Predicting the effect of habitat modification on networks of interacting species

Phillip P. A. Staniczenko (), Owen T. Lewis, Jason M. Tylianakis, Matthias Albrecht, Valérie Coudrain, Alexandra-Maria Klein and Felix Reed-Tsochas
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Phillip P. A. Staniczenko: National Socio-Environmental Synthesis Center (SESYNC)
Owen T. Lewis: University of Oxford, South Parks Road
Jason M. Tylianakis: Centre for Integrative Ecology, School of Biological Sciences, University of Canterbury
Matthias Albrecht: Institute for Sustainability Sciences, Agroscope
Valérie Coudrain: Mediterranean Institute of Marine and Terrestrial Biodiversity and Ecology, Aix-Marseille University, University of Avignon, CNRS, IRD, IMBE
Alexandra-Maria Klein: Chair of Nature Conservation and Landscape Ecology, Faculty of Environment and Natural Resources, University of Freiburg
Felix Reed-Tsochas: CABDyN Complexity Centre, Saïd Business School, University of Oxford

Nature Communications, 2017, vol. 8, issue 1, 1-10

Abstract: Abstract A pressing challenge for ecologists is predicting how human-driven environmental changes will affect the complex pattern of interactions among species in a community. Weighted networks are an important tool for studying changes in interspecific interactions because they record interaction frequencies in addition to presence or absence at a field site. Here we show that changes in weighted network structure following habitat modification are, in principle, predictable. Our approach combines field data with mathematical models: the models separate changes in relative species abundance from changes in interaction preferences (which describe how interaction frequencies deviate from random encounters). The models with the best predictive ability compared to data requirement are those that capture systematic changes in interaction preferences between different habitat types. Our results suggest a viable approach for predicting the consequences of rapid environmental change for the structure of complex ecological networks, even in the absence of detailed, system-specific empirical data.

Date: 2017
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DOI: 10.1038/s41467-017-00913-w

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