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Record W2107033426 · doi:10.1139/f09-023

High niche overlap between two successfully coexisting pairs of Lake Malawi cichlid fishes

2009· article· en· W2107033426 on OpenAlexvenueno aff
Christopher H. Martin, Martin J. Genner

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2009
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsnot available
FundersInstitute of International EducationNational Science Foundation
KeywordsForagingInterspecific competitionEcologyCichlidAbundance (ecology)TerritorialityCompetition (biology)BiologyNicheResource (disambiguation)Ecological nicheHabitatFishery

Abstract

fetched live from OpenAlex

Many species in high-diversity assemblages appear to coexist in similar ecological niches. It has been proposed that interspecific resource partitioning in these assemblages may only occur during periods of resource scarcity. We tested this hypothesis by measuring resource abundance, dietary overlap, foraging rate, and territoriality in a Lake Malawi rock cichlid assemblage over a period of 1 year. Our study examined two pairs of morphologically similar species, with each pair comprising one native species and one invader species that has successfully established after being translocated from another region of the lake. All four species changed their diet and foraging rate in response to seasonal variation in resource abundance. However, dietary overlap within both species pairs remained high in all seasons and was not influenced by resource availability. Similarly, territoriality did not decline during periods of low resource availability, suggesting no decrease in the strength of interspecific competition. These data suggest that these species pairs are successfully coexisting despite substantial niche overlap during resource scarcity. Thus, the coexistence of species within this radiation may not depend on the evolution of divergent resource use patterns.

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 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 score0.033
Threshold uncertainty score1.000

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 science0.0000.000
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 head0.049
GPT teacher head0.308
Teacher spread0.259 · 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

Citations45
Published2009
Admission routes1
Has abstractyes

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