MétaCan
Menu
Back to cohort
Record W2143098193 · doi:10.1111/geb.12154

Seasonality drives global‐scale diversity patterns in waterfowl (<scp>A</scp>nseriformes) via temporal niche exploitation

2014· article· en· W2143098193 on OpenAlexfundno aff
Lars Dalby, Brian J. McGill, Anthony David Fox, Jens‐Christian Svenning

Bibliographic record

VenueGlobal Ecology and Biogeography · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersOticon FondenAarhus Universitets ForskningsfondMcGill University
KeywordsSpecies richnessWaterfowlEcologyBody size and species richnessRange (aeronautics)NicheLatitudeSeasonalityBiologyBreeding bird surveySpecies diversityGeographyHabitat

Abstract

fetched live from OpenAlex

Abstract Aim Birds (class A ves) overall follow the expected latitudinal gradient in species richness. However, there is a poorly understood secondary peak in bird richness at about 45° N latitude, at least in N orth A merica. Waterfowl ( A nseriformes) species richness conforms to this secondary peak, yet this group is commonly excluded from bird species richness studies and consequently the drivers of waterfowl richness are poorly understood. Here, we explore the drivers of waterfowl richness on a global scale, emphasizing the secondary peak. Location Global. Methods We mapped total waterfowl species richness for the breeding and non‐breeding seasons. Considering a wide range of potential environmental drivers (climate, productivity and habitat), we initially ran univariate ordinary least squares models for the candidate set of predictors. We then used regression trees ( RT ) to multivariately assess their importance for the richness pattern while allowing for nonlinear and non‐stationary processes. Results We found seasonal variability in plant productivity (measured by the normalized difference vegetation index, NDVI ) to be the most important predictor of breeding season richness in both univariate regressions and multivariate RT models. For the non‐breeding season, winter actual evapotranspiration ( AET ) was the best predictor. In the regions of highest richness, including the secondary mid‐latitude peak, an overwhelming portion of the species were migratory species. Main conclusions Predictors commonly used to explain the large‐scale richness patterns of birds, such as annual AET or annual NDVI explained little of the variation in richness in A nseriformes. Instead, measures reflecting intra‐annual variability and seasonal productivity in the relevant season were the best predictors. This finding, combined with the high proportion of migrants in the richness peaks, strongly suggests that the patterns of richness in A nseriformes reflect the group's exploitation of seasonal environmental variability via short‐ and long‐distance migration, i.e. temporal niche exploitation at an annual and global scale.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.968

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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 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

Citations61
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Ecology and BiogeographySame topicEcology and Vegetation Dynamics StudiesFrench-language works237,207