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Record W2209510440 · doi:10.5733/afin.056.0319

Studying Earthworms (Annelida: Oligochaeta) in South Africa

2015· article· en· W2209510440 on OpenAlexaff
Thembeka C. Nxele, Siviwe Lamani, John Measey, Adrian J. Armstrong, Jadwiga Danuta Plisko, Sandi Willows‐Munro, Charlene Janion‐Scheepers, John R. Wilson

Bibliographic record

VenueAfrican Invertebrates · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInvertebrate Taxonomy and Ecology
Canadian institutionsCascades (Canada)
FundersDST-NRF Centre of Excellence for Invasion BiologyDepartment of Science and Technology, Ministry of Science and Technology, IndiaNational Research Foundation
KeywordsSampling (signal processing)BiologyEarthwormEcologyOligochaeta (plant)BiodiversityIndigenousInvertebrateGlobal biodiversity

Abstract

fetched live from OpenAlex

Earthworms are an important component of southern African invertebrate diversity, due both to their influential roles in soil ecosystems, and the relatively large number of species. As of 2010, there were 282 indigenous earthworm species (most endemic) known to South Africa belonging to three families: Microchaetidae, Tritogeniidae and Acanthodrilidae. In addition, 44 introduced species from six families had been recorded. However, earthworms are rarely included in environmental monitoring or conservation programmes—partly because sampling and species identification are difficult and many sampling methods are destructive and/or toxic. In this paper we review the earthworm sampling techniques most commonly used by screening data from a digitised literature collection on South African earthworms and on-line global searches. By examining a case study sampling of three vegetation types, this paper highlights taxonomic challenges and the effort required to properly curate specimens. The study provides recommendations for future sampling and highlights some key priorities for future work on the group.From the literature review in early 2012, it is clear that collection techniques are often insufficiently recorded in published work. A total of 10 938 publications from the period 1950 to 2012 were found from the literature search and digitised collection and from these only 32 papers recorded the sampling methodology (mainly hand sorting) for South African research, pointing to the need to adopt standard sampling and reporting protocols. We also tested two of the most popular methodologies in the field. Sampling was conducted in January and February 2012 at four sites, with 24 plots at each site (12 digging and 12 using mustard extraction). A total of 2 094 earthworms collected could be assigned a species name, with introduced species predominating at both disturbed and natural sites. It took a team of three to five people digging and hand collecting all earthworm specimens encountered in a plot of 50 cm × 50 cm × 20 cm deep around 45 to 60 minutes. However, much more time was spent curating and identifying samples. While we recommend following the ISO (ISO11268-3, ISO23611-1) protocol for collecting introduced taxa, to get a complete inventory of South African earthworms a range of sampling techniques will be required; in particular, a large 1 m × 1 m × 20 cm plot is required for many large bodied native taxa, and the collection of giant earthworms will require different approaches. The identification of specimens requires skills that are scarce in the country and so there is an urgent need for training and funding for fundamental work on earthworm taxonomy. An atlasing project could serve as a focal point for future research. In providing some general recommendations based on the long and fruitful history of research on earthworms in South Africa, we are optimistic that a better understanding of the group will help us to both improve our usage of natural resources and provide insights into this vitally important edaphic group.

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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.060
GPT teacher head0.208
Teacher spread0.148 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

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