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Harvard Forest Symposium Abstract 2015

  • Title: Parameterization of a forest landscape model for simulating climate and land-use change impacts in New England
  • Primary Author: Matthew Duveneck (New England Conservatory)
  • Additional Authors: Luca Morreale (Boston University); Jonathan Thompson (Harvard Forest)
  • Abstract:

    Initializing forest landscape models (FLMs) to simulate changes in tree species composition requires accurate fine-scale forest attribute information mapped continuously over large areas. Nearest-neighbor imputation maps, maps developed from multivariate imputation of field plots, have high potential for use as the initial condition within FLMs, but the tendency for field plots to be imputed over large geographical distances can result in species being mapped outside of their home ranges, which is problematic. We developed an approach for evaluating and imputing field plots based on their similarity across multiple spatial environmental variates, their species composition, and their geographical distance between source and imputation to produce a map that is appropriate for initializing an FLM. We used this approach to map 13 million ha of forest throughout the six New England states (Rhode Island, Connecticut, Massachusetts, New Hampshire, Vermont, and Maine). Using both independent state forest and, more extensive, ecoregion validation data sets, we compared the imputation map to field inventory data, based on the dissimilarity of tree community composition and the rank order correlation of tree species abundance. Average Bray-Curtis dissimilarity between the imputation map and field plots was 0.32 and 0.12, for the state forest and ecoregion validation data sets, respectfully. Average Spearman rank order correlation was 0.81 and 0.93 for the state forest and ecoregion validation data sets, respectfully. Our analyses suggest that this approach to imputation can realistically capture regional variation in forest composition (Figure 1). We expect the imputation map will be valuable for several regional forest studies and that the approach could be successfully used in other regions. The final imputation map is available through the Harvard Forest data archive (# 234).





    Landscape simulations of forest change require parameterization of the drivers of forest growth. We calibrated the PnET-Succession extension to LANDIS-II using observed long-term aboveground biomass (AGB) records at six New England sites (Howland Forest (ME), Hubbard Brook (NH), Black Rock (NY), and three Harvard Forest (MA) sites (EMS tower, Lyford plots, and Hemlock tower). Next, we validated PnET-Succession with monthly Net Ecosystem Exchange (NEE) of carbon at sites with eddy-flux towers (EMS, Hemlock, and Howland). Finally, we compared simulated biomass after model-spin up (year zero) to forest inventory plot (FIA) biomass estimates used throughout the landscape imputation. The simulated monthly NEE resulted in a Root Mean Square Error (RMSE) of 56.4, 48.5, and 49.0 (g carbon m-2 month-1) for the EMS, Hemlock, and Howland forest towers, respectively. Results from the EMS tower, for example, suggest that the model dependably simulates growing season dynamics of NEE, however simulations underestimate dormant season respiration (Figure 2). Of the most frequently imputed plots, the simulated AGB resulted in a RMSE of 5.13 (kg biomass m-2) compared to the FIA allometric derived estimates of AGB. Our results suggest that the PnET-Succession extension for LANDIS-II is able to represent species-level forest dynamics throughout New England. These results will greatly benefit future simulations of forest change in New England and will be central to the LTER-V Future Scenarios project.











    Figure 1. Forest type assigned to imputed forest plots in New England. Non-forest classifications come from National Land Cover Database (2011).











    Figure 2. Monthly Eddy flux-tower estimates of Net Ecosystem Exchange (NEE) at the EMS tower at Harvard Forest compared to LANDIS-II PnET-Succession simulations. Results suggest that simulations largely capture the growing season (below zero) dynamics of NEE.











  • Research Category: Ecological Informatics and Modelling

  • Figures:
    • ems_nee_cali1.jpg