<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Cameron Thompson</dc:contributor>
  <dc:contributor>Hassan Moustahfid</dc:contributor>
  <dc:contributor>Jessica Burnett</dc:contributor>
  <dc:contributor>Michael Dietze</dc:contributor>
  <dc:creator>Jacob Aaron Zwart</dc:creator>
  <dc:date>2026</dc:date>
  <dc:description>Ecological forecasting offers critical insights for managing natural resources and safeguarding public well-being. Despite growing demand for these forecasts, progress is hindered by fragmented systems, redundant workflows, and limited interoperability. Drawing lessons from weather forecasting and recent successes like the NEON Ecological Forecasting Challenge, shared cyberinfrastructure is important for advancing ecological prediction. By adopting common standards, open-source tools, and scalable architectures, and fostering transdisciplinary collaboration, the ecological forecasting community can overcome technical and institutional barriers. Such investments could accelerate scientific understanding, improve forecast reliability, and empower decisionmakers to anticipate environmental change and respond effectively.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.1029/2026EO260066</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>American Geophysical Union</dc:publisher>
  <dc:title>How to accelerate advances in ecological forecasting</dc:title>
  <dc:type>article</dc:type>
</oai_dc:dc>