Spatial ecology of species interactions
Why is there variation in the way species interact over space or time? We analyse spatially and temporally replicated datasets of surveys of species interactions to measure the variation in the structure of ecological networks, and describe its dynamics. This research reveals an important aspect of the dissimilarity of ecological communities: the same species can interact in different ways. This opens a lot of stimulating questions. Do species and interactions respond to the same environmental cues and variables? Which are more informative? What is the spatial scale at which variations ceases to matter? How much of this variation is random?
Synthetic datasets and open ecological data
We believe that open data are a treasure trove of knowledge that has not been entirely used yet. We are working on understanding the type of problems that we can solve without having to do new sampling, and how to handle errors and uncertainty. As a bonus, we use the tools we develop to improve the monitoring and reporting abilities of various stakeholders. One of our key question related to this topic at the moment is to examine the “suitability” of existing data to address a given question. We work on measures to identify areas in which information is low, which should be sampled with a higher priority.
Predictive approaches of network structure
Can we predict inter-specific interactions? We are interested in developing predictive models that would use different sources of information (functional traits, local abundances, previous knowledge, …) to predict the probability that two species will interact. These models are required to forecast the structure of novel communities that will emerge through global changes. They also require to develop new tools to merge data, and improved statistical models to fill in the gaps between the different datasets.
Numerical toolkits for network analysis
Addressing these new questions often requires to develop new tools. We develop statistical and mathematical approaches, implement them, and release them as free and open source software to make network analysis user-friendly. We also contribute to the mangal.io project, an open database of species interactions and associated packages. We think a lot about what the best practices for scientific software should be, and do a lot of training. We sincerely believe that good science requires good tools, and we want to help everyone build and use them.
Functional consequences of network structure
We are interested in turning the structure of networks into a predictive variable for community ecology. We are particularly interested in the relationship between network structure and ecosystem functioning, but also work on landscape connectivity, and the transmission of both ideas and diseases. Our flagship project on this theme is to develop networks of habitats patches, to help populations move across highly fragmented areas.