Convergence between ANPP estimation methods in grasslands - A practical solution to the comparability dilemma
Convergence between ANPP estimation methods in grasslands - A practical solution to the comparability dilemma
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DOI：
10.1016/j.ecolind.2013.09.008
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发表时间：
2014
影响因子：
6.9
通讯作者：
J. Ruppert;A. Linstädter
中科院分区：
文献类型：
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作者：
J. Ruppert;A. Linstädter
植被净初级生产力（ANPP）是一个关键的生态系统特征，对陆地生态系统物质和能量通量的各个方面都具有根本的重要性。各种方法估计ANPP是可用的，尽管部分共识的“最佳实践方法”重要的方法问题仍然没有得到解决：ANPP数据获得不同的方法在其幅度，变异性和他们的倾向，过度或低估初级生产。奇怪的是，尽管有大量已发表的ANPP数据，但各研究之间ANPP估计值的可比性有限，实际上导致了汇编的大规模研究的ANPP数据稀缺。我们的目的是克服这些问题，通过建立最常用的ANPP方法之间的转换率，使大量的公布的ANPP数据更具可比性，从而有用的组装大规模studies.Using季节性生物量动态从89个网站代表各种生物群落和气候，我们建立了所有21个组合之间的七个最常见的ANPP估计算法在草为主的植被的线性转换。我们还检查了环境因素的混杂效应，如生物群落和气候干旱。干旱是唯一的因素，有明显的影响ANPP转换，并在六种情况下，我们因此计算出干燥和潮湿的环境中的单独的关系。在这些情况下，旱地的净初级生产力被各自的方法系统地低估了。由于这些方法对从活生物量到衰老生物量的转换过程不敏感，我们假设这种低估与气候引起的生物量转换率差异有关，更干旱的地点具有更高的速率。由此产生的27个转换中的大多数具有高（假）R2值（≥0.65;全范围：0.31-0.92），表明大多数ANPP估计方法之间存在明显的线性关系。考虑到数据集的大小和统计模型的准确性，我们假设大多数转换公式通常是有效的。我们分类转换方面theirR 2值和方法的可比性，并得出结论，16转换可以完全推荐。对于那些在原始生物量数据的基础上重新计算ANPP是不可能的情况下，我们的转换公式提供了一个简单实用的方法来同步不同算法和来源的ANPP估计。
Aboveground net primary production (ANPP) is a key ecosystem characteristic and of fundamental importance for essentially all aspects of matter and energy fluxes in terrestrial ecosystems. Various methods for estimating ANPP are available and despite partial consensus on ‘best practice methods’ important methodological issues remain unresolved: ANPP data obtained with different methods differ in their magnitude, variability and their tendency to over- or underestimate primary production. Paradoxically, despite the large number of published ANPP data, the limited comparability of ANPP estimates across studies de facto leads to a scarcity of ANPP data for assembled large-scale studies. We aimed to overcome these problems by establishing conversion rates between the most commonly used ANPP methods, making the large body of published ANPP data more comparable and thus useful for assembled large-scale studies.Using seasonal biomass dynamics from 89 sites representing various biomes and climata, we established linear conversions for all 21 combinations between the seven most common ANPP estimation algorithms in grass-dominated vegetation. We also checked for confounding effects of environmental factors such as biome and climatic aridity. Aridity was the only factor with a clear influence on ANPP conversions, and in six cases we thus calculated separate relationships for dry and humid environments. In these cases, dryland ANPP was systematically underestimated by the respective methods. As these methods are insensitive to turn-over processes from live to senescent biomass, we assume this underestimation is related to climate-induced differences in biomass turn-over rates, with more arid sites having higher rates.The majority of the resulting 27 conversions had high (pseudo)R2values (≥0.65; full range: 0.31–0.92), indicating clear linear relationships between most ANPP estimation methods. Given the large size of the dataset and the accuracy of statistical models, we assume that most conversion formulae are generally valid. We classified conversions with respect to theirR2values and their methodological comparability, and concluded that 16 conversions can be fully recommended. For those cases where a recalculation of ANPP on basis of original biomass data is not possible, our conversion formulae offer an easy and practical approach to synchronize ANPP estimates from divergent algorithms and sources.