Jiang Yanbin, Wang Tiejun, Wu Yupeng, Hu Ronggui, Huang Ke, Shao Xiaoming
Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River) Ministry of Agriculture College of Resources and Environment Huazhong Agricultural University Wuhan China.
Faculty of Geo-Information Science and Earth Observation (ITC) University of Twente Enschede The Netherlands.
Ecol Evol. 2018 Jul 2;8(15):7436-7450. doi: 10.1002/ece3.4274. eCollection 2018 Aug.
Epiphyllous liverworts form a special group of bryophytes that primarily grow on the leaves of understory vascular plants in tropical and subtropical evergreen broadleaf forests. Being sensitive to moisture and temperature changes, epiphyllous liverworts are often considered to be good indicators of climate change and forest degradation. However, they are a poorly collected and taxonomically complicated group, with an only partly identified distribution pattern. In this study, we built four models based on 24 environmental variables at four different spatial resolutions (i.e., 1 km, 5 km, 10 km, and 15 km) to predict the past distribution of epiphyllous liverworts in China, using Maxent model and 63 historical location records (i.e., presence-only data). Both area under the curve of the receiver operating characteristic (AUC) and true skill statistic (TSS) methods are used to assess the model performance. Results showed that the model with the predictors at a 15-km resolution achieved the highest predictive accuracy (AUC=0.946; TSS=0.880), although there was no statistically significant difference between the four models (>0.05). The most significant environmental variables included aridity, annual precipitation, precipitation of wettest month, precipitation of wettest quarter, and precipitation of warmest quarter, annual mean NDVI, and minimum NDVI. The predicted suitable areas for epiphyllous liverworts were mainly located in the south of Yangtze River and seldom exceed 35°N, which were consistent with the museum and herbarium records, as well as the historical records in scientific literatures. Our study further demonstrated the value of historical data to ecological and evolutionary studies.
叶附生苔类植物构成了苔藓植物中的一个特殊类群，主要生长在热带和亚热带常绿阔叶林下层维管植物的叶片上。叶附生苔类植物对水分和温度变化敏感，常被视为气候变化和森林退化的良好指示物种。然而，它们是一个采集样本较少且分类复杂的类群，其分布模式仅得到部分确认。在本研究中，我们基于24个环境变量，构建了4种不同空间分辨率（即1千米、5千米、10千米和15千米）的模型，以利用最大熵模型（Maxent model）和63条历史位置记录（即仅存在数据）预测中国叶附生苔类植物的过去分布。我们采用了受试者工作特征曲线下面积（AUC）和真技能统计量（TSS）两种方法来评估模型性能。结果表明，尽管4种模型之间没有统计学上的显著差异（>0.05），但预测变量分辨率为15千米的模型具有最高的预测准确率（AUC=0.946；TSS=0.880）。最显著的环境变量包括干旱度、年降水量、最湿润月份降水量、最湿润季度降水量、最温暖季度降水量、年平均归一化植被指数（NDVI）和最小NDVI。预测的叶附生苔类植物适宜分布区主要位于长江以南，很少超过北纬35°，这与博物馆和标本馆记录以及科学文献中的历史记录一致。我们的研究进一步证明了历史数据在生态和进化研究中的价值。
