Functional diversity loss with increasing livestock grazing intensity in drylands: the mechanisms and their consequences depend on the taxa
Bibliographic record
Abstract
Summary Overgrazing is one of the main drivers of desertification in drylands, and livestock production is expected to increase in the next decades. The analysis of functional diversity can clarify the effects of increasing livestock grazing on ecosystem functioning. We assess the effect of livestock grazing intensity on the relationship between taxonomic diversity (TDH) and functional diversity (FDQ) of plants, ants and small mammals, as well as on within‐trait diversity. We compared results using two indices of taxonomic diversity (Shannon and Simpson indices). We used structural equation modelling (SEM) to assess the causal relationship between grazing intensity,TDH,FDQand decomposition rate for each taxa. Correlation betweenTDHvs.FDQvaried across assemblages and seasons, but was consistent between different indices of taxonomic diversity. A similar trajectory ofTDHvs.FDQunder land‐use intensification was found for all taxa, with a correlated loss of species and functional traits. Also, within‐trait diversity was negatively affected by increasing grazing pressure. Vegetation and small mammalSEMmodels show that increasing grazing intensity had a strong and direct effect on decomposition rate. The antSEMmodel was the only one that showed an indirect effect of grazing on decomposition throughFDQ.TDHhad no effect on decomposition for either taxa. We found higher niche differentiation in animal than in plant assemblages. In vegetation, several species seem to have similar trait diversity (i.e. redundancy), perhaps due to a dominant role of environmental constraints. These results were consistent among diversity indices. But increasing disturbance negatively affectedTDHvs.FDQin all assemblages in a similar way. Livestock grazing affected decomposition rate directly, and indirectly only through the effect of antFDQ. Synthesis and applications. Under increasing grazing intensity, all plant and animal assemblages respond with a mirrored reduction in taxonomic diversity and functional diversity, although vegetation seems to have higher functional redundancy. Our results are robust to diversity indices, and show that several taxa respond similarly to land‐use intensification, despite differences in the mechanism behind it. This may facilitate sustainable management. Notably, increasing grazing intensity affects decomposition rate through a stronger direct than indirect effect. The stronger direct effect of livestock on decomposition rate, rather than indirectly through functional diversity, suggests that changes in structure may be more important than changes in community composition.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".