###### Abstract
The network perspective on plankton stability reveals fragilities and connectivity patterns overlooked by traditional methods. Mapping these vulnerabilities enables preventive interventions to bolster resilience. Pinpointing keystone species and critical habitat linkages, for instance, can guide ecosystem conservation approaches. Simulating disruption scenarios quantifies robustness in a dynamic context and provides early warning of imminent regime shifts. Comparing network topologies exposes those designs that are most resilient against failures. These findings allow improvements to be implemented prior to collapse. For example, adding modularity with redundancies to the power grid can help to prevent large-scale blackouts. Increasing heterogeneity in cropland landscapes slows transmission of crop diseases. Thus, maintaining network robustness is critical for managers of natural ecosystems, physical infrastructure, supply chains, and human systems. Network approaches transition the analysis from simple dyadic ties to complex system interdependencies. The related methodologies reveal fragilities that are concealed when studying components in isolation. Quantifying network robustness can aid governance by enhancing robustness. The utility of complex network robustness analysis has been demonstrated across many terrestrial and marine environments. Applications include studying pollination networks [13], trophic interactions in food webs [14], and symbiotic relationships between species [15]. Important discoveries have been made regarding ecosystem stability in forests, coral reefs, and animal gut microbiomes [16]. However, complex network robustness analysis has not been applied to study zooplankton communities. Extending robustness analysis to illuminate zooplankton community dynamics would similarly enhance understanding of this vital biome. The resulting insights can inform conservation strategies to protect marine ecosystems facing growing environmental pressures. In this paper, we argue that analyzing the robustness of complex ecological networks has become an extremely valuable tool for comprehending the stability and resilience of natural communities. Applying complex network approaches to study interactions between zooplankton species can provide unique insights unavailable with traditional methods [17]. In a community, robustness refers to the ability to maintain characteristic behavior under influences such as species extinction and environmental disturbance [18]. Viewing zooplankton communities as complex networks allows critical species that contribute disproportionately to overall ecosystem stability to be identified. The loss of these highly connected \"hub\" species can precipitate catastrophic collapse. In addition, network analysis allows us to model how perturbations might propagate through the system. In order to effectively evaluate marine plankton community resilience, this paper provides insight into different network structures' roles and patterns of robustness development. Specifically, plankton data are used to construct interaction networks of plankton communities. Various network attack strategies are then selected to simulate different types of network damage. We propose a multi-indicator weighted evaluation scheme, namely, the robustness coefficient, to analyze plankton community robustness across diverse network structures under simulated network damage scenarios. For this purpose, we selected three exemplary biological communities for robustness analysis based on continuous plankton recorder monitoring data. These communities exhibit different levels of biomass and interaction. Moreover, by comparing the robustness coefficient with other existing robustness evaluation metrics and analyzing the standard deviations, our comparative experiments provide additional evidence that using a weighted combination of multiple metrics (our proposed robustness coefficient) results in better performance than reliance on any single metric alone.