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通过社交媒体增加生物多样性知识:来自热带孟加拉国的案例研究。

Increasing biodiversity knowledge through social media: A case study from tropical Bangladesh.

作者信息

Chowdhury Shawan, Aich Upama, Rokonuzzaman Md, Alam Shofiul, Das Priyanka, Siddika Asma, Ahmed Sultan, Labi Mahzabin Muzahid, Marco Moreno Di, Fuller Richard A, Callaghan Corey T

机构信息

School of Biological Sciences, University of Queensland, in Saint Lucia, Queensland, Australia.

Institute of Biodiversity, Friedrich Schiller University Jena, in Jena, Germany.

出版信息

Bioscience. 2023 Jun 8;73(6):453-459. doi: 10.1093/biosci/biad042. eCollection 2023 Jun.

Abstract

Citizen science programs are becoming increasingly popular among naturalists but remain heavily biased taxonomically and geographically. However, with the explosive popularity of social media and the near-ubiquitous availability of smartphones, many post wildlife photographs on social media. Here, we illustrate the potential of harvesting these data to enhance our biodiversity understanding using Bangladesh, a tropical biodiverse country, as a case study. We compared biodiversity records extracted from Facebook with those from the Global Biodiversity Information Facility (GBIF), collating geospatial records for 1013 unique species, including 970 species from Facebook and 712 species from GBIF. Although most observation records were biased toward major cities, the Facebook records were more evenly spatially distributed. About 86% of the Threatened species records were from Facebook, whereas the GBIF records were almost entirely Of Least Concern species. To reduce the global biodiversity data shortfall, a key research priority now is the development of mechanisms for extracting and interpreting social media biodiversity data.

摘要

公民科学项目在博物学家中越来越受欢迎,但在分类学和地理方面仍然存在严重偏差。然而,随着社交媒体的爆炸式普及以及智能手机几乎无处不在,许多人在社交媒体上发布野生动物照片。在这里,我们以热带生物多样性丰富的国家孟加拉国为例,说明利用这些数据增强我们对生物多样性理解的潜力。我们将从脸书提取的生物多样性记录与全球生物多样性信息设施(GBIF)的记录进行了比较,整理了1013个独特物种的地理空间记录,其中包括来自脸书的970个物种和来自GBIF的712个物种。尽管大多数观测记录偏向于主要城市,但脸书记录在空间上分布更均匀。约86%的受威胁物种记录来自脸书,而GBIF记录几乎全部是无危物种。为了减少全球生物多样性数据缺口,目前一个关键的研究重点是开发提取和解释社交媒体生物多样性数据的机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a93/10308356/7aab3e50d51f/biad042fig1.jpg

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