The elevational gradient in Andean bird species richness at the local scale: a foothill peak and a high-elevation plateau
The elevational gradient in Andean bird species richness at the local scale: a foothill peak and a high-elevation plateau
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DOI：
10.1111/j.0906-7590.2005.03935.x
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发表时间：
2005-04-01
期刊：
影响因子：
5.9
通讯作者：
Bach, K
中科院分区：
文献类型：
--
作者：
Herzog, SK;Kessler, M;Bach, K
物种丰富度随着海拔的增加而单调下降通常被认为是一种普遍模式，但最近的证据表明，主要模式是驼峰形，最大丰富度出现在中海拔点。为了分析局部范围内物种丰富度与海拔之间的关系，我们对玻利维亚安第斯山脉从低地到林线的鸟类进行了调查。我们将样带划分为 12 个 250 m 的海拔带，并通过基于个体和样本的稀疏度和丰富度估计来标准化每个带的物种丰富度。然后将经验数据与四个解释变量相关联：1）每个海拔带的面积，2）海拔（也代表生态系统生产力），3）几何约束经验范围大小的中域效应（MDE）零模型，4）代表区域物种库假设的南美鸟类的驼峰形模型。当地物种丰富度在海拔约 1000 米处达到峰值，急剧下降至海拔约 1750 米，然后基本保持不变。海拔是最好的单一预测因子，占经验数据方差的 78 - 85%。具有高程、面积和 MDE 的多元回归模型解释了 85 - 90% 的方差。蒙特卡罗模拟表明，1000 米处的丰富度峰值是两种不同鸟类（低地和高地）重叠的结果，并且多元回归中与 MDE 的相关性可能是虚假的。我们建议通过检查范围中点的分布来补充涉及 MDE 预测的相关分析。中海拔地区的急剧下降主要是由于低地物种的迅速消失。这个高海拔高原令人震惊且出人意料，但之前也曾被发现过。目前还无法解释，这说明尽管经过了几十年的研究，海拔梯度仍然没有得到很好的理解。
A monotonic decline in species richness with increasing elevation has often been considered a general pattern, but recent evidence suggests that the dominant pattern is hump-shaped with maximum richness occurring at some mid-elevation point. To analyse the relationship between species richness and elevation at a local scale we surveyed birds from lowlands to timberline in the Bolivian Andes. We divided the transect into 12 elevational belts of 250 m and standardized species richness in each belt with both individual- and sample-based rarefaction and richness estimation. The empirical data were then correlated to four explanatory variables: 1) area per elevational belt, 2) elevation (also representing ecosystem productivity), 3) a mid-domain effect (MDE) null model of geometrically constrained empirical range sizes, and 4) a hump-shaped model derived empirically for South American birds representing the regional species pool hypothesis. Local species richness peaked at ca 1000 m elevation, declined sharply to ca 1750 m, and then remained roughly constant. Elevation was the best single predictor, accounting for 78 - 85% of the variance in the empirical data. A multiple regression model with elevation, area, and MDE explained 85 - 90% of the variance. Monte Carlo simulations showed that the richness peak at 1000 m is the result of an overlap of two distinct avifaunas (lowland and highland) and that the correlation to MDE in the multiple regression was likely spurious. We recommend complementing correlation analyses involving MDE predictions with an examination of the distribution of range midpoints. The steep decline at mid-elevations was mainly due to a rapid loss of lowland species. The high-elevation plateau is striking and unexpected, but has also been found previously. It cannot be explained at present and exemplifies that despite several decades of research elevational gradients are still not well understood.