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Using Distance from Putative Source Woodlots to Predict Occurrence of Forest Birds in Putative Sinks

2005· article· en· W2014241877 on OpenAlexafffund
Erica Nol, Charles M. Francis, Dawn M. Burke

Bibliographic record

VenueConservation Biology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsMinistry of Natural Resources and ForestryBirds CanadaTrent University
FundersIvey FoundationBird Studies CanadaMinistry of Natural ResourcesNational Fish and Wildlife Foundation
KeywordsOccupancyProductivityEcologyForest fragmentationForest structureBiologyGeographyHabitatCanopy

Abstract

fetched live from OpenAlex

Abstract: For some species of forest‐breeding birds, productivity is much higher in large forest fragments than in small forest fragments and is higher than the level needed to replace individuals within the fragment (sources). Thus large forest fragments potentially provide excess individuals to neighboring small fragments where productivity is much lower and not adequate for replacement (sinks). We used occurrence data and distances between putative sources and sinks for four species of forest birds with this pattern of demography in large and small forest fragments to predict occupancy in small fragments. For the Ovenbird ( Seiurus aurocapilla ), Wood Thrush ( Hylocichla mustelina ), Veery ( Catharus fuscescens ), and Rose‐breasted Grosbeak ( Pheucticus ludovicianus ), distance from large woodlots was a significant predictor of occurrence in small woodlots. Distance from large woodlots did not predict occupancy of Red‐eyed Vireos ( Vireo olivaceus ), a species with no apparent difference in productivity in relation to fragment size. Neither vegetation features nor area of the small woodlots adequately explained patterns of occupancy for any species. These results suggest that maintenance of large, productive forest fragments benefit regional occurrence of forest‐breeding birds by providing individuals to neighboring small woodlots. Limited reproduction in these small woodlots means that they may act as a reservoir for excess individuals in the case of catastrophic events that result in vacancies in source woodlots. Conservation and sound forest management of small forests should be directed particularly toward those closer to large regional forests.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.028
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

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

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 head0.036
GPT teacher head0.300
Teacher spread0.264 · 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 teacher head, 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

Citations29
Published2005
Admission routes2
Has abstractyes

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