Resilient ecological networks: A comparative approach

Andrea De Montis, Amedeo Ganciu, Matteo Cabras, Antonietta Bardi, Valentina Peddio, Simone Caschili, Pierangelo Massa, Chiara Cocco and Maurizio Mulas

Land Use Policy, 2019, vol. 89, issue C

Abstract: Resilience is an overarching concept concerning the capacity of complex systems to react to severe crisis by self-organization, innovation and learning and to attain more robust settings than in the original condition. While the theory on resilience has exploded in the last decades, its operationalization is less practiced. A possible way out is the selection of appropriate variables able to measure the behaviour of a system, when it is subject to important stresses. Resilience has been applied to the study of socio-ecological systems, including ecological networks. Ecological networks can reconnect fragmented landscapes through a web of patches intertwined by environmental corridors. In this paper, we aim at assessing the resilience of two ecological networks designed for the towns of Nuoro and Sassari, Italy. The ecological networks are built on the ecological properties of two vegetal target species (i.e. Quercus ilex and Olea europaea) and their seed dispersal through the corresponding frugivorous animal vector species. We have studied the behaviour of the ecological networks under different types of attacks to the patches: at random or according to a deterministic choice. Our method allows to compare the dynamic pattern of resilience (i.e. along the process of elimination of patches) and to observe typical behaviour reported in other cases but also interesting peculiarities.

Keywords: Ecological networks; Resilience; Random attack; Deterministic attack (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:lauspo:v:89:y:2019:i:c:s026483771931035x

DOI: 10.1016/j.landusepol.2019.104207

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