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The provision of ecosystem services (ESs) is driven by land use and biophysical conditions and is thus intrinsically linked to space. Large-scale ES models, developed to inform policy makers on ES drivers, do not usually consider spatial autocorrelation that could be inherent to the distribution of these ESs or to the modeling process. The objective of this study is to estimate the drivers of ecosystem services in France using statistical models and show how taking into account spatial autocorrelation improves the predictive quality of these models. We study six regulating ESs (habitat quality index, water retention index, topsoil organic matter, carbon storage, soil erosion control, and nitrogen oxide deposition velocity) and three provisioning ESs (crop production, grazing livestock density, and timber removal). For each of these ESs, we estimated and compared five spatial statistical models to investigate the best specification (using statistical tests and goodness-of-fit metrics). Our results show that (1) taking into account spatial autocorrelation improves the predictive accuracy of all ES models (Δ R 2 ranging from 0.13 to 0.58); (2) land use and biophysical variables (weather and soil texture) are significant drivers of most ESs; (3) forest was the most balanced land use for provision of a diversity of ESs compared to other land uses (agriculture, pasture, urban, and others); (4) Urban area is the worst land use for provision of most ESs. Our findings imply that further studies need to consider spatial autocorrelation of ESs in land use change and optimization scenario simulations.

Reference:

Moindjie, I.A.; Pinsard, C.; Accatino, F.; Chakir, R.
Interactions between ecosystem services and land use in France: a spatial statistical analysis
in : Frontiers in Environmental Science, (2022), vol. 10. art. 954655. 29 p.

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