The influence of microhabitat, moisture and diet on stable-hydrogen isotope variation in a Neotropical avian food web
Bibliographic record
Abstract
Abstract: The application of stable-hydrogen isotope (δD) measurements to the study of animal movement, resource use and physiology depends on understanding factors driving variation in δD in animal tissues. The source of micro-scale variation in δD is poorly known, yet understanding micro-scale patterns of δD could shed light on important ecological processes and improve our abilities to track animal movements. Using linear and additive models, we explored the influence of micro-scale habitat use, moisture and diet on tissue δD values of Nicaraguan cloud-forest birds. Using mist nets, we captured 211 individuals of 22 resident Neotropical species at 500–1390 m asl and collected feather and claw samples. Based on three years of data from year-round sampling, our results suggest that microhabitat, seasonal shifts in moisture δD, and diet all influence bird tissue δD values. Our model results reveal a previously undescribed microgeographical effect on δD, where foraging level (understorey versus overstorey) and foraging location (forest interior versus adjacent coffee plantation) were significant predictors of δD values in bird claws and feathers. Mean claw and feather δD values among species varied from −83‰ to −19‰. Top models for claws and feathers explained 57% and 52% of variation in δD respectively. Direct comparisons of understorey (mean ± SD of −30‰ ± 15‰) versus overstorey (−50‰ ± 15‰) claw values suggest that δD may be useful in tracking vertical, micro-scale movement. Higher δD values in forest understorey birds reveal a heavy reliance upon recycled, fog moisture. Fragmentation and climate change may result in increasingly desiccated cloud forest that may exert a more negative influence on the food webs of understorey species that seem to be supported by recycled sources of moisture in the dry season.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".