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Record W2789368139

Distribution and habitat of the least bittern and other marsh bird species in southern Manitoba

2006· dissertation· en· W2789368139 on OpenAlexfundaboutno aff
Stacey Hay

Bibliographic record

VenueMspace (University of Manitoba) · 2006
Typedissertation
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersParks CanadaWorld Wildlife Fund
KeywordsMarshHabitatGeographyDistribution (mathematics)EcologyForestryWetlandBiologyMathematics
DOInot available

Abstract

fetched live from OpenAlex

Call-response surveys were conducted to better delineate and estimate the population of the nationally threatened least bittern and their habitat requirements in southern Manitoba, Canada. Other marsh bird species whose populations are believed to be declining due to wetland loss throughout, or in parts of, their range were also surveyed including the American bittern, pied-billed grebe, sora, Virginia rail and yellow rail.\n\nSurveys were conducted during the 2003 and 2004 breeding seasons within 46 different wetlands. Least bitterns were encountered on 26 occasions at 15 sites within 5 wetlands. The sora was the most abundant and widely distributed target species and was encountered on 330 occasions in 39 of the 46 surveyed wetlands. Yellow rails were not detected during either survey year due to survey methodology.\n\nUse of the call-response survey protocol led to an increase in the numbers of all\ntarget species detected. This increase was more significant for the least bittern, sora and\nVirginia rail. \n\nHabitat was assessed as percent vegetation cover within a 50-m radius around the calling sites, and forest resource inventory data were used in a Geographic Information System to determine the landscape composition within a 500-m radius around the sites and within a 5-km radius around the wetlands surveyed. Logistic regression analyses were used to evaluate the relationship between the presence of the target species and the site and landscape characteristics.\n\nThe target species responded differently to different site and landscape characteristics. Least bittern and pied-billed grebe selected areas with higher proportions of Typha spp. and tall shrubs; American bittern also selected areas with higher proportions of tall shrubs. At the 5-km scale, the American bittern responded positively to the amount of wetland and some positive trends were also detected for the pied-billed grebe. Sora and Virginia rail were not associated with any of the measured landscape characteristics.\n\nOne of the most important steps towards the conservation of marsh bird species in Manitoba and elsewhere is the development, adoption, and implementation of a standardized survey protocol. Based on the results of the present study, I recommend that future surveys include both a passive and call-broadcast period for marsh bird species. Future surveys should be conducted in both the morning and evening and sites should be visited 3 times each during the breeding season. In southern Manitoba, call-response surveys should begin as early as the beginning of May to ensure the survey incorporates the period of peak vocalization. I recommend that future yellow rail surveys be conducted after dark.\n\nIn this study many of the target species selected sites that had a greater area of wetland habitat surrounding them. Future wetland conservation efforts should focus on the protection and/or restoration of wetland complexes to ensure that remaining wetlands do not become smaller and increasingly isolated from one another. In addition, the Rat River Swamp was found to be the most productive marsh complex for least bittern in southern Manitoba. Measures should be taken to protect this area from future development and alteration.

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 score0.129
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.006
GPT teacher head0.164
Teacher spread0.157 · 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

Citations7
Published2006
Admission routes2
Has abstractyes

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