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Resistance to disturbance is a diverse phenomenon and does not increase with abundance:The case of oribatid mites

2000· article· en· W1544683881 on OpenAlexvenueno aff
Andréas Prinzing, Sandra Kretzler, Ludwig Beck

Bibliographic record

VenueEcoscience · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicStudy of Mite Species
Canadian institutionsnot available
Fundersnot available
KeywordsInterspecific competitionAbundance (ecology)BiologyEcologyResistance (ecology)Relative species abundanceHabitatPopulationOribatidaMiteIntermediate Disturbance HypothesisCompetition (biology)

Abstract

fetched live from OpenAlex

The resistance of a population to a disturbance can be described by different proxies, such as abundance, long-term abundance trend, and relative abundance in the more disturbed part of the habitat. Each proxy reflects a different aspect of resistance. Here we investigated oribatid mite species and asked: (i) Are the species generally less resistant with respect to one of these aspects than another? (ii) Is there a correlation between different aspects of resistance across species? (iii) Are abundant species more resistant than rare species? We disturbed a forest soil by a single application of diflubenzuron pesticide onto the litter layer and followed 14 taxa from 10 months before to 18 months after application. In the analysis we adopted the concepts of effect size and of phylogenetically independent contrasts. We found large interspecific differences in the degree of resistance. Most species were less resistant with respect to their relative abundance in the more disturbed part of the habitat (the litter layer) than with respect to their abundance or abundance trend. We found no correlation between any two aspects of resistance, or between any aspect of resistance and abundance. We explain interspecific differences in resistance by interspecific differences in life strategies, and by a relaxation of interspecific competition due to the disturbance.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.197
Teacher spread0.187 · 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

Citations3
Published2000
Admission routes1
Has abstractyes

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