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What is Climate?01:16

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Related Experiment Video

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Watershed Planning within a Quantitative Scenario Analysis Framework
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Improving the forecast for biodiversity under climate change.

M C Urban1, G Bocedi2, A P Hendry3

  • 1Institute of Biological Risk, Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA. mark.urban@uconn.edu.

Science (New York, N.Y.)
|September 10, 2016
PubMed
Summary

Realistic biological models predicting climate change impacts need detailed species data, which is currently lacking. A global effort is proposed to collect this mechanistic data for better biodiversity conservation.

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Area of Science:

  • Ecology
  • Climate Change Biology
  • Conservation Biology

Background:

  • Modern biological models increasingly incorporate realistic processes for predicting species' responses to climate change and human disturbances.
  • However, these advanced models demand extensive mechanistic data, which is unavailable for the majority of Earth's species.
  • Current biodiversity monitoring primarily tracks changes rather than collecting the mechanistic data crucial for predictive modeling.

Purpose of the Study:

  • To describe and prioritize essential biological information for improving species response projections under climate change.
  • To highlight the utility of trait-based approaches and adaptive modeling in leveraging sparse data for broader ecological predictions.
  • To outline a coordinated global initiative for collecting critical data to enhance understanding and mitigation of climate change effects on biodiversity.

Main Methods:

  • Prioritization of key biological information required for mechanistic modeling of species' responses.
  • Application of trait-based frameworks to synthesize and extrapolate ecological data.
  • Development of adaptive modeling strategies to address data limitations.
  • Proposal for a global data collection effort focused on mechanistic traits.

Main Results:

  • Identification of critical data gaps hindering accurate predictions of species' responses to environmental change.
  • Demonstration that trait-based and adaptive modeling approaches can effectively utilize limited data for wider predictive power.
  • A framework for a global data collection initiative is proposed to address these knowledge gaps.

Conclusions:

  • There is an urgent need to shift from simple biodiversity monitoring to collecting mechanistic data for predictive ecological modeling.
  • Trait-based and adaptive modeling offer promising avenues for making robust predictions despite data scarcity.
  • A concerted global effort is essential to gather the necessary biological information to anticipate and mitigate climate change impacts on biodiversity.