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首页> 外文期刊>Global change biology >Assessing forest growth across southwestern Oregon under a range ofcurrent and future global change scenarios using a process model, 3-PG
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Assessing forest growth across southwestern Oregon under a range ofcurrent and future global change scenarios using a process model, 3-PG

机译：使用3-PG过程模型评估俄勒冈州西南部森林在当前和未来全球变化情景下的森林生长

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With improvements in mapping regional distributions of vegetation using satellite-derived information, there is an increasing interest in the assessment of current limitations on forest growth and in making projections of how productivity may be altered in response to changing climatic conditions and management policies. We utilised a simplified physiologically based process model (3-PG) across a 54000 km(2) mountainous region of southwestern Oregon, USA, to evaluate the degree to which maximum periodic mean annual increment (PAI) of forests could be predicted at a set of 448 forest inventory plots. The survey data were pooled into six broad forest types (coastal rain forest, interior coast range forest, mixed conifer, dry-site Douglas-fir, subalpine forest, and pine forest) and compared to the 3-PG predictions at a spatial resolution of 1 km(2). We found good agreement (r(2) = 0.84) between mean PAI values of forest productivity for the six forest types with those obtained from field surveys. With confidence at this broader level of integration, we then ran model simulations to evaluate the constraints imposed by (i) soil fertility under current climatic conditions, (ii) the effect of doubling monthly precipitation across the region, and (iii) a widely used climatic change scenario that involves modifications in monthly mean temperatures and precipitation, as well as a doubling in atmospheric CO2 concentrations. These analyses showed that optimum soil fertility would more than double growth, with the greatest response in the subalpine type and the least increase in the coastal rain forests. Doubling the precipitation increased productivity in the pine type (> 50%) with reduced responses elsewhere. The climate change scenario with doubled atmospheric CO2 increased growth by 50% on average across all forest types, primarily as a result of a projected 33% increase in photosynthetic capacity. This modelling exercise indicates that, at a regional scale, a general relationship exists between simulated maximum leaf area index and maximum aboveground growth, supporting the contention that satellite-derived estimates of leaf area index may be good measures of the potential productivity of temperate evergreen forests.
机译：随着使用卫星衍生信息绘制植被区域分布图的改进，人们对评估当前对森林生长的限制以及预测如何根据气候条件和管理政策如何改变生产力产生了越来越大的兴趣。我们利用简化的基于生理过程的模型（3-PG），遍及美国俄勒冈州西南部54000 km（2）的山区，以评估可预测某一森林的最大周期性平均年增率（PAI）的程度448个森林清查地。将调查数据汇总为六种广泛的森林类型（沿海雨林，内陆沿海森林，混合针叶树，旱地道格拉斯冷杉，亚高山森林和松林），并与3 PG预测的空间分辨率进行了比较。 1公里（2）。我们发现这六种森林类型的平均森林生产力的PAI值与实地调查获得的值之间具有良好的一致性（r（2）= 0.84）。在更广泛的整合水平上充满信心，然后我们进行了模型模拟，以评估以下因素所带来的限制：（i）当前气候条件下的土壤肥力;（ii）该地区每月降水量增加一倍的影响;（iii）广泛使用气候变化情景，涉及每月平均温度和降水的变化以及大气中CO2浓度翻倍。这些分析表明，最佳土壤肥力将使土壤肥力提高一倍以上，其中亚高山型的响应最大，而沿海雨林的增长最少。降水量增加一倍，可提高松树类型的生产率（> 50％），而其他地方的响应则减少。大气CO2倍增的气候变化情景在所有森林类型中平均使增长率提高了50％，这主要是由于预计光合作用能力提高了33％。这项模拟工作表明，在区域范围内，模拟的最大叶面积指数与最大地上生长之间存在一般关系，支持以下论点：卫星得出的叶面积指数估计值可能是温带常绿森林潜在生产力的良好衡量标准。

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