Costa Jane, Peterson A Townsend, Beard C Ben
Entomology Branch, Centers for Disease Control and Prevention, Atlanta, Georgia 30341, USA.
Am J Trop Med Hyg. 2002 Nov;67(5):516-20. doi: 10.4269/ajtmh.2002.67.516.
Ecologic niche modeling has allowed numerous advances in understanding the geographic ecology of species, including distributional predictions, distributional change and invasion, and assessment of ecologic differences. We used this tool to characterize ecologic differentiation of Triatoma brasiliensis populations, the most important Chagas' disease vector in northeastern Brazil. The species' ecologic niche was modeled based on data from the Fundação Nacional de Saúde of Brazil (1997-1999) with the Genetic Algorithm for Rule-Set Prediction (GARP). This method involves a machine-learning approach to detecting associations between occurrence points and ecologic characteristics of regions. Four independent "ecologic niche models" were developed and used to test for ecologic differences among T. brasiliensis populations. These models confirmed four ecologically distinct and differentiated populations, and allowed characterization of dimensions of niche differentiation. Patterns of ecologic similarity matched patterns of molecular differentiation, suggesting that T. brasiliensis is a complex of distinct populations at various points in the process of speciation.
生态位建模在理解物种的地理生态学方面取得了诸多进展，包括分布预测、分布变化与入侵以及生态差异评估。我们使用这一工具来描述巴西锥蝽种群的生态分化，巴西锥蝽是巴西东北部恰加斯病最重要的传播媒介。该物种的生态位基于巴西国家卫生基金会（1997 - 1999年）的数据，采用规则集预测遗传算法（GARP）进行建模。此方法涉及一种机器学习方法，用于检测出现点与区域生态特征之间的关联。我们开发了四个独立的“生态位模型”，并用于测试巴西锥蝽种群之间的生态差异。这些模型证实了四个生态上不同且有差异的种群，并能够对生态位分化的维度进行描述。生态相似模式与分子分化模式相匹配，这表明巴西锥蝽是处于物种形成过程中不同阶段的不同种群的复合体。
