Northern flickers only work when they have to: how individual traits, population size and landscape disturbances affect excavation rates of an ecosystem engineer
Bibliographic record
Abstract
Woodpeckers are considered ecosystem engineers because they excavate tree cavities which are used subsequently by many species of secondary cavity nesters for breeding. Woodpeckers have the choice of excavating a new hole or reusing an existing one, and this propensity to excavate ( e ) may affect community dynamics but has rarely been investigated. Using 18 years of data on a population of northern flickers Colaptes auratus , I tested six hypotheses to explain the propensity to excavate ( e ) in a landscape which experienced two types of disturbance: pine beetles and wildfires. Woodpecker age, breeding experience and mate retention had little influence on e which varied between 13–39% annually and averaged 23% for 1843 first nests over the 18 yr. Body size and body condition of males and females were not associated with e but rates of excavation declined seasonally, suggesting time rather than energy costs limited excavation effort. Reduced cavity availability mediated through high conspecific density coupled with wildfires triggered relatively high excavation rates, up to 39% but e decreased to baseline levels three years after the landscape disturbances. Nearly 2/3 of males did not excavate in their lifetime but apparently, e is great enough to balance the average rate of cavity tree loss in this forest which is 11% annually. Excavation propensity in flickers is flexible, but the birds reduce their work levels if there is a surplus of holes available.
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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 | 0.000 |
| 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".