National Laboratory for Scientific Computing (LNCC), Petrópolis, RJ, 25651-075, Brazil.
Sci Rep. 2020 Feb 25;10(1):3358. doi: 10.1038/s41598-020-60134-y.
Biological collections have been historically regarded as fundamental sources of scientific information on biodiversity. They are commonly associated with a variety of biases, which must be characterized and mitigated before data can be consumed. In this work, we are motivated by taxonomic and collector biases, which can be understood as the effect of particular recording preferences of key collectors on shaping the overall taxonomic composition of biological collections they contribute to. In this context, we propose two network models as the first steps towards a network-based conceptual framework for understanding the formation of biological collections as a result of the composition of collectors' interests and activities. Building upon the defined network models, we present a case study in which we use our models to explore the community of collectors and the taxonomic composition of the University of Brasília herbarium. We describe topological features of the networks and point out some of the most relevant collectors in the biological collection as well as their taxonomic groups of interest. We also investigate their collaborative behaviour while recording specimens. Finally, we discuss future perspectives for incorporating temporal and geographical dimensions to the models. Moreover, we indicate some possible investigation directions that could benefit from our approach based on social network analytics to model and analyse biological collections.
生物采集一直被视为生物多样性科学信息的基础来源。它们通常与各种偏差有关，在数据被使用之前，必须对这些偏差进行特征描述和缓解。在这项工作中，我们的动机是分类学和采集者偏差，可以将其理解为关键采集者特定记录偏好对他们所贡献的生物采集总体分类组成的影响。在这种情况下，我们提出了两个网络模型，作为理解由于采集者兴趣和活动的组成而导致生物采集形成的基于网络的概念框架的第一步。在定义的网络模型基础上，我们进行了一个案例研究，我们使用模型来探索巴西利亚大学标本馆的采集者社区和分类组成。我们描述了网络的拓扑特征，并指出了生物采集中一些最相关的采集者及其感兴趣的分类群。我们还研究了他们在记录标本时的合作行为。最后，我们讨论了将时间和地理维度纳入模型的未来展望。此外，我们还指出了一些可能的研究方向，这些方向可以从我们基于社交网络分析的方法中受益，以对生物采集进行建模和分析。
