DOI 10.1007/s00442-010-1563-yPOPULATION ECOLOGY- ORIGINAL PAPER Disjunct performance and distribution in the sedge Carex prasina
Bibliographic record
Abstract
Abstract EVorts to understand species distributions and predict responses to environmental changes depend on specifying how the abiotic environment determines distri-butions. At landscape scales, it is critical to distinguish eVects of environmental factors from other mechanisms such as competition and dispersal limitation. We examined how environmental factors aVect the distribution and per-formance of the sedge Carex prasina across a 10-km2 old-growth forest in southern Québec. We isolated the eVects of soil characteristics by conducting a greenhouse experiment that assessed the performance of C. prasina on soils from a range of wetland habitats where it could potentially occur. This allowed us to compare how the species ’ performance and its distribution across the landscape relate to the same soil characteristics. In the experiment, the biomass and leaf chlorophyll content of C. prasina increased with increasing soil organic matter (OM). Across the landscape, however, the species ’ probability of occurrence and abundance decreased with increasing soil OM. C. prasina had similar biomass on soils from sites where it did and did not occur, but it had higher leaf chlorophyll content on soils from sites where it did not occur. We found no evidence that diVeren-tial performance across environments determines the distri-bution of this species, as C. prasina tended to occur on soils where it showed reduced performance. Rather, other pro-cesses such as competition or dispersal limitation likely override any direct eVects of the soil environment on distri-bution. Our results caution against the common assumption that the environments where a species tends to occur or be most abundant are the environments where it performs best. C. prasina presents a clear example of a species whose per-formance, at least along edaphic gradients, cannot explain its distribution. This example highlights the importance of distinguishing the relative roles of biotic and abiotic factors that shape species distributions across landscapes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.744 | 0.641 |
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".