Moya Wladimir, Jacome Gabriel, Yoo ChangKyoo
Department of Environmental Sciences and Engineering College of Engineering Center for Environmental Studies Kyung Hee University Yongin-si Gyeonggi-do Republic of Korea.
Ecol Evol. 2017 May 28;7(13):4881-4890. doi: 10.1002/ece3.3054. eCollection 2017 Jul.
In order to enhance in terms of accuracy and predict the modeling of the potential distribution of species, the integration of using principal components of environmental variables as input of maximum entropy (MaxEnt) has been proposed in this study. Principal components selected previously from the principal component analysis results performed in ArcGIS in the environmental variables was used as an input data of MaxEnt instead of raw data to model the potential distribution of red spiny lobster from the year 1997 to 2015 and for three different future scenarios 2020, 2050, and 2070. One set of six original environmental variables pertaining to the years 1997-2015 and one set of four variables for future scenarios were transformed independently into a single multiband raster in ArcGIS in order to select the variables whose eigenvalues explains more than 5% of the total variance with the purpose to use in the modeling prediction in MaxEnt. The years 1997 and 1998 were chosen to compare the accuracy of the model, showing better results using principal components instead of raw data in terms of area under the curve and partial receiver operating characteristic as well as better predictions of suitable areas. Using principal components as input of MaxEnt enhances the prediction of good habitat suitability for red spiny lobster; however, future scenarios suggest an adequate management by researches to elaborate appropriate guidelines for the conservation of the habitat for this valuable specie with face to the climate change.
为了提高准确性并预测物种潜在分布的模型，本研究提出将环境变量的主成分作为最大熵（MaxEnt）的输入进行整合。先前从在ArcGIS中对环境变量进行主成分分析结果中选取的主成分，被用作MaxEnt的输入数据，而非原始数据，以模拟1997年至2015年红刺龙虾的潜在分布，并针对2020年、2050年和2070年三种不同的未来情景进行模拟。在ArcGIS中，将一组与1997 - 2015年相关的六个原始环境变量以及一组针对未来情景的四个变量分别独立转换为单个多波段栅格，以便选择其特征值解释总方差超过5%的变量，用于MaxEnt中的建模预测。选择1997年和1998年比较模型的准确性，结果表明，在曲线下面积和部分接受者操作特征方面，使用主成分而非原始数据能得到更好的结果，对适宜区域的预测也更佳。将主成分作为MaxEnt的输入可增强对红刺龙虾良好栖息地适宜性的预测；然而，未来情景表明研究人员应进行适当管理，以制定适当的指导方针，应对气候变化，保护这种珍贵物种的栖息地。
