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Record W7161998213 · doi:10.82308/41348

Phylogenetic, taxonomic, and functional diversity of wetland diptera communities

2017· dissertation· en· W7161998213 on OpenAlexaboutno aff
Amélie Grégoire Taillefer

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicDiptera species taxonomy and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsWetlandSpecies richnessBiodiversityBiological dispersalHabitatSwampAbundance (ecology)MarshBog

Abstract

fetched live from OpenAlex

The goal of this thesis was to describe biodiversity and community composition of Diptera in wetlands across Quebec. Three approaches to community analysis were used to describe patterns: taxonomic identity, functional traits, and phylogenetic relationships. Diptera were sampled using standardized methods (pan traps, sweeping) across three types of wetlands (marsh, swamp, bog) in the Montreal region, and in 15 bogs distributed in three Quebec ecoregions. When comparing three types of wetlands, abundance and species richness did not differ even with wetland areas ranging from 6 to 161 ha. Bogs supported phylogenetically closely related Diptera species filtered by harsher environmental conditions compared to the other two wetland types. Clustering of closely related species was found in bogs, which is probably due to environmental filtering at the initial stage of community assembly postglacially. The slow peat accumulation process and characteristic plant composition adapted to acidic and low nutrient conditions potentially play a role in the structure of the Diptera community. Neutral processes were more important in marshes and swamps, as dispersal limitation explained species abundance dynamics of small and common Diptera species within each wetland type. The assembly of marsh communities is a balance between neutral processes and environmental filtering, while the assembly of swamp habitats is neutral. Clustering, thus environmental filtering, increased with environmental extremes. Rare species tended to be distantly related to common species, based on phylogenetic signal. They have unique habitat requirements and their diversity is maintained by temporal turnover during the active season of species with similar traits filtered by the environment. When the spatial extent of the research was expanded to bogs in three Quebec ecoregions, a selective filtering role of anthropogenic disturbance was found. Recent drastic human modification of the landscape in Eastern Great Lakes Lowland Forest ecoregion, less suitable peatland patches and more barriers to dispersal are adjacent to those bogs, so agriculture and urban development act as filters for the small proportion of species in the regional pool that can disperse in these conditions. In Eastern Canadian Forest and Central Canadian Shield ecoregions, stochastic processes such as dispersal limitation of abundant, small, multivoltine species seem to be the dominant influence. High diversity of Diptera species and different historical disturbances are at the origin of the functional and phylogenetic structure observed for peatland Diptera. Phylogenetic community structure and functional analyses revealed high value and complementarity to standard biodiversity measures. Using only traditional metrics, it would not have been apparent that bog communities are impacted by land-use changes and that these impacts change the species pool capable of inhabiting these isolated habitats. This suggests that the three levels of diversity studied should be used in environmental assessments to have a complete picture of macroecological patterns in wetlands. Conservation of mobile organisms in wetlands will depend on conservation plans focusing on both patch quality and surrounding landscape. Different conservation strategies need to be applied in the different ecoregions.

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 score0.667
Threshold uncertainty score0.670

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.050
GPT teacher head0.219
Teacher spread0.169 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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