Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, United States of America.
PLoS One. 2013;8(2):e56853. doi: 10.1371/journal.pone.0056853. Epub 2013 Feb 25.
Inference involving diversity gradients typically is gathered by mechanistic tests involving single dimensions of biodiversity such as species richness. Nonetheless, because traits such as geographic range size, trophic status or phenotypic characteristics are tied to a particular species, mechanistic effects driving broad diversity patterns should manifest across numerous dimensions of biodiversity. We develop an approach of stronger inference based on numerous dimensions of biodiversity and apply it to evaluate one such putative mechanism: the mid-domain effect (MDE). Species composition of 10,000-km(2) grid cells was determined by overlaying geographic range maps of 133 noctilionoid bat taxa. We determined empirical diversity gradients in the Neotropics by calculating species richness and three indices each of phylogenetic, functional and phenetic diversity for each grid cell. We also created 1,000 simulated gradients of each examined metric of biodiversity based on a MDE model to estimate patterns expected if species distributions were randomly placed within the Neotropics. For each simulation run, we regressed the observed gradient onto the MDE-expected gradient. If a MDE drives empirical gradients, then coefficients of determination from such an analysis should be high, the intercept no different from zero and the slope no different than unity. Species richness gradients predicted by the MDE fit empirical patterns. The MDE produced strong spatially structured gradients of taxonomic, phylogenetic, functional and phenetic diversity. Nonetheless, expected values generated from the MDE for most dimensions of biodiversity exhibited poor fit to most empirical patterns. The MDE cannot account for most empirical patterns of biodiversity. Fuller understanding of latitudinal gradients will come from simultaneous examination of relative effects of random, environmental and historical mechanisms to better understand distribution and abundance of the current biota.
推断多样性梯度通常是通过涉及生物多样性单一维度的机械测试来收集的，例如物种丰富度。然而，由于地理范围大小、营养状态或表型特征等特征与特定物种相关联，因此驱动广泛多样性模式的机械效应应该在生物多样性的众多维度上表现出来。我们开发了一种基于生物多样性众多维度的更强推断方法，并应用它来评估一种这样的假设机制：中域效应（MDE）。通过覆盖 133 种夜翼手目蝙蝠分类群的地理范围图，确定了 10,000 平方公里网格单元的物种组成。我们通过计算每个网格单元的物种丰富度和三个指数（系统发育、功能和表型多样性）来确定新热带地区的经验多样性梯度。我们还根据 MDE 模型创建了每个检查生物多样性指标的 1,000 个模拟梯度，以估计如果物种分布在新热带地区随机放置，则预期的模式。对于每个模拟运行，我们将观察到的梯度与 MDE 预期的梯度进行回归。如果 MDE 驱动经验梯度，那么这种分析的决定系数应该很高，截距与零没有差异，斜率与单位没有差异。MDE 预测的物种丰富度梯度与经验模式拟合。MDE 产生了强烈的分类学、系统发育、功能和表型多样性的空间结构梯度。尽管如此，MDE 为生物多样性的大多数维度生成的预期值与大多数经验模式的拟合程度较差。MDE 不能解释生物多样性的大多数经验模式。更全面地了解纬度梯度将来自于同时检查随机、环境和历史机制的相对影响，以更好地了解当前生物群的分布和丰度。
