Diversity dynamics across scales
| Thesis title in Czech: | Dynamika diverzity napříč škálami |
|---|---|
| Thesis title in English: | Diversity dynamics across scales |
| Key words: | phylogeny, ecology, diversification, statistics |
| English key words: | phylogeny, ecology, diversification, statistics |
| Academic year of topic announcement: | 2012/2013 |
| Thesis type: | dissertation |
| Thesis language: | angličtina |
| Department: | Department of Ecology (31-162) |
| Supervisor: | prof. David Storch, Ph.D. |
| Author: | hidden - assigned by the advisor |
| Date of registration: | 20.10.2012 |
| Date of assignment: | 20.10.2012 |
| Date and time of defence: | 21.06.2018 15:00 |
| Date of electronic submission: | 13.03.2018 |
| Date of proceeded defence: | 21.06.2018 |
| Opponents: | prof. Mgr. Vladimír Remeš, Ph.D. |
| prof. Robert Ricklefs | |
| Preliminary scope of work |
| (1) Motivation
Integration of phylogenetic and ecological research represents one of the most promising challenges in contemporary biology. The growing amount of molecular data, publicly accessible databases of species distributions and life histories pose novel and highly intriguing biological questions. By combining the approaches of biogeography, phylogenetics, and statistics, we can address these novel questions and gain a more profound insight in the phenomena that have long attracted biologists’ attention (e.g. biodiversity gradients, rates of diversification, evolution of life histories). However, bringing all this available information together often is a challenging task. For instance, biogeographic data are generally strongly spatially autocorrelated while data on species traits are phylogenetically dependent. In these cases, conventional statistics cannot be applied, and the correlation structure in the data must be taken into account. Reconstruction of ancestral states, analyses of evolutionary diversification, inference of speciation and extinction events, reconstructing historical dispersals and vicariance, all these critical approaches call for specific analyses that explicitly incorporate evolutionary models and/or spatial autocorrelation. Therefore, a number of novel methods have been designed recently in order to address these effects within likelihood and Bayesian frameworks. (2) Objectives The dissertation will critically summarize these progressive methods which currently emerge in the field of evolutionary ecology. The primary focus of the dissertation will be the analyses that combine phylogenetic data and GIS: advanced evolutionary comparative analyses (PVR, GLS), analyses of diversification rates (LASER, GEIGER), historical biogeography (LAGRANGE, DIVA), trait evolution (PIC, ACE, JUMP). Individual methods will be introduced and their advantages as well as pitfalls critically discussed. Selected methods will be applied to specific biological problems that are in the spotlight of current ecological and evolutionary research (e.g. analysis of mechanistic determinants of biodiversity patterns, niche evolution and niche conservatism, analysis of evolutionary diversification, reconstruction of life -histories). (3) Methods Modern methods of comparative evolutionary analysis, historical biogeography, trait evolution, species distribution modeling, diversification analysis will be employed. The methods are implemented in relevant ‘R’ packages, such as ape, ouch, phytools, dismo, diversitree, etc. Mr. Machac is skilled in statistics and R programming. Computationally exhaustive algorithms will be executed at the MetaCentrum computer cluster. |
| Preliminary scope of work in English |
| (1) Motivation
Integration of phylogenetic and ecological research represents one of the most promising challenges in contemporary biology. The growing amount of molecular data, publicly accessible databases of species distributions and life histories pose novel and highly intriguing biological questions. By combining the approaches of biogeography, phylogenetics, and statistics, we can address these novel questions and gain a more profound insight in the phenomena that have long attracted biologists’ attention (e.g. biodiversity gradients, rates of diversification, evolution of life histories). However, bringing all this available information together often is a challenging task. For instance, biogeographic data are generally strongly spatially autocorrelated while data on species traits are phylogenetically dependent. In these cases, conventional statistics cannot be applied, and the correlation structure in the data must be taken into account. Reconstruction of ancestral states, analyses of evolutionary diversification, inference of speciation and extinction events, reconstructing historical dispersals and vicariance, all these critical approaches call for specific analyses that explicitly incorporate evolutionary models and/or spatial autocorrelation. Therefore, a number of novel methods have been designed recently in order to address these effects within likelihood and Bayesian frameworks. (2) Objectives The dissertation will critically summarize these progressive methods which currently emerge in the field of evolutionary ecology. The primary focus of the dissertation will be the analyses that combine phylogenetic data and GIS: advanced evolutionary comparative analyses (PVR, GLS), analyses of diversification rates (LASER, GEIGER), historical biogeography (LAGRANGE, DIVA), trait evolution (PIC, ACE, JUMP). Individual methods will be introduced and their advantages as well as pitfalls critically discussed. Selected methods will be applied to specific biological problems that are in the spotlight of current ecological and evolutionary research (e.g. analysis of mechanistic determinants of biodiversity patterns, niche evolution and niche conservatism, analysis of evolutionary diversification, reconstruction of life -histories). (3) Methods Modern methods of comparative evolutionary analysis, historical biogeography, trait evolution, species distribution modeling, diversification analysis will be employed. The methods are implemented in relevant ‘R’ packages, such as ape, ouch, phytools, dismo, diversitree, etc. Mr. Machac is skilled in statistics and R programming. Computationally exhaustive algorithms will be executed at the MetaCentrum computer cluster. |