MétaCan
Menu
Back to cohort
Record W3134087664 · doi:10.1002/eap.2324

Migrating Whooping Cranes avoid wind‐energy infrastructure when selecting stopover habitat

2021· article· en· W3134087664 on OpenAlexafffund
Aaron P. Yetter, Kristine L. Metzger, David A. Brandt, Jill A. Shaffer, Mark T. Bidwell, Wade Harrell

Bibliographic record

VenueEcological Applications · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsEnvironment and Climate Change Canada
FundersU.S. Fish and Wildlife ServiceEnvironment and Climate Change Canada
KeywordsGrus (genus)HabitatWhooping coughEnvironmental scienceEcologyEndangered speciesGeographyBiology

Abstract

fetched live from OpenAlex

Electricity generation from renewable-energy sources has increased dramatically worldwide in recent decades. Risks associated with wind-energy infrastructure are not well understood for endangered Whooping Cranes (Grus americana) or other vulnerable Crane populations. From 2010 to 2016, we monitored 57 Whooping Cranes with remote-telemetry devices in the United States Great Plains to determine potential changes in migration distribution (i.e., avoidance) caused by presence of wind-energy infrastructure. During our study, the number of wind towers tripled in the Whooping Crane migration corridor and quadrupled in the corridor's center. Median distance of Whooping Crane locations from nearest wind tower was 52.1 km, and 99% of locations were >4.3 km from wind towers. A habitat selection analysis revealed that Whooping Cranes used areas ≤5.0 km (95% confidence interval [CI] 4.8-5.4) from towers less than expected (i.e., zone of influence) and that Whooping Cranes were 20 times (95% CI 14-64) more likely to use areas outside compared to adjacent to towers. Eighty percent of Whooping Crane locations and 20% of wind towers were located in areas with the highest relative probability of Whooping Crane use based on our model, which comprised 20% of the study area. Whooping Cranes selected for these places, whereas developers constructed wind infrastructure at random relative to desirable Whooping Crane habitat. As of early 2020, 4.6% of the study area and 5.0% of the highest-selected Whooping Crane habitat were within the collective zone of influence. The affected area equates to habitat loss ascribed to wind-energy infrastructure; losses from other disturbances have not been quantified. Continued growth of the Whooping Crane population during this period of wind infrastructure construction suggests no immediate population-level consequences. Chronic or lag effects of habitat loss are unknown but possible for long-lived species. Preferentially constructing future wind infrastructure outside of the migration corridor or inside of the corridor at sites with low probability of Whooping Crane use would allow for continued wind-energy development in the Great Plains with minimal additional risk to highly selected habitat that supports recovery of this endangered species.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.008
GPT teacher head0.221
Teacher spread0.214 · 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.

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

Citations17
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueEcological ApplicationsSame topicAvian ecology and behaviorFrench-language works237,207