Abstract

Understanding the productivity of forestland is essential in sustainable management of forest ecosystems. The most common measure of site productivity is breast height–age site index (BHASI). BHASI has limitations as a productivity measure and can compound error in predictive models. We explored the accuracy of productivity predictions using an alternative productivity measure (10-meter site index) and a nonparametric approach. An orthogonal sampling design ensured samples were collected across the range of conditions known to influence Douglas-fir (Pseudotsuga menziesii var. glauca) height-growth rates. Using climate, soil, and topographic data along with 10-meter site index measurements, we compared five possible models to estimate forest productivity. Model parameters, performance, and predictions were compared. Twelve validation sites were used to test the accuracy of model predictions. Model performance was significantly improved when smoothing span values were optimized and elevation was added as a predictor. A four-predictor nonparametric model with a bias-corrected Akaike information criterion–optimized smoothing span value produced the most accurate results and was used to produce forest productivity maps for the study area. The low resolution of currently available climatic data and the complex nature of the study area landscape necessitate a topographic variable for accurate productivity predictions.

Study Implications

Defining and understanding forest productivity is of interest to a wide variety of natural resource professionals including ecologists, climate change experts, forest biometricians, and forest managers. A new method of defining forest productivity using multipoint height-age pairs at 10 and 20 meters and calculated growth rates combined with an appropriate landscape-scale stratification and a nonparametric approach provides accurate productivity estimates. This method is more widely applicable and more precise for specific locations than previous productivity estimation methods. Better productivity and tree growth information will provide more accurate estimates of future forest condition and structure.

中文翻译：

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使用站点特征，多点测度和非参数方法估算森林生产力

摘要

了解林地的生产力对于森林生态系统的可持续管理至关重要。部位生产率的最常见度量是乳房身高年龄部位指数（BHASI）。BHASI作为生产率度量标准有局限性，并且可能会使预测模型中的错误复杂化。我们使用替代生产率度量（10米站点索引）和非参数方法探讨了生产率预测的准确性。正交抽样设计确保样品穿过的条件已知影响花旗松（范围收集花旗松变种栎）的身高增长率。使用气候，土壤和地形数据以及10米站点索引测量值，我们比较了五个可能的模型来估算森林生产力。比较了模型参数，性能和预测。十二个验证站点用于测试模型预测的准确性。优化平滑范围值并添加高程作为预测变量后，模型性能得到了显着改善。具有偏差校正的Akaike信息标准和最佳化的平滑跨度值的四预测器非参数模型产生的结果最准确，并用于生成研究区域的森林生产力图。当前可用的气候数据分辨率低，而且研究区地貌复杂，因此必须使用地形变量来进行准确的生产力预测。

研究意义

定义和了解森林生产力对包括生态学家，气候变化专家，森林生物统计学家和森林管理者在内的各种自然资源专业人员都非常重要。一种新的方法来定义森林生产力，该方法使用10米和20米处的多点高度-年龄对以及计算的增长率与适当的景观尺度分层和非参数方法相结合，可以提供准确的生产力估算。与以前的生产率估算方法相比，此方法在特定位置更广泛地适用并且更精确。更好的生产力和树木生长信息将提供对未来森林状况和结构的更准确估计。