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Record W4367679250 · doi:10.1002/wmon.1074

Waterfowl distribution and productivity in the Prairie Pothole Region of Canada: tools for conservation planning

2023· article· en· W4367679250 on OpenAlexafffundabout
James H. Devries, Llwellyn M. Armstrong, David W. Howerter, Robert B. Emery

Bibliographic record

VenueWildlife Monographs · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsDucks Unlimited Canada
FundersU.S. Fish and Wildlife ServiceEnvironment and Climate Change CanadaInstitute for Wetland and Waterfowl Research, Ducks Unlimited Canada
KeywordsAnasWaterfowlHabitatAnatidaeNest (protein structural motif)EcologyBreeding bird surveyGeographyPopulationBreeding pairBiology

Abstract

fetched live from OpenAlex

Abstract Species conservation requires an understanding of the factors and interactions affecting species distribution and behavior, habitat availability and use, and corresponding vital rates at multiple temporal and spatial scales. Opportunities to investigate these relationships across broad geographic regions are rare. We combined long‐term waterfowl population surveys, and studies of habitat use and breeding success, to develop models that identify and incorporate these interactions for upland‐nesting waterfowl in the Prairie Pothole Region (PPR) of Canada. Specifically, we used data from the annual Waterfowl Breeding Population and Habitat Survey (1961–2009) at the survey segment level and associated habitat covariates to model and map the long‐term average duck density across the Canadian PPR. We analyzed nest location and fate data from approximately 25,000 duck nests found during 3 multi‐year nesting studies (1994–2011) to model factors associated with nest survival and habitat selection through the nesting season for the 5 most common upland nesting duck species: mallard ( Anas platyrhynchos ), gadwall ( Mareca strepera ), blue‐winged teal ( Spatula discors ), northern shoveler ( Spatula clypeata ), and northern pintail ( Anas acuta ). Duck density was highly variable across the Canadian PPR, reflecting positive responses to local wetland area and count, and amounts of cropland and grassland, a regional positive response to latitude, and a negative response to local amounts of tree cover. Nest survival was affected by temporal and spatial variables at multiple scales. Specifically, nest survival demonstrated interactive effects among species, nest initiation date, and nesting cover type and was influenced by relative annual wetness, population density, and surrounding landscape composition at landscape scales, and broad geographic gradients (east‐west and north‐south). Likewise, species‐specific probability of nest habitat selection was influenced by timing of nest initiation, population density, relative annual wetness, herbaceous cover, and tree cover in the surrounding landscape, and location within the Canadian PPR. We combined these models, with estimates of breeding effort (nesting, renesting, and nest attempts) from existing literature, in a stochastic conservation planning model that estimates nest distribution and success given spatiotemporal variation in duck density, habitat availability, and influential covariates. We demonstrate the use of this model by examining various conservation planning scenarios. These models allow estimation of local, landscape, and regional influence of conservation investments and other landscape changes on the productivity of breeding duck populations across the PPR of Canada. These models lay the groundwork for the incorporation of conservation delivery costs for full return‐on‐investment analyses and scenario analyses of climate, habitat, and land use change in regional and continental population models.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.018
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.247
Teacher spread0.213 · 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

Citations38
Published2023
Admission routes3
Has abstractyes

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