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Record W2804391891

Structure and mechanics of intermittent Wetland communities: Bacteria to Anacondas

2009· article· en· W2804391891 on OpenAlexaff
Tiffany A. Schriever

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFreshwater macroinvertebrate diversity and ecology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWetlandEnvironmental resource managementGeographyEnvironmental scienceEcologyBiology
DOInot available

Abstract

fetched live from OpenAlex

Freshwater wetlands are highly productive and important features of terrestrial landscapes, yet knowledge of their biotas and understanding of their function has lagged behind that of other ecosystems. Superficially, their communities are known to include bacteria, protists, algae, fungi, higher plants, and invertebrate and vertebrate animals that come together to benefit from the rich resources and oftentimes relatively predator-free space that these habitats provide. The cost of these benefits requires adaptation to a highly variable, though often predictable, hydroperiod. This chapter will focus on wetland habitats that are truly intermittent in their water-balance, and will summarize what is known of the structure of the biota at different trophic levels and how they interact at a dynamic land/water interface. For example, fluctuations in flooding regime are known to cause fluxes in carbon, nitrogen and phosphorus in basin sediments as well as in the water column. Such nutrient pulses can lead to increased productivity followed by anoxia and fluxes of methane in these sediments; significantly, microbes that die during the drying period can represent an important source of carbon upon re-wetting at the same time that phosphorus and nitrogen enter the system. These fluxes produce a range of responses from both prokaryote and eukaryote components of the wetland community. Detritivore and herbivore consumers in wetlands are represented by a rich variety of protists and invertebrates, especially crustaceans and insects, many of which appear to be predictably represented, taxonomically, across wetland types. These groups provide the crucial trophic link between producers and the top predators-although the trophic status of some of the latter is variable. For example, many amphibians that breed in wetlands represent both prey and predator depending on the stage in their life cycle. While salamanders may input large amounts of energy via egg deposition, many larger vertebrate inhabitants of wetlands are seen as net removers of energy from these systems (grazing, predation, etc.), with very little return (chiefly wastes)-although intuitively, they must represent important links between aquatic and terrestrial compartments. However, it must be said that the role of vertebrates in nutrient cycling and energy flow in wetlands is not well understood, and that the consequences of, for example the global decline in amphibian populations, are largely unknown. Coverage of these topics will be global in extent. Future research needs to be focused on piecing together the components of intermittent wetland function through study of: 1) physiological and phenological response to natural (e.g., hydroperiod regime) and human-induced stress by individual taxa; 2) community response to these same factors; 3) trophic function, including bottomup resources and top-down pressures; 4) interaction with, especially energy flow through, permanent sections of wetland, the riparian zone, adjacent woodlands and grasslands, and beyond. © 2009 by Nova Science Publishers, Inc. All rights reserved.

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)0.000
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.189
Teacher spread0.178 · 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
Published2009
Admission routes1
Has abstractyes

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