National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, TN, USA.
Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, USA.
J Anim Ecol. 2022 Aug;91(8):1596-1611. doi: 10.1111/1365-2656.13752. Epub 2022 Jun 26.
Understanding the spatial scales at which environmental factors drive species richness patterns is a major challenge in ecology. Due to the trade-off between spatial grain and extent, studies tend to focus on a single spatial scale, and the effects of multiple environmental variables operating across spatial scales on the pattern of local species richness have rarely been investigated. Here, we related variation in local species richness of ground beetles, landbirds and small mammals to variation in vegetation structure and topography, regional climate, biome diversity and glaciation history for 27 sites across the USA at two different spatial grains. We studied the relative influence of broad-scale (landscape) environmental conditions using variables estimated at the site level (climate, productivity, biome diversity and glacial era ice cover) and fine-scale (local) environmental conditions using variables estimated at the plot level (topography and vegetation structure) to explain local species richness. We also examined whether plot-level factors scale up to drive continental scale richness patterns. We used Bayesian hierarchical models and quantified the amount of variance in observed richness that was explained by environmental factors at different spatial scales. For all three animal groups, our models explained much of the variation in local species richness (85%-89%), but site-level variables explained a greater proportion of richness variance than plot-level variables. Temperature was the most important site-level predictor for explaining variance in landbirds and ground beetles richness. Some aspects of vegetation structure were the main plot-level predictors of landbird richness. Environmental predictors generally had poor explanatory power for small mammal richness, while glacial era ice cover was the most important site-level predictor. Relationships between plot-level factors and richness varied greatly among geographical regions and spatial grains, and most relationships did not hold when predictors were scaled up to the continental scale. Our results suggest that the factors that determine richness may be highly dependent on spatial grain, geography, and animal group. We demonstrate that instead of artificially manipulating the resolution to study multiscale effects, a hierarchical approach that uses fine grain data at broad extents could help solve the issue of scale selection in environment-richness studies.
理解环境因素驱动物种丰富度格局的空间尺度是生态学中的一个主要挑战。由于空间粒度和范围之间的权衡，研究往往侧重于单一的空间尺度，而跨越多个空间尺度的多个环境变量对局部物种丰富度模式的影响很少被研究。在这里，我们将地表甲虫、陆鸟和小型哺乳动物的局部物种丰富度变化与植被结构和地形、区域气候、生物多样性和冰川历史变化相关联，研究了 27 个美国站点在两个不同空间粒度下的变化。我们使用在站点水平上估计的变量（气候、生产力、生物多样性和冰川时代冰盖）研究了广泛尺度（景观）环境条件的相对影响，并使用在斑块水平上估计的变量（地形和植被结构）研究了精细尺度（局部）环境条件对局部物种丰富度的影响，以解释局部物种丰富度。我们还检查了斑块水平的因素是否可以扩展到驱动大陆尺度丰富度模式。我们使用贝叶斯层次模型，并量化了不同空间尺度的环境因素对观测丰富度的方差解释量。对于所有三个动物群体，我们的模型解释了大部分局部物种丰富度的变化（85%-89%），但站点水平的变量比斑块水平的变量解释了更多的丰富度方差。对于解释地表甲虫和陆鸟丰富度的变化，温度是最重要的站点水平预测因子。植被结构的某些方面是解释陆鸟丰富度的主要斑块水平预测因子。环境预测因子对小型哺乳动物丰富度的解释能力普遍较差，而冰川时代冰盖是站点水平最重要的预测因子。在不同的地理区域和空间粒度下，斑块水平因素与丰富度之间的关系变化很大，并且当预测因子扩展到大陆尺度时，大多数关系都不成立。我们的结果表明，决定丰富度的因素可能高度依赖于空间粒度、地理位置和动物群体。我们证明，替代人为操纵分辨率来研究多尺度效应的方法是，使用广泛范围的细粒度数据的层次方法可以帮助解决环境丰富度研究中的尺度选择问题。
