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Keywords:

  • diversity;
  • forest;
  • ground flora;
  • indicator species analysis;
  • multi-response permutation procedures;
  • species richness;
  • species–area curves

Abstract

As monitoring plans for the restoration of Pinus ponderosa forests in the southwestern United States evolve toward examining multifactor ecosystem responses to ecological restoration, designing efficient sampling procedures for understory vegetation will become increasingly important. The objective of this study was to compare understory composition and diversity among thin/burn and control treatments in a P. ponderosa restoration, while simultaneously examining the effects of sampling design and multivariate analyses on which conclusions were based. Using multi-response permutation procedures (MRPP), we tested the null hypothesis of no difference in understory species composition among treatments using different data matrices (e.g., frequency and cover) for two different sampling methods. Treatment differences were subtle and were detected by an intensive 50, 1-m2 subplot sampling method for all data matrices but were not detected by a less intensive point-intercept sampling method for any matrix. Sampling methods examined in this study controlled results of multivariate analyses more than the data matrices used to summarize data generated by a sampling method. We partitioned data into plant life form and native/exotic species categories for MRPP, and this partitioning isolated plant groups most responsible for treatment differences. We also examined the effects of number of 1-m2 subplots sampled on mean-species-richness/m2 estimates and found that estimates based on 10 subplots and based on 50 subplots were highly correlated (r = 0.99). Species–area curves indicated that the 50, 1-m2 subplot sampling method detected the common species of sites but failed to detect the majority of rare species. Additional sampling-design studies are needed to develop single sampling designs that produce multifactor data on plant composition, diversity, and spatial patterns amenable to multivariate analyses as part of monitoring plans of vegetation responses to ecological restoration.