No abstract

Assessing forest productivity is important for developing effective management regimes and predicting future growth. Despite some important limitations, the most common means for quantifying forest stand-level potential productivity is site index (SI). Another measure of productivity is gross primary production (GPP). In this paper, SI is compared with GPP estimates obtained from 3-PG and NASA’s MODIS satellite. Models were constructed that predict SI and both measures of GPP from climate variables. Results indicated that a nonparametric model with two climate-related predictor variables explained over 68% and 76% of the variation in SI and GPP, respectively. The relationship between GPP and SI was limited (R2 of 36%–56%), while the relationship between GPP and climate (R2 of 76%–91%) was stronger than the one between SI and climate (R2 of 68%–78%). The developed SI model was used to predict SI under varying expected climate change scenarios. The predominant trend was an increase of 0–5 m in SI, with some sites experiencing reductions of up to 10 m. The developed model can predict SI across a broad geographic scale and into the future, which statistical growth models can use to represent the expected effects of climate change more effectively.

No abstract

No abstract

A technique for estimating the vertical distribution of foliage area in broad-leaved forests was developed. The technique is similar to optical point-quadrat sampling, where estimates are based on heights to the lowest leaves above numerous sample locations beneath a canopy. In optical point-quadrat sampling, heights to lowest leaves are measured with a telephoto lens. Here, heights were measured using a commercially available laser range-finding instrument. The laser point-quadrat technique was tested in field studies conducted under broad-leaved forest canopies in western North Carolina and east-central Minnesota, U.S.A. Foliage-height profiles obtained by laser point-quadrat sampling were consistent with two of four published foliage-height profiles observed in 1995 at the North Carolina field locations. Total leaf area estimates obtained by laser point quadrats were not significantly correlated with values of leaf area index estimated by recent litter fall analyses at the North Carolina and Minnesota field locations. Although further evaluation and refinement of the technique is needed, laser point-quadrat sampling shows promise as a means of obtaining foliage-height profiles at a significantly reduced effort and with greater accuracy than methods commonly in use today.

No abstract

No abstract

As concerns rise over potential effects of greenhouse gas related climate change on terrestrial ecosystems, forest managers require growth and yield modeling capabilities responsive to changing climate conditions. Our goal was to develop prediction models of site index for eastern US forest tree species with climate and soil properties as predictors for use in predicting potential responses of forest productivity to climate change. Species-specific site index data from the USDA Forest Service Forest Inventory and Analysis (FIA) program were linked to contemporary climate data and soil properties mapped in the USDA Soil Survey Geographic (SSURGO) database. Random forest regression tree based ensemble prediction models of site index were constructed based on 37 climate-related and 15 soil attributes. In addition to a species-specific site index, aggregate models were developed for species grouped into two broad categories: conifer (softwood) and hardwood (broadleaved) species groups. Species-specific models based on climate and soil predictors explained the most variation in site index of any models tested (R 2 = 62.5%, RMSE = 3.2 m). Comparable results were found when grouping species into conifer and hardwood groups (R 2 = 63.9%, RMSE = 4.6 m for conifers; R 2 = 35.9%, RMSE = 4.2 m for hardwoods). Model predictions based on multiple global circulation models (GCMs) and Intergovernmental Panel on Climate Change (IPCC) development scenarios were tested for statistical significance using bootstrap resampling methods. Results showed significant increases over the 21st century in mean site index for conifers between +0.5 and +2.4 m. Over the same time period, mean hardwood site index showed decreases of as much as −1.7 m for the scenarios tested. The results demonstrate the utility of using climate and soils data in predicting site index across a large geographic region, and the potential of climate change to alter forest productivity in the eastern US. Additional investigation is needed to interpret spatial patterns and ecological relationships related to predictions from this type of model. Résumé : À cause des préoccupations croissantes au sujet des effets potentiels des changements climatiques liés aux gaz à effet de serre sur les écosystèmes terrestres, les aménagistes forestiers ont besoin de modèles de croissance et de production capables de tenir compte des conditions engendrées par les changements climatiques. Notre but était de mettre au point des modèles de prévision de l'indice de qualité de station pour les espèces d'arbre des forêts de l'est des États-Unis avec le climat et les propriétés du sol comme variables de prédiction pour prévoir les réactions potentielles de la production forestière face aux changements climatiques. Les données d'indice de qualité de station de chaque espèce provenant du programme d'analyse et d'inventaire forestier de l'USDA Forest Service ont été jumelées aux données du climat contemporain et aux propriétés du sol cartographiées dans la base de données SSURGO de l'inventaire g...