. The resulting models have the potential to be incorporated into a wide range of other studies. The aim of this bachelor thesis is to summarize the issues involved in this type of ecological modeling in the context of mountain endemic plants. Emphasis is placed on the individual specifics of the biological and environmental data used for this purpose and on the analysis of the different statistical methods and furthermore on the characterization of endemic taxa of the Balkan Peninsula, which should be the focus of a follow-up thesis.
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Predictive distribution modelling of selected bryophyte species in Bohemian Switzerland National Park
Procházková, Martina ; Man, Matěj (advisor) ; Moudrý, Vítězslav (referee)
The aim of this thesis was to create potential distribution models for Dicranum majus (Greater Fork Moss) and Polytrichum alpinum (Alpine Haircap) in Bohemian Switzerland National Park. In the Czech Republic these bryophyte species occur in cold climatic regions typically with higher altitudes. In Bohemian and Saxon Switzerland they can occur in really low altitudes thanks to unique microclimatic conditions of deep inversion ravines. These bryophyte species had low number of occurence records in studied area before the start of my research (4 occurence localities for Dicranum majus, 8 occurence localities for Polytrichum alpinum). Predictive habitat suitability models can be an effective tool for selecting potential new occurence localities, planning field research or management design. During field research I recorded 34 new occurence localities for Dicranum majus and 29 new occurence localities for Polytrichum alpinum in Bohemian Switzerland National Park. I used 8 topographic parameters derived from digital elevation model with 1 m resolution as environmental data. Using these data I created models of potential distribution of the most suitable habitats for both species with algorithms Artificial neural networks (ANN), Generalised linear model (GLM) and Random forest (RF). RF algorithm had the...
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