Essential Biodiversity Variables (EBVs) as an Optimal Format for Habitat Suitability Index of the Black-Necked Crane Across Life Stages

Effective conservation of the Near Threatened (NT) black-necked crane (Grus nigricollis) requires standardized frameworks for organizing multi-scale biodiversity data. This study proposes the Essential Biodiversity Variables (EBV) system as an optimal structure for archiving and sharing biodiversity data such as Habitat Suitability Index (HSI). Developed by the Group on Earth Observations Biodiversity Observation Network (GEO BON), the EBV framework is an emerging system offering a robust solution for standardizing data exchange. Based on 483,592 valid location records of 106 black-necked cranes using satellite telemetry, we apply species distribution models and demonstrate how the multi-dimensional EBV architecture accommodates distinct life-stage preferences: breeding sites favor mid-elevations modulated by temperature; migration staging relies on precipitation regimes; and wintering grounds are driven by moisture availability and the avoidance of human-modified landscapes. The EBV NetCDF (Network Common Data Form) format functions as a self-describing hypercube that captures spatial, temporal, and life-stage dimensions while ensuring metadata transparency. This integration facilitates critical applications, including the identification of priority conservation areas and climate vulnerability assessments, thereby bridging the gap between species-specific modeling and global biodiversity monitoring standards.

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Publication Details

Journal
Diversity
Published
2026-08-14
DOI
https://doi.org/10.3390/d18080486
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
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article

Essential Biodiversity Variables (EBVs) as an Optimal Format for Habitat Suitability Index of the Black-Necked Crane Across Life Stages

Wendong Xie, Xinhai Li, 孙悦华, Yumin Guo et al.
Diversity
Species Distribution and Climate Change
article

Essential Biodiversity Variables (EBVs) as an Optimal Format for Habitat Suitability Index of the Black-Necked Crane Across Life Stages

Wendong Xie, Xinhai Li, 孙悦华, Yumin Guo, Jia Jia, Yun Fang, Yifei Wang, Yu Zhong
article en

Abstract

Effective conservation of the Near Threatened (NT) black-necked crane (Grus nigricollis) requires standardized frameworks for organizing multi-scale biodiversity data. This study proposes the Essential Biodiversity Variables (EBV) system as an optimal structure for archiving and sharing biodiversity data such as Habitat Suitability Index (HSI). Developed by the Group on Earth Observations Biodiversity Observation Network (GEO BON), the EBV framework is an emerging system offering a robust solution for standardizing data exchange. Based on 483,592 valid location records of 106 black-necked cranes using satellite telemetry, we apply species distribution models and demonstrate how the multi-dimensional EBV architecture accommodates distinct life-stage preferences: breeding sites favor mid-elevations modulated by temperature; migration staging relies on precipitation regimes; and wintering grounds are driven by moisture availability and the avoidance of human-modified landscapes. The EBV NetCDF (Network Common Data Form) format functions as a self-describing hypercube that captures spatial, temporal, and life-stage dimensions while ensuring metadata transparency. This integration facilitates critical applications, including the identification of priority conservation areas and climate vulnerability assessments, thereby bridging the gap between species-specific modeling and global biodiversity monitoring standards.

DiversityVol. 18(8)
Beijing Forestry University (CN), University of Chinese Academy of Sciences (CN), Institute of Zoology (CN)
Climate action
Openalex Percentile: Top 10%
Species Distribution and Climate Change
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Essential Biodiversity Variables (EBVs) as an Optimal Format for Habitat Suitability Index of the Black-Necked Crane Across Life Stages — Wendong Xie, Xinhai Li, et al. · Diversity (2026) | TGRS Research Map | TGRS