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An ecological disturbance is a temporary disruption in the environment resulting from abiotic, biotic, or anthropogenic factors, causing a pronounced change in an ecosystem. The impact of an ecological disturbance, which can depend on its intensity, frequency, and spatial distribution, plays a significant role in shaping the species diversity within the ecosystem.
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Ecological succession is influenced by the processes of facilitation, inhibition, and toleration. Facilitation occurs when early successional species create more favorable ecological conditions for subsequent species, such as enhanced nutrient, water, or light availability. In contrast, inhibition happens when early successional species create unfavorable ecological conditions for potential successive species, such as limiting resource availability. In some cases, later successional species...
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Iterative near-term ecological forecasting: Needs, opportunities, and challenges.

Michael C Dietze1, Andrew Fox2, Lindsay M Beck-Johnson3

  • 1Department of Earth and Environment, Boston University, Boston, MA 02215; dietze@bu.edu.

Proceedings of the National Academy of Sciences of the United States of America
|February 1, 2018
PubMed
Summary

Near-term ecological forecasting is crucial for sustainability. Iterative, near-term ecological forecasts accelerate research, inform decisions, and improve ecological predictions by learning from new data.

Keywords:
ecologyforecastprediction

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Area of Science:

  • Ecological forecasting
  • Environmental science
  • Sustainability science

Background:

  • Current ecological forecasts often focus on long timescales, limiting their utility for near-term environmental decision-making.
  • There is a need for ecological forecasts that allow for iterative refinement based on new observational data to test scientific theories.
  • Near-term forecasting is essential for adaptive management and addressing societal needs under environmental uncertainty.

Purpose of the Study:

  • To identify the immediate scientific and societal needs for iterative near-term ecological forecasting.
  • To outline the opportunities and challenges associated with developing and implementing near-term ecological forecasts.
  • To emphasize the importance of an iterative, learning-by-doing approach to advance ecological prediction.

Main Methods:

  • Review of current ecological forecasting practices and limitations.
  • Identification of advancements in data availability and computational methods.
  • Analysis of challenges in data interoperability, latency, and uncertainty quantification.
  • Discussion of necessary changes in scientific training, culture, and institutions.

Main Results:

  • Increased data volume and accessibility present opportunities but also challenges in data integration and uncertainty quantification.
  • Advances in computational and statistical methods offer potential for improved forecasting.
  • Iterative forecasting requires enhanced cyberinfrastructure, forecast-specific theory, and methods.
  • Cultural and institutional shifts are needed to support a more predictive ecological science.

Conclusions:

  • Iterative, near-term ecological forecasting is vital for societal relevance and sustainable decision-making.
  • Addressing challenges in data, methods, and cyberinfrastructure is critical for advancing ecological prediction.
  • A shift towards a learning-by-doing approach will accelerate progress in ecological forecasting and its application.
  • The time is now to make ecology more predictive to address pressing environmental questions.