Ecological Forecasting

Conducting ecological research in a way that addresses complex, real-world problems requires a diverse, interdisciplinary and quantitatively trained ecology and environmental science workforce. We use ecological forecasting to show how ecology and environmental science undergraduate curriculum could be evaluated and ultimately restructured to address the needs of the 21st century workforce. We developed and implemented programs to increase the accessibility and comprehensiveness of ecological forecasting undergraduate education, including initiatives to engage specifically with Native American undergraduates and online resources for learning quantitative concepts at the undergraduate level.

We scaled a near-term, iterative, water temperature forecasting system to all six NEON lakes in the conterminous US. Forecasts were more accurate than a null model. Lake characteristics interact with weather to control the predictability of thermal structure.

We assessed the skill of seasonal forecasting tools for lakes and reservoirs set up at four sites in Australia and Europe. Lake forecasts forced with an ensemble of boundary conditions resampled from historical meteorology are currently likely to yield higher-quality forecasts in most cases.

Lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. We found that the dominant source of uncertainty varied among the thermal metrics, as thermal metrics associated with the surface waters (surface water temperature, total ice duration) were driven primarily by climate model selection uncertainty, while metrics associated with deeper depths (bottom water temperature, stratification duration) were dominated by lake model selection uncertainty.

In this module, students will generate an ecological forecast for a NEON site and explore how to use ecological data to improve forecast accuracy. This module will introduce students to the concept of data assimilation within an ecological forecast; how data assimilation can be used to improve forecast accuracy; how the level of uncertainty and temporal frequency of observations affects forecast output; and how data assimilation can affect decision-making using ecological forecasts.

Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. Integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts.

We scaled a near-term, iterative water temperature forecasting system to six conterminous NEON lakes. We generated forecasts using a hydrodynamic model. Forecasts were more accurate than a null model. Lake characteristics interact with weather to control the predictability of thermal structure.

We carried out a qualitative assessment of (1) how useful forecasts would have been for a problematic past season and (2) the relevance of any windows of opportunity (seasons and variables where forecasts are thought to perform well) for management. Overall, water managers were optimistic about the potential for improved decision-making and identified actions that could be taken based on forecasts. However, there was often a mismatch between those variables that could best be predicted and those which would be most useful for management.

In this module, students will generate an ecological forecast for a NEON site and quantify the different sources of uncertainty within their forecast. This module will introduce students to the conceptof uncertainty within an ecological forecast; where uncertainty in a forecast comes from; how uncertainty can be quantified within a forecast; and how uncertainty can be managed.

In this module, students will apply the iterative forecasting cycle to develop an ecological forecast for a NEON site. This module will introduce students to the basic components of an ecological forecast; how a simple forecasting model is constructed; how changes to model inputs affect forecast uncertainty; and how productivity forecasts vary across ecoclimatic regions.