用组合模型综合比较的方法分析气候变化对朱鹮潜在生境的影响

Abstract

PDF HTML阅读 XML下载 导出引用 引用提醒 用组合模型综合比较的方法分析气候变化对朱鹮潜在生境的影响 DOI: 10.5846/stxb201103110297 作者: 作者单位: 陕西汉中朱鹮国家级自然保护区管理局,中国科学院动物研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 中国科学院战略性先导科技专项(XDA05080701); 中国科学院战略性先导科技专项(XDA05080701); 中欧ECBP项目"中国生物多样性和气候变化保护战略与行动计划研究" Climate change induced potential range shift of the crested ibis based on ensemble models Author: Affiliation: Shaanxi Hanzhong Crested Ibis National Nature Reserve,Shaanxi province,Institute of Zoology,Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:气候变化的不确定性和物种与环境关系的不确定性使气候变化生物学的研究充满变数。为了降低不确定性,人们开始用组合模型综合比较的方法研究物种对气候变化的响应。以朱鹮(Nipponia nippon)为研究对象,介绍组合模型综合比较方法的特点。朱鹮曾经高度濒危,目前种群大小在迅速恢复中;然而其分布区依旧狭小,气候变化可能是朱鹮面临的新威胁。应用BIOMOD模型中的9种模型,选择了每年的最低温和最高温、温度的季节性变异、每年的总降水量和降水的季节性变异共5个气候因子,依据WorldClim气候数据的CGCM2气候模型的A2a排放情形,计算了朱鹮当前(1950-2000年)的适宜生境和2020年、2050年、2080年3个阶段的潜在生境范围。结果表明朱鹮潜在生境将逐渐北移,生境中心脱离现在的保护区。因此,制定朱鹮的长期保护策略是必要的。9个模型在预测结果上、变量权重上和拟合优度的指标上都有差异,反映了模型本身的不确定性。气候变化的生物学效应比较复杂,应用多个模型进行综合比较,可以尽可能地减少模型所导致的误差。 Abstract:The uncertainty of climate change and uncertainty of species-environment relationship cause great variability in the studies of climate change biology. To reduce such uncertainties, scientists started to use ensemble models in this field. Our objective is to introduce the approach of the ensemble models, and predict the future range shift of one endangered species, the crested ibis (Nipponia nippon) as an example. The crested ibis had been critically endangered, and currently its population is rapidly recovering. The range of the crested ibis is still small after its recovery from the critical endangered status, so that climate change might be a threat to its long term survival. We used the locations of nest site to represent the distribution of the crested ibis, which have a high accuracy level and has being accumulated from 1981 to 2010. We applied nine modes in BIOMOD (a package of R software) to predict the current (1950-2000) and future (i.e. 2020, 2050, and 2080) distribution ranges of the crested ibis using five climate variables (i.e. annual minimum temperature, annual maximum temperature, seasonal variance of temperature, annual total precipitation, and seasonal variance of precipitation) based on CGCM2 climate model A2a emission scenario in WorldClim database. The nice models are Generalized Linear Models, Generalized Additive Models, Classification Tree Analysis, Artificial Neural Networks, Mixture Discriminant Analysis, Multivariate Adaptive Regression Splines, Generalized Boosting Models, Random Forest, and Surface Range Envelope. We compared the current climate conditions with those in 2080, and found that the current habitat of the crested ibis would become warmer and wetter in the future. All nine models indicated that the crested ibis would have a northward range shift (actually a higher elevation shift), and the distribution center would be out of the current nature reserve. Therefore, it is necessary to develop a long term conservation plan for the crested ibis, e.g. adjusting the nature reserve border or design a new nature reserve. The nine models showed differences in predicted ranges, weights of explanatory variables, and goodness-of-fit (based on ROC curves and Cohen's Kappa indices). Among five climate variables, the seasonal variance of precipitation is the most important variable that associated with distribution of the crested ibis; and seasonal variance of temperature is the secondly important variable. The overall performance of all models are very high, indicated the distribution of the crested ibis had a strong pattern (The crested ibis is well constrained by environmental variables, not scattered randomly). The Random Forest has the highest model performance, and the Artificial Neural Networks ranks the second. The high performance of the two models is partly due to their high complexity. We should be cautious whenever using species distribution models to predict the effect of climate change, because such models are based on the assumption that climate variables are the limiting variables restricting the range of the species, and the current population is in its favorite climate niche. As to the crested ibis, the assumption can hardly be satisfied, because other environmental variables such as human disturbance, wetland, and vegetation are also important to the crested ibis. As a result, the predicted range shift of the crested ibis is only a trend or potential distribution pattern in the future. Because of the difference in model prediction and variability of model performance, we suggest to use ensemble models to deal with complex problem such as biological consequences of climate change to decrease the errors from models. 参考文献 相似文献 引证文献