4.1. Rationality of P-IBI Evaluation Results
As a typical comprehensive Biological Indicator Index, the Biological Integrity Index can effectively represent the degree of external environmental disturbances and quantitatively characterize water ecological health status in numerical form [
22]. Compared with fish and large invertebrates, phytoplankton exhibit shorter regeneration times and lifecycles, making them more sensitive to changes in water environmental factors and enabling them to respond more rapidly to human disturbances. Consequently, using the Phytoplankton Integrity Index for evaluation allowed for a more precise reflection of the current water ecological health status under study [
7,
23].
Based on the phytoplankton monitoring results from rivers in Jiangsu Province during the flood season of 2023, Cyanobacteria were found to dominate, followed by green algae and diatoms, forming a Cyanobacteria–green algae–diatom algal structure. Gianbattista Bussi et al. [
24] concluded that water bodies dominated by Cyanobacteria, green algae, and diatoms are typically moderately polluted, which aligns well with the findings of the phytoplankton biological integrity evaluation. The P-IBI evaluation results indicated that the Huai River and Yangtze River Basins were in a sub-healthy state, while the Taihu Lake Basin was rated as “General”, consistent with the overall water quality conditions. Therefore, the use of the P-IBI for assessing water ecological health is both referenceable and feasible.
4.2. Spatial Distribution Characteristics of P-IBI in Rivers of Jiangsu Province
The spatial distribution characteristics of the P-IBI for phytoplankton in rivers of Jiangsu Province are presented in
Figure 9. A one-way ANOVA was conducted on the P-IBI at each sampling point, and the results indicated no significant difference (
p = 0.703 > 0.05). However, overall, the Phytoplankton Integrity Index exhibited spatial heterogeneity, with better phytoplankton integrity observed in the central region compared to the northern and southern regions. This trend aligns with the distribution patterns of phytoplankton species and density: Huai River Basin > Yangtze River Basin > Taihu Lake Basin.
In the Huai River Basin, the average P-IBI was 3.929, indicating a sub-healthy state. The basin contains 25 rivers, among which the Pi-Cang Flood Diversion Channel and Jinbao Waterway were rated as healthy, while the Xintongyang Canal had a poor evaluation result. The remaining 9 rivers were in a sub-healthy state, and 13 rivers had an average evaluation result. The Xintongyang Canal is an artificial waterway used for water diversion, irrigation, and navigation. Its water quality is poor due to severe nutrient and organic oxygen-consuming pollution, coupled with low dissolved oxygen concentrations, leading to poor phytoplankton integrity [
25]. Generally, phytoplankton integrity in the southern part of the basin is better than in the northern part, likely because lakes, such as Hongze Lake, Gaoyou Lake, and Baima Lake, exist in the south, where numerous sluices and dams create weak hydrodynamic conditions that promote nutrient deposition, favoring the growth of phytoplankton, particularly Cyanobacteria. This finding is consistent with the community structure, showing much higher Cyanobacteria densities compared to other groups [
26].
In the Yangtze River Basin, the average P-IBI was 3.896, indicating a sub-healthy state. Phytoplankton integrity showed a west-high, middle-low distribution pattern. Given that most sampling points are located in Nanjing and along the Yangtze River, it is inferred that phytoplankton integrity is higher in the Nanjing area than in the Yangtze River itself. In recent years, Nanjing has implemented several water environment improvement measures, achieving the best water quality monitoring performance in the province and maintaining good ecological conditions, which are conducive to phytoplankton growth. In contrast, most sections of the Yangtze River in Jiangsu Province are artificially constructed, dominated by industrial production and port terminals, resulting in relatively simple habitats. Additionally, frequent ship traffic can cause an increase in the Froude number of water depth (Fr), which restricts the survival and reproduction of phytoplankton [
27]. The eastern section, located in Suzhou, is one of the key areas for water ecological protection, explaining why its P-IBI evaluation result was better than that of the middle section.
In the Taihu Lake Basin, the average P-IBI was 3.701, indicating a “General” state. Among the rivers in this basin, only Xinmeng River achieved a sub-healthy P-IBI evaluation result, surpassing the other five rivers. The Xinmeng River Project is a critical component of national efforts to improve the Taihu Lake water environment and serves as one of the water environment capacity enhancement and drainage channel projects. It promotes water circulation in Taihu Lake and improves surrounding water environmental conditions, creating favorable conditions for phytoplankton growth.
Analysis of similarities (ANOSIM) is a non-parametric test method based on the permutation test and rank sum test, used to test whether there is a significant difference between groups. We conducted pairwise ANOSIM on the phytoplankton density in the Huai River, Yangtze River, and Taihu Lake Basins, and the results are shown in
Figure 10. When the ANOSIM result showed
p < 0.05, there was a significant difference between the groups. The results indicated that there were significant differences in the phytoplankton density community structure between the Huai River Basin and the Yangtze River Basin (
p = 0.011), and between the Yangtze River Basin and the Taihu Lake Basin (
p = 0.043).
Similarity percentage (SIMPER) analysis is a statistical method used to identify the key contributing species that account for the significant differences between different groups of communities. It can calculate the mean similarity among samples within each group based on the Bray–Curtis similarity index and reveal the contribution of each variable to the overall differences.
The SIMPER analysis was used to determine the contribution rates of different algae to the differences in the Huaihe River and Yangtze River Basins. The top 10 species with the highest contribution rates are presented in
Table 8.
The contribution rates of different algae to the differences in the Yangtze River and Taihu Lake Basins were analyzed using SIMPER analysis, and the results are shown in
Table 9.
By comparing the results of the SIMPER analysis between the two groups, it was evident that the species primarily contributing to the differences in phytoplankton density community structure were largely similar. Among the top 10 species contributing to these differences, all belonged to either Cyanobacteria or Chlorophyta phyla, with seven from Cyanobacteria and three from Chlorophyta. Notably, except for Limnospira platensis and Merismopedia tranquilla in the Yangtze River and Huai River Basins, as well as Eudorina elegans and Chroococcus cohaerens in the Yangtze River and Taihu Lake Basins, the remaining eight species were identical. This similarity may stem from the comparable phytoplankton density community structures observed in the Huai River and Taihu Lake Basins (p = 0.446). With the exception of a few species, the densities of other species generally followed the trend Huai River Basin > Yangtze River Basin, and Taihu Lake Basin > Yangtze River Basin. Based on these findings, it can be inferred that the growth conditions of phytoplankton in the Yangtze River Basin were less favorable compared to those in the Huai River and Taihu Lake Basins. This disparity was likely attributable to the adverse impacts of high-intensity human activities, such as industrial development and shipping along the Yangtze River, which have negatively influenced the health of rivers within this basin.
4.3. Relationship Between P-IBI and Environmental Factors
The distribution and succession of the phytoplankton community structure are the result of interactions among various environmental factors, and their changes can serve as indicators of water environmental conditions [
28]. Previous studies have demonstrated that environmental factors, such as N, P, COD
Mn, pH, WT, TDS, and flow velocity, influence both the composition and growth of phytoplankton communities [
29,
30]. Based on the correlation analysis results between the P-IBI and environmental factors presented in this study, the P-IBI values of rivers in Jiangsu Province exhibited significant positive correlations only with total nitrogen (TN) and nitrate nitrogen (NO
3−-N). Within a certain range, the complexity of the phytoplankton community structure increased with rising concentrations of TN and NO
3−-N, findings consistent with those reported by Zhang Shunting et al. [
31] for Yangcheng Lake. This suggests that there were similarities in the relationships between phytoplankton integrity and environmental factors across different aquatic environments.
In contrast to TN and NO
3−-N, the correlations between P-IBI and other environmental factors were relatively weak. This may be attributed to the extensive study area and numerous sampling points, which resulted in relatively stable phytoplankton community compositions that were less susceptible to significant impacts from individual environmental factors. Additionally, the scouring effects of rivers likely exerted some influence on phytoplankton growth [
32]. More critically, rapid economic development, large populations, and urbanization-related human activities within the study region of Jiangsu Province disrupted phytoplankton habitats, thereby affecting the diversity and integrity of phytoplankton communities. For instance, the Taihu Lake Basin, being more economically developed than the Huaihe River and Yangtze River Basins, exhibited poorer evaluations of phytoplankton integrity, supporting the aforementioned inference. Consequently, these multifaceted factors may obscure the relationship between P-IBI and water quality indicators, such as nutrient levels.