My Academic Production

Studies in microbiology have long been mostly restricted to small spatial scales. However‚ recent technological advances‚ such as new sequencing methodologies‚ have ushered an era of large-scale sequencing of environmental DNA data from multiple biomes worldwide. These global datasets can now be used to explore long standing questions of microbial ecology. New methodological approaches and concepts are being developed to study such large-scale patterns in microbial communities‚ resulting in new perspectives that represents a significant advances for both microbiology and macroecology. Here‚ we identify and review important conceptual‚ computational‚ and methodological challenges and opportunities in microbial macroecology. Specifically‚ we discuss the challenges of handling and analyzing large amounts of microbiome data to understand taxa distribution and co-occurrence patterns. We also discuss approaches for modeling microbial communities based on environmental data‚ including information on biological interactions to make full use of available Big Data. Finally‚ we summarize the methods presented in a general approach aimed to aid microbiologists in addressing fundamental questions in microbial macroecology‚ including classical propositions (such as “everything is everywhere‚ but the environment selects”) as well as applied ecological problems‚ such as those posed by human induced global environmental changes.

Size spectra are important indicators of biomass distribution‚ for fundamental understanding as well as for application in management. While many size spectra models have been developed‚ it is unclear how response to stress varies across models and in comparison to data. In the present study‚ we therefor collected papers on empirical size spectra in stressed aquatic ecosystems and compared the results to theoretical models’ predictions from literature. A general decrease of the slope‚ as suggested by most of the studied models‚ is supported by the data found. The investigated empirical studies revealed a strong trend for the slope: 40 out of 57 studies reported a decrease with stress. Deviations from the linear model increased in 10 out of 20 cases and the intercept became larger in 25 out of 43 cases. The data concerning specific impact drivers is generally consistent and supportive of current expert opinions. Our results support that an integration of deviation from the linear model and intercept in the size spectra models could provide better coupling between modeling and field surveys for some stressors.

To understand ecological communities‚ we must understand the complex dynamical patterns that emerge from population interactions. Indeed‚ ecological communities are non-randomly assembled‚ and the interactions between populations have distinct architectures or topologies. Through this structure of interactions‚ populations and species influence each other’s growth in an intricate manner. These dynamics unveils itself in community-wide emergent properties‚ such as community stability. During the last century‚ many central advancements in ecology stemmed from mathematical methods based on dynamical systems theory. More recently‚ the representation of ecological communities as complex networks‚ such as food webs and competitive or mutualistic networks‚ provided additional insights into the ecology of communities. Several authors contributed to our current knowledge on the interplay between the structure and dynamics of complex ecosystems. Despite the extensive developments in this area‚ many questions are not yet settled. Notably‚ the distribution of each type of dynamics (attractors) in ecological models was seldomly studied in favor of the assumption of equilibrium. Additionally‚ an approach exploring the ubiquity of known results linking community stability to structurerelated parameters in the parameter space is of great importance. In this study‚ we aim at contributing to this endeavor by performing simulations based on the generalized Lotka-Volterra model (gLV) across a wide parameter space range. In accordance‚ our research objective is to present an overview of the gLV asymptotic dynamics‚ coexistence‚ and resilience by performing a comprehensive computational exploration integrating variability in type‚ intensity‚ and distribution of interspecific ecological interactions. We found that different dynamics of ecological communities can be described by specific parametrizations of the gLV‚ despite a large prevalence of parameter combinations leading to unlimited growth of some populations in the community. We were also able to detect known patterns‚ such as the negative impact of cooperation upon the resilience of communities and the occurrence of competitive exclusion in speciose communities. Significantly‚ our research contributes to the longstanding question of the interconnections between the structure and stability of ecological communities.

As our world faces ever-increasing pressure upon many natural environments‚ it is essential to understand the stability of ecological communities. One of the crucial aspects of stability in rich communities is resilience‚ which entails information on how quickly the community can recover from small fluctuations in the densities of the populations. Equally relevant is their feasibility‚ which is indicative of how likely all the populations in the community can coexist‚ on the assumption that relative growth rates are variable. Despite substantial advancements in the investigation of these measures of stability‚ their interplay remains largely unexplored. In this work‚ we performed a comprehensive ecologically-informed exploration of the parameter space of the generalized Lotka-Volterra model integrating variability in type‚ intensity‚ and distribution of interspecific ecological interactions to study the broad patterns linking these two aspects of stability. We found a positive correlation between resilience and feasibility‚ suggesting that more resilient communities are more likely to be feasible. Additionally‚ we also found that communities with lower densities and intensities of interactions and more competition/exploitation are more resilient‚ and communities with equal proportions of positive and negative interactions are more feasible. Our study highlights the importance of investigations integrating different aspects of ecological stability.