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<p>Large-scale monitoring networks employing remote sensors have harnessed big data to evaluate conservation efforts on a global scale. While dryland ecosystems are anticipated to expand, and management activities like rewilding and habitat restoration are increasing, the use of data streams and modern analytical methods to plan conservation interventions and quantify their effectiveness remains limited. We recommend establishing a global network for dryland practitioners to bridge critical data gaps, providing two real-world examples. A pilot study in the Kingdom of Saudi Arabia shows how coordinated use of multiple remote sensors and a standardised data pipeline could improve interoperability and facilitate the use of more accurate ecological models. Likewise, the Wildlife Observatory of Australia demonstrates that robust metadata and shared analytical frameworks enable the effective integration of diverse datasets using hierarchical occupancy models. Key steps to build this network include forming a steering committee, engaging stakeholders from various backgrounds, piloting projects in different regions, agreeing on protocols and exploring seed funding opportunities.</p>

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Bridging monitoring gaps in global drylands with big data and collaboration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月16日

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  • Tom Bruce,
  • Rajan Amin,
  • Kausik Banerjee,
  • José Carlos Brito,
  • Bogdan Cristescu,
  • Mohammad S. Farhadinia,
  • Matthew Scott Luskin,
  • Brett Lyons,
  • David Olson,
  • Joaquín Vicente,
  • Robert A. Montgomery

摘要

Large-scale monitoring networks employing remote sensors have harnessed big data to evaluate conservation efforts on a global scale. While dryland ecosystems are anticipated to expand, and management activities like rewilding and habitat restoration are increasing, the use of data streams and modern analytical methods to plan conservation interventions and quantify their effectiveness remains limited. We recommend establishing a global network for dryland practitioners to bridge critical data gaps, providing two real-world examples. A pilot study in the Kingdom of Saudi Arabia shows how coordinated use of multiple remote sensors and a standardised data pipeline could improve interoperability and facilitate the use of more accurate ecological models. Likewise, the Wildlife Observatory of Australia demonstrates that robust metadata and shared analytical frameworks enable the effective integration of diverse datasets using hierarchical occupancy models. Key steps to build this network include forming a steering committee, engaging stakeholders from various backgrounds, piloting projects in different regions, agreeing on protocols and exploring seed funding opportunities.