Patterns of Plant Species Diversity in Remnant and Restored Tallgrass Prairies

To restore diversity of native vegetation, we must understand factors responsible for diversity in targeted communities. These factors operate at different spatial scales and may affect the number and relative abundances of species differently. We measured diversity of plant species and functional groups of species in replicated plots within paired restored and remnant (relic) tallgrass prairies at three locations in central Texas, U.S.A. To determine the contributions of species abundances and of spatial patterns of diversity to differences between prairie types, we separated diversity into richness and evenness (relative biomass) and into within‐plot (α), among‐plot (β), and prairie (γ) components. Species diversity was greater in remnant than in restored prairies at all spatial scales. At the γ scale, both species richness and species evenness were greater in remnants because of greater spatial variation in species composition. At the α scale, remnants were more diverse because of greater richness alone. Mean α richness correlated positively with the size of the species pool in restored prairies only, implying that in remnants, α richness was influenced more by colonization dynamics than by the number of species available for colonization. Plots in remnant prairies contained more functional groups and fewer species per group than did plots in restored prairies, suggesting that resource partitioning was greater in relic prairies. Our results are consistent with the interpretation that local ecological processes, like resource partitioning and limitations on seed dispersal, contribute to the greater diversity of remnant than restored prairies in central Texas. Restoration practices that limit abundances of competitive dominants, increase the number of species in seed mixtures, and increase the proximity of plants of different functional groups thus may be required to better simulate the plant diversity of tallgrass prairies.     

Monitoring an Arizona Ponderosa Pine Restoration: Sampling Efficiency and Multivariate Analysis of Understory Vegetation

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.