No abstract

With expanded interests in sustaining productivity under changing climate, management, and disturbance regimes, we sought a means of mapping the potential productivity of forests across the state of Oregon in the Pacific Northwest, USA. We chose the mapping tool 3-PG, a simplified physiologically based process model that can be driven with monthly averaged climatic data (DAYMET) and estimates of soil fertility based on soil nitrogen content. Maximum periodic mean increment (MAI, m3·ha–1·year–1), a measure of the forest's productive potential, was generated by the 3-PG spatial model and mapped at 1-km2 resolution for the most widely distributed tree species, Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco). Maximum MAI is linearly correlated with yield table site indices and therefore comparable with field-derived estimates of site indices obtained from measurement of tree heights and ages at 5263 federal forest survey points. The model predicted 100-year site index (SI) reasonably well (R2 = 0.55; RMSE = 9.1), considering the difference in spatial resolution between the modeled (1 km2) and field-measured SI (<0.1 ha) and that field plots were offset for confidentiality by 1–3 km. We created a map of the differences between modeled and field-measured SI and found that the 3000 points within ±6 m error were relatively evenly distributed across Oregon. Improving the accuracy in modeling and mapping forest productivity using 3-PG will likely require refinements in soil surveys, the quality of climatic data, the location of field plots, and the model functions and species parameters.

No abstract

At the regional and continental scale, ecologists have theorized that spatial variation in biodiversity can be interpreted as a response to differences in climate. To test this theory we assumed that ecological constraints associated with current climatic conditions (2000-2004) might best be correlated with tree richness if expressed through satellite-derived measures of gross primary production (GPP), rather than the more commonly used, but less consistently derived, net primary production. To evaluate current patterns in tree diversity across the contiguous United States we acquired information on tree composition from the USDA Forest Service's Forest Inventory and Analysis program that represented more than 17,4000 survey plots. We selected 2693 cells of 1000 km2 within which a sufficient number of plots were available to estimate tree richness per hectare. Our estimates of forest productivity varied from simple vegetation indices indicative of the fraction of light intercepted by canopies at 16-d intervals, a product from the MODIS (Moderate Resolution Imaging Spectro-radiometer), to 8- and 10-d GPP products derived with minimal climatic data (MODIS) and SPOT-Vegetation (Systeme Pour l'Observation de la Terre), to 3-PGS (Physiological Principles Predicting Growth with Satellites), which requires both climate and soil data. Across the contiguous United States, modeled predictions of gross productivity accounted for between 51% and 77% of the recorded spatial variation in tree diversity, which ranged from 2 to 67 species per hectare. When the analyses were concentrated within nine broadly defined ecoregions, predictive relations largely disappeared. Only 3-PGS predictions fit a theorized unimodal function by being able to distinguish highly productive forests in the Pacific Northwest that support lower than expected tree diversity. Other models predicted a continuous steep rise in tree diversity with increasing productivity, and did so with generally better or nearly equal precision with fewer data requirements.

A knowledge‐based geographic information system (GIS) model was developed and used to predict net nitrogen mineralization within forest ecosystems of the Midwestern Great Lakes region, USA (Illinois, Indiana, Michigan, Ohio, and Wisconsin). Climate, soil, and forest inventory data were used in conjunction with data relating initial N and lignin concentrations of leaf litter. Net N mineralization (Nnet) from leaf litter of forest ecosystems of the entire region was predicted as a function of litter quality (N:C ratio), annual actual evapotranspiration, soil texture, and litter production. Regional variation was evident in the model results: Nnet decreased from rates exceeding 120 kg·ha−1·yr−1 in the deciduous forest soils of southern Illinois, Indiana, and Ohio to 20 kg·ha−1·yr−1 in the coniferous forest soils of northern and eastern Michigan. Wisconsin’s forest soils had intermediate Nnet rates, predominantly 40–90 kg·ha−1·yr−1. The model was most sensitive to N concentration of litter, which indicated that litter quality of plant species is the most important factor controlling spatial distribution of N mineralization of ecosystems, even at the regional scale. The GIS model in this research provides a means for scaling between stand and regional scales. The model was a practical approach for estimating net N mineralization in forest ecosystems at the regional scale. Such models will become increasingly important in simulating the impacts of global environmental change on forest ecosystems.