Effects of turbulence on alkaline phosphatase activity of phytoplankton and bacterioplankton in Lake Taihu

Alkaline phosphatase (AP), an inducible and hydrolytic enzyme, plays a key role in the biogeochemical cycle of phosphorus (P) in lakes. Activity and regulation of AP has been suggested to be affected by hydrodynamic turbulence. However, many aspects of the coupling of the AP activity (APA) and turbulence are still to be investigated and understood. In this study, mesocosm experiments were carried out to further understand the effects of turbulence on APA and the relative contribution of the different microbial groups to the total APA (TAPA). Specifically, we focused on evaluating the APA of phytoplankton (2–112 µm) and bacterioplankton (0.2–2 µm) and its relationship with P fractions under four turbulence levels. Results showed that turbulent conditions enhanced planktonic APA (PAPA) which dominated TAPA by comprising 66–93% of the total fraction. In particular, PAPA was almost two times higher in the turbulence treatments than in still-water control. On the other hand, bacterioplanktonic APA (BAPA) decreased which could be associated with the competitive advantage of bacteria in nutrient-limited conditions due to surface-to-volume ratio. The results suggest that turbulence can accelerate the biogeochemical cycle of P and plays an important role in P strategies of plankton

Determining the probability of cyanobacterial blooms: the application of Bayesian networks in multiple lake systems

A Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the "high hazardous" category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures

Climate change drives rapid warming and increasing heatwaves of lakes

Climate change could seriously threaten global lake ecosystems by warming lake surface water and increasing the occurrence of lake heatwaves. Yet, there are great uncertainties in quantifying lake temperature changes globally due to a lack of accurate large-scale model simulations. Here, we integrated satellite observations and a numerical model to improve lake temperature modeling and explore the multifaceted characteristics of trends in surface temperatures and lake heatwave occurrence in Chinese lakes from 1980 to 2100. Our model-data integration approach revealed that the lake surface waters have warmed at a rate of 0.11 °C 10a-1 during the period 1980-2021, being only half of the pure model-based estimate. Moreover, our analysis suggested that an asymmetric seasonal warming rate has led to a reduced temperature seasonality in eastern plain lakes but an amplified one in alpine lakes. The durations of lake heatwaves have also increased at a rate of 7.7 d 10a-1. Under the high-greenhouse-gas-emission scenario, lake surface temperature and lake heatwave duration were projected to increase by 2.2 °C and 197 d at the end of the 21st century, respectively. Such drastic changes would worsen the environmental conditions of lakes subjected to high and increasing anthropogenic pressures, posing great threats to aquatic biodiversity and human health.</p

Toxins from harmful algal blooms: How copper and iron render chalkophore a predictor of microcystin production

Research on harmful algal blooms has focused on macronutrients, yet recent research increasingly indicates that understanding micronutrient roles is also important in the development of effective environmental management interventions. Here, we report results on metallophore production from mesocosms amended with copper and iron (enzymatic co-factors in photosynthetic electron transport) to probe questions of how cyanobacteria navigate the divide between copper nutrition, copper toxicity, and issues with iron bioavailability. These experiments utilized Microcystis, Chlorella and Desmodesmus spp., in mono- and mixed-cultures in lake water from a large, hypereutrophic lake (Taihu, China). To initiate experiments, copper and iron amendments were added to mesocosms containing algae that had been acclimated to achieve a state of copper and iron limitation. Mesocosms were analyzed over time for a range of analytes including algal growth parameters, algal assemblage progression, copper/iron concentrations and biomolecule production of chalkophore, siderophore and total microcystins. Community Trajectory Analysis and other multivariate methods were used for analysis resulting in our findings: 1) Microcystis spp. manage copper/iron requirements though a dynamically phased behavior of chalkophore/siderophore production according to their copper and iron limitation status (chalkophore correlates with Cu concentration, R2 = 0.99, and siderophore correlates with the sum of Cu and Fe concentrations, R2 = 0.98). 2) A strong correlation was observed between the production of chalkophore and the cyanobacterial toxin microcystin (R2 = 0.76)-Chalkophore is a predictor of microcystin production. 3) Based on our results and literature, we posit that Microcystis spp. produces microcystin in response to copper/iron availability to manage photosystem productivity and effect an energy-saving status. Results from this work underscore the importance of micronutrients in influencing harmful algal bloom progression and represents a major advance in understanding the ecological function for the cyanobacterial toxin microcystin as a hallmark of micronutrient limitation stress

The unprecedented 2022 extreme summer heatwaves increased harmful cyanobacteria blooms

Heatwaves are increasing and expected to intensify in coming decades with global warming. However, direct evidence and knowledge of the mechanisms of the effects of heatwaves on harmful cyanobacteria blooms are limited and unclear. In 2022, we measured chlorophyll-a (Chla) at 20-s intervals based on a novel ground-based proximal sensing system (GBPSs) in the shallow eutrophic Lake Taihu and combined in situ Chla measurements with meteorological data to explore the impacts of heatwaves on cyanobacterial blooms and the potential relevant mechanisms. We found that three unprecedented summer heatwaves (July 4-15, July 22-August 16, and August 18-23) lasting a total of 44 days were observed with average maximum air temperatures (MATs) of 38.1 ± 1.9 °C, 38.7 ± 1.9 °C, and 40.2 ± 2.1 °C, respectively, and that these heatwaves were characterized by high air temperature, strong PAR, low wind speed and rainfall. The daily Chla significantly increased with increasing MAT and photosynthetically active radiation (PAR) and decreasing wind speed, revealing a clear promotion effect on harmful cyanobacteria blooms from the heatwaves. Moreover, the combined effects of high temperature, strong PAR and low wind, enhanced the stability of the water column, the light availability and the phosphorus release from the sediment which ultimately boosted cyanobacteria blooms. The projected increase in heatwave occurrence under future climate change underscores the urgency of reducing nutrient input to eutrophic lakes to combat cyanobacteria growth and of improving early warning systems to ensure secure water management.</p

Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems impacted by climate change and anthropogenic nutrients

Mitigating the global expansion of cyanobacterial harmful blooms (CyanoHABs) is a major challenge facing researchers and resource managers. A variety of traditional (e.g., nutrient load reduction) and experimental (e.g., artificial mixing and flushing, omnivorous fish removal) approaches have been used to reduce bloom occurrences. Managers now face the additional effects of climate change on watershed hydrologic and nutrient loading dynamics, lake and estuary temperature, mixing regime, internal nutrient dynamics, and other factors. Those changes favor CyanoHABs over other phytoplankton and could influence the efficacy of control measures. Virtually all mitigation strategies are influenced by climate changes, which may require setting new nutrient input reduction targets and establishing nutrient-bloom thresholds for impacted waters. Physical-forcing mitigation techniques, such as flushing and artificial mixing, will need adjustments to deal with the ramifications of climate change. Here, we examine the suite of current mitigation strategies and the potential options for adapting and optimizing them in a world facing increasing human population pressure and climate change

Metabolic Dynamics During Loquat Fruit Ripening and Postharvest Technologies

Loquat is an important fruit widely cultivated worldwide with high commercial value. During loquat fruit development, ripening, and storage, many important metabolites undergo dramatic changes, resulting in accumulation of a diverse mixture of nutrients. Given the value of loquat fruit, significant progresses have been achieved in understanding the metabolic changes during fruit ripening and storage, as well as postharvest technologies applied in loquat fruit in recent years. The objective of the present review is to summarize currently available knowledge and provide new references for improving loquat fruit quality

Metacommunity ecology meets bioassessment : Assessing spatio-temporal variation in multiple facets of macroinvertebrate diversity in human-influenced large lakes

Highlights • We examined drivers of 6 biotic metrics in a metacommunity context in two large lakes. • The relative roles of spatial, human-induced and natural factors were quantified. • The roles of spatial variables are comparable with local environmental conditions. • The relative roles of different drivers varied substantially among seasons. • Spatial processes, natural conditions and temporal variation should be considered.Metacommunity theory emphasizes that local communities are jointly affected by environmental filtering and spatial processes. However, the roles of spatial processes are often given insufficient attention in bioassessment practices, which may bias the assessments of ecological status based on biotic metrics. Here, we quantified the relative importance and the seasonal stability of spatial processes, natural conditions and human-induced factors in structuring variation in different bioassessment metrics based on macroinvertebrate communities. Our study systems were two extensively sampled large and shallow lakes with strong nutrient gradients related to human disturbance. The roles of different drivers were examined for three kinds of indicators: general diversity, trait-based and taxonomic distinctness metrics, and their performance in characterizing human disturbance was evaluated. Overall, human-induced and spatial factors were all important in explaining variation in the three types of bioassessment metrics. Contrary to our expectations, however, we found that the importance of spatial processes on bioassessment metrics can be comparable to the effects of local environmental conditions at the within-lake scale. Furthermore, the results showed substantial seasonal variability in the relative roles of different drivers, which might be linked to life-cycle seasonality of macroinvertebrates. As expected, trait-based metrics generally were best associated with human-induced variables in both lakes, whereas general diversity and taxonomic distinctness metrics performed poorly. The low effectiveness of taxonomic distinctness metrics might due to low species richness associated with high nutrient levels. To conclude, our results suggest that bioassessment cannot exclusively rely on the idea of environmental filtering even if we focus on fine spatial scales. We hence strongly urge that spatial processes, natural drivers and temporal variability should be better considered in combination in the development and application of bioassessment approaches. In addition, taxonomic distinctness measures should be used with caution, especially for the ecosystems and organism groups typically characterized by low species richness

A Global lake ecological observatory network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models

A Global Lake Ecological Observatory Network (GLEON; www.gleon.org) has formed to provide a coordinated response to the need for scientific understanding of lake processes, utilising technological advances available from autonomous sensors. The organisation embraces a grassroots approach to engage researchers from varying disciplines, sites spanning geographic and ecological gradients, and novel sensor and cyberinfrastructure to synthesise high-frequency lake data at scales ranging from local to global. The high-frequency data provide a platform to rigorously validate processbased ecological models because model simulation time steps are better aligned with sensor measurements than with lower-frequency, manual samples. Two case studies from Trout Bog, Wisconsin, USA, and Lake Rotoehu, North Island, New Zealand, are presented to demonstrate that in the past, ecological model outputs (e.g., temperature, chlorophyll) have been relatively poorly validated based on a limited number of directly comparable measurements, both in time and space. The case studies demonstrate some of the difficulties of mapping sensor measurements directly to model state variable outputs as well as the opportunities to use deviations between sensor measurements and model simulations to better inform process understanding. Well-validated ecological models provide a mechanism to extrapolate high-frequency sensor data in space and time, thereby potentially creating a fully 3-dimensional simulation of key variables of interest

Water clarity response to climate warming and wetting of the Inner Mongolia-Xinjiang Plateau: A remote sensing approach

Water clarity (generally quantified as the Secchi disk depth: SDD) is a key variable for assessing environmental changes in lakes. Using remote sensing we calculated and elucidated the SDD dynamics in lakes in the Inner Mongolia-Xinjiang Lake Zone (IMXL) from 1986 to 2018 in response to variations in temperature, rainfall, lake area, normalized difference vegetation index (NDVI) and Palmer's drought severity index (PDSI). The results showed that the lakes with high SDD values are primarily located in the Xinjiang region at longitudes of 75°–93° E. In contrast, the lakes in Inner Mongolia at longitudes of 93°–118° E generally have low SDD values. In total, 205 lakes show significant increasing SDD trends (P < 0.05), with a mean rate of 0.15 m per decade. In contrast, 75 lakes, most of which are located in Inner Mongolia, exhibited significant decreasing trends with a mean rate of 0.08 m per decade (P < 0.05). Pooled together, an overall increase is found with a mean rate of 0.14 m per decade. Multiple linear regression reveals that among the five variables selected to explain the variations in SDD, lake area accounts for the highest proportion of variance (25%), while temperature and rainfall account for 12% and 10%, respectively. In addition, rainfall accounts for 52% of the variation in humidity, 8% of the variation in lake area and 7% of the variation in NDVI. Temperature accounts for 27% of the variation in NDVI, 39% of the variation in lake area and 22% of the variation in PDSI. Warming and wetting conditions in IMXL thus promote the growth of vegetation and cause melting of glaciers and expansion of lake area, which eventually leads to improved water quality in the lakes in terms of higher SDD. In contrast, lakes facing more severe drought conditions, became more turbid