GAM模型和BRT模型在长江口鱼类群落多样性预测中的比较

Comparison of generalized additive model and boosted regression tree in predicting fish community diversity in the Yangtze River Estuary,China

长江口为西太平洋最大的河口,评估其鱼类群落多样性分布能够为长江口生态系统的修复和管理提供科学依据.本研究基于2012—2014年长江口渔业监测数据,分别使用GAM模型和BRT模型建立各站点水域鱼类群落多样性指数与环境和时空因子之间的关系.结合线性回归方程,采用交叉验证的方式对模型的预测能力和拟合效果进行评价,并绘制了2014年长江口鱼类群落多样性指数和丰富度指数的空间分布图.结果表明:盐度、p H和叶绿素a对多样性指数贡献最高,p H、溶解氧和叶绿素a是对丰富度指数贡献率最高的环境因子.BRT模型对于多样性指数和丰富度指数的拟合和预测结果均优于GAM模型.空间分布预测显示,相较于GAM模型,BRT模型能够对长江口小面积水域间的鱼类群落多样性作更好的区分,河口外侧水域的鱼类群落多样性明显高于河口内侧水域,而北支水域的多样性高于南支水域.

Yangtze River Estuary is the biggest estuarine ecosystem in the western Pacific Ocean.Evaluating fish community in this ecosystem can provide scientific basis for its restoration and management. Generalized additive model( GAM) and boosted regression tree( BRT) were built to examine the relationship between fish community diversity and environmental and spatio-temporal variables based on data collected during 2012-2014. Combined with linear regression analysis,a cross validation was used to evaluate the fitness and predictive performance of both models. We plotted the spatial distribution of fish community diversity and richness in each station of the Yangtze River Estuary in 2014. The results showed that salinity,pH and chlorophyll-a had the most contribution on diversity,while pH,dissolved oxygen and chlorophyll-a were the most contributive variables on richness. BRT models showed better fitness and lower prediction error than GAM models. In contrast to GAM models,BRT models could distinguish the fish community index in each station area with respect to the spatial prediction. The diversity index in external water was obviously greater than that in internal water. Meanwhile,the station at higher latitude had a higher diversity index in both external and internal water.