Quantitative reconstructions of vegetation abundance from sediment-derived pollen systems provide unique insights into past ecological conditions. Recently, the use of pollen accumulation rates (PAR, grains cm⁻² year⁻¹) has shown promise as a bioproxy for plant abundance. However, successfully reconstructing region-specific vegetation dynamics using PAR requires that accurate assessments of pollen deposition processes be quantitatively linked to spatially-explicit measures of plant abundance. Our study addressed these methodological challenges. Modern PAR and vegetation data were obtained from seven lakes in the western Klamath Mountains, California. To determine how to best calibrate our PAR-biomass model, we first calculated the spatial area of vegetation where vegetation composition and patterning is recorded by changes in the pollen signal using two metrics. These metrics were an assemblage-level relevant source area of pollen (aRSAP) derived from extended R-value analysis (sensu Sugita, 1993) and a taxon-specific relevant source area of pollen (tRSAP) derived from PAR regression (sensu Jackson, 1990). To the best of our knowledge, aRSAP and tRSAP have not been directly compared. We found that the tRSAP estimated a smaller area for some taxa (e.g. a circular area with a 225 m radius for Pinus) than the aRSAP (a circular area with a 625 m radius). We fit linear models to relate PAR values from modern lake sediments with empirical, distance-weighted estimates of aboveground live biomass (AGL_dw) for both the aRSAP and tRSAP distances. In both cases, we found that the PARs of major tree taxa – Pseudotsuga, Pinus, Notholithocarpus, and TCT (Taxodiaceae, Cupressaceae, and Taxaceae families) – were statistically significant and reasonably precise estimators of contemporary AGL_dw. However, predictions weighted by the distance defined by aRSAP tended to be more precise. The relative root-mean squared error for the aRSAP biomass estimates was 9% compared to 12% for tRSAP. Our results demonstrate that calibrated PAR-biomass relationships provide a robust method to infer changes in past plant biomass.

沉积物衍生的花粉系统对植被丰度的定量重建提供了对过去生态条件的独特见解。最近，使用花粉累积速率（PAR，谷物cm - ² 年^-1）已显示出有望作为植物丰度的生物代理。但是，要使用PAR成功地重建特定区域的植被动态，就需要将花粉沉积过程的准确评估定量地与植物丰度的空间明确度量联系起来。我们的研究解决了这些方法上的挑战。现代PAR和植被数据来自加利福尼亚州西部克拉马斯山的七个湖泊。为了确定如何最好地校准我们的PAR-生物量模型，我们首先计算了植物的空间区域，在该区域中，花粉信号的变化使用两个度量来记录植被组成和模式。这些指标是源自扩展R值分析（sensu）的花粉的组合级相关花粉源区域（aRSAP）Sugita，1993年）以及从PAR回归中得出的特定的分类单元相关花粉来源区域（tRSAP）（sensu Jackson，1990年）。据我们所知，尚未直接比较aRSAP和tRSAP。我们发现，tRSAP估计的某些类群的面积（例如，Pinus半径为225 m的圆形区域）比aRSAP（半径为625 m的圆形区域）估计的面积小。我们拟合线性模型，将现代湖泊沉积物中的PAR值与aRSAP和tRSAP距离的经验性，距离加权的地上生物量（AGL _dw）进行关联。在这两种情况下，我们都发现主要树类群的假单胞菌（Pseudotsuga，Pinus，Notholithocarpus）和TCT（菊科，柏科和紫杉科）–是当代AGL _dw的统计显着且合理精确的估计量。但是，以aRSAP定义的距离加权的预测往往更精确。aRSAP生物量估计的相对均方根误差为9％，而tRSAP为12％。我们的结果表明，校准的PAR-生物量关系提供了一种可靠的方法来推断过去植物生物量的变化。