Convergence between ANPP estimation methods in grasslands — A practical solution to the comparability dilemma

Abstract

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) R2 values (≥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 their R2 values 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.