Do ecological specialization and functional traits explain the abundance–frequency relationship? Arable weeds as a case study
Bibliographic record
Abstract
Aim: The abundance-frequency relationship (AFR) is among the most-investigated pattern in biogeography, yet the relative contributions of niche-based processes related to ecological strategies, and of neutral processes related to spatial colonization-extinction dynamics, remains uncertain. Here, we tested the influences of ecological specialization and functional traits on local abundance and regional frequency, to determine the contribution of niche-based processes. Location: France and the UK. Taxon: Vascular plants. Methods: We used two arable weed surveys covering 1544 fields in Western Europe (France, UK), along with functional traits related to resource acquisition, flowering phenology and dispersal. We quantified specialization both to arable habitat and to individual crop types, and performed phylogenetic path analyses to test competing models accounting for direct and indirect relationships between traits, specialization, abundance and frequency. We performed the analyses for all species in each country, as well as for a subset of the most abundant species. Results: Local abundance of weeds increased with their regional frequency, but the relationship became negative or null when considering only the most abundant weeds. Specialization to arable habitat and to individual crop type either had a similar or opposite effect on regional frequency and local abundance explaining these positive and negative relationships, respectively. Regional frequency was not directly explained by any trait but indirectly by resource requirement traits conferring specialization to the arable habitat. Conversely, high local abundance was directly related to low seed mass, high SLA, early and short flowering. Main Conclusions: Direct/indirect effects of functional traits on local abundance/regional frequency, respectively, supports a significant role of niche-based processes in AFR. Neutral spillover dynamics could further explain a direct linkage of abundance and frequency. Similar causal paths and consistent influences of traits on specialization and abundance in the two studied regions suggest genericity of these findings.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".