生态网络的构建是提升生态质量、保护生物多样性和促进可持续发展的关键途径。通过科学合理地构建和优化生态网络，可以修复生态环境的破碎性，增强物种的连通性，减少生态孤岛效应，从而保障生态网络的完整性和区域生态安全。本研究以宝鸡市为研究区，基于Landsat影像数据、土地利用数据及DEM数据等多源数据，运用遥感生态指数（Remote Sensing Ecological Index, RSEI）、生态系统服务（Ecosystem Services, ES）和形态学空间格局分析（Morphological Spatial Pattern Analysis, MSPA）3种方法对2020年宝鸡市的生态质量、服务功能和生态系统的空间形态分别进行评价并提取生态源地，通过这3种方法组合出7种不同的生态源地识别方法，采用生态网络评价指标与秩和比法确定最优生态源地识别方法。基于最优方法分析2000年、2005年、2010年、2015年和2020年5期宝鸡市生态网络的演变特征。基于2020年宝鸡市生态网络，识别重要生态区域，通过生态源地的增补对生态网络优化，并对比了优化前后的生态网络，围绕生态关键点制定了修复策略，并进一步提出生态网络的优化建议。主要结论如下：

（1）2020年宝鸡市大部分地区生态质量良好，南部和西部的生态质量优于中部和东部。RSEI、ES和MSPA中的高生态质量、高服务功能和生态系统的空间形态核心区分别占宝鸡市总面积48.47%、19.67％、58.52%。多方法组合后的7种方法中，基于MSPA方法识别的生态源地数量最多，RSEI+MSPA和RSEI+ES+MSPA方法识别的数量最少；基于MSPA方法生成的生态廊道数量最多，RSEI+MSPA和RSEI+ES+MSPA方法生成的廊道数量最少。基于RSEI方法构建的生态网络覆盖程度最高，基于ES+MSPA方法的网络结构最复杂、连通性最强，基于MSPA方法的网络流通成本最低，效益最高。秩和比法的综合评价结果表明，基于ES+MSPA方法构建的生态网络表现最优。

（2）2000-2020年宝鸡市的生态源地面积持续增加，从2000年的9990.85km²增长至2020年的10383.41km²，大部分区县的生态源地面积呈增长趋势，但增长幅度和速率有所差异。生态源地数量从2000年的22处增长至2020年的23处，总体变化较为稳定，新增源地主要集中在原有源地周边。宝鸡市的高阻力区域逐步扩大，主要集中在中部城区及道路沿线。低阻力区域保持稳定，主要分布在秦岭山脉等自然保护区。生态廊道数量和总长度呈波动变化，廊道总长度变化趋势为“增加-减少-增加”。廊道密度在2005年与2020年达到峰值，而2000年与2015年达到最低值。生态网络连通性在2010年达到最优状态，尽管2015年略有下降，但2020年仍高于2000年。生态网络成本比在2005年最高，流通成本最大。

（3）2020年宝鸡市共识别5个极重要生态源地和16条极重要生态廊道，生态关键点包括41个生态夹点、26个生态障碍点和26个生态断裂点。通过增补10个生态源地，生成新的生态廊道，优化后的生态网络生态源地为生态廊道为71条，总长度为335.04km。优化后的生态网络在各项指标上均有所提升，整体结构得到改善。最后，本研究针对宝鸡市生态网络的未来发展，提出了相应的优化建议。

The construction of ecological networks is a key approach to improving ecological quality, protecting biodiversity, and promoting sustainable development. By scientifically and rationally building and optimizing ecological networks, it is possible to repair the fragmentation of the ecological environment, enhance species connectivity, and reduce the effects of ecological isolation. This, in turn, ensures the integrity of the ecological network and the ecological security of the region.This study took Baoji City as the study area and utilized multi-source data, including Landsat imagery, land use data, and DEM data. Three methods—Remote Sensing Ecological Index (RSEI), Ecosystem Services (ES), and Morphological Spatial Pattern Analysis (MSPA)—were employed to evaluate the ecological quality, service functions, and spatial morphology of the ecosystem in Baoji City in 2020, respectively, and to extract ecological sources. Based on combinations of these three methods, seven different ecological source identification approaches were proposed. The optimal method was determined using ecological network evaluation indicators and the rank-sum ratio method. The evolution of Baoji's ecological network in 2000, 2005, 2010, 2015, and 2020 was then analyzed based on the optimal method. Important ecological areas in 2020 were identified, and the ecological network was optimized through the supplementation of ecological sources. The structure of the ecological network before and after optimization was compared, restoration strategies were formulated focusing on ecological key points, and further recommendations were proposed for ecological network optimization. The main conclusions are as follows:

(1) In 2020, the ecological quality of most areas in Baoji City was good, with the southern and western regions exhibiting better ecological quality than the central and eastern regions. The high ecological quality, high service functions, and core ecological areas in the MSPA, RSEI, and ES methods accounted for 48.47%, 19.67%, and 58.52% of Baoji City's total area, respectively. Among the seven methods combined, the MSPA method identified the most ecological sources, while the RSEI+MSPA and RSEI+ES+MSPA methods identified the fewest. The MSPA method generated the most ecological corridors, while the RSEI+MSPA and RSEI+ES+MSPA methods generated the fewest. The ecological network constructed based on the RSEI method had the highest coverage, while the network structure based on ES+MSPA was the most complex and had the strongest connectivity. The network based on the MSPA method had the lowest circulation cost and the highest efficiency. The comprehensive evaluation results based on the rank sum ratio method showed that the ecological network constructed with the ES+MSPA method performed the best.

(2) From 2000 to 2020, the area of ecological sources in Baoji City continued to increase, from 9990.85 km² in 2000 to 10383.41 km² in 2020. Most districts and counties showed an increasing trend in the area of ecological sources, although the rate and magnitude of growth varied. The number of ecological sources increased from 22 in 2000 to 23 in 2020, with the overall change being relatively stable, and the newly added sources concentrated around the existing sources. The high-resistance areas in Baoji City gradually expanded, mainly concentrated in the central urban area and along roads. The low-resistance areas remained stable, mainly in natural reserves such as the Qinling Mountains. The number and total length of ecological corridors exhibit fluctuating changes, with the overall trend of corridor length being "increase-decrease-increase.". The corridor density reached its peak in 2005 and 2020, while it was at its lowest in 2000 and 2015. The connectivity of the ecological network reached its optimal state in 2010, and although it slightly decreased in 2015, it was still higher in 2020 compared to 2000. The ecological network cost ratio was highest in 2005, with the greatest circulation cost.

(3) In 2020, Baoji City identified five extremely important ecological sources and 16 extremely important ecological corridors. The key ecological points included 41 ecological pinpoints, 26 ecological barriers, and 26 ecological fracture points. By adding 10 ecological sources and generating new ecological corridors, the optimized ecological network included 71 ecological sources and corridors, with a total length of 335.04 km. The optimized ecological network showed improvements in all indicators, and the overall structure was enhanced. Finally, this study proposed corresponding optimization suggestions for the future development of Baoji City's ecological network.

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