温带森林乔木生物量估算:Logistic模型优于Allometric模型
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国家自然科学基金(41901038);城市与区域生态国家重点实验室开放基金(SKLURE2022-2-4);浙江农林大学科研发展基金(2020RF018)


The logistic model outperforms allometric regression in estimating the biomass of temperate forest
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    摘要:

    作为研究森林生态系统结构与功能的基础要素,准确估算植被生物量关乎温室气体减排政策制定,以及区域气候稳定性评估,具有显著的科学意义和社会需求。Allometric模型提供了量化生物量与个体体型关系的简洁数学形式,但其幂函数结构描述了生物量持续加速且无限增长,与随植株增大而不断加剧的种内种间竞争和资源限制相悖。然而,当前严重缺失大体型植株的不合理样本结构部分掩盖了模型缺陷,是造成该模型获得重复验证的重要潜在原因。相较而言,通过引入限制因子,Logistic模型同时具备描述高速增长和后期渐趋收敛的模型特征,却鲜被用于植被生物量估算。因此,本研究搜集了典型植被类型--温带森林乔木生物量相关的197篇已发表文献(1945-2016年),基于198种针阔叶乔木物种总计26402个叶、茎和地上部分生物量数据,采用Logistic模型分析了生物量随胸径增长的变化规律。结果表明,较之Allometric模型(R2* 0.76、RMSE 0.44 g、AIC 7189.9),Logistic模型呈现更好的拟合优度(R2* 0.81、RMSE 0.39 g、AIC 5809.1)。此外,Logistic模型计算了平衡生物量与平衡生长率,以反映与生境资源相适应的植株生物量及其累积速率,通过识别生物量渐趋稳定的胸径阈值,发现超过该拐点的大体型样本占比不足0.71%,呈现显著的生态学意义。综上,Logistic模型估算温带森林乔木生物量具备统计效力和理论优势。研究显著改进了植被生物量估算模型,有助于揭示植物碳蓄积策略,了解森林能流规律和碳库动态,为制定气候变化应对政策提供依据。

    Abstract:

    Biomass is key to understand the structure and function of forest ecosystems. Precisely estimating tree biomass is critical in reducing greenhouse gas emissions and assessing regional climate stability. The allometric model offers a concise expression of mathematical formulation to quantify the relation between plant biomass and individual size, which has been commonly applied for different life forms in various ecosystems. This power law relationship describes the disproportional increase of biomass as the individual size grows, which indicates a typical J-shaped growth pattern. This contradicts the intensifying inter-/intra-specific competition and resource limits. However, the allometric model often passed model validation in previous studies, partly due to improper data structures characterized by lacking large-sized individuals. By introducing the limited factor, the logistic model describes the accelerating biomass growth at first but later a slowing growth rate with the increasing branch size. A typical S-shaped growth pattern could be noted, accordingly, which displays a more ecologically sound form in theory. However, the logistic model has rarely been applied to estimate biomass and its relation with individual sizes. In this work, we collected data from 197 studies conducted in the temperate forest worldwide during 1945-2016 based on two published datasets. A total of 26402 samples of leaves, stems, and aboveground biomass were collected as well as the data on stem size from the 198 species of broad- and fine-leaved trees. Our results showed that the logistic model had a larger R2* (0.81), smaller RMSE (0.39 g), and smaller AIC (5809.1) than the allometric model (R2* 0.76, RMSE 0.44 g, and AIC 7189.9, respectively). This confirms the better performance of the logistic model relative to the allometric model. The logistic model's better predictive performance was also demonstrated via the linear regression between the estimated biomass and measurements. Furthermore, compared with allometric models, the logistic models were of greater ecological significance by providing the equilibrium biomass, equilibrium growth rate, and points of inflection (POIs) that described the threshold tree sizes starting approaching equilibrium biomass. We further categorized the trees with the bigger-than-POI size as the large-sized samples, which merely took the ≤ 0.71% share in our dataset. This study addresses that the logistic model outperforms the allometric model in predicting tree biomass of temperate forests in statistical performances and ecological significance. This benefits clearly understanding the size-related strategies of carbon sequestration of plants, precisely predicting the dynamics and spatial distribution of forest carbon storage, and mitigating future climate change.

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周佳玉,袁川,马杰敏,毛梦绮,岳晓萍,郭立,王帅,高光耀.温带森林乔木生物量估算:Logistic模型优于Allometric模型.生态学报,2023,43(22):9342~9355

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