Nutrient Resorption and C:N:P Stoichiometry Responses of a Pinus massoniana Plantation to Various Thinning Intensities in Southern China

Understanding the responses of C:N:P stoichiometry and nutrient resorption to thinning is essential to evaluate the effects of management practices on biogeochemical cycling in plantation forest ecosystems. However, nutrient resorption and C:N:P stoichiometry do not always respond in the same way to various thinning intensities, and the underlying mechanisms are not well understood. In this study, we aimed to examine the mechanisms underlying the impacts of thinning on C:N:P stoichiometry in a Pinus massoniana plantation, focusing on interactions among soils, plant tissues (leaves and litter), and soil properties. We conducted four different thinning treatments to determine the effects of thinning on the C:N:P stoichiometric ratios in leaves, litter, and soil in a Pinus massoniana plantation ecosystem. Thinning significantly increased the C, N, and P content of leaves, litter, and soil (p < 0.05). The effects of thinning on C:N:P stoichiometry varied strongly with thinning intensity. Specifically, thinning significantly decreased all C:N:P stoichiometry except leaf N:P and litter C:N (p < 0.05). The N resorption efficiency (NRE) showed no significant change, but thinning significantly decreased the P resorption efficiency (PRE, p < 0.05). This suggests that thinning has inconsistent impacts on N and P cycling in Pinus massoniana plantations. In addition, these different responses suggest that soil physicochemical processes play a crucial role in regulating the effects of thinning. Thinning intensity regulates the biogeochemical cycles of C, N, and P in Pinus massoniana plantation ecosystems by affecting nutrient resorption and soil physicochemical processes. The inconsistent results obtained can be attributed to the complexities of stand environments and the redistribution of site resources following thinning. Therefore, incorporating the effects of thinning intensity into nutrient cycling models may improve predictions related to achieving long-term forest management strategies.