Final Report Summary - SPAECO (Spatial ecology: bringing mathematical theory and data together)
The project acronym SPAECO stands for "Spatial ecology: bringing mathematical theory and data together". The overall aim of the project was to make progress in the understanding of the ecological and evolutionary dynamics of populations inhabiting the heterogeneous and changing landscapes of the real world. To reach this goal, we construct general and mathematically rigorous theories and develop novel statistical approaches to link the theories to data.
The project was highly successful in terms of research activity. Since 2009, we have published 35 papers in peer-reviewed international journals, 2 papers in peer-reviewed edited books, in addition to which ca. 20 manuscripts with substantial contribution from the ERC project have been submitted or are in preparation. Five PhD students of the SPAECO team have defended their thesis during the project. As summarized below, the conducted research reflects very closely the original research plan.
In the mathematical part of the project, we constructed and analyzed spatial and stochastic individual-based models formulated as spatiotemporal point processes. Much of our research focused on the interplay between landscape structure and the processes of local adaptation and evolution of dispersal. Our results show how the feedback between ecological and evolutionary dynamics is influenced by the properties of the species (e.g. genetic architecture of the traits under selection) and on the nature (e.g. stability) of population dynamics. The theory includes testable predictions e.g. on the influence of habitat loss and fragmentation on evolutionary processes. As knowledge transfer, we brought mathematical methods from probability theory and theoretical physics to biology - these methods can be used to study spatio-temporal population dynamics and in particular population extinction and other large fluctuations. As one technical achievement, we developed eigenvalue perturbation methods for spatiotemporal point processes - these are needed evolutionary analyses in the spatial and stochastic context.
In terms of statistical methods, we made progress in three different fronts. First, we developed models of animal movement and methods for their parameterization. We analyzed movement data on diverse taxonomical groups (e.g. butterflies and wolves). As a conceptual development, we derived the characteristic spatial and temporal scales of movement as a unifying currency that enables one to compare structurally different movement models. Second, we developed a mathematical framework and associated statistical methods to address questions in evolutionary quantitative genetics, e.g. whether an observed pattern of population divergence has resulted from neutral genetic drift or from natural selection. Third, we developed hierarchical Bayesian approaches that can be used to analyze data on species communities, including patterns of non-random co-occurrence among the species, and the analysis of large but sparse datasets dominated by rare species.
We continued the strong interaction between empirical studies and modelling in the Glanville fritillary butterfly. We built and parameterized an individual-based model that describes the (meta)population dynamics of this species in a network of some 4,000 habitat patches. We studied in detail the interplay between genotypic and phenotypic variability, focusing on dispersal-related traits. In a metacommunity study of wood-decaying fungi, we combined conventional fruit body inventories with high throughput sequencing to study the relationships between mycelial and fruit body occurrences in the field. We showed that specialist species have an especially low fruiting rate, and thus they are not as rare at the mycelial stage than at the fruit-body stage. This implies that the main bottleneck for many of the threatened species is fruit-body formation rather than colonization.
As a summary, we have addressed a wide range of research questions in ecology and evolutionary biology, the approaches ranging from empirical to theoretical. The project has demonstrated the value of interdisciplinary research: the involvement of a mathematical component can bring additive value to research in biology.
The project was highly successful in terms of research activity. Since 2009, we have published 35 papers in peer-reviewed international journals, 2 papers in peer-reviewed edited books, in addition to which ca. 20 manuscripts with substantial contribution from the ERC project have been submitted or are in preparation. Five PhD students of the SPAECO team have defended their thesis during the project. As summarized below, the conducted research reflects very closely the original research plan.
In the mathematical part of the project, we constructed and analyzed spatial and stochastic individual-based models formulated as spatiotemporal point processes. Much of our research focused on the interplay between landscape structure and the processes of local adaptation and evolution of dispersal. Our results show how the feedback between ecological and evolutionary dynamics is influenced by the properties of the species (e.g. genetic architecture of the traits under selection) and on the nature (e.g. stability) of population dynamics. The theory includes testable predictions e.g. on the influence of habitat loss and fragmentation on evolutionary processes. As knowledge transfer, we brought mathematical methods from probability theory and theoretical physics to biology - these methods can be used to study spatio-temporal population dynamics and in particular population extinction and other large fluctuations. As one technical achievement, we developed eigenvalue perturbation methods for spatiotemporal point processes - these are needed evolutionary analyses in the spatial and stochastic context.
In terms of statistical methods, we made progress in three different fronts. First, we developed models of animal movement and methods for their parameterization. We analyzed movement data on diverse taxonomical groups (e.g. butterflies and wolves). As a conceptual development, we derived the characteristic spatial and temporal scales of movement as a unifying currency that enables one to compare structurally different movement models. Second, we developed a mathematical framework and associated statistical methods to address questions in evolutionary quantitative genetics, e.g. whether an observed pattern of population divergence has resulted from neutral genetic drift or from natural selection. Third, we developed hierarchical Bayesian approaches that can be used to analyze data on species communities, including patterns of non-random co-occurrence among the species, and the analysis of large but sparse datasets dominated by rare species.
We continued the strong interaction between empirical studies and modelling in the Glanville fritillary butterfly. We built and parameterized an individual-based model that describes the (meta)population dynamics of this species in a network of some 4,000 habitat patches. We studied in detail the interplay between genotypic and phenotypic variability, focusing on dispersal-related traits. In a metacommunity study of wood-decaying fungi, we combined conventional fruit body inventories with high throughput sequencing to study the relationships between mycelial and fruit body occurrences in the field. We showed that specialist species have an especially low fruiting rate, and thus they are not as rare at the mycelial stage than at the fruit-body stage. This implies that the main bottleneck for many of the threatened species is fruit-body formation rather than colonization.
As a summary, we have addressed a wide range of research questions in ecology and evolutionary biology, the approaches ranging from empirical to theoretical. The project has demonstrated the value of interdisciplinary research: the involvement of a mathematical component can bring additive value to research in biology.