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美丽高原湖泊湿地“花—鱼—螺蚌—鸟”生态修复新路径及其应用

受人类活动和气候变化等因素的叠加影响，云南高原湖泊湿地不同程度地面临水位降低、水面面积缩小、水质污染问题，土著水生生物性丧失严重，许多土著物种濒危甚至消失。20世纪80年代以来实施的生态修复方式基本采用外来物种为主，不可避免地带来外来物种对高原湿地生态系统和土著物种的负面影响。针对这些问题，在有关项目的支持下，文章提出了基于土著旗舰物种为主的“花—鱼—螺蚌—鸟”生态修复新路径，并在滇池和洱海进行了试验示范。结果表明，这一生态修复新路径重建了退化湖泊湿地生态系统能量流动链条中缺失的环节，完善了生态系统的整体性功能，使得水体中氮、磷等富营养化物质循着“藻—鱼—鸟（或人） ”和“花—鱼—鸟（或人） ”2条路径顺利离水上岸；在试验区内还收获了海菜花和金线鱼等云南传统名贵食材，白瓣黄蕊的海菜花密集浮于水面形成了云南高原湖泊湿地特有的美丽景观；基于滇池和洱海试验示范工作，并结合云南高原各湖泊湿地生物多样性特点和现状，提出了针对不同类别湿地生态状况的生态修复和治理措施。     

长江上游赤水河示范区水生态修复的成效与挑战

“深入开展绿色发展示范，推进赤水河流域生态环境保护”是《中华人民共和国国民经济和社会发展第十四个五年规划和2035年远景目标纲要》提出的重大决策部署。加强赤水河水生态环境保护对于维系长江上游鱼类多样性、促进区域高质量发展、开创生态文明和美丽中国建设新局面均具有示范和引领作用。文章分析了赤水河在长江上游的生态功能，阐述了近年来赤水河实施的全面禁渔、支流小水电清理整改等保护修复措施的成效，从流域整体保护与系统修复角度提出了进一步加强赤水河水生态保护修复的对策建议。     

Video demonstrations can predict the intention to use digital learning technologies

The technology acceptance model (TAM) uses perceived usefulness and perceived ease of use to predict the intention to use a technology which is important when deciding to invest in a technology. Its extension for e-learning (the general extended technology acceptance model for e-learning; GETAMEL) adds subjective norm to predict the intention to use. Technology acceptance is typically measured after the technology has been used for at least three months. This study aims to identify whether a minimal amount of exposure to the technology using video demonstrations is sufficient to predict the intention to use it three months later. In two studies—one using TAM and one using GETAMEL—we showed students of different cohorts (94 and 111 participants, respectively) video demonstrations of four digital technologies (classroom response system, classroom chat, e-lectures, mobile virtual reality). We then measured technology acceptance immediately after the demonstration and after three months of technology use. Using partial least squares modelling, we found that perceived usefulness significantly predicted the intention to use three months later. In GETAMEL, perceived usefulness significantly predicted the intention to use for three of the four learning technologies, while subjective norm only predicted the intention to use for mobile virtual reality. We conclude that video demonstrations can provide valuable insight for decision-makers and educators on whether students will use a technology before investing in it.

Practitioner notes

What is already known about this topic

• The technology acceptance model helps decision-makers to determine whether students and teachers will adopt a new technology.
• Technology acceptance is typically measured after users have used the technology for three to twelve months.
• Perceived usefulness is a strong predictor of intention to use the technology.
• The predictive power of perceived ease of use for the intention to use varies from insignificant to strong.

What this paper adds

• For the four digital learning technologies (classroom chat, classroom response system, e-lectures and mobile virtual reality), we measure technology acceptance after a video demonstration and again after three months of usage.
• Using structural equation modelling, we are able to predict intention to use after three months, with perceived usefulness measured after the video demonstration.
• We replicate these findings with a second study using the general extended technology acceptance model.

Implications for practice and/or policy

• Short video demonstrations can provide information for educators to predict whether students will use a technology.
• Early impressions of perceived usefulness are very important and valuable to predict whether students will use a technology.