Study on Beta Diversity of Ancient and Famous Trees in Taizhou

Based on the 2017 survey results of ancient and famous trees in Taizhou City, Zhejiang Province, this study divides the city into units according to 0°10′ longitude intervals, 0°10′ latitude intervals, or 100 m elevation intervals, and investigates the spatial gradient patterns of beta diversity among ancient and famous trees in the city. The results showed that: (1) The overall β diversity of ancient and famous trees in Taizhou City is mainly determined by the spatial rotation of tree species along latitude, longitude, or altitude gradients. (2) The top eight species contributing most to overall beta diversity based on abundance were consistent across latitude, longitude, and elevation gradients: Rhododendron fortunei, Cinnamomum camphora, Pinus massoniana, Cryptomeria japonica, Castanopsis eyrei, Liquidambar formosana, Castanopsis sclerophylla, and Cupressus funebris. (3) The Sorensen, Jaccard, Bray-Curtis, and Morisita-Horn indices between adjacent regions initially increased and then decreased with increasing latitude. Regardless of abundance or presence-absence binary methods, Region 8 (29°10′0″-29°20′0″ N) contributed most to overall beta diversity. Along the longitudinal gradient, these indices exhibited a wave-like pattern of initial decrease followed by increase. Region 5 (120°0′0″−121°10′0″ E) showed the highest contribution to beta diversity. Along the elevational gradient, the indices displayed a wave-like trend of initial increase followed by decrease, with Region 1 (1−100 m elevation) contributing most significantly. The findings indicate that species with wide and concentrated distributions contribute most to beta diversity when abundance-based metrics are used, while species with sparse but uniform distributions contribute more under presence-absence binary methods. When selecting beta diversity indices, it is essential to consider species abundance or relative dominance to avoid overestimating the role of rare species, which may lead to biased conclusions.