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Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems

Authors

  • Quan Wang,

    Corresponding author
    1. Department of Plant Ecology, University of Bayreuth, D-95440 Bayreuth, Germany;
      *Correspondence, Quan Wang, Department of Plant Ecology, University of Bayreuth, D-95440 Bayreuth, Germany. E-mail: quan.wang@uni-bayreuth.de
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  • Jian Ni,

    1. Laboratory of Quantitative Vegetation Ecology, Institute of Botany, Chinese Academy of Sciences, 100093 Beijing, China; and
    2. Max Planck Institute for Biogeochemistry, PO Box 100164, D-07701 Jena, Germany
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  • John Tenhunen

    1. Department of Plant Ecology, University of Bayreuth, D-95440 Bayreuth, Germany;
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*Correspondence, Quan Wang, Department of Plant Ecology, University of Bayreuth, D-95440 Bayreuth, Germany. E-mail: quan.wang@uni-bayreuth.de

ABSTRACT

Aim  The objective of this paper is to obtain a net primary production (NPP) regression model based on the geographically weighted regression (GWR) method, which includes spatial non-stationarity in the parameters estimated for forest ecosystems in China.

Location  We used data across China.

Methods  We examine the relationships between NPP of Chinese forest ecosystems and environmental variables, specifically altitude, temperature, precipitation and time-integrated normalized difference vegetation index (TINDVI) based on the ordinary least squares (OLS) regression, the spatial lag model and GWR methods.

Results  The GWR method made significantly better predictions of NPP in simulations than did OLS, as indicated both by corrected Akaike Information Criterion (AICc) and R2. GWR provided a value of 4891 for AICc and 0.66 for R2, compared with 5036 and 0.58, respectively, by OLS. GWR has the potential to reveal local patterns in the spatial distribution of a parameter, which would be ignored by the OLS approach. Furthermore, OLS may provide a false general relationship between spatially non-stationary variables. Spatial autocorrelation violates a basic assumption of the OLS method. The spatial lag model with the consideration of spatial autocorrelation had improved performance in the NPP simulation as compared with OLS (5001 for AICc and 0.60 for R2), but it was still not as good as that via the GWR method. Moreover, statistically significant positive spatial autocorrelation remained in the NPP residuals with the spatial lag model at small spatial scales, while no positive spatial autocorrelation across spatial scales can be found in the GWR residuals.

Conclusions  We conclude that the regression analysis for Chinese forest NPP with respect to environmental factors and based alternatively on OLS, the spatial lag model, and GWR methods indicated that there was a significant improvement in model performance of GWR over OLS and the spatial lag model.

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