On the shape and origins of the freshwater species–area relationship. Issue 3 (6th January 2023)

• Record Type: Journal Article
• Authors: Passy, Sophia I.; Mruzek, Joseph L.; Budnick, William R.; Leboucher, Thibault; Jamoneau, Aurélien; Chase, Jonathan M.; Soininen, Janne; Sokol, Eric R.; Tison‐Rosebery, Juliette; Vilmi, Annika; Wang, Jianjun; Larson, Chad A.
• Is Part Of: Ecology. Volume 104:Issue 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-06
• Subjects: biodiversity -- climate -- diatoms -- environmental heterogeneity -- fish -- insects -- intraspecific spatial aggregation -- metacommunity -- scale -- skewness -- species abundance distribution -- stream; Ecology -- Periodicals; Ecology -- Periodicals; Écologie -- Périodiques; Ecologie; Écologie; Écologie animale; Écologie végétale; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecy.3917
• Languages: English
• ISSNs: 0012-9658

Abstract

Abstract: 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 2 . First, we found that all models provided good fits ( R 2 ≥ 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.