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Record W14994395

Seed bank strategies in a Kalahari ecosystem in relation to grazing and habitats

2011· dissertation· en· W14994395 on OpenAlexvenueno aff
Anne Elisabeth Johannsmeier

Bibliographic record

VenueThe Journal of Rheumatology · 2011
Typedissertation
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersBundesministerium für Bildung und ForschungUniversity of PretoriaNational Research Foundation
KeywordsGrazingHabitatEcosystemRelation (database)GeographyEcologyEnvironmental scienceForestryEnvironmental resource managementAgroforestryBiologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

The seed bank dynamics of five habitats as well as a grazing gradient in the southwestern Kalahari, South Africa were studied. Soil samples were collected in the following habitats: dune crests, dune slopes, dune streets, a calcrete outcrop and a riverbed on the farm Alpha. Soil samples were also collected along a grazing gradient from a watering point. Three methods of soil seed bank analysis were used to analyse the soil samples and to gain insight into soil seed bank response to habitat type and to grazing pressure, over four seasons in the year 2004. Results from the three methods of analysis were also compared to each other. They included the direct seedling germination method, the seedling germination re-examination and the seed extraction method. These analyses were used to (a) estimate seed bank size and composition in response to habitat type and grazing pressure; (b) the differences between the standing vegetation- and the seed bank-flora in different habitats and along a grazing gradient and (c) the type of seed banks that tend to form in certain habitats and in response to grazing pressure. Analyses of soil seed bank size along a grazing gradient showed that the seedling emergence re-examinations estimated a larger size for the seed bank than the direct seedling emergence method. The seed extraction method estimated a significantly larger seed bank size than the other two methods. Heavy grazing pressure favoured annual/opportunistic species such as Schmidtia kalahariensis, which formed very large seed banks in heavily trampled areas. When Schmidtia kalahariensis data was removed from the seed bank analyses, it was found that, in contrast to previous results, the direct germination method mostly estimated a larger seed bank size than the re-examination. Also, the estimation of seed bank size by the flotation method, in this case, was much smaller. The flotation method produced data mostly for hard-seeded species, while the seedling emergence method produced data for species with small seeds and which were readily germinable. In all seasons, the dune crest habitat always had the smallest seed bank and the riverbed habitat always had the largest seed bank. All the dune habitats were characterised by perennial grasses. Perennial grasses formed transient seed banks which were relatively small. The riverbed habitat’s vegetation was mostly composed of annuals. Annual plants formed persistent seed banks which were relatively large. Species richness of the readily germinable seed bank in all habitats, fluctuated between the four seasons and was usually largest in summer. The difference in species richness between the above- and belowground floras fluctuated over four seasons. The dune habitats showed a large difference between the species richness of the above- and the below-ground flora, while the riverbed habitat showed a much smaller difference. The dune habitats had many species with transient seed banks while the riverbed was characterised by many species with short-term persistent and ‘permanent’ seed banks.

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)0.001
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.241
Teacher spread0.231 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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