| 郑伟成,陈智强,郑子洪,王宇,冯磊,郭坤,马力,丁国骅,余著成,上官海平,郭洪兴.2021.浙江九龙山国家级自然保护区黑熊活动监测 及其华东地区潜在分布区预测.动物学杂志,56(4):509-521. |
| 浙江九龙山国家级自然保护区黑熊活动监测 及其华东地区潜在分布区预测 |
| Monitoring of Activity of the Asiatic Black Bear (Ursus thibetanus) in Zhejiang Jiulongshan National Natural Reverse and Prediction of Potential Suitable Distribution in East China |
| 投稿时间：2020-12-21 修订日期：2021-06-18 |
| DOI：10.13859/j.cjz.202104004 |
| 中文关键词: 黑熊 浙江九龙山国家级自然保护区 红外相机 物种分布模型 潜在分布 |
| 英文关键词:Ursus thibetanus Zhejiang Jiulongshan National Nature Reserve Infrared camera Species distribution models Potential distribution |
| 基金项目:浙江省环境保护专项资金项目（浙财建[2018]121号），遂昌县县校合作项目（No. 2020-H11） |
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| 中文摘要: |
| 2017至2020年，在浙江九龙山国家级自然保护区内及周边网格化布设63台Ltl-6210 MC红外相机，对区内的黑熊（Ursus thibetanus）及其同域物种进行调查。监测期间，5台红外相机共拍摄到9张黑熊活动照片和6次黑熊活动视频，提取到7次黑熊有效照片。根据本次监测到的黑熊位点与文献记录，共确定华东地区黑熊出现位点14个。基于黑熊栖息地特征选择土地利用和7个生境因子为预测背景，利用物种分布模型预测黑熊在华东地区的潜在分布区。为提高预测的精准度，采用了biomod 2软件包中的10种模型算法，并用真实技能统计值（TSS）和曲线下面积值（AUC）来评估这10种算法，只有当TSS值超过0.8且AUC值大于0.9时，才使用该模型算法预测物种的潜在分布区。结果表明：（1）推测保护区内现存2或3只黑熊；（2）最冷月份最低温（Bio6）和最湿季节降水量（Bio16）是限制黑熊分布的主要环境因子；（3）黑熊在华东地区存在3个主要潜在分布区，即浙–赣潜在分布区、浙–赣–皖潜在分布区和浙–皖潜在分布区，适生区面积约为317 km2。综上所述，这些结果可为加强华东地区黑熊的保护提供一定的资料。 |
| 英文摘要: |
| From 2017 to 2020, 63 Ltl-6210 MC infrared cameras were grid-deployed in and around the Zhejiang Jiulongshan National Nature Reserve (Fig. 1) to study the Asiatic Black Bear (Ursus thibetanus) and other sympatric species. During the monitoring period, we obtained nine photographs and six videos of the activity of U. thibetanus from five infrared cameras and extracted seven effective pieces of information regarding U. thibetanus (Fig. 3). Based on our monitoring site data and records of U. thibetanus from literature, a total of 14 locations of U. thibetanus were confirmed in East China. Based on the habitat characteristics of U. thibetanus and correlation of climate factors, land use and seven habitat factors (mean diurnal range, Bio2; maximum temperature of the warmest month, Bio5; minimum temperature of the coldest month, Bio6; mean temperature of the wettest quarter, Bio8; precipitation of the driest month, Bio14; precipitation seasonality, Bio15; and precipitation of the wettest quarter, Bio16) (Fig. 2) were selected as the prediction background, and the potential distribution areas of U. thibetanus in East China were predicted through species distribution models. To develop an accurate projection for U. thibetanus, we used an ensemble modeling approach with 10 algorithms (artificial neural networks, ANN; classification tree analysis, CTA; flexible discriminant analysis, FDA; generalized additive model, GAM; generalized boosting model, GBM; generalized linear model, GLM; multiple adaptive regression splines, MARS; maximum entropy, MaxEnt; random forests, RF; and surface range envelope) using the package ‘biomod 2.’ We evaluated predictive performances of the 10 algorithms using two criteria parameters, true skill statistics (TSS > 0.8) and area under the curve (AUC > 0.9) (Fig. 4). Then, we selected nine algorithms (ANN, CTA, FDA, GAM, GBM, GLM, MARS, MaxEnt, and RF) to estimate the potential suitable distribution of U. thibetanus in East China. Our results showed that (1) 2﹣3 U. thibetanus are speculated to inhabit the nature reserve; (2) the minimum temperature of the coldest month (Bio6) and precipitation of the wettest quarter (Bio16) were the main environmental factors limiting the distribution of U. thibetanus (Fig. 5); and (3) there are three potential distribution areas of U. thibetanus in East China, including Zhejiang-Jiangxi, Zhejiang-Jiangxi-Anhui, and Zhejiang-Anhui (Fig. 6). In conclusion, these results can serve as basic information for strengthening the protective measures for U. thibetanus conservation in East China. |
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