An evaluation of statistical models of microcystin detection in lakes applied forward under varying climate conditions.

Algal blooms can threaten human health if cyanotoxins such as microcystin are produced by cyanobacteria. Regularly monitoring microcystin concentrations in recreational waters to inform management action is a tool for protecting public health; however, monitoring cyanotoxins is resource- and time-intensive. Statistical models that identify waterbodies likely to produce microcystin can help guide monitoring efforts, but variability in bloom severity and cyanotoxin production among lakes and years makes prediction challenging. We evaluated the skill of a statistical classification model developed from water quality surveys in one season with low temporal replication but broad spatial coverage to predict if microcystin is likely to be detected in a lake in subsequent years. We used summertime monitoring data from 128 lakes in Iowa (USA) sampled between 2017 and 2021 to build and evaluate a predictive model of microcystin detection as a function of lake physical and chemical attributes, watershed characteristics, zooplankton abundance, and weather. The model built from 2017 data identified pH, total nutrient concentrations, and ecogeographic variables as the best predictors of microcystin detection in this population of lakes. We then applied the 2017 classification model to data collected in subsequent years and found that model skill declined but remained effective at predicting microcystin detection (area under the curve, AUC ≥ 0.7). We assessed if classification skill could be improved by assimilating the previous years' monitoring data into the model, but model skill was only minimally enhanced. Overall, the classification model remained reliable under varying climatic conditions. Finally, we tested if early season observations could be combined with a trained model to provide early warning for late summer microcystin detection, but model skill was low in all years and below the AUC threshold for two years. The results of these modeling exercises support the application of correlative analyses built on single-season sampling data to monitoring decision-making, but similar investigations are needed in other regions to build further evidence for this approach in management application.

Large scale seasonal forecasting of peak season algae metrics in the Midwest and Northeast U.S.

In recent decades, many inland lakes have seen an increase in the prevalence of potentially harmful algae. In many inland lakes, the peak season for algae abundance (summer and early fall in the northern hemisphere) coincides with the peak season for recreational use. Currently, little information regarding expected algae conditions is available prior to the peak season for productivity in inland lakes. Peak season algae conditions are influenced by an array of pre-season (spring and early summer) local and global scale variables; identifying these variables for forecast development may be useful in managing potential public health threats posed by harmful algae. Using the LAGOS-NE dataset, pre-season local and global drivers of peak-season algae metrics (represented by chlorophyll-a) are identified for 178 lakes across the Northeast and Midwest U.S. from readily available gridded datasets. Forecasting models are built for each lake conditioned on relevant pre-season predictors. Forecasts are assessed for the magnitude, severity, and duration of seasonal chlorophyll concentrations. Regions of pre-season sea surface temperature, and pre-season chlorophyll-a demonstrate the most predictive power for peak season algae metrics, and resulting models show significant skill. Based on categorical forecast metrics, more than 70% of magnitude models and 90% of duration models outperform climatology.  Forecasts of high and severe algae magnitude perform best in large mesotrophic and oligotrophic lakes, however, high algae duration performance appears less dependent on lake characteristics. The advance notice of elevated algae biomass provided by these models may allow lake managers to better prepare for challenges posed by algae during the high use season for inland lakes.

A synthesis of modern organic carbon accumulation rates in coastal and aquatic inland ecosystems.

Organic carbon accumulation in the sediments of inland aquatic and coastal ecosystems is an important process in the global carbon budget that is subject to intense human modification. To date, research has focused on quantifying accumulation rates in individual or groups of aquatic ecosystems to quantify the aquatic carbon sinks. However, there hasn't been a synthesis of rates across aquatic ecosystem to address the variability in rates within and among ecosystems types. Doing so would identify gaps in our understanding of accumulation rates and potentially reveal carbon sinks vulnerable to change. We synthesized accumulation rates from the literature, compiling 464 rate measurements from 103 studies of carbon accumulated in the modern period (ca. 200 years). Accumulation rates from the literature spanned four orders of magnitude varying substantially within and among ecosystem categories, with mean estimates for ecosystem categories ranging from 15.6 to 73.2 g C m-2 y-1 within ecosystem categories. With the exception of lakes, mean accumulation rates were poorly constrained due to high variability and paucity of data. Despite the high uncertainty, the estimates of modern accumulation rate compiled here are an important step for constructing carbon budgets and predicting future change.

Terrestrial support of lake food webs: Synthesis reveals controls over cross-ecosystem resource use.

Widespread evidence that organic matter exported from terrestrial into aquatic ecosystems supports recipient food webs remains controversial. A pressing question is not only whether high terrestrial support is possible but also what the general conditions are under which it arises. We assemble the largest data set, to date, of the isotopic composition (δ2H, δ13C, and δ15N) of lake zooplankton and the resources at the base of their associated food webs. In total, our data set spans 559 observations across 147 lakes from the boreal to subtropics. By predicting terrestrial resource support from within-lake and catchment-level characteristics, we found that half of all consumer observations that is, the median were composed of at least 42% terrestrially derived material. In general, terrestrial support of zooplankton was greatest in lakes with large physical and hydrological connections to catchments that were rich in aboveground and belowground organic matter. However, some consumers responded less strongly to terrestrial resources where within-lake production was elevated. Our study shows that multiple mechanisms drive widespread cross-ecosystem support of aquatic consumers across Northern Hemisphere lakes and suggests that changes in terrestrial landscapes will influence ecosystem processes well beyond their boundaries.

Reversal of a cyanobacterial bloom in response to early warnings.

Directional change in environmental drivers sometimes triggers regime shifts in ecosystems. Theory and experiments suggest that regime shifts can be detected in advance, and perhaps averted, by monitoring resilience indicators such as variance and autocorrelation of key ecosystem variables. However, it is uncertain whether management action prompted by a change in resilience indicators can prevent an impending regime shift. We caused a cyanobacterial bloom by gradually enriching an experimental lake while monitoring an unenriched reference lake and a continuously enriched reference lake. When resilience indicators exceeded preset boundaries, nutrient enrichment was stopped in the experimental lake. Concentrations of algal pigments, dissolved oxygen saturation, and pH rapidly declined following cessation of nutrient enrichment and became similar to the unenriched lake, whereas a large bloom occurred in the continuously enriched lake. This outcome suggests that resilience indicators may be useful in management to prevent unwanted regime shifts, at least in some situations. Nonetheless, a safer approach to ecosystem management would build and maintain the resilience of desirable ecosystem conditions, for example, by preventing excessive nutrient input to lakes and reservoirs.

Response of plankton to nutrients, planktivory and terrestrial organic matter: a model analysis of whole-lake experiments.

Terrestrial organic matter can be assimilated by aquatic consumers but implications for biomass and production are unresolved. An ecosystem model was fit to estimate effects of phosphorus (P) load, planktivory, and supply rate of terrestrial particulate organic carbon (TPOC) on phytoplankton and zooplankton in five whole-lake experiments. Phytoplankton biomass increased with P load and planktivory and decreased with TPOC supply rate. Zooplankton biomass increased with P load and responded weakly to planktivory and TPOC supply rate. Zooplankton allochthony (proportion of carbon from terrestrial sources) decreased with P load and planktivory and increased with TPOC supply rate. Lakes with low allochthony (< 0.3) had wide ranges of phytoplankton and zooplankton biomass and production, depending on P load and planktivory. Lakes with high allochthony (> 0.3) had low biomass and production of both phytoplankton and zooplankton. In summary, terrestrial OC inhibits primary production and is a relatively low-quality food source for zooplankton.