Spatial models with covariates improve estimates of peat depth in blanket peatlands.
Spatial models with covariates improve estimates of peat depth in blanket peatlands.
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DOI：
10.1371/journal.pone.0202691
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Ray S
中科院分区：
文献类型：
--
作者：
Young DM;Parry LE;Lee D;Ray S
泥炭地是一种空间异质性的生态系统，其发展是由于一套复杂的自生物理和生物地球化学过程以及气候和地形等外源因素。它们是全球土壤碳的重要储存，因此预测泥炭地的深度是对其规模进行准确评估的重要组成部分。然而，在预测泥炭地的深度时，很少有人试图同时考虑内部和外部过程。使用毯泥炭地在英国作为一个案例研究，我们比较了线性和地质统计（空间）模型和协变量适用于泥炭地在世界各地的丘陵或起伏的地形。我们假设，空间模型将作为一个代理的自生过程中泥炭地，可以调解泥炭高原或浅坡的积累。我们的研究结果表明，在所有情况下，空间模型的性能都优于线性模型--均方根误差（RMSE）更低，95%的预测区间更窄。在支持我们的假设，空间模型也更好地预测更深的泥炭区，我们表明，其预测性能在深泥炭区是依赖于空间自相关的深度观测。如果它们不是，空间模型的性能仅略优于线性模型。因此，我们建议进行深度调查的从业人员充分考虑预测位置的地形特征的变化，并采取抽样方法，使观测空间自相关。
Peatlands are spatially heterogeneous ecosystems that develop due to a complex set of autogenic physical and biogeochemical processes and allogenic factors such as the climate and topography. They are significant stocks of global soil carbon, and therefore predicting the depth of peatlands is an important part of establishing an accurate assessment of their magnitude. Yet there have been few attempts to account for both internal and external processes when predicting the depth of peatlands. Using blanket peatlands in Great Britain as a case study, we compare a linear and geostatistical (spatial) model and several sets of covariates applicable for peatlands around the world that have developed over hilly or undulating terrain. We hypothesized that the spatial model would act as a proxy for the autogenic processes in peatlands that can mediate the accumulation of peat on plateaus or shallow slopes. Our findings show that the spatial model performs better than the linear model in all cases—root mean square errors (RMSE) are lower, and 95% prediction intervals are narrower. In support of our hypothesis, the spatial model also better predicts the deeper areas of peat, and we show that its predictive performance in areas of deep peat is dependent on depth observations being spatially autocorrelated. Where they are not, the spatial model performs only slightly better than the linear model. As a result, we recommend that practitioners carrying out depth surveys fully account for the variation of topographic features in prediction locations, and that sampling approach adopted enables observations to be spatially autocorrelated.
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