Linking Dynamic Habitat Indices to Resident Bird Richness: Evidence from a National-Scale Analysis in China

Dynamic Habitat Indices (DHIs) are crucial for understanding species richness patterns and provide a powerful tool for large-scale biodiversity conservation research. DHIs summarize three key aspects of vegetation productivity: (a) cumulative annual productivity, (b) minimum productivity, and (c) intra-annual variability. While DHIs have demonstrated potential for predicting richness patterns globally and in tropical regions, their predictive ability across climate zones and using multiple sources of species richness data remains untested. We assess the feasibility of using DHIs to predict the richness of resident birds in China and explore approaches to improve model performance. We used (a) IUCN range maps of terrestrial resident birds in China, and (b) species distribution models (SDMs) to delineate bird richness patterns, classifying species into six habitat guilds: forest, shrubland, grassland, cropland, wetland, and all resident birds. We linked DHIs to richness in each guild and quantified their predictive power using three modeling approaches: linear (GLM) and non-linear models (GAM, Random Forest). We also recorded the relative importance of each DHI component in the models. Our results show that DHIs best predicted cropland bird richness (SDM-based richness, adjusted R2 = 0.73), while for IUCN-based guilds, adjusted R2 ranged from 0.68 to 0.71. The Random Forest model achieved the highest performance and interpretability. Among DHI components, cumulative DHI consistently played the most dominant role in predicting richness from both SDM and IUCN sources. DHIs effectively capture the link between energy availability and resident bird richness in China, demonstrating considerable potential for biodiversity assessment and conservation planning.