Abstract
Abstract. With an increase in the rate of species extinction, we should choose right methods that are sustainable on the basis of appropriate science and human needs to conserve ecosystems and rare species. Species distribution modeling (SDM) uses 3S technology and statistics and becomes increasingly important in ecology. Brainea insignis (cycad-fern, CF) has been categorized a rare, endangered plant species, and thus was chosen as a target for the study. Five sampling schemes were created with different combinations of CF samples collected from three sites in Huisun forest station and one site, 10 km farther north from Huisun. Four models, MAXENT, GARP, generalized linear models (GLM), and discriminant analysis (DA), were developed based on topographic variables, and were evaluated by five sampling schemes. The accuracy of MAXENT was the highest, followed by GLM and GARP, and DA was the lowest. More importantly, they can identify the potential habitat less than 10% of the study area in the first round of SDM, thereby prioritizing either the field-survey area where microclimatic, edaphic or biotic data can be collected for refining predictions of potential habitat in the later rounds of SDM or search areas for new population discovery. However, it was shown unlikely to extend spatial patterns of CFs from one area to another with a big separation or to a larger area by predictive models merely based on topographic variables. Follow-up studies will attempt to incorporate proxy indicators that can be extracted from hyperspectral images or LIDAR DEM and substitute for direct parameters to make predictive models applicable on a broader scale.
Highlights
Biodiversity is very important for humans and all other species on the Earth
For the first data-merged models in SS-3, the kappa values of four models lifted back to almost the same values as those in SS-1 from SS-2 or even better, and the four models still kept the same order in accuracy as that in SS-1
As the first data-merged models built in SS-3 were applied to a larger area in SS-4 including Tong-Mao Mountain, with 10 km away from the three sites at Huisun, the kappa values of maximum entropy (MAXENT) and discriminant analysis (DA) declined to near zero, as well as genetic algorithm for rule-set prediction (GARP) and generalized linear models (GLM) could not work possibly due to a limit on the size of data layer, a big difference in the domain values of predictor variables between Huisun and Tong Mao, or some other possible unknown factors which we will figure out later
Summary
Biodiversity is very important for humans and all other species on the Earth. Without the diversity of species, ecosystems are more fragile to natural disasters and climatic change. With an increase in the rate of species extinction, we must conserve ecosystems and rare species by choosing right methods that are sustainable on the basis of appropriate science and human needs. Species distribution modeling (SDM) could apply in conservation and protection rare species, ecology, epidemiology, disaster and management in forestry (Pearson et al, 2007; Asner et al, 2008; Cayuela et al, 2009). SDM needs to utilize the combination of 3S technology and statistics, and has become increasingly important in ecology (Côté and Reynolds, 2002; Guisan and Thuiller, 2005). Nowadays a variety of statistical methods have been used to model ecological niches and predict the geographical distributions of species, such as BIOCLIM, maximum entropy (MAXENT), DOMAIN, genetic algorithm for rule-set prediction (GARP), generalized linear models (GLM), generalized additive model (GAM) and discriminant analysis (DA) (Elith et al, 2006; Hernandez et al, 2006; Guisan et al, 2007; Peterson et al, 2007; Wisz et al, 2008; Ke et al, 2010)
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