Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, Iowa, United States of America.
Department of Statistics, Iowa State University, Ames, Iowa, United States of America.
PLoS One. 2023 Feb 15;18(2):e0279839. doi: 10.1371/journal.pone.0279839. eCollection 2023.
Soil respiration is a major source of atmospheric CO2. If it increases with warming, it will counteract efforts to minimize climate change. To improve understanding of environmental controls over soil CO2 emission, we applied generalized linear modeling to a large dataset of in situ measurements of short-term soil respiration rate, with associated environmental attributes, which was gathered over multiple years from four locations that varied in climate, soil type, and vegetation. Soil respiration includes many CO2-producing processes: we theorized that different environmental factors could limit each process distinctly, thereby diminishing overall CO2 emissions. A baseline model that included soil temperature, soil volumetric water content, and their interaction was effective in estimating soil respiration at all four locations (p < 0.0001). Model fits, based on model log likelihoods, improved continuously as additional covariates were added, including mean daily air temperature, enhanced vegetation index (EVI), and quadratic terms for soil temperature and water content, and their interactions. The addition of land cover and its direct interactions with environmental variables further improved model fits. Significant interactions between covariates were observed at each location and at every stage of analysis, but the interaction terms varied among sites and models, and did not consistently maintain importance in more complex models. A main-effects model was therefore tested, which included soil temperature and water content, their quadratic effects, EVI, and air temperature, but no interactions. In that case all six covariates were significant (p < 0.0001) when applied across sites. We infer that local-scale soil-CO2 emissions are commonly co-limited by EVI and air temperature, in addition to soil temperature and water content. Importantly, the quadratic soil temperature and moisture terms were significantly negative: estimated soil-CO2 emissions declined when soil temperature exceeded 22.5°C, and as soil moisture differed from the optimum of 0.27 m3 m-3.
土壤呼吸是大气 CO2 的主要来源。如果它随着气候变暖而增加,它将抵消为最小化气候变化而做出的努力。为了更好地了解环境对土壤 CO2 排放的控制作用,我们应用广义线性模型对大量的原位测量短期土壤呼吸速率及其相关环境属性的数据进行了分析,这些数据是在多年时间内从四个气候、土壤类型和植被各不相同的地点收集的。土壤呼吸包括许多产生 CO2 的过程:我们推测,不同的环境因素可能会明显限制每个过程,从而减少总的 CO2 排放。一个包含土壤温度、土壤体积含水量及其相互作用的基线模型,在四个地点都能有效地估计土壤呼吸(p < 0.0001)。基于模型对数似然的模型拟合随着附加的协变量(包括日平均气温、增强植被指数(EVI)、土壤温度和水分的二次项及其相互作用)的增加而不断提高。土地覆盖及其与环境变量的直接相互作用的加入进一步提高了模型拟合度。在每个地点和分析的每个阶段都观察到了协变量之间的显著相互作用,但这些相互作用项在不同的地点和模型之间存在差异,并且在更复杂的模型中并不始终保持重要性。因此,测试了一个主要效应模型,该模型包括土壤温度和水分、它们的二次效应、EVI 和气温,但没有相互作用。在这种情况下,当应用于所有站点时,所有六个协变量都是显著的(p < 0.0001)。我们推断,除了土壤温度和水分之外,局部尺度的土壤-CO2 排放通常还受到 EVI 和气温的共同限制。重要的是,土壤温度和水分的二次项具有显著的负效应:当土壤温度超过 22.5°C 时,估计的土壤-CO2 排放量下降,而当土壤水分与 0.27 m3 m-3 的最佳值不同时,排放量也会下降。