Do ecological specialization and functional traits explain the abundance–frequency relationship? Arable weeds as a case study
Bibliographic record
Abstract
Abstract Aim The abundance–frequency relationship (AFR) is among the most‐investigated patterns 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 1,544 fields in Western Europe (France, UK), along with functional traits related to resource acquisition, resource requirements, 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 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 | 0.001 |
| 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".