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Record W2734633689 · doi:10.1071/mf16375

Ontogenetic shifts in habitat use during the dry season by an amphidromous shrimp in a tropical lowland river

2017· article· en· W2734633689 on OpenAlexfundno aff
Peter A. Novak, Peter Bayliss, Erica A. García, Brad Pusey, Michael M. Douglas

Bibliographic record

VenueMarine and Freshwater Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersMcMaster UniversityHolsworth Wildlife Research Endowment
KeywordsHabitatDry seasonEcologyMacrophyteBiologyShrimpWet seasonAbundance (ecology)WetlandEcosystem

Abstract

fetched live from OpenAlex

Caridean shrimp have considerable effects on ecosystem processes and, thus, understanding their use of key habitats is important for determining their potential ecological effect. The present study examined the meso-habitat use of Macrobrachium spinipes, a large-bodied and important amphidromous species, in the Daly River, northern Australia. We examined shrimp abundance at four common meso-habitat types; sand bank, rock bars, undercut and structurally complex banks and mid-channel areas at five sites on three occasions during the dry season (May to October). We found that habitat use changed considerably first, with ontogeny, and, second, with the colonisation of habitats with algae and macrophytes as the dry season progressed. As juveniles, their habitat use was strongly associated with well structured bank environments early in the dry season. By the mid-dry season, juveniles were more abundant within sand habitats recently colonised with macrophytes and filamentous algae. Females showed little change in habitat use, whereas large-bodied dominant males generally favoured rock bars and heavily structured bank environments. The present study has provided significant insights into the changes in use of key riverine habitats throughout the dry season by an ecologically important species. This information will be of considerable value to the determination of environmental flow requirements and food-web investigations.

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 score0.626
Threshold uncertainty score0.989

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.002
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 head0.032
GPT teacher head0.288
Teacher spread0.256 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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