Tang Lili, Wang Runxi, He Kate S, Shi Cong, Yang Tong, Huang Yaping, Zheng Pufan, Shi Fuchen
College of Life Sciences, NanKai University, Tianjin, China.
Department of Biological Sciences, Murray State University, Murray, KY, USA.
PeerJ. 2019 Apr 9;7:e6731. doi: 10.7717/peerj.6731. eCollection 2019.
As global climate change accelerates, ecologists and conservationists are increasingly investigating changes in biodiversity and predicting species distribution based on species observed at sites, but rarely consider those plant species that could potentially inhabit but are absent from these areas (i.e., the dark diversity and its distribution). Here, we estimated the dark diversity of vascular plants in China and picked up threatened dark species from the result, and applied maximum entropy (MaxEnt) model to project current and future distributions of those dark species in their potential regions (those regions that have these dark species).
We used the Beals probability index to estimate dark diversity in China based on available species distribution information and explored which environmental variables had significant impacts on dark diversity by incorporating bioclimatic data into the random forest (RF) model. We collected occurrence data of threatened dark species (, , , , , and ) and related bioclimatic information that can be used to predict their distributions. In addition, we used MaxEnt modeling to project their distributions in suitable areas under future (2050 and 2070) climate change scenarios.
We found that every study region's dark diversity was lower than its observed species richness. In these areas, their numbers of dark species are ranging from 0 to 215, with a generally increasing trend from western regions to the east. RF results showed that temperature variables had a more significant effect on dark diversity than those associated with precipitation. The results of MaxEnt modeling showed that most threatened dark species were climatically suitable in their potential regions from current to 2070.
The results of this study provide the first ever dark diversity patterns concentrated in China, even though it was estimated at the provincial scale. A combination of dark diversity and MaxEnt modeling is an effective way to shed light on the species that make up the dark diversity, such as projecting the distribution of specific dark species under global climate change. Besides, the combination of dark diversity and species distribution models (SDMs) may also be of value for ex situ conservation, ecological restoration, and species invasion prevention in the future.
随着全球气候变化加速，生态学家和保护主义者越来越多地研究生物多样性的变化，并根据在各地点观察到的物种来预测物种分布，但很少考虑那些可能栖息但在这些地区不存在的植物物种（即隐性多样性及其分布）。在此，我们估计了中国维管植物的隐性多样性，并从结果中挑选出受威胁的隐性物种，应用最大熵（MaxEnt）模型来预测这些隐性物种在其潜在区域（有这些隐性物种的区域）当前和未来的分布。
我们基于现有的物种分布信息，使用比尔兹概率指数来估计中国的隐性多样性，并通过将生物气候数据纳入随机森林（RF）模型，探究哪些环境变量对隐性多样性有显著影响。我们收集了受威胁隐性物种（、、、、和）的出现数据以及可用于预测其分布的相关生物气候信息。此外，我们使用MaxEnt建模来预测它们在未来（2050年和2070年）气候变化情景下在适宜区域的分布。
我们发现每个研究区域的隐性多样性都低于其观察到的物种丰富度。在这些区域，它们的隐性物种数量从0到215不等，总体上呈现从西部地区到东部地区逐渐增加的趋势。RF结果表明，温度变量对隐性多样性的影响比与降水相关的变量更为显著。MaxEnt建模结果表明，从当前到2070年，大多数受威胁的隐性物种在其潜在区域气候上是适宜的。
本研究结果首次提供了集中在中国的隐性多样性模式，尽管这是在省级尺度上进行估计的。隐性多样性和MaxEnt建模相结合是一种有效的方法，可用于揭示构成隐性多样性的物种，例如预测全球气候变化下特定隐性物种的分布。此外，隐性多样性与物种分布模型（SDMs）的结合在未来的迁地保护、生态恢复和物种入侵预防方面可能也具有价值。
