Explaining Productivity Differences Among Tree Species via Biotic and Abiotic Factors

Greenhouse gases emitted by humans have exacerbated global climate change. Forests can effectively sequester atmospheric carbon dioxide through photosynthesis, and afforestation has been widely adopted worldwide to mitigate climate change. Cunninghamia lanceolata and Pinus massoniana, as major afforestation tree species, are extensively cultivated in southern China. However, the mechanisms by which climate, topography, biodiversity, forest structure, and forest growth status affect the productivity of these two species remain unclear. This study used forest inventory data from Lishui City combining the Biomod2 model with a structural equation model (SEM) to investigate the differential effects of biotic and abiotic factors on the productivity of the two tree species. The results showed that at the same diameter at breast height (DBH), the biomass of P. massoniana reached 384.67 kg, accounting for 188.75% of that of C. lanceolata (211.07 kg). The dominant climatic factors affecting C. lanceolata and P. massoniana were different; the most important climatic factors affecting C. lanceolata were Bio 17, Bio 15, Bio 05, Bio 08, and Bio 02, while those affecting P. massoniana were Bio 18, Bio 04, and Bio 01. Furthermore, the explanatory power of the structural equation model (SEM) optimized by the Biomod2 model was effectively improved. Biodiversity and forest growth factors were the most important biotic factors affecting C. lanceolata (p < 0.01), while structural diversity and forest growth factors were the most important biotic factors affecting P. massoniana (p < 0.05). Biodiversity and structural diversity exerted divergent effects on C. lanceolata and P. massoniana in different growth stages, exerting negative effects in the early growth stage and positive effects in the late growth stage. These outcomes were jointly driven by the selection effect and niche complementarity. This study recommends the forest management practices should select tree species based on local conditions.