Seasonal Variation in the Capacity for Plant Trait Measures to Predict Grassland Carbon and Water Fluxes
Seasonal Variation in the Capacity for Plant Trait Measures to Predict Grassland Carbon and Water Fluxes
复制标题
植物性状测量预测草地碳和水通量能力的季节性变化
作者：
G. Everwand;Ellen L. Fry;Till Eggers;P. Manning
有必要在野外条件下准确预测生态系统碳（C）和水通量。以往的研究表明，生态系统的属性可以预测从社区丰富加权平均（CWM）的植物功能性状和性状变异的措施在一个社区（FDvar）。性状预测碳（C）和水通量的能力，以及这些性状功能关系的季节依赖性尚未得到充分探讨。在这里，我们测量了白天的C和水通量超过四个季节在草地上的一系列演替年龄在英格兰南部。在模型选择过程中，我们将这些通量与环境协变量和植物生物量措施，然后添加CWM和FDvar植物性状措施，这些措施是从温室条件下生长的单个植物的措施中放大的。在低生物活动期间描述通量的模型包含很少的预测因子，这通常是非生物因子。在更生物活跃的时期，模型包含更多的预测因子，包括植物性状的措施。基于现场的植物生物量的措施，一般更好的预测通量比水煤浆和FDvar性状。然而，当这些措施被用于组合性状占额外的变化。特征显着的预测，他们的身份往往反映了季节性植被动态。这些结果表明，数据库衍生的性状措施，可以提高生态系统C和水通量的预测。需要进行对照研究和涉及更详细通量测量的研究，以验证和探索这些研究结果，考虑到使用简单植被测量来帮助预测小尺度通量的潜力，这是一项值得的努力。
There is a need for accurate predictions of ecosystem carbon (C) and water fluxes in field conditions. Previous research has shown that ecosystem properties can be predicted from community abundance-weighted means (CWM) of plant functional traits and measures of trait variability within a community (FDvar). The capacity for traits to predict carbon (C) and water fluxes, and the seasonal dependency of these trait-function relationships has not been fully explored. Here we measured daytime C and water fluxes over four seasons in grasslands of a range of successional ages in southern England. In a model selection procedure, we related these fluxes to environmental covariates and plant biomass measures before adding CWM and FDvar plant trait measures that were scaled up from measures of individual plants grown in greenhouse conditions. Models describing fluxes in periods of low biological activity contained few predictors, which were usually abiotic factors. In more biologically active periods, models contained more predictors, including plant trait measures. Field-based plant biomass measures were generally better predictors of fluxes than CWM and FDvar traits. However, when these measures were used in combination traits accounted for additional variation. Where traits were significant predictors their identity often reflected seasonal vegetation dynamics. These results suggest that database derived trait measures can improve the prediction of ecosystem C and water fluxes. Controlled studies and those involving more detailed flux measurements are required to validate and explore these findings, a worthwhile effort given the potential for using simple vegetation measures to help predict landscape-scale fluxes.
影响因子：
8.8
作者：
Milcu, Alexandru;Roscher, Christiane;Roy, Jacques
通讯作者：
Roy, Jacques
影响因子：
6.6
作者：
Nakagawa, Shinichi;Schielzeth, Holger
通讯作者：
Schielzeth, Holger