Department of Ecology and Evolutionary Biology, Graduate School of Life Sciences, Tohoku University, 6-3 Aoba, Aramaki-aza, Aoba-ku, Sendai, Miyagi, 980-8578, Japan.
Department of Biology, Faculty of Science, Yamagata University, 1-4-12 Kojirakawa, Yamagata-shi, Yamagata, 990-8560, Japan.
Sci Rep. 2017 Sep 11;7(1):11215. doi: 10.1038/s41598-017-10581-x.
Citizen science is a powerful tool for collecting large volumes of observational data on various species. These data are used to estimate distributions using environmental factors with Species Distribution Models (SDM). However, if citizens are inexperienced in recognizing organisms, they may report different species as the subject species. Here we show nation-wide bumblebee distributions using photographs taken by citizens in our project, and estimated distributions for six bumblebee species using land use, climate, and altitude data with SDM. We identified species from photographic images, and took their locations from GPS data of photographs or the text in e-mails. When we compared our data with conventional data for specimens in the Global Biodiversity Information Facility (GBIF), we found that the volume and the number of species were larger, and the bias of spatial range was lower, than those of GBIF. Our estimated distributions were more consistent with bumblebee distributions reported in previous studies than with those of GBIF. Our method was effective for collecting distribution data, and estimating distributions with SDM. The estimated SDM allows us to predict the previous and future species distributions, and to develop conservation policies taking account of future city planning and/or global climate changes.
公民科学是一种收集各种物种大量观测数据的有力工具。这些数据可用于使用物种分布模型（SDM）中的环境因素来估计分布。然而，如果公民在识别生物方面没有经验，他们可能会将不同的物种报告为目标物种。在这里，我们使用我们项目中的公民拍摄的照片展示了全国范围内的熊蜂分布，并使用土地利用、气候和海拔数据以及 SDM 估计了六种熊蜂物种的分布。我们从摄影图像中识别物种，并从照片的 GPS 数据或电子邮件中的文本中获取它们的位置。当我们将我们的数据与全球生物多样性信息设施（GBIF）中标本的常规数据进行比较时，我们发现，与 GBIF 相比，我们的数据在数量和物种数量上更大，空间范围的偏差更小。我们的估计分布与以前的研究中报告的熊蜂分布更一致，而与 GBIF 的分布不一致。我们的方法对于收集分布数据和使用 SDM 进行估计非常有效。估计的 SDM 使我们能够预测以前和未来的物种分布，并制定考虑到未来城市规划和/或全球气候变化的保护政策。
