A global, remote sensing‐based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".