## 4 Discussion
### Methodological Advantages  
Network approaches offer distinct advantages over traditional techniques for studying community robustness. Representing communities as networks enables the modeling of component interdependencies. Dynamic simulations can assess responses to perturbations in a way that is impossible with static methods. Moreover, our proposed multi-indicator weighted evaluation provides a more nuanced appraisal of robustness than single metrics such as connectivity.  
Comparing our network-based approach to existing plankton community robustness assessment methods reveals many advantages of our method. Specifically, our technique can be compared to the plankton population dynamics model developed by [PERSON] et al. [36]. The Cropp model uses a series of nonlinear equations to simulate fluctuations in plankton biomass under various environmental conditions. Compared to the [PERSON] model, our network approach has the following strengths:  
* It captures the intricate relationships between plankton types, rather than treating genera in isolation, helping to assess robustness at the ecosystem scale;
* It can simulate community dynamics during response to perturbations through different attack strategies;
* Its computational efficiency allows rapid investigation of multiple scenarios;
* The proposed multi-metric evaluation enables more nuanced measurement of robustness;
* Visual network representations provide intuitive understanding of topological vulnerabilities.  
However, the Cropp model has more detailed biological characterizations of individual genera and their behaviors. Thus, the network technique offers higher-level robustness assessments that complement the individual-based modeling insights. In the future, an integrated framework combining both approaches may provide a more comprehensive perspective on plankton community resilience. While no single technique can provide all the answers, network analysis reveals community-scale phenomena that reductionist approaches cannot. Further synergistic application of systems and traditional methods can advance our understanding of these vital marine ecosystems.  
In addition, we computed the standard deviation between the multi-metric weighted evaluation indicators we proposed and the single metrics, demonstrating that our multi-metric approach has better stability across the evaluation indicators compared to relying on any individual metric. A single metric may be susceptible to noise, and may fail to capture key aspects of ecosystem dynamics. In contrast, a composite indicator based on multiple metrics helps to smooth over variability in individual measurements and integrate diverse aspects of stability. The lower standard deviation that we obtained verifies that our multi-metric technique yields more consistent evaluations of plankton community robustness.  
The calculated variation in the standard deviation for the evaluation indicators under the four attack strategies is shown Table 3. The standard deviation values in Table 3 show that the proposed multi-indicator weighting evaluation scheme, that is, the robustness coefficient, achieves more stable measurement than the other evaluation indicators under different attack strategies. The maximum standard deviation value is 0.33 and the minimum is 0.32, fluctuating by only about 3%, which is significantly less than the other evaluation indicators. In addition, the average standard deviation is the lowest among the different evaluation indicators.  
Overall, based on the differences in the trend lines and standard deviations of the different indicators, the robustness coefficient proposed in this paper demonstrates better stability and reliability.