Prospects for Integrating Disturbances, Biodiversity and Ecosystem Functioning Using Microbial Systems

Biodiversity is a key driver of ecosystem functioning, while disturbances are a key driver of biodiversity. Consequently, disturbances crucially influence ecosystem functioning– both directly via affecting ecosystem processes but also indirectly via altering biodiversity. We thus need to disclose the joint relationships between disturbances, biodiversity and functioning (DBF) to understand and predict ecosystem dynamics under realistic conditions. However, biodiversity responses to disturbances have so far insufficiently been studied together with biodiversity effects on functions. For many ecosystems, such integrative exploration of DBF relationships would require too extensive manipulations and observations over unfeasible spatial and temporal scales. We argue that microbial systems offer a bright perspective to overcome these limitations, and present a roadmap for doing so. Microbial systems allow us exposing different, well characterized communities to multiple, reproducible disturbance regimes, and precisely measuring both biodiversity and associated functions over time. Comprehensive data can be obtained by systematically varying and replicating representative environmental scenarios. These data can further be explored and explained with computational models. Microbial systems thus reveal mechanisms that underlie DBF relationships and allow scrutinizing ecological hypotheses. This advancement of theory will be essential for ecology as a whole. It is particularly relevant in the context of global change, which is expected to promote disturbances as well as loss of biodiversity and functions in many ecosystem

Individual-based modeling of soil organic matter in NetLogo: transparent, user-friendly, and open

Soil organic matter dynamics are essential for terrestrial ecosystem functions as they affect biogeochemical cycles and, thus, the provision of plant nutrients or the release of greenhouse gases to the atmosphere. Most of the involved processes are driven by microorganisms. To investigate and understand these processes, individual-based models allow analyzing complex microbial systems' behavior based on rules and conditions for individual entities within these systems, taking into account local interactions and individual variations.Postprint (author's final draft

Local buffer mechanisms for population persistence

Assessing and predicting the persistence of populations is essential for the conservation and control of species. Here, we argue that local mechanisms require a better conceptual synthesis to facilitate a more holistic consideration along with regional mechanisms known from metapopulation theory. We summarise the evidence for local buffer mechanisms along with their capacities and emphasise the need to include multiple buffer mechanisms in studies of population persistence. We propose an accessible framework for local buffer mechanisms that distinguishes between damping (reducing fluctuations in population size) and repelling (reducing population declines) mechanisms. We highlight opportunities for empirical and modelling studies to investigate the interactions and capacities of buffer mechanisms to facilitate better ecological understanding in times of ecological upheaval.acceptedVersio

Visualization of causation in social-ecological systems

In social-ecological systems (SES), where social and ecological processes are intertwined, phenomena are usually complex and involve multiple interdependent causes. Figuring out causal relationships is thus challenging but needed to better understand and then affect or manage such systems. One important and widely used tool to identify and communicate causal relationships is visualization. Here, we present several common visualization types: diagrams of objects and arrows, X-Y plots, and X-Y-Z plots, and discuss them in view of the particular challenges of visualizing causation in complex systems such as SES. We use a simple demonstration model to create and compare exemplary visualizations and add more elaborate examples from the literature. This highlights implicit strengths and limitations of widely used visualization types and facilitates adequate choices when visualizing causation in SES. Thereupon, we recommend further suitable ways to account for complex causation, such as figures with multiple panels, or merging different visualization types in one figure. This provides caveats against oversimplifications. Yet, any single figure can rarely capture all relevant causal relationships in an SES. We therefore need to focus on specific questions, phenomena, or subsystems, and often also on specific causes and effects that shall be visualized. Our recommendations allow for selecting and combining visualizations such that they complement each other, support comprehensive understanding, and do justice to the existing complexity in SES. This lets visualizations realize their potential and play an important role in identifying and communicating causation.Peer reviewe

Modelling Bacterial Growth, Dispersal and Biodegradation: An experiment-based modelling study of the spatiotemporal dynamics of bacterial colonies, their responses to dispersal networks, and their performance in degrading organic contaminants

Successful bioremediation of polluted soils is often limited by the bioavailability of organic contaminants to degrading bacteria. Recent studies revealed that fungal hyphae have the potential to promote bacterial dispersal, and thus raised the idea of specifically stimulating the establishment of fungal networks in soils to increase contaminant bioavailability. Can such bacterial dispersal networks improve biodegradation performance considerably? If so, how are the improvements affected by abiotic conditions and by the spatial structure of dispersal networks? This doctoral thesis aims at answering these research questions. To this end, laboratory experiments are performed and a bacterial simulation model is developed, incorporating both microbiological and ecological theory. Manifold simulations and analyses of the microbial ecosystems’ spatiotemporal dynamics under different environmental scenarios reveal key factors and processes controlling biodegradation performance and determining benefits from bacterial dispersal networks

Data from: Spatially structured intraspecific trait variation can foster biodiversity in disturbed, heterogeneous environments

Trait variation within populations is an important area of research for empirical and theoretical ecologists. While differences between individuals are doubtlessly ubiquitous, their role for species coexistence is much less clear and highly debated. Both unstructured (random) and structured (linked to space, time or inheritance) intraspecific trait variation (ITV) may modify species interactions with nontrivial consequences for emerging community compositions. In many ecosystems, these compositions are further driven by prevalent disturbance regimes. I therefore explored the effects of unstructured as well as spatially structured ITV under disturbances in a generic ecological model of competing sessile species. Using spatially explicit, individual-based simulations, I studied how intraspecific variation in life history traits together with interspecific trade-offs and disturbance regimes shape long-term community composition. I found that (i) unstructured ITV does not affect species coexistence in the given context, (ii) spatially structured ITV may considerably increase coexistence, but (iii) spatially clumped disturbances reduce this effect of spatially structured ITV, especially if interspecific trade-offs involve dispersal distance. The findings suggest that spatially structured ITV with individual trait responses to local habitat conditions differing among species may create or expand humps in disturbance-diversity relationships. Hence, if present, these forms of spatially structured ITV should be included in ecological models and will be important for reliably assessing community responses to environmental heterogeneity and change

Simulation data for manuscript 10.1111/oik.05787

Simulation results. All details in readme.txt

Prospects for Integrating Disturbances, Biodiversity and Ecosystem Functioning Using Microbial Systems

Biodiversity is a key driver of ecosystem functioning, while disturbances are a key driver of biodiversity. Consequently, disturbances crucially influence ecosystem functioning– both directly via affecting ecosystem processes but also indirectly via altering biodiversity. We thus need to disclose the joint relationships between disturbances, biodiversity and functioning (DBF) to understand and predict ecosystem dynamics under realistic conditions. However, biodiversity responses to disturbances have so far insufficiently been studied together with biodiversity effects on functions. For many ecosystems, such integrative exploration of DBF relationships would require too extensive manipulations and observations over unfeasible spatial and temporal scales. We argue that microbial systems offer a bright perspective to overcome these limitations, and present a roadmap for doing so. Microbial systems allow us exposing different, well characterized communities to multiple, reproducible disturbance regimes, and precisely measuring both biodiversity and associated functions over time. Comprehensive data can be obtained by systematically varying and replicating representative environmental scenarios. These data can further be explored and explained with computational models. Microbial systems thus reveal mechanisms that underlie DBF relationships and allow scrutinizing ecological hypotheses. This advancement of theory will be essential for ecology as a whole. It is particularly relevant in the context of global change, which is expected to promote disturbances as well as loss of biodiversity and functions in many ecosystem

Prospects for Integrating Disturbances, Biodiversity and Ecosystem Functioning Using Microbial Systems

Biodiversity is a key driver of ecosystem functioning, while disturbances are a key driver of biodiversity. Consequently, disturbances crucially influence ecosystem functioning– both directly via affecting ecosystem processes but also indirectly via altering biodiversity. We thus need to disclose the joint relationships between disturbances, biodiversity and functioning (DBF) to understand and predict ecosystem dynamics under realistic conditions. However, biodiversity responses to disturbances have so far insufficiently been studied together with biodiversity effects on functions. For many ecosystems, such integrative exploration of DBF relationships would require too extensive manipulations and observations over unfeasible spatial and temporal scales. We argue that microbial systems offer a bright perspective to overcome these limitations, and present a roadmap for doing so. Microbial systems allow us exposing different, well characterized communities to multiple, reproducible disturbance regimes, and precisely measuring both biodiversity and associated functions over time. Comprehensive data can be obtained by systematically varying and replicating representative environmental scenarios. These data can further be explored and explained with computational models. Microbial systems thus reveal mechanisms that underlie DBF relationships and allow scrutinizing ecological hypotheses. This advancement of theory will be essential for ecology as a whole. It is particularly relevant in the context of global change, which is expected to promote disturbances as well as loss of biodiversity and functions in many ecosystem

Individual-based modeling of soil organic matter in NetLogo: Transparent, user-friendly, and open

Soil organic matter dynamics are essential for terrestrial ecosystem functions as they affect biogeochemical cycles and, thus, the provision of plant nutrients or the release of greenhouse gases to the atmosphere. Most of the involved processes are driven by microorganisms. To investigate and understand these processes, individual-based models allow analyzing complex microbial systems' behavior based on rules and conditions for individual entities within these systems, taking into account local interactions and individual variations