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Record W1575918301

Mallard duckling survival and habitat selection in the Canadian prairie pothole region

2010· article· en· W1575918301 on OpenAlexfundaboutno aff
Pauline M. Bloom

Bibliographic record

VenueUniversity Library - University of Saskatchewan (University of Saskatchewan) · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersInstitute for Wetland and Waterfowl Research, Ducks Unlimited Canada
KeywordsPothole (geology)HabitatSelection (genetic algorithm)GeographyEcologyAnatidaeBiologyForestry
DOInot available

Abstract

fetched live from OpenAlex

Like life-history theory, wildlife management decisions are typically predicated on trade-offs between benefits associated with investing resources to achieve higher reproductive or survival rates versus costs or risks of achieving those goals. On the Canadian prairies, most waterfowl conservation resources are directed to policies and programs that seek to increase duck nesting success. Limited attention has focused on post-hatching life-cycle stages, yet, despite considerable recent work on duckling survival rates, many uncertainties remain concerning how abiotic and biotic factors affect duckling survival rates. The role of upland habitat characteristics may be important but has received limited attention. I evaluated hypothesized sources of variation in duckling survival for 617 mallard (Anas platyrhynchos) broods on 27 Canadian prairie-parkland sites, with emphasis on assessing effects of managed and remnant natural upland habitats. I contrasted suites of a priori and post hoc exploratory models that incorporated effects of landscape, weather, female and brood-related variables to explain variation in duckling survival rates. Survival was lower for ducklings that used areas with high proportions of semi-permanent wetlands, as well as for broods that travelled farther overland. Exploratory analyses revealed further that survival of ducklings was negatively related to the amount of managed hayland. In contrast, duckling survival was positively associated with the amount managed grassland. There was no evidence of trade-offs between benefits of managing habitat to enhance duck nesting success versus costs in terms of lower subsequent duckling survival. I also addressed unresolved questions about how birds balance costs and benefits of selecting habitats by determining the survival consequences of habitat choices made during brood-rearing. In theory, fitness should be higher in preferred habitats, but this assumption is rarely tested. Fitness consequences (i.e., duckling survival) of habitat selection patterns were determined at landscape and local scales using logistic regression and information-theoretic model selection techniques. Best-approximating landscape-level models indicated that mallard females selected brood-rearing areas with a high proportion of wetland and perennial upland habitats, but duckling survival was not related to habitat selection patterns at this scale. At finer spatial scales, females selected brood-rearing areas with high proportions of wetland habitats, but, contrary to expectation, duckling survival was lower when females raised their broods in these areas. Females avoided areas with abundant perennial cover and wetlands with little vegetative cover and, consistent with prediction, duckling survival was higher when females selected areas with low perennial cover. Thus, females did not consistently select brood-rearing habitats that conferred the highest fitness benefits. Rather, the relationship between habitat selection and duckling survival depended on spatial scale and habitats considered.

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 score0.040
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.007
GPT teacher head0.160
Teacher spread0.153 · 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

Citations8
Published2010
Admission routes2
Has abstractno

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