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Record W2347377361 · doi:10.1111/oik.03366

Isodars unveil asymmetric effects on habitat use caused by competition between two endangered species

2016· article· en· W2347377361 on OpenAlexaff
Rocío Tarjuelo, Juán Traba, Manuel B. Morales, Douglas W. Morris

Bibliographic record

VenueOikos · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsLakehead University
FundersEuropean Commission
KeywordsBustardInterspecific competitionHabitatEndangered speciesEcologyCompetition (biology)Biology

Abstract

fetched live from OpenAlex

In order for competing species to coexist, segregation on some ecological niche component is required and is often mediated by differential habitat use. When unequal competitors are involved, the dominant species tends to displace the subordinate one to its less preferred habitat. Here, we use habitat isodars, an approach which reflects evolutionary stable strategies of habitat selection, to evaluate whether interspecific competition between two competing species with distinct habitat preferences, the little bustard Tetrax tetrax and the great bustard Otis tarda, modulates their habitat use. Field data on these endangered species demonstrate that unequal competitors can coexist without completely segregating on their preferred habitats. The negatively sloped isodar of the subordinate little bustard unveils its competition with the dominant great bustard. Interference from great bustards in secondary cereal habitats reinforces use of preferred natural habitat by little bustards. Studies of density‐dependent habitat selection by a single‐species can thus aid in identifying the effects of competition on community composition, and guide the conservation of at‐risk species. Isodars, in particular, represent a promising method to gain clear knowledge on interspecific competition for species in which experimental manipulations are not feasible.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.998

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)0.0010.003

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 head0.014
GPT teacher head0.218
Teacher spread0.204 · 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.

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

Citations24
Published2016
Admission routes1
Has abstractyes

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