解释变量的选择对于模拟响应气候变化的物种分布变化的预测非常重要。在这项研究中,我们评估了变量选择在物种分布模型中的重要性.我们比较了两种不同类型的模型来预测蚂蚁物种的分布:仅温度以及温度和降水。从2006年到2009年,在韩国的343个森林地点收集了蚂蚁。我们使用广义加性模型(GAM)来预测16种物种的未来分布,这些物种对气候因素(温度和/或降水)的变化表现出显着响应。构造了四种类型的GAM:温度,温度与降水的相互作用,温度和降水没有相互作用,温度和降水相互作用。大多数物种在仅温度与温度和降水模型之间显示出相似的结果。物种丰富度预测变化的结果与仅温度模型不同。这表明物种丰富度预测的不确定性更高,这是通过结合每个物种分布变化的预测结果获得的,而不是分布变化的预测。预测蚂蚁组合的周转率随着温度的降低和海拔的增加而增加,这与其他研究一致。最后,我们的结果表明,由于模型中使用的变量引起的模型输出的高度变异性,因此对响应气候变化的生物分布或多样性的预测是不确定的。
The selection of explanatory variables is important in modeling prediction of changes in species distribution in response to climate change. In this study, we evaluated the importance of variable selection in species distribution models. We compared two different types of models for predicting the distribution of ant species: temperature-only and both temperature and precipitation. Ants were collected at 343 forest sites across South Korea from 2006 through 2009. We used a generalized additive model (GAM) to predict the future distribution of 16 species that showed significant responses to changes in climatic factors (temperature and/or precipitation). Four types of GAMs were constructed: temperature, temperature with interaction of precipitation, temperature and precipitation without interaction, and temperature and precipitation with interaction. Most species displayed similar results between the temperatureonly and the temperature and precipitation models. The results for predicted changes in species
richness were different from the temperature-only model. This indicates higher uncertainty in the prediction of species
richness, which is obtained by combining the prediction results of distribution change for each species, than in the prediction of distribution change. The turnover rate of the ant assemblages was predicted to increase with decreases in temperature and increases in elevation, which was consistent with other studies. Finally, our results showed that the prediction of the distribution or diversity of organisms responding to climate change is uncertain because of the high variability of the model outputs induced by the variables used in the models.