A global, remote sensing‐based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling
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A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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- Teacher spread
- 0.198 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Abstract Aim Habitat heterogeneity has long been recognized as a key landscape characteristic determining biodiversity patterns. However, a lack of standardized, large‐scale, high‐resolution and temporally updatable heterogeneity information based on direct observations has limited our understanding of this connection and its effective use for biodiversity conservation. To address this, we develop here remote sensing‐based metrics to characterize global habitat heterogeneity at 1‐km resolution and assess their value for biodiversity modelling. Location Global. Methods We develop 14 heterogeneity metrics (available at http://www.earthenv.org ) based on the textural features of the enhanced vegetation index ( EVI ) imagery from the Moderate Resolution Imaging Spectroradiometer ( MODIS ), and closely examine a complementary core set of six of these metrics. We evaluate their ability to provide fine‐grain habitat heterogeneity by comparing the heterogeneity information captured by them with that measured by 30‐m Landsat‐based land‐cover data. Using spatial autoregressive models, we then compare their utility with that of more conventional metrics (derived from topography or categorical land‐cover data) for modelling the species richness of bird communities across the conterminous U nited S tates based on B reeding B ird S urvey data. Results The newly derived metrics capture different aspects of habitat heterogeneity and provide fine‐grain information for locations deemed homogeneous by traditional land‐cover classifications at both continental and global extents. Most of them strongly exceed conventional heterogeneity variables in capturing the spatial variation in bird species richness, with H omogeneity emerging as the strongest predictor. Main conclusions This study develops and validates the performance of readily usable metrics of textural measures capturing fine‐grain habitat heterogeneity. The presented metrics outperform conventional measures in capturing detailed spatial variation in habitats and in predicting key biodiversity patterns. They provide a rigorous and comparable basis for understanding heterogeneity–diversity relationships, and offer a powerful tool for monitoring and understanding the responses of biodiversity and ecosystems to the changing environment.
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The record
- Venue
- Global Ecology and Biogeography
- Topic
- Land Use and Ecosystem Services
- Field
- Environmental Science
- Canadian institutions
- —
- Funders
- Division of Biological InfrastructureNational Center For Environmental AssessmentMcGill UniversityNational Aeronautics and Space Administration
- Keywords
- Spatial heterogeneitySpecies richnessBiodiversityLand coverHabitatGeographyVegetation (pathology)Moderate-resolution imaging spectroradiometerEcologyRemote sensingEnvironmental sciencePhysical geographyLand useBiologySatellite
- Has abstract in OpenAlex
- yes