Modeling the assembly and disassembly of complex ecological systems
Dr Miguel Lurgi, Swansea University
https://computational-ecology-lab.git...
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Abstract:
Understanding the factors that influence the persistence and stability of complex ecological networks is a central focus of ecology. Research into these factors has attempted to unveil the ecological processes and network structural constraints modulating ecosystem stability using modellng approaches. Mechanistic models of community assembly in turn play a fundamental role in obtaining a better understanding of the potential effects of different aspects of global change, such as habitat loss, warming, and invasions, on complex ecosystems.
In parallel, attention has also been given to understanding the consequences of evolution within complex ecological settings, as the interplay between ecology and evolution has been recognised as fundamental to understand the formation of ecological communities. The development of eco-evolutionary theory and models would ultimately allow us to obtain a comprehensive understanding of the drivers of assembly and disassembly of natural communities.
In this talk I will present a few modeling approaches we have developed over the last few years to better understand the drivers of assembly in species interactions networks. Using a spatially-explicit, individual-based model we explored how spatial dynamics and a mixture of interaction types affect the stability of complex food webs. This model is then put into practice to investigate the effects of habitat loss and fragmentation on the composition and stability of food webs. Our results show how different types of habitat loss affect ecosystem stability in different ways. These effects are mediated by changes in the strength of interactions between species.
Switching to a mean-field approach to community dynamics, I will present our findings on how the synergistic effects of increases in temperature and invasions impact the structure and stability of complex food webs. In the last part of the talk, I will extend this type of network dynamics model into the evolutionary realm. Using a mutualistic population dynamical model incorporating evolutionary adaptation events, we relate ecological aspects of mutualistic community stability to the stability of evolutionary pathways that allow their persistence.
This research illustrates the pivotal role of dynamical modeling in obtaining a better understanding of ecological community assembly and in developing tools to predict the potential effects of anthropogenic environmental change on complex ecosystems.
Bio:
Miguel obtained his PhD in Terrestrial Ecology from the Autonomous University of Barcelona in 2014. He developed his doctoral research jointly at the Marine Sciences Institute (ICM-CSIC) and the Centre for Ecological Research and Forestry Applications (CREAF). After his PhD, Miguel completed his postdoctoral training at the University of Adelaide, Australia (2014-2016) and the Theoretical and Experimental Ecology Station of the CNRS, France (2016-2019). Since 2019 he leads the Computational Ecology Lab at Swansea University, UK where he is an Associate Professor.
Miguel is interested in obtaining a better understanding of the ecological and evolutionary mechanisms behind the assembly of complex species interaction networks across spatial scales and ecosystem types. Further, he is interested in applying this knowledge to understand the impacts of different aspects of global change on complex ecosystems. To tackle these challenges, he studies a variety of systems at various scales, from microbial communities to continent-wide food webs. His research combines analyses of complex datasets of species distributions and interactions with the development of theoretical models of community assembly incorporating ecological and evolutionary processes. This approach ultimately seeks to contribute to the mechanistic understanding of large-scale biodiversity patterns and how they break in the face of environmental perturbations.
#modeling #simulation #climatechange #remotesensing #ml #ai #ecology #biodiversity #foodweb #ecological #habitats #habitatloss #complexsystems