Abstract
Over recent decades, the combination of deforestation, peat drainage and fires have resulted in widespread degradation of Southeast Asia's tropical peatlands. These disturbances are generally thought to increase peat soil bulk density through peat drying and shrinkage, compaction, and consolidation. Biological oxidation and fires burning across these landscapes also consume surface peat, exposing older peat strata. The prevalence and severity of deforestation, peat drainage and fire are typically greater closer to canals, built to drain peatlands and provide access routes for people. We compared bulk densities of 240cm peat profiles from intact forests and degraded peatlands broadly, and also assessed differences between degraded peatlands near-to-canals (50–200m from the nearest canal) and far-from-canals (300+ m from the nearest canal). The effects of vegetation type and fire frequency on bulk density, irrespective of the distance from canal, were also investigated. Mean bulk density values ranged between 0.08 and 0.16gcm−3 throughout the 240cm peat profiles. Drainage of peat near-to-canals increased bulk density of peat above the minimum water table depth. Degradation by deforestation and fire also increased bulk densities of upper peat strata, albeit with greater variability. Peat sampled further from canals experienced less intense water table drawdowns, buffering them from drainage effects. These areas were also more commonly forested and burnt less frequently. Differences in bulk densities below minimum water table levels are less clear, but may reflect lowering of the current peat surface in degraded peatlands broadly. These results clearly show that important differences in bulk density exist across degraded peatlands that are spatially dependent on distance from canals and disturbance history. These landscape features should be taken into account when designing future bulk density sampling efforts and peatland restoration programs, or when extrapolating from existing sources in order to make accurate inferences from them.
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