title: Avoiding treatment bias of REDD+ monitoring by sampling with partial replacement.
abstract: BACKGROUND
  Implementing REDD+ renders the development of a measurement, reporting and verification (MRV) system necessary to monitor carbon stock changes. MRV systems generally apply a combination of remote sensing techniques and in-situ field assessments. In-situ assessments can be based on 1) permanent plots, which are assessed on all successive occasions, 2) temporary plots, which are assessed only once, and 3) a combination of both. The current study focuses on in-situ assessments and addresses the effect of treatment bias, which is introduced by managing permanent sampling plots differently than the surrounding forests. Temporary plots are not subject to treatment bias, but are associated with large sampling errors and low cost-efficiency. Sampling with partial replacement (SPR) utilizes both permanent and temporary plots.
  
  
  RESULTS
  We apply a scenario analysis with different intensities of deforestation and forest degradation to show that SPR combines cost-efficiency with the handling of treatment bias. Without treatment bias permanent plots generally provide lower sampling errors for change estimates than SPR and temporary plots, but do not provide reliable estimates, if treatment bias occurs, SPR allows for change estimates that are comparable to those provided by permanent plots, offers the flexibility to adjust sample sizes in the course of time, and allows to compare data on permanent versus temporary plots for detecting treatment bias. Equivalence of biomass or carbon stock estimates between permanent and temporary plots serves as an indication for the absence of treatment bias while differences suggest that there is evidence for treatment bias.
  
  
  CONCLUSIONS
  SPR is a flexible tool for estimating emission factors from successive measurements. It does not entirely depend on sample plots that are installed at the first occasion but allows for the adjustment of sample sizes and placement of new plots at any occasion. This ensures that in-situ samples provide representative estimates over time. SPR offers the possibility to increase sampling intensity in areas with high degradation intensities or to establish new plots in areas where permanent plots are lost due to deforestation. SPR is also an ideal approach to mitigate concerns about treatment bias.
authors.affiliation: University of Hamburg, Institute for World Forestry, Leuschnerstrasse 91, 21031 Hamburg, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Köhl
authors.affiliation: USDA Forest Service, Northern Research Station, Forest Inventory and Analysis, 11 Campus Blvd, Suite 200, Newtown Square, PA 19073 USA.
authors.forename: Charles T
authors.initials: CT
authors.lastname: Scott
authors.affiliation: USDA Forest Service, Northern Research Station, Forest Inventory and Analysis, 11 Campus Blvd, Suite 200, Newtown Square, PA 19073 USA.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Lister
authors.affiliation: Centre for Agricultural Research in Suriname (CELOS), Postbus 1914, Paramaribo South, Suriname.
authors.forename: Inez
authors.initials: I
authors.lastname: Demon
authors.affiliation: University of Hamburg, Institute for World Forestry, Leuschnerstrasse 91, 21031 Hamburg, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Plugge
keywords: Forest carbon stock and carbon stock change estimation; Measurement; Reporting and verification (MRV); Representativeness over time