Temporal dynamics of a commensal network of cavity-nesting vertebrates: increased diversity during an insect outbreak
Bibliographic record
Abstract
Network analysis offers insight into the structure and function of ecological communities, but little is known about how empirical networks change over time during perturbations. “Nest webs” are commensal networks that link secondary cavity-nesting vertebrates (e.g., bluebirds, ducks, and squirrels, which depend on tree cavities for nesting) with the excavators (e.g., woodpeckers) that produce cavities. In central British Columbia, Canada, Northern Flicker (Colaptes auratus) is considered a keystone excavator, providing most cavities for secondary cavity-nesters. However, roles of species in the network, and overall network architecture, are expected to vary with population fluctuations. Many excavator species increased in abundance in association with a pulse of food (adult and larval beetles) during an outbreak of mountain pine beetle (Dendroctonus ponderosae), which peaked in 2003–2004. We studied nest-web dynamics from 1998 to 2011 to determine how network architecture changed during this resource pulse. Cavity availability increased at the onset of the beetle outbreak and peaked in 2005. During and after the outbreak, secondary cavity-nesters increased their use of cavities made by five species of beetle-eating excavators, and decreased their use of flicker cavities. We found low link turnover, with 74% of links conserved from year to year. Nevertheless, the network increased in evenness and diversity of interactions, and declined slightly in nestedness and niche overlap. These patterns remained evident seven years after the beetle outbreak, suggesting a legacy effect. In contrast to previous snapshot studies of nest webs, our dynamic approach reveals how the role of each cavity producer, and thus quantitative network architecture, can vary over time. The increase in interaction diversity with the beetle outbreak adds to growing evidence that insect outbreaks can increase components of biodiversity in forest ecosystems at various temporal scales. The observed changes in (quantitative) network architecture contrast with the relatively stable (qualitative) architecture of empirical mutualistic networks that have been studied to date. However, they are consistent with recent theory on the importance of population fluctuations in driving network architecture. Our results support the view that models should allow for the possibility of rewiring (species switching partners) to avoid overestimation of secondary extinction risk.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".