ABSTRACT:The prediction of species distribution has become a focus in ecology. For predicting a result more effectively and accurately, some novel methods have been proposed recently, like support vector machine (SVM) and maximum entropy (MAXENT). However, high complexity in the forest, like that in Taiwan, will make the modeling become even harder. In this study, we aim to explore which method is more applicable to species distribution modeling in the complex forest. Castanopsis carlesii (long-leaf chinkapin, LLC), growing widely in Taiwan, was chosen as the target species because its seeds are an important food source for animals. We overlaid the tree samples on the layers of altitude, slope, aspect, terrain position, and vegetation index derived from SOPT-5 images, and developed three models, MAXENT, SVM, and decision tree (DT), to predict the potential habitat of LLCs. We evaluated these models by two sets of independent samples in different site and the effect on the complexity of forest by changing the background sample size (BSZ). In the forest with low complex (small BSZ), the accuracies of SVM (kappa = 0.87) and DT (0.86) models were slightly higher than that of MAXENT (0.84). In the more complex situation (large BSZ), MAXENT kept high kappa value (0.85), whereas SVM (0.61) and DT (0.57) models dropped significantly due to limiting the habitat close to samples. Therefore, MAXENT model was more applicable to predict species' potential habitat in the complex forest; whereas SVM and DT models would tend to underestimate the potential habitat of LLCs.

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ABSTRACT:How to effectively describe ecological patterns in nature over broader spatial scales and build a modeling ecological framework has become an important issue in ecological research. We test four modeling methods (MAXENT, DOMAIN, GLM and ANN) to predict the potential habitat of Schima superba (Chinese guger tree, CGT) with different spatial scale in the Huisun study area in Taiwan. Then we created three sampling design (from small to large scales) for model development and validation by different combinations of CGT samples from aforementioned three sites (Tong-Feng watershed, Yo-Shan Mountain, and Kuan-Dau watershed). These models combine points of known occurrence and topographic variables to infer CGT potential spatial distribution. Our assessment revealed that the method performance from highest to lowest was: MAXENT, DOMAIN, GLM and ANN on small spatial scale. The MAXENT and DOMAIN two models were the most capable for predicting the tree's potential habitat. However, the outcome clearly indicated that the models merely based on topographic variables performed poorly on large spatial extrapolation from Tong-Feng to Kuan-Dau because the humidity and sun illumination of the two watersheds are affected by their microterrains and are quite different from each other. Thus, the models developed from topographic variables can only be applied within a limited geographical extent without a significant error. Future studies will attempt to use variables involving spectral information associated with species extracted from high spatial, spectral resolution remotely sensed data, especially hyperspectral image data, for building a model so that it can be applied on a large spatial scale.

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ABSTRACT:Spatial extrapolation has become a sine qua non and an ad hoc major research focus in applied ecology in the latter half 20 th century. Progressive innovations in data acquisition and processing technologies over the last few decades, especially in the fields of 3S (RS, GIS and GPS) and statistical modeling method, have greatly enhanced ecologists' capacity to face the challenge by enabling them to to describe patterns in nature over larger spatial scales and a greater level of details than ever before. Elaeocarpus japonicas (Japanese Elaeocarpus tree, JET) was selected for applying in the concurrent developed technology, such as ecological distribution modeling and ecological extrapolation. The GPS-located JET samples were introduced in a GIS for overlaying with five environmental layers (elevation, slope, aspect, terrain position and vegetation index derived from two-date SPOT-5 images) for ecological information extraction and model building. We created three sampling designs (SD), Tong-Feng samples for SD1, Kuan-Dau samples for SD2, and the merge of the two former datasets for SD3, according to watersheds, and the three SDs were used individually to test the extrapolation ability of predictive models. The results of the two-way extrapolation indicated it is hard to extend the predicted distribution patterns through different watersheds. The main reasons resulting in this outcome were the difference in microclimate and micro-terrain between these two watersheds. Consequently, the models built with SD3 were the more robust. The information of vegetation index in this study poorly improved the models, so we will adopt the hyperspectral data to overcome the shortage of the SPOT-5 images.