# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## 4 Discussion
### Drivers of Global GPP Changes from 2000 to 2022  
GPP quantifies the carbon uptake of plants through photosynthesis, which is influenced by factors such as temperature, available water, and CO\({}_{2}\) concentration and is crucial in determining the overall productivity of plant ecosystems (Figure 8). Globally, the sensitivity of environmental variables to GPP was assessed and compared with 10 remote sensing models. It was found that the main cause of the increase in GPP was the positive response of plants to increases in atmospheric CO\({}_{2}\) concentration, air temperature, and precipitation, with mean values of \(23.9\pm 12.62\) PgC year\({}^{-1}\) 100 ppm\({}^{-1}\), 6.655.3 PgC year\({}^{-1}\) \(\circ\)C\({}^{-1}\), and 2.62.03 PgC year\({}^{-1}\) 100 mm\({}^{-1}\), respectively [82]. The C-Fix model evaluated the impact of climatic factors on GPP trends, spanning from 1982 to 2015, revealing that the global average contributions of atmospheric CO\({}_{2}\) concentration, air temperature, and water to the GPP trend were 65.37%, 13.07%, and 11.74%, respectively [83]. Atmospheric CO\({}_{2}\) concentration was a key environmental factor that affected global GPP and made the largest positive contribution to its increase [84]. In this study, we investigated the correlation of global summed GPP with the climate variables of air temperature, atmospheric CO\({}_{2}\) concentration, and precipitation to quantify the total effect of environmental variables on long-term changes in GPP. Over the last two  
Figure 7: **(A)** Comparison of site elevation and the average elevation within a 5 km radius area of the site. **(B)** Comparison of annual average site LAI and annual average LAI within a 5 km radius of the site. The blue line is the regression line, and the red dashed line is the 1:1 line. The regression equation, correlation coefficients, statistical significance, mean square error, and bias are shown on the bottom. **(C)** The proportion of vegetation types at the site relative to vegetation types within a 5 km radius of the site. WSA: woody savannas; SAV: savannas; GRA: grasslands; EBF: evergreen broadleaf forests; CRO: croplands; MF: mixed forests; ENF: evergreen needleleaf forests; OSH: open shrulands; WET: permanent wetlands; DBF: deciduous broadleaf forests; DNF: deciduous needleleaf forests). decades, global trends have shown continuous increases in atmospheric CO2 concentration, air temperature, and precipitation [85], with increases of 2.19 ppm per year [86; 87], 0.0405 \({}^{\circ}\)C per year [88], and 0.377 mm per year, respectively. The correlation between GPP and air temperature, atmospheric CO2 concentration, and precipitation was 0.866, 0.927, and 0.131, respectively (Figure 8). The emission of greenhouse gases such as CO2 contributed to global warming through the greenhouse effect and led to a prolonged growing season, especially at high and mid-latitudes, resulting in increased GPP [89]. The analysis of global process model results suggested that part of the increase in terrestrial carbon uptake could be attributed to increased vegetation productivity caused by the fertilization effect of elevated atmospheric CO2 concentrations [90; 91; 92]. Adequate precipitation effectively boosted GPP by enhancing soil moisture and plant growth, while excessive precipitation posed the risk of causing flooding, damaging plants, and inhibiting GPP due to altered soil properties. Structural equation modeling (SEM) is a significant method for the analysis of ecological data that is capable of quantifying both direct and indirect causal relationships among multiple variables (Figure 9). SEM not only determines the individual path coefficient for each relationship but also assesses the overall model fit to provide a comprehensive understanding of natural systems [67; 68; 69]. A structural equation model was developed to explore the impacts of storm frequency on both the structure of kelp forest communities and the complexity of their food webs with a \(p\) value = 0.115 [67; 68].