{"date_updated":"2023-09-07T13:11:39Z","has_accepted_license":"1","author":[{"full_name":"Szep, Eniko","first_name":"Eniko","last_name":"Szep","id":"485BB5A4-F248-11E8-B48F-1D18A9856A87"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","year":"2020","_id":"8574","alternative_title":["ISTA Thesis"],"doi":"10.15479/AT:ISTA:8574","publication_status":"published","month":"09","citation":{"short":"E. Szep, Local Adaptation in Metapopulations, Institute of Science and Technology Austria, 2020.","ieee":"E. Szep, “Local adaptation in metapopulations,” Institute of Science and Technology Austria, 2020.","apa":"Szep, E. (2020). <i>Local adaptation in metapopulations</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:8574\">https://doi.org/10.15479/AT:ISTA:8574</a>","ama":"Szep E. Local adaptation in metapopulations. 2020. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8574\">10.15479/AT:ISTA:8574</a>","chicago":"Szep, Eniko. “Local Adaptation in Metapopulations.” Institute of Science and Technology Austria, 2020. <a href=\"https://doi.org/10.15479/AT:ISTA:8574\">https://doi.org/10.15479/AT:ISTA:8574</a>.","mla":"Szep, Eniko. <i>Local Adaptation in Metapopulations</i>. Institute of Science and Technology Austria, 2020, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8574\">10.15479/AT:ISTA:8574</a>.","ista":"Szep E. 2020. Local adaptation in metapopulations. Institute of Science and Technology Austria."},"ddc":["570"],"status":"public","date_created":"2020-09-28T07:33:38Z","language":[{"iso":"eng"}],"degree_awarded":"PhD","file_date_updated":"2020-09-28T07:25:37Z","publication_identifier":{"eissn":["2663-337X"]},"article_processing_charge":"No","title":"Local adaptation in metapopulations","day":"20","page":"158","type":"dissertation","supervisor":[{"full_name":"Barton, Nicholas H","first_name":"Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","last_name":"Barton","orcid":"0000-0002-8548-5240"}],"publisher":"Institute of Science and Technology Austria","file":[{"access_level":"open_access","success":1,"file_id":"8575","file_name":"thesis_EnikoSzep_final.pdf","relation":"main_file","content_type":"application/pdf","file_size":6354833,"date_created":"2020-09-28T07:25:35Z","date_updated":"2020-09-28T07:25:35Z","creator":"dernst","checksum":"20e71f015fbbd78fea708893ad634ed0"},{"file_name":"thesisFiles_EnikoSzep.zip","relation":"source_file","content_type":"application/x-zip-compressed","file_size":23020401,"date_created":"2020-09-28T07:25:37Z","date_updated":"2020-09-28T07:25:37Z","creator":"dernst","checksum":"a8de2c14a1bb4e53c857787efbb289e1","access_level":"closed","file_id":"8576"}],"department":[{"_id":"NiBa"}],"oa_version":"Published Version","date_published":"2020-09-20T00:00:00Z","oa":1,"abstract":[{"lang":"eng","text":"This thesis concerns itself with the interactions of evolutionary and ecological forces and the consequences on genetic diversity and the ultimate survival of populations. It is important to understand what signals processes \r\nleave on the genome and what we can infer from such data, which is usually abundant but noisy. Furthermore, understanding how and when populations adapt or go extinct is important for practical purposes,  such as the genetic management of populations, as well as for theoretical questions, since local adaptation can be the first step toward speciation. \r\nIn Chapter 2, we introduce the method of maximum entropy to approximate the demographic changes of a population in a simple setting, namely the logistic growth model with immigration. We show that this method is not only a powerful \r\ntool in physics but can be gainfully applied in an ecological framework. We investigate how well it approximates the real \r\nbehavior of the system, and find that is does so, even in unexpected situations. Finally, we illustrate how it can model changing environments.\r\nIn Chapter 3, we analyze the co-evolution of allele frequencies and population sizes in an infinite island model.\r\nWe give conditions under which polygenic adaptation to a rare habitat is possible. The model we use is based on the diffusion approximation, considers eco-evolutionary feedback mechanisms (hard selection), and treats both \r\ndrift and environmental fluctuations explicitly. We also look at limiting scenarios, for which we derive analytical expressions. \r\nIn Chapter 4, we present a coalescent based simulation tool to obtain patterns of diversity in a spatially explicit subdivided population, in which the demographic history of each subpopulation can be specified. We compare \r\nthe results to existing predictions, and explore the relative importance of time and space under a variety of spatial arrangements and demographic histories, such as expansion and extinction. \r\nIn the last chapter, we give a brief outlook to further research. "}]}