Re-Analysis of Breeding Bird Density in Eastern Pennsylvania Woodlots

R. Mancke, T. Gavin
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Abstract

Previously, we developed statistical models for the densities of 36 breeding bird species occurring in 176 woodlots in eastern Pennsylvania, USA. Here, we present a re-analysis of the same data after eliminating many highly correlated variables (|r| > 0.5), decreasing our number of candidate models from thousands to 12, and correcting a statistical error that we had made previously. Our distance-to-edge variable was a measure of depth in the woodlot that depended on distances to 4 woodlot edges. We could not use our area variable because of its very high correlation with our distance-to-edge variable. Our isolation variable was not highly correlated with either our distance-to-edge or area variable. Two other key variables related to buildings at the woodlot edges. As previously, our models were nonlinear, and we used bootstrapped data sets to help ascertain the best model for each species. We determined that 3 species were forest-interior species, and 2 of these also responded negatively to increasing isolation. We found 3 edge species, plus 2 modified edge species that occurred only at suburban or urban edges; both of the latter are known to nest on buildings. Our results agreed well but were conservative compared to an extensive literature. However, they did not agree well with our previous analysis. In particular, we were not able to confirm our previous conclusion that buildings near the edges of woodlots were important to many bird species in eastern Pennsylvania, USA.
宾夕法尼亚州东部林地繁殖鸟类密度的再分析
在此之前,我们建立了美国宾夕法尼亚州东部176个林地中36种繁殖鸟类的密度统计模型。在这里,我们在消除了许多高度相关的变量(|r| > 0.5)之后,对相同的数据进行了重新分析,将候选模型的数量从数千个减少到12个,并纠正了我们之前犯的统计错误。我们的距离边缘变量是林地深度的测量,它取决于到4个林地边缘的距离。我们不能使用面积变量,因为它与距离到边缘的变量高度相关。我们的隔离变量与我们到边缘的距离或面积变量都不高度相关。另外两个关键变量与林地边缘的建筑有关。如前所述,我们的模型是非线性的,我们使用自举数据集来帮助确定每个物种的最佳模型。3种为林内种,其中2种对隔离度的增加也有负响应。发现3种边缘种,外加2种仅发生在城郊或城市边缘的改良边缘种;后者都以在建筑物上筑巢而闻名。我们的结果很一致,但与广泛的文献相比是保守的。然而,他们不同意我们之前的分析。特别是,我们无法证实我们之前的结论,即在美国宾夕法尼亚州东部,林地边缘附近的建筑物对许多鸟类很重要。
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