Projected climate change impact on cyanobacterial bloom phenology in temperate rivers based on temperature dependency.

Climate warming is linked to earlier onset and extended duration of cyanobacterial blooms in temperate rivers. This causes an unpredictable extent of harm to the functioning of the ecosystem and public health. We used Microcystis spp. cell density data monitored for seven years (2016-2022) in ten sites across four temperate rivers of the Republic of Korea to define the phenology of cyanobacterial blooms and elucidate the climatic effect on their pattern. The day of year marking the onset, peak, and end of Microcystis growth were estimated using a Weibull function, and linear mixed-effect models were employed to analyze their relationships with environmental variables. These models identified river-specific temperatures at the beginning and end dates of cyanobacterial blooms. Furthermore, the most realistic models were employed to project future Microcystis bloom phenology, considering downscaled and quantile-mapped regional air temperatures from a general circulation model. Daily minimum and daily maximum air temperatures (mintemp and maxtemp) primarily drove the timing of the beginning and end of the bloom, respectively. The models successfully captured the spatiotemporal variations of the beginning and end dates, with mintemp and maxtemp predicted to be 24℃ (R2 = 0.68) and 16℃ (R2 = 0.35), respectively. The beginning and end dates were projected to advance considerably in the future under the Representative Concentration Pathway 2.6, 4.5, and 8.5. The simulations suggested that the largest uncertainty lies in the timing of when the bloom ends, whereas the timing of when blooming begins has less variation. Our study highlights the dependency of cyanobacterial bloom phenology on temperatures and earlier and prolonged bloom development.

Time-series modelling of harmful cyanobacteria blooms by convolutional neural networks and wavelet generated time-frequency images of environmental driving variables.

Early warning systems for harmful cyanobacterial blooms (HCBs) that enable precautional control measures within water bodies and in water works are largely based on inferential time-series modelling. Among deep learning techniques, convolutional neural networks (CNNs) are widely applied for recognition of pictorial, acoustic and thermal images. Time-frequency images of environmental drivers generated by wavelets may provide crucial signals for modelling of HCBs to be recognized by CNNs. This study applies CNNs for time-series modelling of HCBs of Microcystis sp. in four South Korean rivers between 2016 and 2022 by means of time-frequency images of environmental drivers within the lead time of HCBs. After estimating the cardinal dates of beginning, peak, and ending of HCBs, wavelet analysis identified key drivers by phase analysis and generated time-frequency images of the drivers within the cardinal dates for 3, 4 and 5 years. Performances of CNNs were compared in terms of four determinants of input images: methods of estimating critical timings, the number of segments, time-series continuity, and image size. The resulting CNNs predicted high or low intensities of HCBs with a mean accuracy of 97.79 ± 0.06% and F1-score 97.49 ± 0.06% for training dataset, and a mean accuracy of 95.01 ± 0.06% and F1-score 93.30 ± 0.07% for testing dataset. Predictions of Microcystis abundances by CNNs achieved a mean MSE of 2.58 ± 2.46 and a mean R2 of 0.78 ± 0.20 for training, and a mean MSE of 2.76 ± 2.42 and a mean R2 of 0.55 ± 0.20 for testing dataset. Precipitation and discharge appeared to be the best performing drivers for qualitative and quantitative predictions of HCBs pointing at the nonstationary nature of river habitats. This study highlights the opportunities of time-series modelling by CNNs driven by wavelet generated time-frequency images of key environmental variables for forecasting of HCBs.

Influence of land cover, point source pollution, and granularity on the distribution of metals, metalloids, and organic matter in the river and stream sediments in the Republic of Korea.

With increasing anthropogenic activities, rivers and streams have become vulnerable to pollution; therefore, monitoring potential contaminants and the pollution status of surface sediments is essential. This study analyzed the concentrations of organic matter, metals, and metalloids; indices for organic, metal, and metalloid pollution; and ecological risk in river and stream sediments at 82 stations across Korea in 2017, 2018, and 2020. We performed bootstrapped analysis of variance, principal component analysis, and cluster analysis and used a structural equation model (SEM) to investigate spatiotemporal changes in the pollution status, main pollutant chemicals, and the exogenous factors affecting pollution status. The results suggest no significant differences in any of the twelve single chemical parameters and three pollution indices across the surveyed years. Metals, metalloids (Cu, Zn, Pb, and Hg), and organic matter with nutrients were identified as the main pollutants. The SEM demonstrated the significant influence of pollution sources-water used for industrial purposes, landfill wastewater discharge, and industrial wastewater discharge-on organic pollution, metal and metalloid pollution load, and environmental toxicity. This study identified consistently polluted areas, proposed additional management policies and stricter regulations on major point pollution sources rather than on broader land-use types, and suggested the combined consideration of metal toxicity risk with nutrient accumulation for future risk assessments.

Is scat marking a reliable tool for otter census and surveys at the landscape scale?

Biological significance of scat marking by otters has been a controversial subject among scientists. Using multiyear (2014-2017) data of otter spraint counts in South Korea, this study aimed to test whether the observed pattern of spraint presence/absence is driven by detection error and if/how scat counts can be a proxy for otter abundance at the landscape scale. To test the first hypothesis, spraint presence/absence was analyzed through occupancy models, which relied on environmental variables related to otter detectability and presence. Spraint count models were used to test the second hypothesis against resource-related covariates in combination with landscape, anthropogenic, and climate variables through machine learning algorithms (MLAs). The detection probability has specifically decreased in areas characterized by high rainfall and human population densities, whereas the probability has increased near food-rich sites, characterized by high marking frequencies. The temporal trends of spraint count predictions were in line with changes in the diversity of fish communities in 2014-2017 instead of fish biomass, suggesting that the availability of feeding resources is higher where fish communities are more diverse. Because diverse fish communities can attract otters, fish diversity conservation is critical for preserving this mammal's populations. This fine scale four-year monitoring has contributed to the disentanglement of the role of spraint presence/absence and spraint counts in detectability and population trends. This will assist in identifying key resource areas and planning strategies to promote otter conservation and dispersal dynamics.

Implications of flow regulation for habitat conditions and phytoplankton populations of the Nakdong River, South Korea.

Anthropogenic regulation of hydrographs is a widespread approach to river management; however, the effects of river regulation on habitat conditions and aquatic communities have rarely been studied. In this study, we analyzed the physical, chemical, and biological data from the lower Nakdong River in South Korea from 2005 to 2009 before weir construction and from 2012 to 2016 after weir construction. A partial least square path model (PLS-PM) was applied to delineate the complex interrelationships of diatoms and cyanobacteria with physicochemical parameters, nutrients, zooplankton grazing, and hydrological parameters. Inferential modeling using the hybrid evolutionary algorithm (HEA) allowed the identification of differences in the importance and threshold conditions of population dynamics drivers of diatoms and cyanobacteria before and after flow regulation. The annually averaged trajectories of limnological variables displayed significant shifts in seasonality and magnitudes of phytoplankton, zooplankton, and nutrient concentrations between the two periods. The results of PLS-PM indicated that, after flow regulation, diatoms and cyanobacteria were directly affected by nutrients and zooplankton densities and the path coefficients of hydrological parameters decreased or even were insignificant. The inferential models suggested that diatom dynamics were essentially shaped by threshold conditions of water temperature (WT) and pH before regulation, but mainly by those of rotifers (below 51.1 ind. L-1) after regulation. As for cyanobacteria dynamics, WT was identified as a critical threshold condition before and after regulation, and the threshold of PO4- concentration above 145.4 L-1 was identified as the reason for occasional blooms during the post-regulation period. Overall, the results suggest that flow regulation gradually alters habitat conditions typically of rivers to those of stagnant waters. These findings must be taken into account for sustainable management strategies of regulated rivers.

Spatial patterning of chlorophyll a and water-quality measurements for determining environmental thresholds for local eutrophication in the Nakdong River basin.

Management of water-quality in a river ecosystem needs to be focused on susceptible regions to eutrophication based on proper measurements. The stress-response relationships between nutrients and primary productivity of phytoplankton allow the derivation of ecologically acceptable thresholds of stressors under field conditions. However, spatio-temporal variations in heterogeneous environmental conditions have hindered the development of locally applicable criteria. To address these issues, we utilized a combination of a geographically specialized artificial neural network (Geo-SOM, geo-self-organizing map) and linear mixed-effect models (LMMs). The model was applied to a 24-month dataset of 54 stations that spanned a wide spatial gradient in the Nakdong River basin. The Geo-SOM classified 1286 observations in the basin into 13 clusters that were regionally and seasonally distinct. Inclusion of the random effects of Geo-SOM clustering improved the performance of each LMM, which suggests that there were significant spatio-temporal variations in the Chla-stressor relationships. These variations arise owing to differences in background seasonality and the effects of local pollutant variables and land-use patterns. Among the 16 environmental variables, the major stressors for Chla were total phosphate (TP) as a nutrient and biological oxygen demand (BOD) as a non-nutrient according to the results of both Geo-SOM and LMM analyses. Based on LMMs with the random effect of the Geo-SOM clusters on the intercept and the slope, we can propose recommended thresholds for TP (18.5 µg L-1) and BOD (1.6 mg L-1) in the Nakdong River. The combined method of LMM and Geo-SOM will be useful in guiding appropriate local water-quality-management strategies and in the global development of large-scale nutrient criteria.

Drivers shaping episodic and gradual changes in phytoplankton community succession: Taxonomic versus functional groups.

Describing temporal changes in phytoplankton communities is complicated owing to (i) multivariate environmental drivers, (ii) inter-specific relationships, and (iii) various species. With long-term research data from the lower Nakdong River from 1993 to 2016, we examined the temporal changes at two scales-episodic (from weekly to monthly) and long-term (yearly)-and screened the potential environmental drivers. Phytoplankton community component patterns were modeled with the drivers as covariates, using multivariate autoregressive state-space (MARSS) models, to assess their response to environmental drivers and biotic interactions. We assumed that compared to taxonomic classification, functional classification would obtain a better identification of community response to temporal variability. Over 24 years, the succession patterns of the dominant taxonomic and functional groups decreased in diversity, with the greatest decreases in biomass of Bacillariophyceae and group D (mainly the diatom Stephanodiscus hantzschii), and coincided with the introduction of group H1 (dinitrogen-fixing nostocaleans). The potential drivers for these changes were precipitation, water level, and total nitrogen (TN) for taxonomic groups and TN, total phosphorus, and euphotic zone depth for functional groups. The results of the MARSS model and temporal trends for each driver indicated that the increases in the water level and light availability were mostly related with the taxonomic and functional groups, respectively. The model for functional groups proposed a total of 24 significant inter-group relationships, where five relationships supported the succession patterns of dominant groups in the Nakdong River. Combined with the effects of increased light availability, a positive relationship between groups H1 and M (mainly Cyanobacteria and Microcystis aeruginosa) appears to induce cyanobacterial bloom development over a long period. These results can be fundamental information for river system management concerning the resulting cascading effects of changes in environmental drivers and inter-group relationships on the phytoplankton community composition.