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Record W7006502894

The use of seagrass (Zostera muelleri) habitat by Canada geese (Branta canadensis) in Waikato estuaries.

2021· dissertation· en· W7006502894 on OpenAlexaboutno aff

Bibliographic record

VenueResearch Commons (University of Waikato) · 2021
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsnot available
FundersWaikato Regional Council
KeywordsSeagrassWaterfowlZosteraHabitatZostera marinaForagingBiodiversityDisturbance (geology)EcosystemForageIntertidal zone
DOInot available

Abstract

fetched live from OpenAlex

Seagrass beds are highly biodiverse habitats delivering key ecosystem functions and services to mankind. Zostera muelleri is New Zealand’s single seagrass species, and occurs intertidally within several estuaries and sheltered harbours. However, these habitats are globally in decline due to the impacts of multiple stressors including eutrophication, turbidity, coastal urbanisation, sedimentation, and sea level rise. Herbivory by waterfowl is a relatively unknown biotic disturbance that may cause additional stress to these vulnerable seagrass habitats. Canada geese (Branta canadensis) were introduced to New Zealand in 1905, and have since been increasing in numbers since a change in species management. In response to increase in Canada geese populations and use of estuaries along the West coast, the Waikato Regional Council commissioned this MSc (Research) study to investigate the consumption of Zostera by Canada geese in Kawhia and Whāingaroa (Raglan) harbours, West coast of the North Island, New Zealand.
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\nIn order to better understand the grazing pressure placed on seagrass habitats, a three part investigation was conducted. Behaviours of Canada geese on Zostera beds were observed in January and February (2019), at two sites in Whāingaroa Harbour, with geese numbers varying between 8 to 200 at any one time. Observations indicated that foraging incorporated a large proportion of their behavioural budget (> 85%), and birds utilised several destructive methods to forage on both above and below-ground Zostera biomass. Foraging was significantly reduced by disturbance events less than 30 m away and was also influenced by group size. Repeat observations in June and July 2019, were not possible as geese were no longer present on the Zostera beds.
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\nCanada geese samples were collected to investigate bird diet across a temporal scale; from Kawhia between July to November 2019 (n = 33), and from Raglan between August to September 2020 (n = 26). Gut contents analysis showed that more than 70% of specimens consumed solely pasture in the two hours prior to sampling. Bayesian mixing models in MixSIAR were used for δ¹⁵N and δ¹³C stable isotope analysis to evaluate the assimilated diet three to four days (plasma), three to four weeks (red blood cells) and several months (primary feathers) prior to sampling. Pasture was the dominant food source (75 to 93%) contributing to all three tissue types.
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\nThis study aimed to provide insight into the consumption of seagrass (Zostera muelleri) by Canada geese, and determine the proportion of their diet that came from Zostera relative to pasture grass. Although Canada geese were observed feeding on Zostera during the dry summer months, gut and isotope samples could not support this, as they were collected during the winter/spring months. This difference indicates that the period where Canada geese exploit seagrass was not captured in the isotope study. Post-moult gut and tissue sampling (from late January) would confirm if these birds use the more digestible Zostera to meet their nutritional demands during the dry summer season as pasture grass becomes less nutritious or digestible.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.232
Teacher spread0.190 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

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