浅议动物学野外实习中标本采集应注意的问题

动物学野外实习是高等师范院校生物系动物学教学的一个重要内容。标本采集是动物学野外实习当中一个很重要的环节。本人带领学生野外实习多年,对在实习教学中标本采集应注意的几个问题进行了总结和归纳。     

青海湖高寒湿地生态系统野外试验台站简介

青海湖高寒湿地生态系统观测试验地处青藏高原东北部的青海湖,目前主要有两个野外观测站——小泊湖观测站和瓦颜山观测站。小泊湖观测站位于青海湖东岸,距共和县湖东种羊场西北约6公里,地理坐标北纬36°41′734″~36°42′256″,东经100°46′85″~100°47′21″,站区地形开阔,海拔3216~3221m,面积约14.8km~2,是青海湖水位下降遗留下来的沼泽草甸湿地。瓦颜山观测站位于青海湖北岸,距刚察县西北52公里瓦颜山河源处,地理坐标北纬37°43′38.03″-37°46′20″,东经100°01′58.87″~100°05′04.17″,海拔3720~3850m,面积约11.03 km~2,地势开阔,是沙柳河上游支流——瓦颜曲的河源湿地。     

Obtaining the best possible predictions of habitat selection for wintering Great Bustards in Cangzhou,Hebei Province with rapid machine learning analysis

Great Bustards （Otis tarda dybowskii） are one of the world＇s heaviest flying birds, occupying grassland hab- itats in Eastern Asia. Our study is located at the most eastern Chinese wintering site in Cangzhou, Hebei Province, where approximately 100 individuals are concentrated in a small area （17.53 km2）. Solid information is still lacking about the wintering areas for this subspecies in its eastern range and specifically for China. The study area consists of intensely used farmland in proximity to humans and is lacking con- servation areas and wild, open fields. Here, we present our results from two years of field data collection on habitat selection. We choose a machine learning model approach based on a rapid assessment methodology for the winter habitat of the Great Bustard. It is based on a spatial analysis of the best available environmental data, which were col- lected relatively quickly. These relatively new methods in ecology are based on an ensemble of decision trees and include algorithms such as TreeNet, Random Forest and CART used in parallel. In this study, we collected bustard droppings （presence only） from 48 locations between December 2011 and January 2012 and used the sites as training data. Droppings from 23 locations were collected in November 2012, and those sites were used as test data. We used eight environmental variables as predictor layers for the response variable of bustard presence/availability. We employed a Geographic Information System （ArcGIS 10.1and Geospatial Modelling Environment） and Google Earth. Compared with the other three models, we found that pre- dictions from Random Forest obtained a significant differ- ence between presence and absence. According to this model, the three most important factors for wintering Great Bustards are distance to residential area, distance to water pools, and farmland area. Our model shows that wintering Great Bustards prefer locations that are over 400 m away from residential areas, within 900 m of water pools and on areas o