In order to evaluate and protect ecosystem services provided by peat and peaty soils, accurate estimations for the depth of the surface organic horizon are required. This study uses linear mixed models (LMMs) to test how topographic (elevation, slope, aspect) and superficial geology parameters can contribute to improved depth estimates across a Scottish upland catchment. Mean (n = 5) depth data from 283 sites (representing full covariate ranges) were used to calibrate LMMs, which were tested against a validation dataset. Models were estimated using maximum likelihood, and the Akaike Information Criterion was used to test whether the iterative addition of covariates to a model with constant fixed effects was beneficial. Elevation, slope and certain geology classes were all identified as useful covariates. Upon addition of the random effects (i.e. spatial modelling of residuals), the RMSE for the model with constant-only fixed effects reduced by 24%. Addition of random effects to a model with topographic covariates (fixed effects = constant, slope, elevation) reduced the RMSE by 13%, whereas the addition of random effects to a model with topographic and geological covariates (fixed effects = constant, slope, elevation, certain geology classes) reduced the RMSE by only 3%. Therefore, much of the spatial pattern in depth was explained by the fixed effects in the latter model. The study contributes to a growing research base demonstrating that widely available topographic (and also here geological) datasets, which have national coverage, can be included in spatial models to improve organic horizon depth estimations.

中文翻译：

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使用具有地形和地质协变量的线性混合模型估算苏格兰高地集水区泥炭和泥炭土壤的有机表面层深度

为了评估和保护泥炭和泥炭土壤提供的生态系统服务，需要准确估计地表有机层的深度。本研究使用线性混合模型 (LMM) 来测试地形（高程、坡度、坡向）和表层地质参数如何有助于改进苏格兰高地集水区的深度估计。平均值 ( n = 5) 来自 283 个站点（代表完整协变量范围）的深度数据用于校准 LMM，并根据验证数据集对其进行测试。使用最大似然估计模型，并使用 Akaike 信息准则来测试向具有恒定固定效应的模型迭代添加协变量是否有益。高程、坡度和某些地质类别都被确定为有用的协变量。在添加随机效应（即残差的空间建模）后，仅具有常量固定效应的模型的 RMSE 降低了 24%。向具有地形协变量（固定效应 = 常数、坡度、高程）的模型添加随机效应使 RMSE 降低了 13%，而向具有地形和地质协变量（固定效应 = 常量、坡度、高程）的模型添加随机效应, 某些地质类别）仅将 RMSE 降低了 3%。因此，后一种模型中的固定效应解释了深度的大部分空间模式。该研究有助于不断扩大的研究基础，证明覆盖全国的广泛可用的地形（以及这里的地质）数据集可以包含在空间模型中，以改进有机层深度估计。