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Record W2599566389

Non-dawn vocalizations by birds, survey improvements and scale-dependent habitat selection

2015· dissertation· en· W2599566389 on OpenAlexfundno aff
Van T. La

Bibliographic record

VenueThe Atrium (University of Guelph) · 2015
Typedissertation
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScholarshipChristian ministryEngineering researchResearch councilHabitatNatural resourceScale (ratio)ForestryLibrary scienceGeographyEnvironmental resource managementPolitical scienceEcologyEngineeringEnvironmental scienceCartographyComputer scienceTelecommunicationsBiology
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.212
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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