The analysis of drought propagation has garnered mounting attention in the changing global environment. The current studies tend to focus on the propagation characteristics from meteorological to hydrological drought in rivers. Lakes, despite being a key component of watershed ecosystems, have received little attention to their response to meteorological and hydrological droughts. To this end, here, we investigated the characteristics of propagation from meteorological to hydrological drought for a lake in a changing environment. To determine the drought propagation time from meteorological to hydrological drought, we analyzed correlations between the standardized precipitation index (SPI), standardized runoff index (SRI), and standardized water level index (SWI). Lake Baiyangdian in China served as the case study. The results showed that meteorological droughts occur at high frequency but are short in duration, indicating that not every meteorological drought will necessarily lead to a hydrological drought. By contrast, lake hydrological droughts have low frequency and long duration and feature more severe consequences. Comparing drought characteristics before and after a changing environment, we found a reduced frequency of the SPI, SRI, and SWI, yet their duration was prolonged. For the SWI especially, these results were even more pronounced, which suggests the changing environment enabled further intensification of the lake hydrological drought. In addition, more time was needed for a meteorological drought to transition into a lake hydrological drought after a changing environment.

Measures based on concurrent alterations of an environment’s physical, chemical, and biological factors are commonly adopted to control harmful algal blooms (HABs). It was postulated that the combinations and interactions of multiple measures could exert cumulative effects (as the overall effect may or may not be equal to the additive sum for each measure individually). However, few studies have further assessed whether the cumulative effect is synergistic, additive, or antagonistic. This study proposed a framework to distinguish and quantify the cumulative effects. We also designed an experiment to investigate the cumulative effect of the combined utilization of physical (flow velocity), chemical (copper), and biological (propionamide) measures on algae growth inhibition. The results show that the cumulative effect of physical and chemical measures on algae growth inhibition was antagonistic; the cumulative effect of physical and biological measures was antagonistic; the cumulative effect of chemical and biological measures was synergistic, and the cumulative effect of all the measures together tended to be antagonistic. These results showed that the synergistic interactions between chemical and biological measures produced antagonistic effects when physical measures were added. Through response surface methodology analysis, we also found that the physical factor was the most significant factor affecting the cumulative effect, followed by the chemical factor and then the biological factor. Our results provide a more detailed understanding of the interaction patterns among multiple measures that affect algal growth. Importantly, this understanding can be further integrated into future strategy development to fully exploit the potential of the cumulative effect at its maximum performance.

Water level variability and temporal change are critical for shaping the structure of aquatic vegetation. Much research has examined the response of aquatic vegetation to hydrological metrics. However, the hydrological sequence is a fundamental driver of aquatic ecosystem structure and function. Given the aleatory uncertainty of future water levels under an unstable climate, how aquatic vegetation responds to changing dynamics in hydrological processes, especially shifting water level sequences, remains insufficiently explored. In this paper, we establish an evaluation framework to study the response of vegetation diversity to variation in water level sequences during a drought event. To do this, the uncertainty and variability of water level processes are both considered. Altering water level processes was achieved using two types of scenarios in order to explore the effects of differing water level sequences (i.e., changing the order of high vs. low water levels) on the probability distribution of four indexes of aquatic plant diversity (e.g., Margalef’s, Simpson’s, Shannon’s, and Pielou’s evenness index). Our results show that altering the order of water level state can lead to differences in the diversity of aquatic vegetation, with a pronounced impact on vegetation complexity. This suggests that the specific sequence of water level events is critical for shaping aquatic vegetation structure. In addition, we found that a uniform distribution of water level state is beneficial for enhancing a species’ dominance in aquatic vegetation. Our findings provide guidance for improving the future development of freshwater ecosystem protection and lake management.

As an important primary producer in aquatic ecosystems, the various parameters within the mathematical models are used to describe the growth of microalgae and need to be estimated by carefully designed experiments. Non-uniform sampling has proved to generate a deliberately optimized sampling temporal schedule that can benefit parameter estimation. However, the current non-uniform sampling method depends on prior knowledge of the nominal values of the model parameters. It also largely ignores the uncertainty associated with the nominal values, thus inducing unacceptable parameter estimates. This study focuses on the uncertainty problem and describes a new sampling design that couples the traditional uniform and non-uniform sampling schedules to benefit from the merits of both methods. Based on D-optimal design, we first derive the non-uniform optimal sampling points by maximizing the determinant of the Fisher information matrix. Then the confidence interval around the non-uniform sampling points is determined by Monte Carlo simulations based on the prior knowledge of parameter distribution. Finally, we wrap the non-uniform sampling points with the uniform sampling points within the confidence interval to obtain the ultimate optimal experimental design. Scenedesmus obliquus, whose growth curve follows a four-parameter model, was used as a case study. Compared with the traditional sampling design, the simulation results show that our proposed coupled sampling schedule can partly eliminate the uncertainty in parameter estimates caused by fixed systematic errors in observations. Our coupled sampling can also retain some advantages belonging to non-uniform sampling, in exploiting information maximization and managing the cost of sampling.

In coastal ecosystems, heavy metals partition between the water column and sediments, which act as dual contaminant compartments that can alter plankton communities. Disentangling this metal signal is difficult because these contaminants covary strongly with the salinity and nutrient gradients driven by river-sea interactions. Because these gradients themselves shape plankton, they generate a statistical collinearity that can mask metal-driven associations. To resolve this, we quantified the salinity-nutrient gradient alongside heavy-metal indices for the water column and sediments, linking them to plankton communities across 23 stations in Qinzhou Bay, China (April 2022). Redundancy analysis revealed that the gradient and metal indices jointly accounted for 27.4% of plankton variation (P = 0.0004). Variation partitioning attributed unique fractions of 17.0% and 14.9% to the gradient and metal blocks, with effectively no shared fraction. A piecewise structural equation model identified strong negative direct paths to the primary phytoplankton axis from the gradient (-0.757), the water-column index (-0.599), and the sediment index (-0.551). The isolated metal signal carried positive coefficients on phytoplankton standing stock and negative coefficients on diversity; by contrast, neither the direct paths to zooplankton nor the indirect path through phytoplankton was supported. Notably, the gradient’s total association with this axis was only -0.174, because metal-mediated indirect paths (+0.583) offset 77% of the direct path. This statistical suppression explains why simple bivariate tests detected none of these relationships. Ultimately, in estuaries where stressors share riverine sources, single-predictor surveys obscure associations that joint estimation recovers, demonstrating that standing stock alone can misrepresent community condition.