Linking climate, gross primary productivity, and site index across forests of the western United States

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 ([R.sup.2] of 36%-56%), while the relationship between GPP and climate ([R.sup.2] of 76%-91%) was stronger than the one between SI and climate ([R.sup.2] 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. Il est important d'evaluer la productivite forestiere pour elaborer des regimes d'amenagement efficace et predire la croissance future. Malgre certaines limitations importantes, le moyen le plus frequemment utilise pour quantifier la productivite potentielle A l'echelle du peuplement forestier est l'indice de qualite de station (IQS). La production primaire brute (PPB) est une autre mesure de la productivite. Dans cet article, IQS est compare aux estimations de PPB obtenues A l'aide du modele 3-PG et de l'instrument satellitaire MODIS de la NASA. Nous avons bati des modeles qui predisent IQS et les deux mesures de PPB A partir des variables climatiques. Les resultats ont montre qu'un modele non parametrique avec deux variables explicatives reliees au climat expliquait respectivement 68 % et 76 % de la variation de IQS et de PPB. La relation entre PPB et IQS etait faible ([R.sup.2] de 36 % A 56 %) tandis que la relation entre PPB et le climat ([R.sup.2] de 76 % A 91 %) etait plus forte que la relation entre IQS et le climat ([R.sup.2] de 68 % A 78 %). Le modele qui a ete developpe pour IQS a ete utilise pour predire IQS A partir de differents scenarios previsibles de changement climatique. Une augmentation de IQS de 0 A 5 m etait la tendance predominante alors que certaines stations subissaient des reductions allant jusqu'A 10 m. Le modele qui a ete developpe peut predire IQS pour une large gamme d'echelles geographiques ainsi que dans le futur et la valeur de IQS ainsi obtenue peut etre utilisee par les modeles statistiques de croissance pour illustrer plus efficacement les effets anticipes du changement climatique. [Traduit par la Redaction]
Introduction Measures of productivity are critical in efforts to understand and maintain forest ecosystems sustainably for a range of products and services, while managing risks related to wildfire, pests, and [...]