DOI: 10.5061/dryad.r7sqv9scz
title: Precipitation variability can bias estimates of ecological controls on ecosystem productivity response to precipitation change

description: Annual vegetation aboveground net primary productivity (ANPP) exhibits a
 nonlinear dependence on annual precipitation. A common pattern of
 nonlinearity, called asymmetry, arises when productivity responses in wet
 years are larger than declines in dry years. To-date, ANPP asymmetry has
 been attributed primarily to vegetation water stress, an internal
 ecosystem response to precipitation and soil water availability. However,
 when quantified via the asymmetry index (AI) estimated from productivity
 measurements, the asymmetry can be a sampling artifact that arises from a
 positively skewed annual precipitation distribution. In this paper, we
 aimed to separate the sampling effect (from external precipitation
 variability) from the nonlinear response of the system (the internal
 ecosystem dynamics). We constructed a probabilistic model that integrates
 the precipitation distribution with the precipitation-productivity
 response curve (PPT-ANPP curve), derived using empirical formulae and a
 process-based soil water balance model. The model was used to derive the
 probability density function of AI and to attribute its shape to the PPT
 distribution and the PPT-ANPP response curve. The models were compared to
 data from 47 grasslands. Results demonstrated that positively skewed
 precipitation produces a positive AI as a statistical artifact. The
 nonlinear ecosystem PPT-ANPP dependence can further enhance or dampen this
 statistical artifact. In all sites, the precipitation skew highly affected
 the probability of correctly identifying asymmetry using AI. Observed
 negative asymmetry arises from a larger soil water holding capacity, and
 positive asymmetry from plant water stress. More robust statistical
 indicators of nonlinear ecological responses to climate variability are
 needed to improve ecosystem forecasts.
description: # Precipitation variability can bias estimates of ecological controls on
 ecosystem productivity response to precipitation change
 [https://doi.org/10.5061/dryad.r7sqv9scz](https://doi.org/10.5061/dryad.r7sqv9scz) Precipitation variability can bias estimates of ecological controls on ecosystem productivity response to precipitation change Parolari and Paschalis (2021), Ecohydrology, doi:10.1002/eco.2384 DATA FILES \* Precipitation and Above-ground Net Primary Productivity (ANPP) data are contained described in the following references: Andales, A. A., Derner, J. D., Ahuja, L. R., & Hart, R. H. (2006). Strategic and tactical prediction of forage production in northern mixed-grass prairie. Rangeland Ecology and Management, 59(6). https://doi.org/10.2111/06-001R1.1 Bai, Y., Han, X., Wu, J., Chen, Z., & Li, L. (2004). Ecosystem stability and compensatory effects in the Inner Mongolia grassland. Nature, 431(7005). https://doi.org/10.1038/nature02850 Bai, Y. F., Li, L. H., Huang, J. H., & Chen, Z. Z. (2001). The influence of plant diversity and functional composition on ecosystem stability of four Stipa communities in the inner Mongolia Plateau. Acta Botanica Sinica, 43(3). Knapp, A. K., Ciais, P., & Smith, M. D. (2017). Reconciling inconsistencies in precipitation–productivity relationships: implications for climate change. New Phytologist, 214(1), 41–47. https://doi.org/10.1111/nph.14381 Smoliak, S. (1986). Influence of Climatic Conditions on Production of Stipa-Bouteloua Prairie over a 50-Year Period. Journal of Range Management, 39(2). https://doi.org/10.2307/3899276 \* Precipitation - ANPP models previously developed for the case study sites by Hsu et al. (2012) are summarized in the file hsu_data.txt: Hsu, J. S., Powell, J., & Adler, P. B. (2012). Sensitivity of mean annual primary production to precipitation. Global Change Biology, 18(7), 2246–2255. https://doi.org/10.1111/j.1365-2486.2012.02687.x DATA AND MODEL ANALYSIS \* Run paper figure files in order: Figure2_AI_distributions.m Figure3to5_partition_Hsu_etal.m Figure4_asymmetry_examples.m Figure6_Konza_Prairie_example.m Figure7_Konza_AI_distribution.m Figure8_Konza_Prairie_model.m Figure9_AI_sensitivity.m Figure10_drought_sensitivity.m