Postdoc, University of Chicago
The study of how ecological systems interact and change in space and time
Ecological systems are complex: the contain many organisms which interact in multiple ways. Predator-prey, parasitism and mutualism are just a few examples. A bee which pollinates a flower is also a prey of spiders. Ecological systems are also dynamic and constantly change in space and time. These multiple dimensions are particularly challenging and require adequate mathematical frameworks which consider all the components of the system, and their interactions. Therefore, in the past 3 decades, the study of networks has been a fundamental component of population and community ecology. But ecological networks have been typically studied in isolation – that is, they involve a single type of interaction or represent a particular community that is not connected to others. Recent advances in the field of multilayer networks in network science, together with an increase in the availability of adequate ecological data, now provide an exciting opportunity for ecologists to move beyond studies of isolated networks. This direction entails an explicit integration of multilayer networks into network ecology. Ecological multilayer networks enable us to explore how complex ecological systems interact (what is the effect of bee predation for the population of the flower?), as well as their intrinsic dynamics. They also enable us to explore multiple levels: from the gene to the ecosystem. In this emerging field, ecologists aim to explore ecological variation across interconnected ecological networks such as those interconnected in space, time, or involving several interaction types. Of particular importance is to understand how highly-dimensinoal ecological systems respond to perturbations: from antibiotics in bacterial communities to human interference in large-scale ecosystems.
Abstract: Networks provide a powerful approach to study a variety of ecological systems, but their formulation does not typically account for multiple interaction types, for interactions that vary in space and time, or for interconnected systems such as networks of species networks. The emergent field of 'multilayer networks' provides a natural framework for extending analyses of ecological systems to include such multiple layers of complexity, as it allows one to differentiate between and simultaneously model 'intralayer' and 'interlayer' connectivity. The framework provides a set of concepts and tools that can be adapted and applied to ecology, facilitating research on high-dimensional, heterogeneous systems in nature. Here, we formally define ecological multilayer networks based on a review of previous related approaches, illustrate their application and potential insights with analyses of existing data, and discuss limitations, challenges, and future applications. The integration of multilayer network theory into ecology offers an exciting perspective to tackle ecological complexity, with the potential to provide new theoretical and empirical insights into the architecture and dynamics of ecological systems.
Pub.: 11 Jul '16, Pinned: 29 Jun '17
Abstract: Species are linked to each other by a myriad of positive and negative interactions. This complex spectrum of interactions constitutes a network of links that mediates ecological communities' response to perturbations, such as exploitation and climate change. In the last decades, there have been great advances in the study of intricate ecological networks. We have, nonetheless, lacked both the data and the tools to more rigorously understand the patterning of multiple interaction types between species (i.e., "multiplex networks"), as well as their consequences for community dynamics. Using network statistical modeling applied to a comprehensive ecological network, which includes trophic and diverse non-trophic links, we provide a first glimpse at what the full "entangled bank" of species looks like. The community exhibits clear multidimensional structure, which is taxonomically coherent and broadly predictable from species traits. Moreover, dynamic simulations suggest that this non-random patterning of how diverse non-trophic interactions map onto the food web could allow for higher species persistence and higher total biomass than expected by chance and tends to promote a higher robustness to extinctions.
Pub.: 04 Aug '16, Pinned: 29 Jun '17
Abstract: Understanding species' interactions and the robustness of interaction networks to species loss is essential to understand the effects of species' declines and extinctions. In most studies, different types of networks (such as food webs, parasitoid webs, seed dispersal networks, and pollination networks) have been studied separately. We sampled such multiple networks simultaneously in an agroecosystem. We show that the networks varied in their robustness; networks including pollinators appeared to be particularly fragile. We show that, overall, networks did not strongly covary in their robustness, which suggests that ecological restoration (for example, through agri-environment schemes) benefitting one functional group will not inevitably benefit others. Some individual plant species were disproportionately well linked to many other species. This type of information can be used in restoration management, because it identifies the plant taxa that can potentially lead to disproportionate gains in biodiversity.
Pub.: 01 Mar '12, Pinned: 29 Jun '17