Introduction. Integration of ecology and endocrinology in avian reproduction: a new synthesis
Bibliographic record
Abstract
Birds are some of the most familiar organisms of global ecosystems. Changes in the visibility and abundance of birds are therefore excellent indicators of population and physiological responses to habitat changes and are a major focus for public concern about detrimental environmental changes. In order to understand how birds respond to these challenges, it is essential to determine how the environment affects reproduction under natural conditions. The continuum from environmental variables (cues) to reproductive life-history traits depends upon a cascade of neural and physiological processes that determine the extent and rate at which birds will be able to adapt to changes in their environment. For a full understanding of this ability to adapt, ecologists and endocrinologists need to collaborate and build a common framework. The objective of this theme issue is to bring together a series of papers addressing how evolutionary ecologists and endocrinologists can collaborate directly using avian reproduction as a model system. First, we address the need to integrate ecology and endocrinology and what benefits to biological knowledge will be gained. The papers collected in this issue represent a new synthesis of ecology and endocrinology as discussed in three E-BIRD workshops. The three main foci are trade-offs and constraints, maternal effects and individual variation. Authors within each group present ecological and endocrinological aspects of their topics and many go on to outline testable hypotheses. Finally, we discuss where the major problems remain and how this issue points out where these need collaborative efforts of ecologists and endocrinologists. Specific challenges are raised to future researchers to break through intellectual barriers and explore new frontiers. This framework of topics will ultimately apply to all taxa because the principles involved are universal and hopefully will have direct application to programmes integrating organisms and genes throughout biological sciences.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".