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Record W6964968690 · doi:10.26191/rdtf-4v69

Bioassessment of freshwater lentic wetlands using the reference condition approach

2021· article· en· W6964968690 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUniversity of Canberra Research Portal · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsLake ecosystemWetlandHydrology (agriculture)Water qualityEcosystem

Abstract

fetched live from OpenAlex

Almost half of the world‟s wetlands have disappeared as a result of anthropogenic influences, with few wetlands remaining in an undisturbed condition in temperate regions. Conservation and restoration of extant wetlands need programs that can assess wetland condition and report on change. Rapid assessment methods are increasingly seen as central for the implementation of freshwater bioassessment and monitoring programs, with protocols well established for rivers and Great Lakes. Bioassessment programs that use the reference condition approach (RCA) and predictive models to assess river health, are common in the United Kingdom, North America, Australia and Canada. In this thesis, I develop and implement the RCA to biological assessment of lentic wetlands, using macroinvertebrates as indicators. My study is based in Tasmania, Australia (a temperate climate zone) where many wetlands are regarded as having high conservation values with more than 60% located within protected areas. From an initial dataset of approximately 20 000 mapped wetlands and waterbodies, 80 were identified to be in best available condition and suitable for sampling macroinvertebrates for the purposes of my research. Using a Geographic Information System (GIS), existing spatial data, expert local knowledge and ground-truthing, sites located in a range of landforms in protected areas were selected. Sites were categorised using a broad scale classification, refined using a hierarchical rule set, then selected based on the absence of human disturbance and the presence of four habitats considered to be optimal for macroinvertebrate communities. Of these wetlands, multivariate analysis identified six groups, based on 40% similarity. The environmental attributes of land tenure, water regime, dominant habitat and protection zone were significantly associated with the six groups of wetlands. This method of site selection provided an accurate, rapid and cost-effective selection of sites required for this study and would be suitable for other monitoring programs to use.\nTo consider the influence of spatial and temporal variability on the application of field sampling methods, 48 macroinvertebrate samples were collected from two Ramsar wetlands over a three year period. Samples were collected from four discrete habitats at each wetland, from the austral spring 2009 to autumn 2011.A rapid assessment method was used, employing live picking in the field and family-level identification in the laboratory. No significant differences were detected in the composition of macroinvertebrate assemblages between habitats within wetlands, between seasons or between years. However, assemblages differed significantly between the wetlands. I concluded that a protocol based on the collection of a single (austral spring), macroinvertebrate sample (representing a composite of four within-wetland habitats) was appropriate for the rapid assessment of wetlands in this region. The rapid assessment protocol was used to collect macroinvertebrates from 66 protected wetlands. Two hundred and eighteen taxa were identified with an average of 33 species (or morphospecies) and 18 families recorded per wetland. The wetland assemblages were idiosyncratic, four families contributed 21% of the total recorded and only two families contributed greater than 10%. Wetlands were not significantly nested on the basis of the composition of their macroinvertebrate assemblages. No single environmental attribute had a strong relationship with macroinvertebrate richness or assemblage composition and neither species richness nor assemblage composition varied significantly between different types of protected areas. The state of the proximal zone and the type of aquatic habitat present were the most important determinants of macroinvertebrate richness and assemblage composition across all types of protected wetlands. Using the acquired data, four RCA models were tested. The best model was developed from 46 wetlands, using macroinvertebrate data at the family level with rare families removed. The four predictor variables identified to group similar wetlands were: type of aquatic habitat present, water colour, electrical conductivity and average annual rainfall. Model performance was evaluated using both independent reference-site data and simulated biological impairment data to test both Type 1 and Type 2 errors. The model performed well with respect to Type 1 errors by correctly assigning independent reference-sites to band A and Type 2 errors by correctly detecting three levels of simulated impairment.\nThe adoption of the RCA to bioassessment of temperate lentic wetlands has been limited, this thesis adds to the global knowledge of the bioassessment of freshwaters using the RCA. Finding environmental attributes associated with macroinvertebrate assemblages and predictor variables to group similar wetlands, is important for the global understanding of wetland macroinvertebrates. The findings have a crucial role in improving our understanding, management and protection of: freshwater ecosystems in Tasmania; wetlands in temperate regions of the world and, macroinvertebrate communities on a global scale. Results suggest that for temperate austral wetlands, bioassessment can be undertaken using macroinvertebrates as indicators and the reference condition approach.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.053
GPT teacher head0.297
Teacher spread0.244 · 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