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Record W6892201143 · doi:10.5061/dryad.50557nm

Breaking ecological barriers: anthropogenic disturbance leads to habitat transitions, hybridization, and high genetic diversity

2019· dataset· en· W6892201143 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2019
Typedataset
Languageen
Field
Topic
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiological dispersalGenetic diversityHabitatPopulationGenetic structureDisturbance (geology)ObligatePopulation genetics

Abstract

fetched live from OpenAlex

Genetic diversity is expected to erode in disturbed habitats through strong selection, local extinctions, and recolonization associated with genetic bottlenecks and restricted gene flow. Despite this general prediction and over three decades of population genetics studies, our understanding of the long-term effect of environmental disturbance on local and regional genetic diversity remains limited. We conducted a population genetic survey of the microcrustacean Daphnia across a landscape subject to anthropogenic stressors from a century of industrial mining. At the local scale we found moderate genetic diversity (i.e., low clonal diversity), characteristic of habitat-specific selective sweeps and local extinctions, but high diversity and strong genetic structure at the regional scale despite the shared watershed of many lakes and exceptional dispersal ability of daphniids. Many habitats experienced changes in species assemblages, with the obligate asexual Daphnia pulex lineages—known only to inhabit ponds—dominating disrupted urban lakes. This habitat transition (pond to lake) was likely facilitated by the disruption of ecological barriers maintaining the genomic separation of these young species. Thus, disrupted habitats can exhibit complex and unexpected genetic patterns of local extinctions and recolonizations, followed by habitat transitions, hybridization and potential speciation events that are difficult to predict and should not be underestimated.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.200
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.005

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 head0.251
Teacher spread0.237 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2019
Admission routes1
Has abstractyes

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