Human impacts and aridity differentially alter soil<scp>N</scp>availability in drylands worldwide
Bibliographic record
Abstract
Abstract Aims Climate and human impacts are changing the nitrogen ( N ) inputs and losses in terrestrial ecosystems. However, it is largely unknown how these two major drivers of global change will simultaneously influence the N cycle in drylands, the largest terrestrial biome on the planet. We conducted a global observational study to evaluate how aridity and human impacts, together with biotic and abiotic factors, affect key soil variables of the N cycle. Location Two hundred and twenty‐four dryland sites from all continents except A ntarctica widely differing in their environmental conditions and human influence. Methods Using a standardized field survey, we measured aridity, human impacts (i.e. proxies of land uses and air pollution), key biophysical variables (i.e. soil pH and texture and total plant cover) and six important variables related to N cycling in soils: total N , organic N , ammonium, nitrate, dissolved organic:inorganic N and N mineralization rates. We used structural equation modelling to assess the direct and indirect effects of aridity, human impacts and key biophysical variables on the N cycle. Results Human impacts increased the concentration of total N , while aridity reduced it. The effects of aridity and human impacts on the N cycle were spatially disconnected, which may favour scarcity of N in the most arid areas and promote its accumulation in the least arid areas. Main conclusions We found that increasing aridity and anthropogenic pressure are spatially disconnected in drylands. This implies that while places with low aridity and high human impact accumulate N , most arid sites with the lowest human impacts lose N . Our analyses also provide evidence that both increasing aridity and human impacts may enhance the relative dominance of inorganic N in dryland soils, having a negative impact on key functions and services provided by these ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".