探索在线数据源的潜力,通过全球蝙蝠剥削案例研究来加强物种威胁映射
Sara Bronwen Hunter1, Malik Oedin2, Julie Weeds3
1School of Life Sciences, University of Sussex, Brighton, UK.
概括
在线数据揭示了全球蝙蝠剥削模式,提供了超越学术研究的新见解. 虽然对识别威胁有价值,但时间趋势可能会受到COVID-19流行病等外部事件的影响.
科学领域:
- 野生动物保护 野生动物保护
- 生态生态学 生态生态学
- 数字数据分析 数字数据分析
背景情况:
- 人与自然的相互作用,包括野生动物的剥削,可以使用社交媒体和在线新闻等数字数据源进行研究.
- 这些数字来源在扩大或偏向对威胁分布和强度的理解方面的实用性需要全面评估.
研究的目的:
- 量化记录蝙蝠剥削的在线记录的地理和时间分布.
- 将这些在线数据与学术文献和国际自然保护联盟 (IUCN) 红色清单威胁数据进行比较.
- 评估在线数据的价值和潜在偏差,以了解野生动物剥削.
主要方法:
- 社交媒体 (Facebook,Twitter) 和搜索引擎 (谷歌,Bing) 的自动搜索被用来收集在线记录.
- 机器分类过了收集的数据,产生了953个相关帖子/网页,其中有来自84个国家的1099个蝙蝠剥削的独特记录.
- 数据与系统的文献搜索和国际自然保护联盟 (IUCN) 威胁物种红色名单数据进行了比较.
主要成果:
- 与学术文献相比,在线记录提供了额外的地理位置和更近期的蝙蝠剥削数据,包括22个未受研究覆盖的国家.
- 在线数据显示,2020年和2014年的剥削时间峰值,2020年的峰值可能受到COVID-19猜测的影响.
- 每个国家的在线记录数量在很大程度上是由学术研究的数量预测的,这表明数据源之间的关系.
结论:
- 在线数据源显著提高了野生动物剥削的地理代表性,提供了更完整的全球图景.
- 这些数字资源可以作为对新出现的野生动物剥削威胁的早期预警系统,并确定有针对性的研究领域.
- 在分析来自在线数据的时空趋势时,仔细考虑混变量至关重要,以避免误解事件.
关键词:
长腿动物 (Chiroptera) 是一个类动物.自动学习是自动学习.数字化保护数字化保护数字化保护数字化保护野生动物的利用.机器学习是机器学习.野生动物市场社交网络 社交网络社交媒体 社交媒体剥削野生动物,利用野生动物.野生动物贸易贸易数字保护 数字保护机器学习 机器学习社交媒体 社交媒体脊椎动物 脊椎动物野生动物贸易野生动物资源开发更多相关视频
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