scholarly journals Targeting Extreme Events: Complementing Near-Term Ecological Forecasting With Rapid Experiments and Regional Surveys

2019 ◽  
Vol 7 ◽  
Author(s):  
Miranda D. Redmond ◽  
Darin J. Law ◽  
Jason P. Field ◽  
Nashelly Meneses ◽  
Charles J. W. Carroll ◽  
...  
Keyword(s):  
Extreme Events ◽  
Ecological Forecasting ◽  
Near Term

scholarly journals Near‐term ecological forecasting for dynamic aeroconservation of migratory birds

2021 ◽  
Author(s):  
Kyle G. Horton ◽  
Benjamin M. Van Doren ◽  
Heidi J. Albers ◽  
Andrew Farnsworth ◽  
Daniel Sheldon
Keyword(s):  
Migratory Birds ◽  
Ecological Forecasting ◽  
Near Term

Increased adoption of best practices in ecological forecasting enables comparisons of forecastability across systems

2021 ◽  
Author(s):  
Abigail S. L. Lewis ◽  
Whitney M. Woelmer ◽  
Heather L. Wander ◽  
Dexter W. Howard ◽  
John W. Smith ◽  
...  
Keyword(s):  
Decision Support ◽  
Best Practices ◽  
Forecast Skill ◽  
Robust Analysis ◽  
Ecological Forecasting ◽  
Workflow Automation ◽  
Future Developments ◽  
Current State ◽  
Near Term ◽  
Over Time

Near-term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross-ecosystem analysis of near-term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near-term ecological forecasting papers to understand the development and current state of near-term ecological forecasting literature and compare forecast skill across ecosystems and variables. Our results indicate that near-term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, a number of best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near-term ecological forecasting has the potential to make significant contributions to our understanding of predictability across scales and variables. In this study, we found that forecast skill decreased in predictable patterns over 1–7 day forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in predictability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables.

scholarly journals Near-term spatial hydrologic forecasting in Everglades, USA for landscape planning and ecological forecasting

2020 ◽  
Vol 132 ◽  
pp. 104783 ◽  
Author(s):  
Leonard G. Pearlstine ◽  
James M. Beerens ◽  
Gregg Reynolds ◽  
Saira M. Haider ◽  
Mark McKelvy ◽  
...  
Keyword(s):  
Landscape Planning ◽  
Ecological Forecasting ◽  
Near Term

scholarly journals Developing an automated iterative near-term forecasting system for an ecological study

2018 ◽  
Author(s):  
Ethan P. White ◽  
Glenda M. Yenni ◽  
Shawn D. Taylor ◽  
Erica M. Christensen ◽  
Ellen K. Bledsoe ◽  
...  
Keyword(s):  
Software Development ◽  
Scientific Progress ◽  
List Type ◽  
Ecological Forecasting ◽  
Natural Systems ◽  
Long Term Study ◽  
New Models ◽  
Forecasting System ◽  
Near Term

AbstractMost forecasts for the future state of ecological systems are conducted once and never updated or assessed. As a result, many available ecological forecasts are not based on the most up-to-date data, and the scientific progress of ecological forecasting models is slowed by a lack of feedback on how well the forecasts perform.Iterative near-term ecological forecasting involves repeated daily to annual scale forecasts of an ecological system as new data becomes available and regular assessment of the resulting forecasts. We demonstrate how automated iterative near-term forecasting systems for ecology can be constructed by building one to conduct monthly forecasts of rodent abundances at the Portal Project, a long-term study with over 40 years of monthly data. This system automates most aspects of the six stages of converting raw data into new forecasts: data collection, data sharing, data manipulation, modeling and forecasting, archiving, and presentation of the forecasts.The forecasting system uses R code for working with data, fitting models, making forecasts, and archiving and presenting these forecasts. The resulting pipeline is automated using continuous integration (a software development tool) to run the entire pipeline once a week. The cyberinfrastructure is designed for long-term maintainability and to allow the easy addition of new models. Constructing this forecasting system required a team with expertise ranging from field site experience to software development.Automated near-term iterative forecasting systems will allow the science of ecological forecasting to advance more rapidly and provide the most up-to-date forecasts possible for conservation and management. These forecasting systems will also accelerate basic science by allowing new models of natural systems to be quickly implemented and compared to existing models. Using existing technology, and teams with diverse skill sets, it is possible for ecologists to build automated forecasting systems and use them to advance our understanding of natural systems.

scholarly journals Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting

Inland Waters ◽  
2021 ◽  
pp. 1-14
Author(s):  
Cayelan C. Carey ◽  
Whitney M. Woelmer ◽  
Mary E. Lofton ◽  
Renato J. Figueiredo ◽  
Bethany J. Bookout ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Water Quality Management ◽  
Reservoir Water ◽  
Ecological Forecasting ◽  
Near Term

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

2018 ◽  
Vol 115 (7) ◽  
pp. 1424-1432 ◽  
Author(s):  
Michael C. Dietze ◽  
Andrew Fox ◽  
Lindsay M. Beck-Johnson ◽  
Julio L. Betancourt ◽  
Mevin B. Hooten ◽  
...  
Keyword(s):  
Decision Making ◽  
Learning By Doing ◽  
Ecological Processes ◽  
Ecological Forecasting ◽  
Environmental Decision Making ◽  
Societal Needs ◽  
Data Volume ◽  
Near Term ◽  
New Evidence ◽  
Climate Responses

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.

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