Taiwan Hemlock concentrates in range from 2000 to 3100 m in elevation, 2000 to 3100 mm in annual precipitation, and 25 to 40 degrees in slope and consists of 4 main vegetation type, Taiwan Hemlock-Taiwan Cypress-Taiwan Yellow Cypress dominance, Taiwan Hemlock-Pine species dominance, Taiwan Hemlock-Taiwan Fir dominance, and Taiwan Hemlock-Taiwan Spruce dominance vegetation types. The classification is response to the environmental variables mainly by elevation and warmth index which is highly relative with elevation. Whether CART, CIT or Maxent method chose the elevation variable for representing the characteristics of distribution of Taiwan Hemlock.
Other variables used in this study were minor to affect the distribution of Taiwan Hemlock.
The analysis of species distribution and environmental relationships reveals the extrinsic effects on species’ distribution. None of the best model is defined as the universal tools for predicting species distribution, however, the attempt to analysis those relationships gives the implication of how the species reacts to any environmental disturbance and where does the species can escape from this impact of changes. Clemen (1989) concluded model combination as:
“Combining forecasts has been shown to be practical, economical and useful.
Underlying theory has been developed, and many empirical tests have demonstrated the value of composite forecasting. We no longer need to justify this methodology. We do need to find ways to make the implementation of the technique easy and efficient.”
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The analysis of the relationship between species and environmental variables is quantified by many new approaches rather than using just a traditional statistical analysis. Machine learning methods jump over the traditional method due to the multi-consideration of the algorithm with modern computer intensive ability to analyze more precisely prediction. Many tasks of SDM discussed in this study such as how to select appropriate environmental variables, if more homogeneous samples affects the selection of environmental variables?, and resolution of the environmental layers and the cell space to be predicted. However, restricted to the unavailable data quality, the accuracy of the SDM still needs to be revised by actually examined by a specific experiment on id the potential environmental condition really suitable for the target species or the potential predicted area is really suitable for planting or growing of the target species? Although nowadays the predicted modeling is widely spread the model needs more experiments by further study to support the PVM.
Maxent performed well if suitable environmental variables were puts into it.
CART and CIT successfully analyzed and chose the most effective environmental variable to the distribution of Taiwan Hemlock and this approach is useful while too many irrelevant environmental variables are available.
Combining model approach makes the SDM and its relevant model such as ENM more flexible to apply in a specific purpose. However, it still need further study for completing it and encourages the recent scientists to have the foundation to establish new combination approaches, as in this study pays efforts on changing combination target from model techniques to species and vegetation units, which is followed the
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plant community conception. How can ecological theory help the model performance of accuracy is still needs further study, however, this research gives an initial implication and hopes for more interesting ideas.
For conservation management, further alpine ecological researches are needed in Taiwan to adapt the climate change impact. A physical based model is the possible approach to improve the cons in the statistic models, i.e. the parameters are still robust under the climate change?
122
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Su, H.J. (1984b) Studies on the climate and vegetation types of the natural forests in