Evidence of a load-lightening helper effect in Florida Scrub-Jays: implications for translocation
Bibliographic record
Abstract
The Florida Scrub-Jay (Aphelocoma coerulescens) is an imperiled cooperatively breeding species endemic to Florida scrub habitats. Translocation of non-reproductive helpers has been proposed as a conservation tool to increase population size and connectivity. However, the potential consequences of helper removal on the source population remain unclear because the benefits provided by helpers are complex and not consistently observed. We used nest monitoring and nest camera data to examine the effects of helpers on provisioning rates, nestling mass, nest survival, and productivity for 111 family groups at Ocala National Forest, which supports the largest remaining population of Florida Scrub-Jays. In groups with helpers, male breeders and helpers provisioned nestlings at higher rates than did female breeders. In contrast, provisioning rate of female breeders was reduced by half in groups with helpers compared to groups without helpers, revealing a load-lightening helper effect in this population. The compensatory benefit of helpers on maternal provisioning rates in this study may have easily been overlooked without the use of nest cameras. Helpers provisioned less and nestling mass was lower in 2019 than 2018. Helpers did not influence nestling mass, nest survival, or nest productivity, suggesting that the effect of helpers on these metrics is either minimal or masked by other environmental factors. Future study is needed to understand how indirect helper benefits may affect female breeder survival and future productivity. In the meantime, the load-lightening effect of helpers on maternal provisioning and its potential effect on the donor population should be acknowledged when evaluating the net benefits of future translocation projects proposing the removal of helpers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".