Bridging the divide between ecological forecasts and environmental decision making

A framework for developing a real-time lake phytoplankton forecasting system to support water quality management in the face of global change

Phytoplankton blooms create harmful toxins, scums, and taste and odor compounds and thus pose a major risk to drinking water safety. Climate and land use change are increasing the frequency and severity of blooms, motivating the development of new approaches for preemptive, rather than reactive, water management. While several real-time phytoplankton forecasts have been developed to date, none are both automated and quantify uncertainty in their predictions, which is critical for manager use. In response to this need, we outline a framework for developing the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty, thereby enabling managers to adapt operations and mitigate blooms. Implementation of this system calls for new, integrated ecosystem and statistical models; automated cyberinfrastructure; effective decision support tools; and training for forecasters and decision makers. We provide a research agenda for the creation of this system, as well as recommendations for developing real-time phytoplankton forecasts to support management.

LSTM networks provide efficient cyanobacterial blooms forecasting even with incomplete spatio-temporal data

Cyanobacteria are the most frequent dominant species of algal blooms in inland waters, threatening ecosystem function and water quality, especially when toxin-producing strains predominate. Enhanced by anthropogenic activities and global warming, cyanobacterial blooms are expected to increase in frequency and global distribution. Early Warning Systems (EWS) for cyanobacterial blooms development allow timely implementation of management measures, reducing the risks associated to these blooms. In this paper, we propose an effective EWS for cyanobacterial bloom forecasting, which uses 6 years of incomplete high-frequency spatio-temporal data from multiparametric probes, including phycocyanin (PC) fluorescence as a proxy for cyanobacteria. A probe agnostic and replicable method is proposed to pre-process the data and to generate time series specific for cyanobacterial bloom forecasting. Using these pre-processed data, six different non-site/species-specific predictive models were compared including the autoregressive and multivariate versions of Linear Regression, Random Forest, and Long-Term Short-Term (LSTM) neural networks. Results were analyzed for seven forecasting time horizons ranging from 4 to 28 days evaluated with a hybrid system that combined regression metrics (MSE, R2, MAPE) for PC values, classification metrics (Accuracy, F1, Kappa) for a proposed alarm level of 10 µg PC/L, and a forecasting-specific metric to measure prediction improvement over the displaced signal (skill). The multivariate version of LSTM showed the best and most consistent results across all forecasting horizons and metrics, achieving accuracies of up to 90 % in predicting the proposed PC alarm level. Additionally, positive skill values indicated its outstanding effectiveness to forecast cyanobacterial blooms from 16 to 28 days in advance.

Transferability of ecological forecasting models to novel biotic conditions in a long-term experimental study

Ecological forecasting models play an increasingly important role for managing natural resources and assessing our fundamental knowledge of processes driving ecological dynamics. As global environmental change pushes ecosystems beyond their historical conditions, the utility of these models may depend on their transferability to novel conditions. Because species interactions can alter resource use, timing of reproduction, and other aspects of a species' realized niche, changes in biotic conditions, which can arise from community reorganization events in response to environmental change, have the potential to impact model transferability. Using a long-term experiment on desert rodents, we assessed model transferability under novel biotic conditions to better understand the limitations of ecological forecasting. We show that ecological forecasts can be less accurate when the models generating them are transferred to novel biotic conditions and that the extent of model transferability can depend on the species being forecast. We also demonstrate the importance of incorporating uncertainty into forecast evaluation with transferred models generating less accurate and more uncertain forecasts. These results suggest that how a species perceives its competitive landscape can influence model transferability and that when uncertainties are properly accounted for, transferred models may still be appropriate for decision making. Assessing the extent of the transferability of forecasting models is a crucial step to increase our understanding of the limitations of ecological forecasts.

Predictability of ecological and evolutionary dynamics in a changing world

Ecological and evolutionary predictions are being increasingly employed to inform decision-makers confronted with intensifying pressures on biodiversity. For these efforts to effectively guide conservation actions, knowing the limit of predictability is pivotal. In this study, we provide realistic expectations for the enterprise of predicting changes in ecological and evolutionary observations through time. We begin with an intuitive explanation of predictability (the extent to which predictions are possible) employing an easy-to-use metric, predictive power PP(t). To illustrate the challenge of forecasting, we then show that among insects, birds, fishes and mammals, (i) 50% of the populations are predictable at most 1 year in advance and (ii) the median 1-year-ahead predictive power corresponds to a prediction R 2 of only 20%. Predictability is not an immutable property of ecological systems. For example, different harvesting strategies can impact the predictability of exploited populations to varying degrees. Moreover, incorporating explanatory variables, accounting for time trends and considering multivariate time series can enhance predictability. To effectively address the challenge of biodiversity loss, researchers and practitioners must be aware of the information within the available data that can be used for prediction and explore efficient ways to leverage this knowledge for environmental stewardship.