贫困区旅游地社会-生态系统恢复力时空分异研究——以安徽境内大别山区12个贫困县（市）为例

基于可变模糊识别模型分析 2008—2017年安徽境内大别山区12个贫困县（市）社会-生态系统恢复力的时空演变过程，采用障碍度模型揭示恢复力影响因素和特征。研究表明：① 时序变化上，社会-生态系统恢复力呈现稳步上升趋势，但总体仍处中低阶段。其中，社会子系统恢复力整体微升，局部年份有突变；经济子系统恢复力发展逐年提高；生态子系统恢复力“上升-下降-上升”反复波动，整体呈下降趋势；② 空间变化上，社会-生态系统恢复力总体上呈“极低-较低-中等”递进发展，各县（市）由分化到趋同，中部最快，南北均衡并进。其中，社会子系统恢复力空间关联性相对较弱，部分相邻区域阶段一致，总体北部快于南部；经济子系统恢复力空间关联性较强，连片提升，进度相似，总体南部快于北部；生态子系统恢复力空间关联性居中，部分区域短时间内步调一致，总体南慢北快。③ 旅游发展、人地关系、生态环境是贫困区旅游地社会-生态系统恢复力的主要影响因素。各县（市）越临近，影响因素相似度越高，但随地势复杂性增加，相似度由北至南递减。

Spatio-temporal Differentiation of Tourism Socio-ecological System Resilience in Poor Area:A Case Study of 12 Poverty-stricken Counties in the Dabie Mountain of Anhui Province

With the frequent occurrence of social crises and natural disasters, improving resilience has become an important means for socio-ecological systems to cope with external disturbances. Scientific evaluation of socio-ecological resilience has important practical significance for local decision-making. Based on the variable fuzzy recognition model, the article analyzed the spatio-temporal evolution of socio-ecological systems resilience in 12 poverty-stricken counties in the Dabie Mountain of Anhui Province from 2008 to 2017. Then, by using the obstacle degree model, the article revealed the main influencing factors and characteristics of resilience. The results showed that: 1) In terms of temporal changes, the resilience of the socio-ecological system showed a steady upward trend, but the overall level was not high. Among them, the resilience of the social subsystem had risen slightly, and there had been a sudden change in some years; the resilience of the economic subsystem had increased year by year, with obvious time periods; the resilience of the ecological subsystem had a ‘rising-falling-rising’ fluctuation repeatedly, but the overall trend was downward. 2) In terms of spatial dynamic changes, the resilience of the socio-ecological system generally showed a ‘very low-lower-medium’ upward trend. The socio-ecological system resilience changed from differentiation to convergence among 12 counties, and changes was faster among central counties, slower among north and south counties. To social subsystem, except for some adjacent counties, the spatial correlation of the resilience was relatively weak, and the resilience in north area improved faster than in the south. To economic subsystem, the change of their resilience was correlated spatially, improved continuously, changed consistently among 12 counties, and the resilience in south area improved faster than in the north. To ecological subsystem, the resilience was correlated partly among 12 counties, and south area was slower than north. 3) For socio-ecological system resilience, tourism development, Man-land relationship and ecological environment were the main influencing factors, especially tourism development was the most; economic development, medical education, resource endowment and poverty alleviation policies were secondary influencing factors. More closer the counties were, more higher the similarity of influencing factors were, but as the complexity of the terrain changed, the similarity decreased from north to south. Some problems still deserve further study: 1) In the socio-ecological system of tourism destination, how to quantitatively separate the impact of tourism and other factors, and explore the effect of tourism development on the resilience of socio-ecological system more carefully? 2) In order to evaluate the effect of tourism development, how to compare socio-ecological system resilience between poverty-stricken areas and non poverty-stricken areas, tourism areas and non tourism areas? 3) Tourism development in poverty-stricken areas started relatively late, and how to explore more effective methods to reveal the law of socio-ecological system resilience in poverty-stricken areas in a limited time scale?