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Developing a biological network approach to quantify indirect costs and benefits of natural ecosystems to tropical agriculture (network)
Date du début: 1 août 2012, Date de fin: 31 juil. 2016 PROJET  TERMINÉ 

Ecosystem services (ES) have risen to prominence as a motivation for conserving biodiversity. The role of biodiversity in ES is however complex and depends on the intricate ways in which the components of ecosystems interact. We have long been aware that “tinkering” with one part of an ecosystem can have unforeseen consequences elsewhere in that system, as effects are transmitted through the network of biological interactions. Advances in network theory, and empirical studies, are now providing insight into how these complex ecological systems operate, and how influencing one part of a network can have consequences for the entire system. NETWORK will bring together scientist with expertise in the empirical (UK), theoretical (France) and applied (South Africa) aspects that are required to make the transition from fundamental ecology to practice possible. It will focus on a proof-of-concept case study on the interactions between mango plantations and surrounding natural ecosystems. Natural vegetation near farmland can sustain populations of natural enemies of pest insects and of pollinators and thereby provide an important ecosystem service but can also be a source of the pest insects themselves (as farmers believe is the case in the mango system). A dynamic ecological network approach will be employed in this project to quantify these opposing effects in an important tropical crop (mango) under different management conditions. The project will be one of the first studies to take an in-depth ecological look at the tradeoffs between ES and the costs of natural systems, by studying the dynamics of, and interactions between, all the component species in the network. This will give insight into the quantitative effect of the natural ecosystem on crop production, and the development of methodology for testing alternative management strategies in silico to aid informed decision making.



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