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• W2099596113 abstract "Ecosystem management and biodiversity conservation are usually implemented using information of several targeted species or cover‐types and usually do not include information about communities. This is not because community‐level information is unimportant for management purposes, but because the detailed fieldwork required for gathering community‐level information at the scale for ecosystem management is usually impractical. We propose two methods to estimate the geographical distribution of plant communities with the objectives of covering large areas with minimal field efforts. The first method estimates the geographical distribution of plant communities by combining clustering methods with vegetation modeling, and the second extrapolates the geographical distribution of gradients in plant communities by combining gradient analysis with vegetation modeling. Vegetation modeling with clustering methods can be used to allocate sites with potentially higher alpha diversity, with the benefit of having a list of species associated with the clustered type. Vegetation modeling with gradient analysis can be used to identify regions with potentially the highest beta diversity by means of selecting regions with the widest range or highest variability in major DCA axes scores, and thereby help to preserve the scope of environmental conditions that lead to diversity in species assemblages. This is especially important because biological entities such as species, communities, or even ecosystems may cease to exist in the long run, and the preservation of processes that lead to biodiversity will eventually become more meaningful. We conclude that new methods to study and manage the processes that contribute to biodiversity at all scales should be and can be developed." @default.
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• W2099596113 date "2004-08-09" @default.
• W2099596113 modified "2023-10-15" @default.
• W2099596113 title "Predicting the geographical distribution of plant communities in complex terrain - a case study in Fushian Experimental Forest, northeastern Taiwan" @default.
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• W2099596113 doi "https://doi.org/10.1111/j.0906-7590.2004.03852.x" @default.
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