Non-dawn vocalizations by birds, survey improvements and scale-dependent habitat selection
Bibliographic record
Abstract
Knowledge of habitat selection derived from surveys is an important component of conservation planning. However, mismatches between survey timing and the behaviour of target species can result in observation bias, which can misconstrue the ability to determine species-habitat relationships. For birds, large-scale, long-term surveys are based on the songbird dawn chorus, suggesting that birds which exhibit non-dawn chorusing behaviour may either be only incidentally detected or overlooked. I quantified bias and improved standard morning surveys to evaluate coarse-scale models to predict fine-scale occupancy of non-dawn chorusing birds. I found that nocturnal vocalizations occur in at least 30% of 749 species across 18 of 22 orders, establishing the need for an investigation of bias in standard morning surveys that do not account for birds that exhibit non-dawn chorusing behaviour. Subsequently, I used automated acoustic recorders to collect repeated recordings throughout 24-h periods to compare with results from standard morning surveys, and found that the latter surveys underestimated total species richness (Chapter 2) and waterfowl and songbird occupancy (Chapter 3). Further, I developed a novel subsampling approach for extended acoustic recordings and compared statistical estimators, to efficiently estimate total species richness (Chapter 2). I also investigated the effect of revisitation schedules - same or different day, as well as increased sampling effort- to estimate occupancy for birds with different diel vocalization patterns (Chapter 3). In Chapter 4, I used improved estimates of waterfowl occupancy from extended acoustic recordings to evaluate the ability of previously published coarse-scale models to predict fine-scale distributions, as well as models augmented with additional fine-scale habitat data. Lack of significant increase in model performance with the inclusion of fine-scale habitat data suggested that waterfowl select habitat based more on coarser than finer cues. Nevertheless, no models predicted distribution well enough at fine scales for practical application, suggesting no available shortcuts to conducting fine-scale surveys for local conservation planning. Ultimately, my thesis comprehensively demonstrated that an understanding of vocal behaviour is required for developing effective surveys for birds and illustrated how improved sampling designs can be applied to address important questions in conservation and management.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".