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利用博物馆标本和公民科学数据预测记录不充分的物种北方黑寡妇(Latrodectus variolus)和黑囊蛛(Sphodros niger)的分布。

Predicting the distribution of poorly-documented species, Northern black widow (Latrodectus variolus) and Black purse-web spider (Sphodros niger), using museum specimens and citizen science data.

机构信息

Department of Natural Resource Sciences, McGill University, Montreal, Quebec, Canada.

Canada Research Chair on Northern Biodiversity, Université du Québec à Rimouski, Rimouski, Québec, Canada.

出版信息

PLoS One. 2018 Aug 8;13(8):e0201094. doi: 10.1371/journal.pone.0201094. eCollection 2018.

Abstract

Predicting species distributions requires substantial numbers of georeferenced occurrences and access to remotely sensed climate and land cover data. Reliable estimates of the distribution of most species are unavailable, either because digitized georeferenced distributional data are rare or not digitized. The emergence of online biodiversity information databases and citizen science platforms dramatically improves the amount of information available to establish current and historical distribution of lesser-documented species. We demonstrate how the combination of museum and online citizen science databases can be used to build reliable distribution maps for poorly documented species. To do so, we investigated the distribution and the potential range expansions of two north-eastern North American spider species (Arachnida: Araneae), the Northern black widow (Latrodectus variolus) and the Black purse-web spider (Sphodros niger). Our results provide the first predictions of distribution for these two species. We also found that the Northern black widow has expanded north of its previously known range providing valuable information for public health education. For the Black purse-web spider, we identify potential habitats outside of its currently known range, thus providing a better understanding of the ecology of this poorly-documented species. We demonstrate that increasingly available online biodiversity databases are rapidly expanding biogeography research for conservation, ecology, and in specific cases, epidemiology, of lesser known taxa.

摘要

预测物种分布需要大量的地理参考数据和获取远程感应的气候及土地覆盖数据。大部分物种的可靠分布估计都不可用,因为数字化的地理参考分布数据要么很罕见,要么没有数字化。在线生物多样性信息数据库和公民科学平台的出现极大地增加了建立当前和历史上记录较少的物种分布的信息量。我们展示了如何结合博物馆和在线公民科学数据库来构建可靠的记录较少的物种分布图。为此,我们研究了两种东北北美蜘蛛物种(蛛形纲:蜘蛛目),即北方黑寡妇(Latrodectus variolus)和黑袋网蛛(Sphodros niger)的分布和潜在的范围扩张。我们的结果首次预测了这两个物种的分布。我们还发现,北方黑寡妇已经向北扩展到了其以前已知的范围,这为公众健康教育提供了有价值的信息。对于黑袋网蛛,我们确定了其当前已知范围以外的潜在栖息地,从而更好地了解了这个记录较少的物种的生态。我们证明,越来越多的在线生物多样性数据库正在迅速扩大生物地理学研究,以保护、生态学,以及在特定情况下,保护那些不太知名的分类群的流行病学研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a83/6082516/e7e41a9420e7/pone.0201094.g001.jpg

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