4.5 Article

Sampling and processing roots from rocky forest soils

期刊

ECOSPHERE
卷 8, 期 6, 页码 -

出版社

WILEY
DOI: 10.1002/ecs2.1863

关键词

bias; biomass; carbon; error; fine roots; power analysis; soil coring; Special Feature: Uncertainty Analysis; uncertainty

类别

资金

  1. National Science Foundation
  2. QUEST (Quantifying Uncertainty in Ecosystem Studies)
  3. NSF Research Coordination Network
  4. Direct For Biological Sciences
  5. Division Of Environmental Biology [1633026] Funding Source: National Science Foundation
  6. Division Of Environmental Biology
  7. Direct For Biological Sciences [0949854, 1257906] Funding Source: National Science Foundation

向作者/读者索取更多资源

Quantifying root biomass in rocky forest soils is challenging. This report provides practical advice for field sampling and laboratory processing of root biomass in these settings. Manual coring is the most efficient method for sampling fine root biomass in the upper soil profile (we sampled to 30 cm). However, careful correction for coarse fragment volume is needed because manual coring is impeded by rocks. Unbiased estimation of root biomass below obstructions requires either excavating a pit or power coring. We recommend power coring because of the very high field labor costs of pit excavation. Roots can be separated from soil either by dry picking or by wet sieving. For surface organic matter-rich horizons typical of many forest soils, only dry picking is feasible. A timed interval approach can greatly reduce laboratory processing time. Because sorting live from dead roots is necessarily subjective, efforts to avoid fragmentation of root systems obtained from cores are strongly recommended. Sample size requirements for detecting changes or differences in root biomass at the stand level are presented based on extensive sampling in northern hardwood forests. Detecting 20% differences in fine root (< 1 mm) biomass in 0-30 cm soil using ten 5-cm manual cores generally would require about nine sample plots in a stand, whereas detecting such differences in deep soil (30-50 cm) would be virtually impossible because of extreme spatial variation. Power analyses such as these can help improve experimental designs, as the spatial intensity of sampling determines the detectable difference, which can in turn guide decisions about the temporal frequency of sampling.

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