山西芦芽山褐马鸡越冬栖息地选择的多尺度研究

1998～2000年在山西芦芽山自然保护区对褐马鸡的越冬栖息地选择进行了研究。采用4种空间尺度(10m、100m、300m和距离尺度)，对影响褐马鸡越冬栖息地选择的主要因子进行了深入分析，并建立了褐马鸡越冬栖息地选择的逻辑斯谛回归模型。在300m尺度上．活动点和非活动点的生境类型有针叶林、针阔混交林、灌木林和草丛等。活动点周围针叶林面积显著高于非活动点(F=-3．116，P=0．002)，虽然针阔混交林在两者中的面积比例都较小，但活动点周围针阔混交林的面积明显地低于非活动点(F=-2．255，P=0．024)．在灌木林和草丛的面积上两者无显著差异。这表明褐马鸡在300m尺度上喜欢活动于针叶林较多的地域，由于冬季针阔混交林不如针叶林能提供很好的隐蔽条件，褐马鸡避免选择针阔混交林；在100m尺度上，活动点和非活动点的生境类型有针叶林、针阔混交林和草丛，无灌木林生境，活动点的针叶林面积明显地高于非活动点(F=-2．931，P=0．003)。这表明褐马鸡在100m尺度上虽然倾向于选择针叶林，但对其它类型的生境如针阔混交林和草丛是可以利用的，这可能与其广泛取食活动有关。褐马鸡大尺度上的隐蔽条件满足以后，在小尺度上主要是为了获取更为丰富的食物。在距离尺度上活动点距居民点的距离、距道路的距离显著大于非活动点(F=15．621；6．048，P=0．000；0．018)。通过逐步逻辑斯谛回归分析，发现距灌草丛的距离、距居民点的距离、100m范围内针叶林的面积、树高以及食物的丰盛度是冬季褐马鸡栖息地选择的重要因子。以另外一个研究地收集的数据对所建立的栖息地选择模型的可靠程度进行了检验，结果表明该模型能有效地对褐马鸡的越冬栖息地进行预测。

Scale-dependent wintering habitat selection by brown-eared pheasant in Luyashan Nature Reserve of Shanxi, China

The selection of different spatial scales is critical when investigating ecological processes, because different patterns emerge in spatial data at different scales. The wintering habitat selection of Brown-eared Pheasant Crossoptilon mantchuricum was studied on four spatial scales (10m, 100m, 300m and distance scale) in Luyashan Nature Reserve of Shanxi from 1998 to 2000. At the 300m scale, the area of coniferous forests around selected sites was larger than that of unselected sites (F=(-3.116,)P=0.002), and the area of coniferous-deciduous mixed forests was less than that of unselected sites (F=-2.255,P=0.024), though the percent area of mixed forests was lower around the two kinds of sites. In addition, there was no difference for shrubs and grass. The results at the 300m scale show that the pheasants prefer to use coniferous forests rather than use mixed forests that cannot supply better shield in winter. At the 100m scale, there were no shrubs, and the area of conifer forests around the selected sites was also larger than that of unselected sites (F=-2.931,P=0.003), but for mixed forests and grass, there was no difference between the two kinds of sites. The results suggest that the pheasants occur in coniferous forests, and also freely use mixed forests and grass at the 100m scale. So it is concluded that after the better shields are satisfied on larger scale, Brown-eared Pheasant can take more food by using all available habitats on smaller scale. The distance scale applied showed some variables, such as the distance to villages and roads, had significant differences between the selected and unselected sites (F=15.621; 6.048, P=0.000; 0.018). Furthermore, the model based on data collected at four different spatial scales was developed by stepwise logistic regression. Presence of Brown-eared Pheasant was best predicted by the distance to shrubs, distance to villages, area of coniferous forests of 100m around the selected sites, height of trees and food richness. We tested the model's ability to predict the presence of Brown-eared Pheasant by an independent data set collected at another study area in this nature reserve. In general, the model has a good ability to predict the presence of Brown-eared Pheasant.