The hummingbird and the hawk-moth: Species distribution, geographical partitioning, and macrocompetition across the United States
Bibliographic record
Abstract
ABSTRACT We introduce a new concept called macrocompetition – defined as the mutual suppression of diversity/species richness of competing clades – and investigate evidence for its existence. To this end, we analyzed the distribution of two convergent nectarivorous families, hawk-moths and hummingbirds, over the continental United States to determine whether there is geographic partitioning between the families and its potential causes. Using stepwise regression, we tested for latitudinal and longitudinal biases in the species richness of both taxa and the potential role of 10 environmental variables in their distribution pattern. Hawk-moth species richness increases with longitude (eastward-bias) while that of hummingbirds declines (westward-bias). Similar geographic patterns can be seen across Canada, Mexico and South America. Hawk-moth species richness is positively correlated with higher overall temperatures (especially summer minimums), atmospheric pressure, and summer precipitation; hummingbird species richness is negatively correlated with atmospheric pressure and positively correlated with winter daily maxima. The species richness patterns reflect each family’s respective anatomical differences and support the concept of macrocompetition between the two taxa. Hawk-moth species richness was highest in states with low elevation, summer-time flowering, and warm summer nights; hummingbird species richness is highest in the southwest with higher elevation, greater cool season flowering and high daytime winter temperatures. Hawk-moths and hummingbirds as distinct evolutionary technologies exhibit niche overlap and geographical partitioning. These are two of three indicators suggested by Brown and Davidson for inter-taxonomic competition. We intend the patterns revealed here to inspire further exploration into competition and community structuring between hawk-moths and hummingbirds.
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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 | 0.000 | 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 | 0.000 | 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".