Spatial models with covariates improve estimates of peat depth in blanket peatlands.

Dylan M Young,Surajit Ray,Lauren E Parry,Duncan Lee,Budiman Minasny

doi:10.1371/journal.pone.0202691

Abstract

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.

Highlights

Peatlands are globally significant stores of soil carbon, are important for biodiversity, and provide many important ecosystem services such as water regulation, and the provision of food, fibre, and fuel [1,2]
For fitted predictions based on peat depths sampled using the stratified method (ST), root mean square prediction error (RMSE) varied from 2.54 cm to 2.62 cm for the spatial model, and from 8.53 cm to 15.70 cm for the linear model
When we used the dataset obtained by the gridded sampling approach (GR) the differences between models were smaller, which is likely caused by the smaller range of peat depth values and the lack of spatial autocorrelation in this dataset

Summary

Introduction

Peatlands are globally significant stores of soil carbon, are important for biodiversity, and provide many important ecosystem services such as water regulation, and the provision of food, fibre, and fuel [1,2]. Spatial models with covariates improve estimates of peat depth and fate of peatland carbon (C) [5], their impact can be mediated by internal negative feedback mechanisms [6,7] This combination of allogenic factors and autogenic feedbacks results in heterogeneous peat accumulation across a peatland [8,9] which make it challenging to assess peat depth over large areas [10]. Blanket peatlands are hyperoceanic ombrotrophic bogs that develop over hilly or undulating terrain covering large areas including all but the steepest slopes. They are found in Europe (Great Britain, Iceland, Ireland, Norway), North America (Labrador, Newfoundland, Alaska), South America (Falkland Islands, Patagonia, Ecuador, Columbia), Asia (e.g. Kamchatka), and Australasia (Tasmania, New Zealand). Similar peatland types (condensation mires), occur in the European Alps and Ruwenzori Mountains in Uganda [11] Because the accumulation of peat in blanket peatlands can occur over varying terrain, allogenic, topographic factors, are perhaps more influential on peat accumulation than on any other peatland type [12,13]

Methods

Results

Discussion

Conclusion