1295: Discrimination of semi-natural plant communities from abandoned fields by ordination and neural networks
(2003) Discrimination of semi-natural plant communities from abandoned fields by ordination and neural networks. Applied Vegetation Science.**
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Summary
This study deals with the succession from abandoned fields to semi-natural grassland and heathland vegetation and the discrimination between these types of habitat.
We hypothesised that semi-natural condition may be indicated by the species composition of vascular plants. A statistical classification model was developed, based on 2059 reference samples from Danish ancient grasslands and heathlands, and abandoned fields of varying age. This Succession Model was shown to discriminate effectively between abandoned fields and semi-natural habitats, and was suggested to be useful for the detection of conservation-worthy abandoned fields. A test of four hypotheses regarding the model prediction of naturalness of abandoned fields revealed that successional age, period of abandonment and successional trajectory had significant impact on the naturalness of abandoned fields. The implications of the results for restoration of semi-natural habitats and the usefulness of the model in practical conservation management are discussed.
| Document Language: | English |
|---|---|
| Keywords: | Artificial neural networks, biodiversity, conservation, habitat quality, old field, restoration, secondary succession, set-aside, supervised classification |
| Subject Areas: | Environmental aspects > Biodiversity and ecosystem services |
| Research affiliation: | Denmark > DARCOF II (2000-2005) > III.5 Nature quality in organic farming |
| Funding Part: | 5-25% |
| Orgprints ID Number: | 1295 |
| Contact: | Ejrnæs, senior scientist Rasmus |
| Deposited On: | 23 September 2003 |
| EPrint Type: | Journal paper |
| Published?: | Unpublished |
| Peer Review Status: | Submitted for peer-review but not yet accepted |
| Additional Publishing Information: | Submitted for peer review in Applied Vegetation Science June 2003 |
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