## 3 Results
### Analysis on Influencing Factors of Plant Diversity in the Yellow River Delta
#### 3.3.2 Relationship Between Plant Diversity and Influencing Factors  
Prior to the influencing analysis, we performed a collinearity analysis of the influencing factors to ensure the stability of the regression model (Table 9). The VIF analysis method showed that there was no collinearity among the factors. First, in order to explore the driving factors of plant diversity in the riverbank of the main stream and tributaries of the Yellow River Delta, we conducted a stepwise regression analysis of plant diversity and influencing factors (Table 10). The human interference degree (HID) was the sole variable entering the models of R, H, and D, with \(R_{adj}^{~{}2}\) of 0.554, 0.580, and 0.509, respectively. SEC emerged as the only significant factor explaining variation in the J index. The results indicated that the R, H, and D indexes increased with the decrease of HID, and the J index increased with the decrease of SEC in the main stream. For the tributaries, SEC was the sole factor in all regression models of plant diversity indexes, indicating its predominant role as the key limiting factor for plant diversity in the tributaries. Figure 8: Analysis of the soil property: (**a**) soil properties of all sampling sites; (**b**) soil properties at different distanced from the sea. Different lowercase letters indicate significant differences between different land–sea zones. Then, in order to explore the key influencing factors of plant diversity in the Yellow River Delta region under the land-sea gradient, we conducted RDA analysis. Since the DCA results showed that the maximum gradient in the four axes did not exceed three, which was consistent with the RDA model. The RDA results (Figure 9) revealed that the overall explanatory rate of the constraint axis was 35.5%, in which the explanatory rate of the RDA 1 and RDA 2 axes reached 34% and 1.37%, respectively, and the cumulative interpretation rate accounted for 99.72% of the total variance. The projection length of R, H, D, and J were long, showing a strong positive correlation between them. The R index had a strong positive correlation with height, and a strong negative correlation with SEC, the nearest distance from the Yellow River (DR), and HID. The H and D indexes were strongly positively correlated with height, and negatively correlated with SEC, SMC, DR, longitude (Lon), and HID. The J index had a strong negative correlation with SEC, SMC, DR, and Lon. Then, the hierarchical segmentation method was used to analyze the comprehensive contribution of each factor to plant diversity (Figure 10). The results indicated that the explanatory rate of SEC was 62.37%, which had the greatest impact on plant diversity. The next was SMC, which was explained by 20.65%. Latitude (Lat), slope, and SPH had the lowest explanatory rates, which almost had no significant effect on plant diversity. Therefore, the influence of each factor on plant diversity were as follows: SEC \(>\) SMC \(>\) Height \(>\) HID \(>\) DR \(>\) Lon \(>\) Lat \(\approx\) Slope \(\approx\) SPH. In terms of the relationship among sampling sites, plant diversity, and environmental factors, most of the sampling sites along the main stream of the Yellow River (M1-M2, M4-M6, M8, and M10-M12) and the Shenxian Ditch (S1-S3) had a high plant diversity level and were greatly affected by height. Specifically, the plant diversity was positively correlated with height, and negatively correlated with SEC, SMC, HID, and DR. The surrounding environment was conducive to the development of plant diversity. The sampling sites close to the sea (such as Y3, X3, and M14) were more susceptible to SEC, SMC, DR, and HID, resulting in the poor level of plant diversity.