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Record W3123550459 · doi:10.1111/ibi.12931

Knowledge gaps and biases in the Pantanal indicate future directions for ornithological research in large wetlands

2021· article· en· W3123550459 on OpenAlexaboutno aff
Gilberto Fernández-Arellano, Alberto L. Teixido, Bianca Bernardon, Elaine R. Bueno, Tiago Valadares Ferreira, Stela Rosa Amaral Gonçalves, Moisés Jesus, Kamila Prado Cruz Serra Thomas, Mayara Zucchetto, Vítor de Queiroz Piacentini, João Batista de Pinho

Bibliographic record

VenueIbis · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsWetlandOrnithologyEcologyBiodiversityThreatened speciesGeographySpecies richnessAbundance (ecology)Bird conservationEcosystemBiologyHabitatSouthern Hemisphere

Abstract

fetched live from OpenAlex

While taxonomic and biogeographical biases are often acknowledged, those for certain biological responses and species traits are routinely overlooked, generating major gaps in knowledge and conservation of biodiversity. Biases in research on birds ‒ an over‐sampled, diverse vertebrate class ‒ may be readily detectable, and wetlands are important species‐rich ecosystems in which to identify biases and research gaps for birds. The Pantanal, one of the world’s largest wetlands, is globally relevant for bird conservation. In this wetland, we determined spatial, temporal, taxonomic and biological response‐related biases in ornithological studies to guide future research in this ecosystem and, ultimately, in major global wetlands. Avian research was geographically biased, with 61 studies conducted in the Brazilian Pantanal and only one in Bolivia. Most studies were concentrated near urban centres, with poorly explored areas in the central Pantanal. Research was also over‐represented during the dry season when field conditions are more favourable, but such temporal bias may hamper migration studies. Considering their richness, some families were studied disproportionately more (e.g. Jacanidae) or less (e.g. Tyrannidae). Some species (e.g. Wood Stork Mycteria americana and Yellow‐billed Cardinal Paroaria capitata) were included in > 25% of studies, whereas a relatively low number of threatened bird species were studied. Behaviour was the most studied response, followed by abundance and reproduction, which were considered for > 65% of species studied. We conclude that further research needs to be focused on unexplored areas and periods, less detectable species, and ecological processes (e.g. interspecific interactions). Additionally, our results can provide useful information to better address future work and bird conservation actions in other large wetlands. For example, major gaps detected here constitute a primary roadmap to guide research in under‐sampled regions, such as the Canadian peatlands and Tonlé Sap Lake. Specifically, more studies on waterbirds in highly diverse wetlands from low‐income countries (e.g. Okavango and Sundarban Delta) may help to disentangle the essential functional role provided for these species and to prioritize conservation actions in regions with limited research capacity.

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)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0020.005
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.084
GPT teacher head0.355
Teacher spread0.271 · 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.

Study designObservational
DomainMethods
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

Citations10
Published2021
Admission routes1
Has abstractyes

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