Height-diameter (H-D) allometry plays an important role in exploring forest growth dynamics and estimating forest biomass and carbon storage. Ponderosa pine (Pinus ponderosa) is one of the most economically and ecologically valuable tree species in North America. However, our understanding about the H-D allometry of ponderosa pine and its variation along environmental gradients is still limited. To quantify the region-scale variation in H-D allometry, 87715 pairs of height and diameter from 4034 plots in seven states in the western United States were used to compile a dataset together with neighborhood competition, species diversity, climatic and topographic variables. We used the power-law function as the basic H-D allometric model and generalized it to incorporate competition, climate, diversity and topography. Then, the nonlinear mixed-effects (NLME) modeling method was used to further fit the H-D allometry. The results showed that the allometric exponents in seven states changed near the theoretical 2/3 exponent, indicating that tree allometry was plastic rather than fixed, and its allometric variation was related to the difference of growth environment. The quality of the multi-level (state, inventory year, site and plot) NLME allometric model was significantly better than that of the basic and the generalized allometric model. Further study showed that tree height was positively correlated with DBH dominance (DOMI, a neighborhood-based competition index), mean annual precipitation (MAP) and mean annual temperature (MAT), however it was negatively correlated with species mingling (MING). Moreover, we also found that the variations in H-D allometry across climate gradients were far weaker than those in competition gradients, which indicated that competition dominated tree allometric relationship. It could be proved that the allometry of ponderosa pine should be driven by two opposite factors: competition from neighbors drove trees to invest more in height, whereas tree height tended to be shorter under arid and cold conditions. Therefore, the large-scale variation in H-D allometry of ponderosa pine was mainly explained by neighboring competition and partly by climate, diversity, or topography. Large amounts of data, wide environmental gradients and statistically reliable H-D allometric model help us make convincing conclusions about allometric variation. Our results reveal the response mechanism of tree resource allocation to environmental gradients, which is bound to be beneficial to make rational and scientific forest management decision under future environmental changes.

高度直径 (HD) 异速生长在探索森林生长动态和估计森林生物量和碳储存方面发挥着重要作用。黄松（黄松) 是北美最具经济和生态价值的树种之一。然而，我们对黄松 HD 异速生长及其随环境梯度变化的了解仍然有限。为了量化 HD 异速生长的区域尺度变化，来自美国西部七个州 4034 个地块的 87715 对高度和直径被用于编译一个数据集以及邻里竞争、物种多样性、气候和地形变量。我们使用幂律函数作为基本的 HD 异速生长模型，并将其概括为包含竞争、气候、多样性和地形。然后，使用非线性混合效应（NLME）建模方法进一步拟合 HD 异速生长。结果表明，7种状态的异速生长指数变化接近理论2/3指数，说明树木异速生长是可塑性的而不是固定的，其异速生长变异与生长环境的差异有关。多级（州、库存年份、地点和地块）NLME 异速生长模型的质量显着优于基本和广义异速生长模型。进一步的研究表明，树高与 DBH 优势度（DOMI，基于邻域的竞争指数）、平均年降水量（MAP）和年平均温度（MAT）呈正相关，而与物种混合（MING）呈负相关。此外，我们还发现，跨气候梯度的 HD 异速生长变化远弱于竞争梯度中的变化，这表明竞争主导了树木异速生长关系。可以证明，黄松异速生长应该由两个相反的因素驱动：邻居的竞争促使树木在高度上投入更多，而在干旱和寒冷条件下，树木高度往往更短。因此，黄松 HD 异速生长的大规模变化主要由邻近竞争解释，部分由气候、多样性或地形解释。大量数据、广泛的环境梯度和统计上可靠的 HD 异速生长模型帮助我们得出关于异速生长变异的令人信服的结论。我们的研究结果揭示了树木资源配置对环境梯度的响应机制，必将有利于在未来环境变化下做出合理、科学的森林经营决策。来自邻居的竞争促使树木在高度上投资更多，而在干旱和寒冷的条件下，树木的高度往往更短。因此，黄松 HD 异速生长的大规模变化主要由邻近竞争解释，部分由气候、多样性或地形解释。大量数据、广泛的环境梯度和统计上可靠的 HD 异速生长模型帮助我们得出关于异速生长变异的令人信服的结论。我们的研究结果揭示了树木资源配置对环境梯度的响应机制，必将有利于在未来环境变化下做出合理、科学的森林经营决策。来自邻居的竞争促使树木在高度上投资更多，而在干旱和寒冷的条件下，树木的高度往往更短。因此，黄松 HD 异速生长的大规模变化主要由邻近竞争解释，部分由气候、多样性或地形解释。大量数据、广泛的环境梯度和统计上可靠的 HD 异速生长模型帮助我们得出关于异速生长变异的令人信服的结论。我们的研究结果揭示了树木资源配置对环境梯度的响应机制，必将有利于在未来环境变化下做出合理、科学的森林经营决策。黄松 HD 异速生长的大规模变化主要是由邻近竞争解释的，部分原因是气候、多样性或地形。大量数据、广泛的环境梯度和统计上可靠的 HD 异速生长模型帮助我们得出关于异速生长变异的令人信服的结论。我们的研究结果揭示了树木资源配置对环境梯度的响应机制，必将有利于在未来环境变化下做出合理、科学的森林经营决策。黄松 HD 异速生长的大规模变化主要是由邻近竞争解释的，部分原因是气候、多样性或地形。大量数据、广泛的环境梯度和统计上可靠的 HD 异速生长模型帮助我们得出关于异速生长变异的令人信服的结论。我们的研究结果揭示了树木资源配置对环境梯度的响应机制，必将有利于在未来环境变化下做出合理、科学的森林经营决策。