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Many taxa show their highest species richness at intermediate elevations, but the underlying reasons for this remain unclear. Here, we suggest that the physiological tolerance hypothesis can explain species richness patterns along elevational gradients, and we used functional diversity to test this hypothesis. We analyzed herb species richness, functional diversity, and environmental conditions along a 1300 m elevational gradient in a temperate forest, Beijing, China. We found that herb richness exhibited a “hump-shaped” relationship with elevation, with peak richness at approximately 1800 m. Functional diversity showed a significant unimodal relationship with elevation. The duration of high temperatures (≥ 300C: DHT) was the best predictor for herb richness and functional diversity along the gradient from 1020 to 1800 m, which suggest richness is limited by high temperature at low elevations. While along the gradient from 1800 to 2300 m, the duration of low temperatures (≤ 0°C: DLT) was the most powerful explanatory variable, which indicated at high elevations, richness reduced due to low temperature. Our analyses showed that the functional diversity of traits related to drought-tolerance (leaf mass per area, leaf area, and leaf hardiness) exhibited negative relationships with DHT, while functional diversity of traits related to freezing-tolerance (leaf thickness and hair density) exhibited negative relationships with DLT. Taken together, our results demonstrated that the richness-elevation relationship is consistent with the physiological tolerance hypothesis: at low elevations, richness is limited by high temperatures, and at high elevations, richness is reduced due to low temperatures. We concluded that our results provide trait-based support for the physiological tolerance hypothesis, suggesting that mid-elevations offer the most suitable environmental conditions, thus higher numbers of species are able to persist.

Can the physiological tolerance hypothesis explain herb richness patterns along an elevational gradient? A trait-based analysis