Spatial scale drives pedodiversity-elevation relationship in Botswana
Bibliographic record
Abstract
Elevational distribution affects pedodiversity (a measure or indicator of soil diversity) by controlling factors like climate, vegetation, and water drainage. It plays a vital role as a constant variable in soil formation processes ( e.g ., mineralization, eluviation, and illuviation, etc. ) thus promoting diverse soil types at different toposequence formations. However, the relationship between pedodiversity and elevation at various spatial scales remains poorly understood and obscure, especially for dryland regions. Here, we first derive a national-scale pedodiversity map of Botswana, explaining more than 50 % of the variance. For this map, elevation is among the most important environmental covariates influencing soil diversity. We further examine how spatial scale indicators such as spatial extent ( i.e ., countrywide, and locally) and resolution ( i.e ., 90 m, 900 m, 9000 m, and 90,000 m) systematically influence landscape pedodiversity. While viewing the data countrywide, the relationship between pedodiversity and elevation maintained a negative or inverse linear trend ( i.e ., meaning that as elevation increases, pedodiversity decreases), but when viewed locally—for the small district, it showed a positive or direct linear trend ( i.e ., both elevation and pedodiversity increase simultaneously). This can be explained by differences in elevation patterns together with complex and dynamic interactions between scale-dependent soil-forming factors like land use type which tend to dominate local scales. Significant differences in pedodiversity related to the spatial resolution of geodata inputs were noticeable, for example, the local coefficient of determination values ranged from 0.06 to 0.65 for fine to coarse spatial resolutions respectively. Together, our findings demonstrate that the relationships between pedogenesis factors ( e.g . elevation) and pedodiversity are scale-dependent. Even a small change in spatial resolution can lead to significant variations in pedodiversity, especially in semi-arid areas. Therefore, taking this into account can reduce overly optimistic conclusions about the landscape patterns we observe. • Scale-dependent variations define the pedodiversity-elevation relationship. • Global South drylands are critical ecosystems in pedodiversity studies. • Land management strategies should integrate scale-specific insights. • Modelling effectively captures interactions within diverse landscapes.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".