Department of Biology, University of Texas at Arlington, Arlington, Texas, USA.
Forestry and Environmental Conservation Department, Clemson University, Clemson, South Carolina, USA.
Ecology. 2023 Mar;104(3):e3917. doi: 10.1002/ecy.3917. Epub 2023 Jan 6.
The species-area relationship (SAR) has over a 150-year-long history in ecology, but how its shape and origins vary across scales and organisms remains incompletely understood. This is the first subcontinental freshwater study to examine both these properties of the SAR in a spatially explicit way across major organismal groups (diatoms, insects, and fish) that differ in body size and dispersal capacity. First, to describe the SAR shape, we evaluated the fit of three commonly used models, logarithmic, power, and Michaelis-Menten. Second, we proposed a hierarchical framework to explain the variability in the SAR shape, captured by the parameters of the SAR model. According to this framework, scale and species group were the top predictors of the SAR shape, climatic factors (heterogeneity and median conditions) represented the second predictor level, and metacommunity properties (intraspecific spatial aggregation, γ-diversity, and species abundance distribution) the third predictor level. We calculated the SAR as a sample-based rarefaction curve using 60 streams within landscape windows (scales) in the United States, ranging from 160,000 to 6,760,000 km . First, we found that all models provided good fits (R ≥ 0.93), but the frequency of the best-fitting model was strongly dependent on organism, scale, and metacommunity properties. The Michaelis-Menten model was most common in fish, at the largest scales, and at the highest levels of intraspecific spatial aggregation. The power model was most frequent in diatoms and insects, at smaller scales, and in metacommunities with the lowest evenness. The logarithmic model fit best exclusively at the smallest scales and in species-poor metacommunities, primarily fish. Second, we tested our framework with the parameters of the most broadly used SAR model, the log-log form of the power model, using a structural equation model. This model supported our framework and revealed that the SAR slope was best predicted by scale- and organism-dependent metacommunity properties, particularly spatial aggregation, whereas the intercept responded most strongly to species group and γ-diversity. Future research should investigate from the perspective of our framework how shifts in metacommunity properties due to climate change may alter the SAR.
种-面积关系（SAR）在生态学中已有 150 多年的历史，但它在不同尺度和生物之间的形状和起源如何变化仍不完全清楚。这是第一个对不同体型和扩散能力的主要生物类群（硅藻、昆虫和鱼类）进行的跨大陆淡水 SAR 这两个特性进行空间明确检验的研究。首先，为了描述 SAR 形状，我们评估了三种常用模型（对数、幂和米氏方程）的拟合度。其次，我们提出了一个分层框架来解释 SAR 形状的可变性，这是通过 SAR 模型的参数来捕获的。根据该框架，尺度和物种群是 SAR 形状的主要预测因子，气候因素（异质性和中值条件）代表第二个预测因子水平，而集合群落属性（种内空间聚集、γ-多样性和物种丰度分布）是第三个预测因子水平。我们使用美国景观窗口（尺度）内的 60 条溪流计算了 SAR，范围从 160,000 到 6,760,000 公里 2 。首先，我们发现所有模型都提供了很好的拟合度（R ²≥0.93），但最佳拟合模型的频率强烈依赖于生物、尺度和集合群落属性。米氏方程模型在鱼类中最常见，在最大尺度上，在种内空间聚集度最高的情况下最常见。幂模型在硅藻和昆虫中最常见，在较小的尺度上，在均匀度最低的集合群落中最常见。对数模型仅在最小尺度和物种贫乏的集合群落中拟合得最好，主要是鱼类。其次，我们使用最广泛使用的 SAR 模型的参数（幂模型的对数-对数形式），使用结构方程模型来测试我们的框架。该模型支持我们的框架，并揭示 SAR 斜率最好由尺度和生物依赖的集合群落属性预测，特别是空间聚集，而截距对物种群和γ-多样性的响应最强。未来的研究应该从我们的框架角度调查由于气候变化导致的集合群落属性的变化如何改变 SAR。
