期刊
JOURNAL OF VEGETATION SCIENCE
卷 28, 期 6, 页码 1260-1269出版社
WILEY
DOI: 10.1111/jvs.12564
关键词
Conservation prioritization; Landscape evaluation; Multilayer model; PNV; Predictive vegetation model; Probability distribution of vegetation types; Probability re-scaling; Restoration; Vegetation stochasticity
资金
- Hungarian Scientific Research Fund (OTKA) [PD-83522]
- Hungarian Academy of Sciences [TAMOP 4.2.1/B-09/1/KMR-2010-0005]
- Swiss SNF [40FA40_158395]
- EEA
- REC
- [GINOP-2.3.2-15-2016-00019]
- Swiss National Science Foundation (SNF) [40FA40_158395] Funding Source: Swiss National Science Foundation (SNF)
Questions: Multiple potential natural vegetation (MPNV) is a framework for the probabilistic andmultilayer representation of potential vegetation in an area. How can an MPNV model be implemented and synthesized for the full range of vegetation types across a large spatial domain such as a country? What additional ecological and practical information can be gained compared to traditional potential natural vegetation (PNV) estimates? Location: Hungary. Methods: MPNV was estimated by modelling the occurrence probabilities of individual vegetation types using gradient boosting models (GBM). Vegetation data from the Hungarian Actual Habitat Database (META) and information on the abiotic background (climatic data, soil characteristics, hydrology) were used as inputs to the models. To facilitate MPNV interpretation a new technique for model synthesis (re - scaling) enabling comprehensive visual presentation (synthetic maps) was developed which allows for a comparative view of the potential distribution of individual vegetation types. Results: The main result of MPNV modelling is a series of raw and re - scaled probability maps of individual vegetation types for Hungary. Raw probabilities best suit within - type analyses, while re - scaled estimations can also be compared across vegetation types. The latter create a synthetic overview of a location's PNV as a ranked list of vegetation types, and make the comparison of actual and potential landscape composition possible. For example, a representation of forest vs grasslands in MPNV revealed a high level of overlap of the potential range of the two formations in Hungary. Conclusion: The MPNV approach allows viewing the potential vegetation composition of locations in far more detail than the PNV approach. Re - scaling the probabilities estimated by the models allows easy access to the results by making potential presence of vegetation types with different data structure comparable for queries and synthetic maps. The wide range of applications identified for MPNV (conservation and restoration prioritization, landscape evaluation) suggests that the PNV concept with the extension towards vegetation distributions is useful both for research and application.
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