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Spatial ecology of the mulgara in arid Australia: impact of fire history on home range size and burrow use

2007· article· en· W2038173228 on OpenAlexaff
Gerhard Körtner, Chris R. Pavey, Fritz Geiser

Bibliographic record

VenueJournal of Zoology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsDepartment of Environment and Conservation
FundersAustralian Research Council
KeywordsBurrowHome rangeEcologyHabitatAridBiologyPredationRange (aeronautics)

Abstract

fetched live from OpenAlex

Abstract Knowledge about the spatial ecology of small mammals in relation to fire history in arid zones in general and Australia in particular is limited. Here, we report data on the spatial ecology of the brush‐tailed mulgara Dasycercus blythi in the hummock grasslands of Ulu r u – Kata Tju t a National Park during winter 2006, the beginning of the breeding season for this species. About 73% of the study area had been burnt in 2002 and spinifex cover was sparse. Mulgaras Marsupialia: Dasyuridae (six males and three females) were implanted with radio‐transmitters and monitored daily for between 6 and 55 days. All mulgaras appeared to use defined home ranges, which overlapped extensively with those of several neighbours. Spatial overlap occurred between as well as within sexes. On average, males (25.5 ha) occupied significantly larger home ranges than females (10.8 ha). Mulgaras used a number of burrows within home ranges and several were used by more than one individual. Moreover, occasionally, two individuals used the same burrow simultaneously. Home ranges and burrows encompassed both mature spinifex Triodia basedowii and open regrowth areas and mulgaras did not exhibit a significant preference for either habitat type. However, three males were killed by introduced‐predators and they all lived predominantly in the open regrowth area. We conclude that mulgaras do not select the dense cover of mature spinifex habitat, and might be subjected to increased risk from introduced predators, especially following fire.

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 score0.003
Threshold uncertainty score0.759

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)0.0010.000

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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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

Citations51
Published2007
Admission routes1
Has abstractyes

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