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Identifying Biodiversity Hotspots by Predictive Models: A Case Study Using Taiwan's Endemic Bird Species

机译：通过预测模型识别生物多样性热点：使用台湾特有鸟类的案例研究

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Predicting species distributions and identifying biodiversity hotspots are essential in designing conservation strategies. Because of different spatial scales and/or species characteristics, uncertainty still exist as to which model is the best. Several models have been proposed to calculate the probability of species occurrences, predict biodiversity hotspots, and decide importance levels of those hotspots. We constructed predictive distribution models for 14 of 16 endemic bird species in Taiwan using a fine-resolution (1 x 1 km) breeding bird distribution dataset compiled over the past decade as well as environmental variables. We compared the performances of the 4 models: logistic regression (LR), multiple discriminant analysis (MDA), genetic algorithm for rule-set prediction (GARP), and artificial neural network (ANN). Maps for biodiversity hotspots were generated based on the species distributions from the 4 models. To account for potential uncertainty, we constructed hotspot maps using a frequency histogram and probability density function approaches. Based on the distribution maps and the area under the curve (AUC) of the receiver operating characteristic, all of our models made good predictions for each species (all AUC values were > 0.75). The nonlinear models (GARP, ANN, and LR) provided better predictions than did the linear (MDA) model. GARP was the most consistent model when evaluated by it kappa, sensitivity, accuracy, and specificity values for each species and the 3 species categories (common, uncommon, and rare species). The prevalence of all species did not affect the final predictive performance. The 5 biodiversity hotspot maps derived from the frequency histogram approach showed a relatively similar pattern to maps generatedby the probability density function, which indicated that of mid- to high-elevation areas had higher probabilities. In spite of some inconsistencies, the hotspot maps identified from these 2 approaches were fairly representative when evaluated against currently known hotspots. A GAP analysis indicated only 25% of the hotspots are currently protected by national parks. We concluded that the LR, GARP, ANN, and MDA models are all feasible to use for modeling bird species distributions. Although there weresome limitations, we suggest using a combination approach to identify common features and conservation priorities of biodiversity hotspots. Comparing known and predicted hotspots can promote the reliability of the models as well as provide managers withgreater confidence when planning conservation policies. Finally, this approach to identifying common features and conservation priorities of biodiversity hotspots can be applied to evaluate conservation efforts and provide a better tool to achieve efficient conservation. http://zoolstud.sinica.edu.tw/Journals/48.3/418.pdf
机译：在设计保护策略时，预测物种分布和识别生物多样性热点至关重要。由于不同的空间尺度和/或物种特征，关于哪种模型最好是不确定的。已经提出了几种模型来计算物种出现的可能性，预测生物多样性热点并确定这些热点的重要性级别。我们使用过去十年汇编的高分辨率（1 x 1 km）繁殖鸟类分布数据集以及环境变量，构建了台湾16种特有鸟类的14种预测分布模型。我们比较了4种模型的性能：逻辑回归（LR），多判别分析（MDA），规则集预测的遗传算法（GARP）和人工神经网络（ANN）。根据四个模型的物种分布生成了生物多样性热点的地图。为了解决潜在的不确定性，我们使用频率直方图和概率密度函数方法构造了热点图。根据接收器工作特性的分布图和曲线下面积（AUC），我们所有的模型都对每种物种做出了良好的预测（所有AUC值均> 0.75）。与线性（MDA）模型相比，非线性模型（GARP，ANN和LR）提供了更好的预测。当通过GARP评估每种物种以及3个物种类别（常见，罕见和稀有物种）的kappa，敏感性，准确性和特异性值时，GARP是最一致的模型。所有物种的患病率均未影响最终的预测性能。从频率直方图方法获得的5个生物多样性热点图显示出与概率密度函数生成的图相对相似的模式，这表明中高海拔地区的概率更高。尽管存在一些不一致之处，但根据当前已知的热点进行评估时，从这两种方法中识别出的热点图仍具有相当的代表性。 GAP分析表明，目前只有25％的热点受到国家公园的保护。我们得出的结论是，LR，GARP，ANN和MDA模型都可用于对鸟类物种分布进行建模。尽管存在一些局限性，但我们建议使用组合方法来确定生物多样性热点的共同特征和保护重点。比较已知和预测的热点可以提高模型的可靠性，并在规划保护政策时为管理人员提供更大的信心。最后，这种确定生物多样性热点共同特征和保护重点的方法可用于评估保护工作，并为实现有效保护提供更好的工具。 http://zoolstud.sinica.edu.tw/Journals/48.3/418.pdf

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