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Record W2614742532 · doi:10.1111/jbi.13022

A test of the habitat amount hypothesis as an explanation for the species richness of forest bird assemblages

2017· article· en· W2614742532 on OpenAlexafffundabout
Rémi Torrenta, Marc‐André Villard

Bibliographic record

VenueJournal of Biogeography · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversité du Québec à RimouskiUniversité de Moncton
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHabitatSpecies richnessEcologyGeographyDeciduousSpatial heterogeneityBiology

Abstract

fetched live from OpenAlex

Abstract Aim For the past 20 years, researchers have been challenged to demonstrate that the spatial arrangement of habitat patches actually influences the distribution of organisms and the persistence of their populations, beyond the effects of its sheer amount. More recently, it has been argued that habitat amount in the ‘local landscape’ surrounding a site is sufficient to predict species richness (SR) in that site, irrespective of habitat configuration. Here, we tested four predictions derived from the habitat amount hypothesis (HAH). Location Eastern Ontario, Canada ( c . 44°55′–45°15′ N, 75°10′–75°45′ W). Methods Point counts ( n = 157) were conducted in five subregions to estimate forest bird SR while accounting for detectability. Surveys were conducted in mature, deciduous‐dominated forest fragments, and landscape structure was quantified at three spatial scales (500, 1000 and 1500 m). Results Although we found a significant positive correlation between SR and either fragment area (FA) or habitat amount in the local landscape, predictions emphasizing the dominant influence of habitat amount and the lower influence of FA were either not supported or weakly so. Main conclusions Contrary to the HAH, we conclude that habitat amount in the local landscape is not a sufficient predictor of SR on its own. However, we agree with the contention that, in most landscape types, ‘local landscapes’ represent more natural spatial units than habitat fragments or ‘patches’.

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 score0.007
Threshold uncertainty score0.353

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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
Published2017
Admission routes3
Has abstractyes

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