Lecheta Melise Cristine, Corrêa Rodrigo César, Moura Mauricio Osvaldo
Departamento de Zoologia, Universidade Federal do Paraná, Av. Cel. Francisco H. dos Santos, s/n, Caixa Postal 19020, Curitiba, PR 81531-980, Brazil.
F.L.I.E.S Facility, Department of Entomology, Texas A&M University, College Station, 370 Olsen Blvd, TX 77843.
Environ Entomol. 2017 Oct 1;46(5):1051-1059. doi: 10.1093/ee/nvx124.
For all species, abiotic factors directly affect performance, survival and reproduction, and consequently, their geographic distribution. Species distribution models (SDMs) are important tools to predict the influence of abiotic factors in species distributions and has been more applied over the years. However, these models can be built under different algorithms and using different methods to select environmental predictors, which can lead to different results. Five different algorithms and two sets of environmental predictors were compared to predict the geographic distribution of the blowfly Sarconesia chlorogaster (Wiedemann) (Diptera: Calliphoridae). This species has several occurrence points and a considerable amount of biological data available, which makes S. chlorogaster a good model system to compare environmental predictors. Two sets of environmental predictors (mainly derived from temperature and humidity) were built, and the set based on the influence of abiotic variables on the ecophysiology of S. chlorogaster showed better results than the principal component analysis (PCA) approach using 19 climatic variables. We also employed five modeling algorithms-Envelope Score, Mahalanobis Distance, GARP, Support Vector Machines, and Maxent-and the latter two showed the best performances. The results indicate that temperature is the main factor shaping geographic distribution of S. chlorogaster through its effect on fitness. Furthermore, we showed that this species is mainly distributed in south, southeastern, and some northwestern and southwestern sites of South America. In addition, our results also predicted suitable areas in Ecuador and Colombia, countries without previous records.
对于所有物种而言，非生物因素直接影响其表现、生存和繁殖，进而影响其地理分布。物种分布模型（SDMs）是预测非生物因素对物种分布影响的重要工具，多年来得到了更广泛的应用。然而，这些模型可以基于不同的算法构建，并使用不同的方法选择环境预测因子，这可能导致不同的结果。比较了五种不同的算法和两组环境预测因子，以预测绿蝇Sarconesia chlorogaster（Wiedemann）（双翅目：丽蝇科）的地理分布。该物种有多个出现点且有大量可用的生物学数据，这使得绿蝇成为比较环境预测因子的良好模型系统。构建了两组环境预测因子（主要源自温度和湿度），基于非生物变量对绿蝇生态生理学影响的那一组预测因子比使用19个气候变量的主成分分析（PCA）方法显示出更好的结果。我们还采用了五种建模算法——包络得分、马氏距离、GARP、支持向量机和最大熵模型，后两种算法表现最佳。结果表明，温度是通过影响适合度来塑造绿蝇地理分布的主要因素。此外，我们发现该物种主要分布在南美洲的南部、东南部以及一些西北部和西南部地区。此外，我们的结果还预测了厄瓜多尔和哥伦比亚的适宜区域，而此前这些国家并无相关记录。
