Source pool geometry and the assembly of continental avifaunas
Bibliographic record
Abstract
Classical niche-assembly models propose that the composition of biotic communities in continental landscapes is determined chiefly by the autecology of species, interspecific competition, and the diversity of resources and habitats within a region. In contrast, stochastic models propose that simulation algorithms can replicate the macroecological patterns, if not the mechanisms, of community assembly. Despite fundamental differences in assumptions, both categories of models assume that species are drawn from regional source pools. We explored the implications of source pool geometry on the assembly of avian communities with an analysis of assemblage dispersion fields, which can be visualized by overlaying the geographic ranges of all species that occur in an assemblage. Contours of species richness surrounding focal quadrats illustrate the decay rate of assemblage similarity with distance and the probable geometry of assemblage source pools. We used a geographic database for 2,891 species of South American birds to characterize dispersion fields for assemblages sampled by 1 degrees latitude-longitude quadrats (n = 1,676). We show that the median range size of dispersion fields varies by an order of magnitude across the continent. Because abundance generally correlates with geographic range size within taxonomic groups, the number of individuals per species in avifaunal source pools must also vary by an order of magnitude. Most significantly, dispersion field geometry was surprisingly asymmetrical and exhibited complex geographical patterns that were associated with the distribution of biomes. These results are broadly consistent with the predictions of niche-assembly models but offer little support for stochastic assembly models.
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 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 | 0.001 | 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 | 0.003 |
| 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".