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Record W4320731368 · doi:10.1111/aec.13290

Mortality rates of desert vegetation during high‐intensity drought at <scp>Uluru‐Kata</scp> Tjuta National Park, Central Australia

2023· article· en· W4320731368 on OpenAlexaff
Boyd R. Wright, Martin Nipper, Nathan Nipper, Samuel D. Merson, Tracey Guest

Bibliographic record

VenueAustral Ecology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsGovernment of Northwest Territories
Fundersnot available
KeywordsNational parkGeographyAridBiodiversityVegetation (pathology)EcologyNative plantBiologyIntroduced species

Abstract

fetched live from OpenAlex

Abstract Precipitation variability and heatwaves are expected to intensify over much of inland Australia under most projected climate change scenarios. This will undoubtedly have impacts on the biota of Australian dryland systems. However, accurate modelling of these impacts is presently impeded by a lack of empirical research on drought/heatwave effects on native arid flora and fauna. During the 2018–2021 Australian drought, many parts of the continent's inland experienced their hottest, driest period on record. Here, we present the results of a field survey in 2021 involving indigenous rangers, scientists and national parks staff who assessed plant dieback during this drought at Uluru‐Kata Tjuta National Park (UKTNP), central Australia. Spatially randomized quadrat sampling of eight common and culturally important plants indicated the following plant death rates across UKTNP (in order of drought susceptibility): desert myrtle (Aluta maisonneuvei subsp. maisonneuvei) (91%), yellow flame grevillea (Grevillea eriostachya) (79%), Maitland's wattle (Acacia maitlandii) (67%), waxy wattle (A. melleodora) (65%), soft spinifex grass (Triodia pungens) (53%), mulga (A. aneura) (42%), desert oak (Allocasuarina decaisneana) (22%) and quandong (Santalum acuminatum) (0%). The sampling also detected that seedling recruitment was absent or minimal for all plants except soft spinifex, while a generalized linear mixed model (GLMM) indicated two‐way interactions among species, plant size and stand density as important predictors of drought survival of adult plants. A substantial loss of biodiversity has occurred at UKTNP during the recent drought, with likely drivers of widespread plant mortality being extreme multi‐year rainfall deficit (2019 recorded the lowest‐ever annual rainfall at UKTNP [27 mm]) and record high summer temperatures (December 2019 recorded the highest‐ever temperature [47.1°C]). Our findings indicate that widespread plant death and extensive vegetation restructuring will occur across arid Australia if the severity and frequency of droughts increase under climate change.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.081
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
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 head0.031
GPT teacher head0.271
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2023
Admission routes1
Has abstractyes

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