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Record W2141573792 · doi:10.2193/2007-518

Postlogging Succession and Habitat Usage of Shrubland Birds

2009· article· en· W2141573792 on OpenAlexaboutno aff
Scott Schlossberg, David I. King

Bibliographic record

VenueJournal of Wildlife Management · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersU.S. Forest Service
KeywordsShrublandEcological successionAbundance (ecology)HabitatVegetation (pathology)EcologyRelative species abundanceRegeneration (biology)LoggingGeographyBiology

Abstract

fetched live from OpenAlex

ABSTRACT In the first decades after logging, the vegetation structure of harvested areas changes rapidly due to succession. For shrubland birds, many of which specialize on regeneration of specific ages, the changing vegetation structure makes determining how much habitat is available for individual species difficult. We conducted a meta‐analysis to determine how populations of shrubland birds in the eastern United States and Canada respond to succession in the first 20 years after timber harvest. Based on those results, we used the area under the abundance‐time regression curves to estimate the proportion of regenerating forest actually used by each bird species. Of the 28 species for which we had sufficient data, 14 showed significant changes in abundance over time. For 6 species, abundance was highest immediately after logging and decreased thereafter. Abundances of 7 other species were initially low, peaked roughly 10 years after harvest, and declined thereafter. Based on these results, shrubland birds would be expected to occupy a mean of just 53% (SD = 17%) of regenerating forests up to 20 years old. Thus, current estimates of habitat availability for shrubland birds may be too high by a factor of 2. Our findings also suggest that managed openings should be maintained on longer rotations than are currently used, providing habitat for birds that prefer older regeneration.

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)0.001
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 score0.349
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.220
Teacher spread0.215 · 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

Citations73
Published2009
Admission routes1
Has abstractyes

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