Dynasym

Interactions between species are ubiquitous in nature and determine ecosystem properties and processes. Mechanistic insights about these relationships are required to understand the processes structuring ecological communities. Most interactions can be summarized as symbiotic interactions, which refer to the close association of two or more different species living in close proximity. They can take different forms and include parasitic, commensal, mutualistic, competitive, and predator-prey relationships. Contrary to the classical assumption in community ecology, the form of symbiosis is not always fixed and can shift along different continua depending on the context. One important context that determines symbiosis form is partner density. Other context dependences that have been shown include resource availabilities, abiotic conditions such as temperature, or the presence and density of other interacting species. A shift in symbiosis form may have consequences for population dynamics, for example when the interaction shifts from antagonism to mutualism. Whether and how the symbiosis form may affect population dynamics and vice versa is not yet understood. Furthermore, as density is one factor that can lead to context dependent symbiosis forms, and the symbiosis form can affect the densities in return, there is also a possibility for feedbacks between density and symbiosis form. Developing an understanding of density-dependent symbiosis form and population dynamics requires more detailed information on i) symbiosis form and their shifts, ii) the processes that determine symbiosis form and shifts, iii) the context in which these shifts can be observed, iv) how the context dependent symbiosis form determines population dynamics and v) a general framework for studying density-symbiosis feedback. While some information can be found in the literature, we currently lack a systematic overview of this information. This project aims to synthesize current knowledge and results of the research unit, to test and apply analysis tools for time series data to detect shifts in symbiosis form, and to develop novel concepts based on the syntheses and on modelling.