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Record W4307563023 · doi:10.1111/fwb.14004

Dark diversity in Amazonian stream fish communities: What factors determine species absence along environmental gradients?

2022· article· en· W4307563023 on OpenAlexfundno aff
Lorrane Gabrielle Cantanhêde, Fabrício Barreto Teresa, David J. Hoeinghaus, Kirk O. Winemiller, Luciano Fogaça de Assis Montag

Bibliographic record

VenueFreshwater Biology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFish biology, ecology, and behavior
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoVale Canada Limited
KeywordsEcologyAmazonianAlpha diversitySpecies diversityHabitatEcosystem diversityBeta diversityBiodiversityGeographyBiologyAmazon rainforest

Abstract

fetched live from OpenAlex

Abstract Species distribution models often fail to predict observed patterns of species diversity, and this is because some species within a regional pool that are tolerant of conditions at a given location may nevertheless be absent from the local community. These missing species have been termed “dark diversity”. In the present study, we investigated which factors explain dark diversity among fish assemblages in Amazonian streams. We sampled 71 streams in areas with different types of land use within two river basins and estimated dark diversity from patterns of species co‐occurrence, using Beals’ index, along environmental gradients. From this procedure, taxa are designated as dark diversity components when they are absent from a given stream, but often co‐occur with the local species at other streams, indicating similar ecological requirements. We used generalised linear models both to determine whether environmental or landscape variables, connectivity, instream environmental heterogeneity or some combination of these factors explained dark diversity of fishes, and to evaluate whether ecomorphology is associated with the extent to which a species contributes to dark diversity and which specific traits contribute the most to explaining variation in dark diversity. Mean local diversity exceeded observed dark diversity. The magnitude of dark diversity was directly associated with the proportion of secondary forest in the immediate catchment and with the index of proximity to anthropogenic impact. Species that have high affinity for environments with higher current velocity, low swimming ability and that capture food mainly on the surface contributed more to dark diversity, which suggests that swimming ability, habitat preference and aspects related to diet are key predictors of the probability that a given species will be present at locations with suitable habitat. Our findings reinforce the idea that dark diversity results from interactions between species traits and environmental factors, including anthropogenic impacts. Understanding the interplay among environmental factors and species traits that contribute to dark diversity provides targets for improved ecosystem restoration and sustainability of native species assemblages.

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 score0.093
Threshold uncertainty score0.981

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.000

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 head0.028
GPT teacher head0.222
Teacher spread0.193 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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