Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density.

Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.

Indian spices: past, present and future challenges as the engine for bio-enhancement of drugs: impact of COVID-19.

Spices are natural plant products enriched with the history of being used as herbal medicine for prevention of diseases. India is also known as the 'Land of Spices'. Out of 109 spices recognized by the International Organization for Standardization (ISO) more than 52-60 spice crops are grown in India. The major spices exported by India are turmeric, cumin, coriander, fenugreek, peppers, etc. The Indian spices are divided into three era viz. early period, middle age and early modern period. Spices are used in beverages, liquors, and pharmaceutical, cosmetic and perfumery products. The major issue with spices is their handling and storage. This review article mainly focuses on two aspects: at the outset the handling and storage of the spices is an essential factor as spices are available in different forms like raw, processed, fresh, whole dried, or pre-ground dried. Therefore, the need of processing, packaging, storage and handling of the spices is important as the deterioration of spices can lead to the loss of therapeutic activity. Furthermore, many herbal constituents have the capability to enhance the bioavailability of drugs. Therefore, an attempt has been made to throw a light on the bioenhancer activity and therapeutic activity along with their mechanism of action of some Indian spices which are regularly used for cooking purpose on a daily basis to enhance the taste of food. The spices suggested by ministry of AYUSH which is relevant to its medicinal and biological property in treatment and prevention from COVID-19 are discussed. © 2022 Society of Chemical Industry.

Earlier winter/spring runoff and snowmelt during warmer winters lead to lower summer chlorophyll-a in north temperate lakes.

Winter conditions, such as ice cover and snow accumulation, are changing rapidly at northern latitudes and can have important implications for lake processes. For example, snowmelt in the watershed-a defining feature of lake hydrology because it delivers a large portion of annual nutrient inputs-is becoming earlier. Consequently, earlier and a shorter duration of snowmelt are expected to affect annual phytoplankton biomass. To test this hypothesis, we developed an index of runoff timing based on the date when 50% of cumulative runoff between January 1 and May 31 had occurred. The runoff index was computed using stream discharge for inflows, outflows, or for flows from nearby streams for 41 lakes in Europe and North America. The runoff index was then compared with summer chlorophyll-a (Chl-a) concentration (a proxy for phytoplankton biomass) across 5-53 years for each lake. Earlier runoff generally corresponded to lower summer Chl-a. Furthermore, years with earlier runoff also had lower winter/spring runoff magnitude, more protracted runoff, and earlier ice-out. We examined several lake characteristics that may regulate the strength of the relationship between runoff timing and summer Chl-a concentrations; however, our tested covariates had little effect on the relationship. Date of ice-out was not clearly related to summer Chl-a concentrations. Our results indicate that ongoing changes in winter conditions may have important consequences for summer phytoplankton biomass and production.

Storm impacts on phytoplankton community dynamics in lakes.

In many regions across the globe, extreme weather events such as storms have increased in frequency, intensity, and duration due to climate change. Ecological theory predicts that such extreme events should have large impacts on ecosystem structure and function. High winds and precipitation associated with storms can affect lakes via short-term runoff events from watersheds and physical mixing of the water column. In addition, lakes connected to rivers and streams will also experience flushing due to high flow rates. Although we have a well-developed understanding of how wind and precipitation events can alter lake physical processes and some aspects of biogeochemical cycling, our mechanistic understanding of the emergent responses of phytoplankton communities is poor. Here we provide a comprehensive synthesis that identifies how storms interact with lake and watershed attributes and their antecedent conditions to generate changes in lake physical and chemical environments. Such changes can restructure phytoplankton communities and their dynamics, as well as result in altered ecological function (e.g., carbon, nutrient and energy cycling) in the short- and long-term. We summarize the current understanding of storm-induced phytoplankton dynamics, identify knowledge gaps with a systematic review of the literature, and suggest future research directions across a gradient of lake types and environmental conditions.

Multivariate Analyses of Phytoplankton Pigment Fluorescence from a Freshwater River Network.

Monitoring phytoplankton classes in river networks is critical to understanding phytoplankton dynamics and to predicting the ecosystem response to changing land-use and seasons. Applicability of phytoplankton fluorescence as a quick and effective ecological monitoring approach is relatively unexplored in freshwater ecosystems. We used multivariate analyses of fluorescence from pigment extracted in 90% acetone to assess the variability in phytoplankton classes, herbivory, and organic matter quality in a freshwater river network. A total of four models developed by the parallel factor analysis (PARAFAC) of fluorescence excitation and emission matrices identified six components: Model 1 (pheophytin-A and chlorophyll-A), Model 2 (chlorophyll-B and chlorophyll-C), Model 3 (pheophytin-B), and Model 4 (pheophytin-C). Redundancy analyses revealed that in the summer, urban and agricultural streams were abundant in chlorophylls, fresh organic matter, and organic nitrogen, whereas in winter, streams were high in phaeopigments. A slow-moving, light-limited wetland stream was an exception as high phaeopigment abundance was observed in both seasons. The PARAFAC components were used to develop a partial least-squares regression-based model (r2 = 0.53; Nash-Sutcliffe efficiency = 0.5; n = 147) that successfully predicted chlorophyll-A concentrations from an external subset of river water samples (r2 = 0.41; p < 0.0001; n = 75). Thus, combining multivariate analyses and fluorescence spectroscopy is useful for monitoring and predicting phytoplankton dynamics in large river networks.

Hydro-Climatological Influences on Long-Term Dissolved Organic Carbon in a Mountain Stream of the Southeastern United States.

In the past decade, significant increases in surface water dissolved organic carbon (DOC) have been reported for large aquatic ecosystems of the Northern Hemisphere and have been attributed variously to global warming, altered hydrologic conditions, and atmospheric deposition, among other factors. We analyzed a 25-yr DOC record (1988-2012) available for a forested headwater stream in the United States and documented two distinct regimes of stream DOC trends. From 1988 to 2001, annual mean volume-weighted DOC concentration (DOC, mg L) and annual DOC flux (kg ha yr) declined by 34 and 56%, respectively. During 1997 to 2012, the decline in DOC and DOC flux increased by 141 and 165%, respectively. Declining DOC from 1988 to 2001 corresponded to a decline in growing season runoff, which has the potential to influence mobilization of DOC from uplands to streams. Increasing DOC from 1997 to 2012 corresponded to increased precipitation early in the growing season and to an increase in the number and intensity of short-duration fall storms capable of mobilizing long-accrued DOC from forest litter and soils. In contrast, total annual runoff declined throughout the period. Rising air temperature, atmospheric acid deposition, and nitrogen depositions did not offer any plausible explanation for the observed bidirectional annual trends of stream DOC. Our study highlights the critical role of long-term datasets and analyses for understanding the impacts of climate change on carbon and water cycles and associated functions of aquatic and terrestrial ecosystems.

What makes a cyanobacterial bloom disappear? A review of the abiotic and biotic cyanobacterial bloom loss factors.

Cyanobacterial blooms present substantial challenges to managers and threaten ecological and public health. Although the majority of cyanobacterial bloom research and management focuses on factors that control bloom initiation, duration, toxicity, and geographical extent, relatively little research focuses on the role of loss processes in blooms and how these processes are regulated. Here, we define a loss process in terms of population dynamics as any process that removes cells from a population, thereby decelerating or reducing the development and extent of blooms. We review abiotic (e.g., hydraulic flushing and oxidative stress/UV light) and biotic factors (e.g., allelopathic compounds, infections, grazing, and resting cells/programmed cell death) known to govern bloom loss. We found that the dominant loss processes depend on several system specific factors including cyanobacterial genera-specific traits, in situ physicochemical conditions, and the microbial, phytoplankton, and consumer community composition. We also address loss processes in the context of bloom management and discuss perspectives and challenges in predicting how a changing climate may directly and indirectly affect loss processes on blooms. A deeper understanding of bloom loss processes and their underlying mechanisms may help to mitigate the negative consequences of cyanobacterial blooms and improve current management strategies.

The role of organic nutrients in structuring freshwater phytoplankton communities in a rapidly changing world.

Carbon, nitrogen, and phosphorus are critical macroelements in freshwater systems. Historically, researchers and managers have focused on inorganic forms, based on the premise that the organic pool was not available for direct uptake by phytoplankton. We now know that phytoplankton can tap the organic nutrient pool through a number of mechanisms including direct uptake, enzymatic hydrolysis, mixotrophy, and through symbiotic relationships with microbial communities. In this review, we explore these mechanisms considering current and projected future anthropogenically-driven changes to freshwater systems. In particular, we focus on how naturally- and anthropogenically- derived organic nutrients can influence phytoplankton community structure. We also synthesize knowledge gaps regarding phytoplankton physiology and the potential challenges of nutrient management in an organically dynamic and anthropogenically modified world. Our review provides a basis for exploring these topics and suggests several avenues for future work on the relation between organic nutrients and eutrophication and their ecological implications in freshwater systems.

Chromophoric dissolved organic matter composition and load from a coastal river system under variable flow regimes.

Chromophoric dissolved organic matter (CDOM) exported from riverine catchments can influence biogeochemical processes in coastal environments with implications for water quality and carbon budget. Despite recent efforts to quantify C fluxes during high flow events, knowledge gaps exist regarding the fluxes and yield of terrestrial, reactive vs. recalcitrant CDOM under episodic to base-flow conditions from uplands to downstream estuaries. We used stream dissolved organic carbon (DOC) concentrations and CDOM optical properties using parallel factor analysis to characterize composition and fluxes under variable flow conditions for a coastal river basin in the SE USA. Our findings showed that episodic flows (>75th percentile) were marked by the elevated flux of humic acid-like CDOM and lower in-stream autochthonous production, or microbial degradation. Further, 70% of the terrestrial CDOM was exported during high flows, with a 3-fold increase in CDOM flux during episodic events, including Hurricane Irene in 2011. While, low flows (<25th percentile) were marked by an increased abundance of microbial, humic CDOM that can be easily processed within the estuary. Due to greater wetland coverage in the Neuse, the annual CDOM yield was 5-6 times higher than the larger rivers, such as the Mississippi, USA, and Changjiang, China. We suggest that similar coastal watersheds in SE USA or elsewhere may contribute substantial amounts of reactive CDOM to the estuaries during high flow conditions and can have negative water quality implications for the coastal C dynamics. These findings can help predict the evolution of coastal C cycling under projected climate change and inform the development of appropriate management strategies.

Spatial patterns in dissolved organic matter composition controlled by watershed characteristics in a coastal river network: The Neuse River Basin, USA.

The effect of watershed characteristics (land use land cover and morphology) on spatial variability in dissolved organic matter (DOM) composition, and concentrations of dissolved organic carbon [DOC] and nitrogen [DON] was assessed in a coastal river basin draining into Pamlico Sound in eastern North Carolina, USA. Understanding the factors that influence DOM concentration and composition i.e., structurally complex molecules with high molecular weight versus low molecular weight, simple molecules can provide insights on DOM cycling and water composition implications. Such information is imperative for large coastal river networks undergoing rapid and intense land use and land cover (LULC) changes. DOM composition was estimated using optical indices calculated from DOM absorbance and fluorescence measurements. DOM was derived from terrestrial sources, and ordination analysis indicated that LULC, in particular, % wetland area was the most significant control on DOM composition and concentration. Wetland and agricultural coastal streams were abundant in humic and complex DOM, whereas forested and urban streams were least abundant in humic DOM. We speculate that greater availability of mobilizable DOM in wetland and agricultural watersheds contributed to this observation. In comparison, mixed urbanized and forested streams in North Carolina's Piedmont region were abundant in [DOC], less complex, low molecular weight DOM, as well as greater amounts [DON] due to higher urban runoff and elevated DOM production in these streams. Our results indicated that physiographic transition from Piedmont to coastal plain and varying LULC influenced the spatial variability in DOM composition and concentration. Our findings highlight that increasing anthropogenic alterations might increase the abundance of reactive DOM in coastal rivers and estuaries resulting in severe water quality issues. This information is important for monitoring and developing land use policies.

A Bayesian framework to unravel food, groundwater, and climate linkages: A case study from Louisiana.

Advancing our understanding of the connections among groundwater, food, and climate is critical to meet global food demands while optimizing water resources usage. However, our understanding of the linkages among groundwater, food, and climate is still limited. Here, we offer a Bayesian framework to simulate crop yield at a regional scale and quantify its relationships and associated uncertainty with climate, groundwater, agricultural, and energy-related variables. We implemented the framework in the rice-producing regions of Louisiana from 1960-2015. To build a parsimonious model, we used a probability-based variable selection approach to detect the key drivers of rice yield. Rice yield increased, groundwater declined, and area planted declined or did not change over 56yrs. The number of irrigation wells, groundwater level, air temperature, and area planted were found to be the key drivers of rice yield. The regression coefficients showed that rice yield was positively related to groundwater level, and negatively related to area planted and the number of irrigation wells. The limited influence of N fertilizer was noted on rice yield for the period when fertilizer data were available. The inverse relationship between rice yield and area planted pointed to the adaption of efficient crop management practices that maintained or increased yield, despite the decline in area planted. The farmers' ability to install irrigation wells during droughts sustained the yields over long-term but not short-term. This decline in rice yield in response to drought over the short-term might explain the negative relation between yield and irrigation wells. Overall, this work highlighted the uncertainty in relationships between rice yield and key drivers and quantified the intimate connection between food and groundwater. This work may have implications for managing two highly competing commodities (i.e., groundwater and food) in agricultural regions.

Ecological response to hydrological variability and catchment development: Insights from a shallow oxbow lake in Lower Mississippi Valley, Arkansas.

The ecological response of shallow oxbow lakes to variability in hydrology and catchment development in large river floodplain ecosystems (RFE) in Arkansas remains largely unknown. Investigating these responses will advance our understanding of ecological evolution of oxbow lakes in response to the major environmental drivers, which will establish baseline conditions required to develop effective management practices for RFE. In this pilot study, we examined the potential of using a dated surface sediment core from Adams Bayou, a floodplain lake located within the Cache-Lower White River Ramsar site in SE Arkansas. Stratigraphic records of diatoms and sediment geochemistry were used to ascertain variation in Adams Bayou's ecological condition. During 1968-2008, in response to hydrological and anthropogenic changes, Adams Bayou's diatom assemblages progressed from predominantly benthic (Gomphonema parvulum and Meridion circulare) to primarily planktonic assemblage (Aulacoseira granulata and Cyclotella meneghiniana), along with a decrease in magnetic susceptibility (k) and % silt. Statistical analyses reveled that during 1968-2000, higher hydrological connectivity and catchment alterations drove Adams Bayou's ecosystem. After 2000, lower hydrological connectivity and increase in cultivation were the major drivers. The potential impact of increasing air temperature was also noted. The shift in Adams Bayou from a connected, clear, mesotrophic state to a relatively isolated, turbid and nutrient enriched state is consistent with regime shift models and highlights its sensitivity to a combination of environmental stresses prevalent in the catchment. Although fluvial systems pose challenges in establishing clear chronologies, oxbow lake sediments can be a effective paleoecological archives. Our work provides clear evidence for the change in the ecological character of this wetland of international significance and flags the need for a wider assessment of water bodies across this site under obligations to the Ramsar Convention.