Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Abstract

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

A concise guide to developing and using quantitative models in conservation management

“…Uncertainty in model inferences is influenced by two main factors, which will contribute to the overall uncertainty and ambiguity in conservation actions (Chatfield, 2006;Dietze, 2017;Dietze et al, 2018;Milner-Gulland et al, 2017;Regan, Colyvan, & Burgman, 2002). Including measures of uncertainty when presenting inferences on model structure and model parameters is fundamental to informed conservation actions.…”

“…Output distributions can be interpreted in terms of risk assessments, a key tool in conservation management, as distributions provide a measure of the likelihood of occurrence of an event (Burgman, 2005). Nonetheless, that is a hurdle that can be overcome through effective communication and translation, and we posit that the benefits of this recommendation outweigh the potential complications (Burgman, 2005;Cartwright et al, 2016;Dietze et al, 2018;Groves & Game, 2016;Parrott, 2017). The main shortcoming of this recommendation is that output distributions can be difficult to communicate to conservation managers (Cartwright et al, 2016;Hoffrage, Lindsey, Hertwig, & Gigerenzer, 2000;Parrott, 2017).…”

“…Being explicit when using thresholds is crucial to providing transparent applications of model results. The situation is slightly different when the model is part of an adaptive conservation management program (Addison et al, 2013;Conroy & Peterson, 2013;Dietze et al, 2018;Salafsky et al, 2016;Schmolke et al, 2010). Thresholds can sometimes be arbitrary and misleading when they are used in the context of conservation management, and it always is important to explain and justify their use (Bestelmeyer, 2006;Field, Tyre, Jonzén, Rhodes, & Possingham, 2004;Liu, Berry, Dawson, & Pearson, 2005;Wasserstein & Lazar, 2016) The development of optimal thresholds to discontinue surveillance or the removal of invasive species, by estimating the costs and benefits associated with deploying different surveying efforts, is a good example of a welldesigned and justifiable threshold in the context of conservation management (Gormley,Anderson,& Nugent,FIGURE 3 Two histograms illustrating recommendation 7 ("communicating the uncertainty in model results to end-users broadens its utility").…”

Improving estimates and forecasts of lake carbon dynamics using data assimilation

“…Identifying essential processes to include in lake C models across a diverse set of lakes is an important next step for advancing broad‐scale modeling of lake C cycling. Likewise, forecasting lake C states and fluxes and confronting these forecasts with new observations through data assimilation can accelerate lake C research by identifying processes we do and do not know well, and what data we need to collect to help us learn more about these important ecosystems (Dietze et al ).…”

Integrating Environmental Science and the Economy: Innovative Partnerships between the Private Sector and Research Infrastructures

“…This would indeed assist them in determining if industry has to transition toward different markets or plan for transportation costs to access the desired commodity. Predictive ecology is an strategic area of research and is being advanced through the data ERIs provide (Dietze et al, 2017).…”