A novel conceptual framework is presented that proposes to apply trait-based approaches to predicting the impact of environmental change on ecosystem service delivery by multi-trophic systems. Development of the framework was based on an extension of the response-effect trait approach to capture functional relationships that drive trophic interactions. The framework was populated with worked examples to demonstrate its flexibility and value for linking disparate data sources, identifying knowledge gaps and generating hypotheses for quantitative models. A novel conceptual framework, based on an extension of the plant response - effect trait approach, proposes to apply trait-based approaches to predicting the impact of environmental change on ecosystem services delivered by multiple trophic levels. We demonstrate the flexibility and value of the framework for linking disparate data sources, identifying knowledge gaps and generating hypotheses for quantitative models. © 2013 International Association for Vegetation Science.
A novel framework for linking functional diversity of plants with other trophic levels for the quantification of ecosystem services
Mulder, ChristianConceptualization
;
2013-01-01
Abstract
A novel conceptual framework is presented that proposes to apply trait-based approaches to predicting the impact of environmental change on ecosystem service delivery by multi-trophic systems. Development of the framework was based on an extension of the response-effect trait approach to capture functional relationships that drive trophic interactions. The framework was populated with worked examples to demonstrate its flexibility and value for linking disparate data sources, identifying knowledge gaps and generating hypotheses for quantitative models. A novel conceptual framework, based on an extension of the plant response - effect trait approach, proposes to apply trait-based approaches to predicting the impact of environmental change on ecosystem services delivered by multiple trophic levels. We demonstrate the flexibility and value of the framework for linking disparate data sources, identifying knowledge gaps and generating hypotheses for quantitative models. © 2013 International Association for Vegetation Science.File | Dimensione | Formato | |
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MUL-118 Lavorel et al. (2013) Journal of Vegetation Science.pdf
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