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Group Overview

The computational ecology group seeks to develop innovative models and data analysis techniques to better understand complex ecological systems. The current main research focus of the lab is on the interplay between ecological and evolutionary mechanisms that enable the assembly of complex networks of ecological interactions. An important aspect of this research is the spatial context on which communities assemble. This is incorporated into models using a meta-community framework in which local patches represent communities defined by complex ecological interaction networks.

This research takes advantage of a mixed empirical-theoretical approximation in which theoretical ecological models of species interactions are combined with analysis of big data (e.g. networks with up to half a million nodes) to formulate theoretical models and test predictions. Within this framework, research in the lab focuses on the application of dynamical systems models, such as systems of ordinary differential equations or individual-based models, incorporating both ecological dynamics of species interactions and evolution through random change and selection. The aim of this is to construct a comprehensive body of theory that will allow to (1) study the formation (assembly) of complex communities from an evolutionary perspective, and (2) predict the effects of exogenous disturbances on these communities (disassembly).

The study of complex ecological systems from a computational perspective has also enabled research within the group that ranges from the study of the spatial scaling of biodiversity and its architecture (e.g. Galiana et al. 2018, Nat. Ecol. Evol.), through an understanding of the effects of space and multiple interaction types on ecological stability (e.g. Lurgi et al. 2016a, Theor. Ecol.), and the effects of climate warming on ecological communities (e.g. Lurgi et al. 2012a-b, Philos. Trans. Royal Soc. B), to a better understanding of the microbiome associated to multicellular hosts (e.g. Thomas et al. 2016, Nat. Commun.; Lurgi et al. 2019, Nat. Commun.).

An important research axis within the group focuses on biological conservation. In particular, we are interested in developing applied tools and enhance our understanding of different aspects related to biological conservation using ecological theory as a backbone. Research in this area has produced a meta-population framework to better understand the effects of the spatial configuration of local communities of invasive species when responding to management actions. This framework was first applied to obtain a better understanding of European rabbit (Oryctolagus cuniculus) eradication in Australia (Lurgi et al. 2016b, PLoS One), and more recently to brushtail possum (Trichosurus vulpecula) eradication in New Zealand (García-Díaz et al. 2019, Landscape Ecol.).

An important challenge in biological conservation is to adopt a multi-species community perspective. Threatened and invasive species are often embedded in complex webs of interactions, and hence their removal can have profound, indirect consequences over other species in the network. This challenge has encouraged further research within the group. In particular, we aim at developing dynamical multi-species networks models to better predict the effects of biological invasions on complex communities (e.g. Lurgi et al. 2014, Front. Ecol. Evol.; Galiana et al. 2014, Oikos); or the effect conservation actions such as invasive species eradication, as it is the case of the European rabbit in Australia (Lurgi et al. 2018, J. Appl. Ecol.). Frameworks such as this can also be used to determine the cascading effects of eradicating top predators (Wallach et al. 2017, Methods Ecol. Evol.), a recurrent topic in our research.

To deal with all the challenges associated to the study of complex ecological communities we use a combination of complex networks analysis, geographical information systems (GIS), dynamical modelling of spatial networks and ecological interactions networks, and statistical modelling and analysis.