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Project

Path dependency of population and community dynamics in heterogeneous landscapes: a theoretical and statistical approach

Understanding the processes that drive the distribution of genes and species in heterogeneous landscapes is a central goal in biology. However, the proportion of the patterns in gene or species distribution that can be explained is often limited. Path dependency results in historical contingencies in current-day patterns and might partly be responsible for our limited capacity to explain gene and species occurrences. In the proposed research, I aim at understanding such mechanisms and their influence on ecological and evolutionary processes, in the context of landscapes that undergo changes. First, I will use mathematical models to determine whether sustained environmental perturbations can lead to unexpected transitions, so-called regime shifts, in the degree of adaptation in populations. This would imply that populations can either exist in a well- or maladapted state, depending on the governing conditions, their historical trajectory and the surrounding landscape. Second, I will determine whether these regime shifts can cascade through other species co-occurring in the same community, invoking them to also undergo adaptational regime shift as a result of such an event in another community member. Finally, I will use statistical models to detect signatures of historical contingencies in existing datasets. In addition to the analysis of rigorously collected datasets, I will also make use of citizen science datasets with a very large resolution through space and time.
 

Date:1 Nov 2019 →  5 Nov 2023
Keywords:environmental change, eco-evolutionary dynamics, spatial ecology, hierarchical modelling, citizen science, local adaptation
Disciplines:Community ecology, Evolutionary biology not elsewhere classified, Conservation and biodiversity, Landscape ecology
Project type:PhD project