Nutrient Enrichment and Planktonic Biomass Ratios in Lakes

Water quality-fisheries tradeoffs in a changing climate underscore the need for adaptive ecosystem-based management

Changes driven by both unanticipated human activities and management actions are creating wicked management landscapes in freshwater and marine ecosystems that require new approaches to support decision-making. By linking a predictive model of nutrient- and temperature-driven bottom hypoxia with observed commercial fishery harvest data from Lake Erie (United States-Canada) over the past century (1928-2022) and climate projections (2030-2099), we show how simple, yet robust models and routine monitoring data can be used to identify tradeoffs associated with nutrient management and guide decision-making in even the largest of aquatic ecosystems now and in the future. Our approach enabled us to assess planned nutrient load reduction targets designed to mitigate nutrient-driven hypoxia and show why they appear overly restrictive based on current fishery needs, indicating tradeoffs between water quality and fisheries management goals. At the same time, our temperature results show that projected climate change impacts on hypoxic extent will require more stringent nutrient regulations in the future. Beyond providing a rare example of bottom hypoxia driving changes in fishery harvests at an ecosystem scale, our study illustrates the need for adaptive ecosystem-based management, which can be informed by simple predictive models that can be readily applied over long time periods, account for tradeoffs across multiple management sectors (e.g., water quality, fisheries), and address ecosystem nonstationarity (e.g., climate change impacts on management targets). Such approaches will be critical for maintaining valued ecosystem services in the many aquatic systems worldwide that are vulnerable to multiple drivers of environmental change.

Combining national and state data improves predictions of microcystin concentration

Data collected from lakes at national (regional) scales and state (local) scales can provide different insights regarding relationships between environmental factors and biological responses, and combining these two types of data can potentially yield more precise and accurate understanding of ecological phenomena. National data can include many measures, cover large spatial areas, and span broad environmental gradients. Because of these characteristics, analyses of these data can yield accurate estimates of relationships among different lake characteristics. However, the number of samples in a national data set that is available for estimating a relationship specific to waterbodies within a smaller region, like a single state, is limited. Conversely, state monitoring data provide intensive sampling of lakes within a smaller area, but these data span a narrower range of conditions and may only include a subset of relevant measurements. Here, a Bayesian network model is described that represents the causal linkages between observations of chlorophyll a concentration, cyanobacterial biovolume, and microcystin concentration. This network model was fit to national data and provided a context for modeling observations of chlorophyll a and microcystin collected from lakes in Iowa. Using the knowledge inherent in the national network model improved the accuracy of predictions of microcystin concentrations in Iowa compared to a model based only on Iowa data.

Chlorophyll a predictability and relative importance of factors governing lake phytoplankton at different timescales

Assessing the key drivers of eutrophication in lakes and reservoirs has long been a challenge, and many studies have developed empirical models for predicting the relative importance of these drivers. However, the relative roles of various parameters might differ not only spatially (between regions or localities) but also at a temporal scale. In this study, the relative roles of total phosphorus, total nitrogen, ammonia, wind speed and water temperature were selected as potential drivers of phytoplankton biomass by using chlorophyll a as a proxy for biomass. A generalized additive model (GAM) and a random forest model (RF) were developed to assess the predictability of chlorophyll a and the relative importance of various predictors driving algal blooms at different timescales in a freshwater lake. The results showed that the daily datasets yielded better predictability than the monthly datasets. In addition, at a daily scale, water temperature was a more important predictor of chlorophyll a than nutrients, and the importance of phosphorus was comparable to that of nitrogen. In contrast, at a monthly scale, nutrients are more important predictors than water temperature and phosphorus is a better predictor than nitrogen. This study indicates that the drivers of phytoplankton fluctuations vary at different timescales and that timescale has an influence on the relative roles of nitrogen and phosphorus limitation in lakes, which suggests that the temporal scale should be considered when explaining phytoplankton fluctuations. Moreover, this study provides a reference for the monitoring of phytoplankton fluctuations and for understanding the mechanisms underlying phytoplankton fluctuations at different timescales.

Changes in the relationship between zooplankton and phytoplankton biomasses across a eutrophication gradient

The relationship between zooplankton biomass and phytoplankton biomass can provide insight into the structure and function of lake biological communities. We used a Bayesian network model to analyze a continental-scale data dataset to estimate changes in the relationship between zooplankton (Z) and phytoplankton (P) biomasses along a eutrophication gradient. The Bayesian network model allowed us to combine two different measurements of phytoplankton biomass (chlorophyll a concentration and directly observed biovolume) to improve the precision of estimates of true biomass within each sample. The model also allowed us to estimate separate relationships between P and zooplankton abundance and between P and mean individual zooplankton biomass and then to combine these two relationships into an estimate of seasonal mean zooplankton biomass. The resulting analysis indicated that seasonal mean zooplankton biomass increased proportionally with phytoplankton biomass in oligotrophic lakes, yielding a constant ratio between Z and P and suggested that bottom-up forces determined zooplankton biomass in these systems. In eutrophic lakes, seasonal mean zooplankton biomass was nearly constant with increases in phytoplankton biomass, yielding a decrease in the ratio between Z and P with increasing eutrophication. Bottom-up forces, as quantified by an increase in the proportion of cyanobacteria, accounted for approximately one fifth of the residual variance in the model as the relationship between Z and P changed from direct proportionality in oligotrophic lakes to the nearly constant value of Z observed in eutrophic lakes, suggesting that a combination of both top-down and bottom-up forces likely determined zooplankton biomass in eutrophic lakes.

Fifteen forms of biodiversity trend in the Anthropocene

Humans are transforming the biosphere in unprecedented ways, raising the important question of how these impacts are changing biodiversity. Here we argue that our understanding of biodiversity trends in the Anthropocene, and our ability to protect the natural world, is impeded by a failure to consider different types of biodiversity measured at different spatial scales. We propose that ecologists should recognize and assess 15 distinct categories of biodiversity trend. We summarize what is known about each of these 15 categories, identify major gaps in our current knowledge, and recommend the next steps required for better understanding of trends in biodiversity.

Community stoichiometry in a changing world: combined effects of warming and eutrophication on phytoplankton dynamics

The current changes in our climate will likely have far-reaching consequences for aquatic ecosystems. These changes in the climate, however, do not act alone, and are often accompanied by additional stressors such as eutrophication. Both global warming and eutrophication have been shown to affect the timing and magnitude of phytoplankton blooms. Little is known about the combined effects of rising temperatures and eutrophication on the stoichiometry of entire phytoplankton communities. We exposed a natural phytoplankton spring community to different warming and phosphorus-loading scenarios using a full-factorial design. Our results demonstrate that rising temperatures promote the growth rate of an entire phytoplankton community. Furthermore, both rising temperatures and phosphorus loading stimulated the maximum biomass built up by the phytoplankton community. Rising temperatures led to higher carbon: nutrient stoichiometry of the phytoplankton community under phosphorus-limited conditions. Such a shift towards higher carbon: nutrient ratios, in combination with a higher biomass buildup, suggests a temperature-driven increase in nutrient use efficiency, the phytoplankton community. Importantly, with higher carbon: nutrient stoichiometry, phytoplankton is generally of poorer nutritional value for zooplankton. Thus, although warming may result in higher phytoplankton biomass, this may be accompanied by a stoichiometric mismatch between phytoplankton and their grazers, with possible consequences for the entire aquatic food web.

Study of phytopigments in river bed sediments: effects of the organic matter, nutrients and metal composition

Phytopigment estimation has a considerable interest in the evaluation of freshwater bodies' quality, because it takes into account the synergistic effect of nutrients like phosphorus or nitrogen on algal growth producing eutrophication. Furthermore, their increasing concentration constitutes the first step in the formation of biofilms on the surface sediments, adding a new and very important element to the dynamic nature of the surface sediments. In this study the distribution of phytoplankton--in terms of chlorophyll-a, chlorophyll-b, phaeophytin-a, phaeophytin-b, total carotenoids, total chlorophyll, and total phaeophytin--was evaluated in river bed sediments. Samples collected at sites with low levels of nutrients (P,N) and metal concentrations showed lower phytopigment concentrations than those collected at the sampling sites affected by sources of pollution. Phytoplankton concentrations were directly and highly related to the organic matter concentrations--in particular to the humic fraction--as well as to the total nitrogen (N), total phosphorus (P(T)) and available phosphorus (P(A)) concentrations in sediments. In addition, phytoplankton also correlates positively with Cu, Zn, Fe and Al extracted in oxalate, being Cu the variable that most influences the phytopigment growth. These are essential metals for the metabolism of the phytoplankton, so therefore the increase in metal concentrations can increase algal growth, unless they reach toxic levels.