Near-Term Ecological Forecasting

A topical collection in Forecasting (ISSN 2571-9394). This collection belongs to the section "Environmental Forecasting".

Viewed by 6491

Editor


E-Mail Website1 Website2
Guest Editor
Department of Earth & Environment, Boston University, 685 Commonwealth Ave Rm 130, Boston, MA, 02115, USA
Interests: ecological forecasting; data assimilation; model-data cyberinfrastructure; terrestrial carbon cycle; natural disturbance; soil microbiome; phenology; tick-borne disease

Topical Collection Information

Dear Colleagues,

Near-term (daily to interannual) ecological forecasting is a rapidly growing research area focused on producing continually updated, actionable predictions of the future state of ecosystems, their services (e.g., water quality, forest products, fisheries, carbon sequestration), and risks (e.g., infectious disease, invasive pests) that increase scientific understanding and improve environmental decision making, management, and conservation.

This Special Issue is open to papers that address individual ecological forecasts, multi-forecast  comparisons/competitions, or other topics that advance research in this area, such as theory, standards, best practices, protocols, decision support, methods, and cyberinfrastructure. Examples include, but are in no way limited to, papers coming out of the Ecological Forecasting Initiative’s NEON forecasting challenge. Papers are open to multiple approaches (statistical, machine learning, process-based, expert judgement) and systems (terrestrial, freshwater, marine; population, community, ecosystem) and could draw upon knowledge and research in the biological, social, computational, and physical environmental sciences.

Dr. Michael C. Dietze
Guest Editor

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Keywords

  • ecology
  • ecosystem
  • environmental
  • natural resources
  • terrestrial
  • freshwater
  • marine
  • competition

Published Papers (2 papers)

2022

29 pages, 5484 KiB  
Review
Ecological Forecasting and Operational Information Systems Support Sustainable Ocean Management
by Chaojiao Sun, Alistair J. Hobday, Scott A. Condie, Mark E. Baird, J. Paige Eveson, Jason R. Hartog, Anthony J. Richardson, Andrew D. L. Steven, Karen Wild-Allen, Russell C. Babcock, Dezhou Yang, Rencheng Yu and Mathieu Mongin
Forecasting 2022, 4(4), 1051-1079; https://doi.org/10.3390/forecast4040057 - 16 Dec 2022
Cited by 6 | Viewed by 2395
Abstract
In times of rapid change and rising human pressures on marine systems, information about the future state of the ocean can provide decision-makers with time to avoid adverse impacts and maximise opportunities. An ecological forecast predicts changes in ecosystems and its components due [...] Read more.
In times of rapid change and rising human pressures on marine systems, information about the future state of the ocean can provide decision-makers with time to avoid adverse impacts and maximise opportunities. An ecological forecast predicts changes in ecosystems and its components due to environmental forcing such as climate variability and change, extreme weather conditions, pollution, or habitat change. Here, we summarise examples from several sectors and a range of locations. We describe the need, approach, forecast performance, delivery system, and end user uptake. This examination shows that near-term ecological forecasts are needed by end users, decisions are being made based on forecasts, and there is an urgent need to develop operational information systems to support sustainable ocean management. An operational information system is critical for connecting to decision makers and providing an enduring approach to forecasting and proactive decision making. These operational systems require significant investment and ongoing maintenance but are key to delivering ecological forecasts for societal benefits. Iterative forecasting practices could provide continuous improvement by incorporating evaluation and feedback to overcome the limitations of the imperfect model and incomplete observations to achieve better forecast outcomes and accuracy. Full article
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30 pages, 3651 KiB  
Article
Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module
by Tadhg N. Moore, R. Quinn Thomas, Whitney M. Woelmer and Cayelan C. Carey
Forecasting 2022, 4(3), 604-633; https://doi.org/10.3390/forecast4030033 - 30 Jun 2022
Cited by 6 | Viewed by 2736
Abstract
Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, [...] Read more.
Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, “Introduction to Ecological Forecasting,” at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students’ ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts. Full article
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