Unraveling the Influence of Elevation on Moss Species–Area Relationships and the Effect of Spatial Scale on Elevational Richness Patterns in Mt Wutai With a Nested‐Plot Sampling Design

ABSTRACT Understanding how species–area relationships (SARs) vary with elevation as well as how elevational richness patterns vary across spatial scales is critical for biodiversity conservation in montane systems. On Mt Wutai of China, we sampled sets of nested plots and collected moss species along the elevational gradient. We examined how the c ‐value (richness per unit area) and z ‐value (rate of richness increase with area) of SARs vary with elevation and how elevational richness patterns vary under different scales. We analyzed the driving factors behind the variation of c ‐ and z ‐values along the elevational gradient as well as the variation of elevational richness patterns across spatial scales through a machine learning method. Last, we explored how the driving factors of elevational richness patterns vary with spatial scale. We found a positively skewed hump‐shaped pattern in c ‐values along the elevational gradient and a monotonic increasing trend in z ‐values with rising elevation. NPP and precipitation of the driest month (Bio14) were the most influential predictors for the variation of c ‐ and z ‐values, respectively. A positively skewed hump‐shaped pattern in species richness along the elevational gradient was found at small spatial scales, whereas a decelerating increasing trend with a less distinct mid‐elevation peak was found at larger spatial scales. A stronger relationship between elevational richness pattern and environmental variables was detected as sampling scale increased. With increasing spatial scale, the relative importance of the mid‐domain effect and Bio14 declined, whereas that of NDVI, NPP, and annual temperature range rose significantly when explaining variations in species richness. The scale‐dependent elevational richness patterns of mosses, marked by a fine‐scale mid‐elevation peak and shifting predictor importance, are driven by their sensitivity to microhabitat, climate, energy, and scale‐dependent ecological processes. Given scale‐dependent elevational richness patterns, we emphasize the need to establish an integrated “large‐scale guiding, small‐scale refining” conservation framework.