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Record W2567181381 · doi:10.1093/plankt/fbw092

Changes in cladoceran assemblages from tropical high mountain lakes during periods of recent climate change

2016· article· en· W2567181381 on OpenAlexaff
Andrew L. Labaj, Neal Michelutti, John P. Smol

Bibliographic record

VenueJournal of Plankton Research · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsCladoceraBranchiopodaClimate changePaleolimnologyEnvironmental scienceEcologyZooplanktonOceanographyGeographyBiologyGeology

Abstract

fetched live from OpenAlex

The Andes Mountain range of South America is one of the most rapidly warming regions in the world. Alpine lakes from Cajas National Park (Ecuador) have shown evidence of increased thermal stratification and associated shifts in algal communities in recent decades, consistent with regionally warmer air temperatures and reduced wind speeds. Here, we use paleolimnological approaches to examine the impacts of recent climate change on Cladocera (Crustacea, Branchiopoda) in three equatorial alpine lakes. Each lake experienced a shift in abundance from the small pelagic grazer Bosmina spp. to larger Daphnia spp. In two of the lakes, Daphnia spp. increased from an average of ~5 to 35–40% relative abundance within the last ~20 years. Meanwhile, in the third lake, Daphnia spp. increased to ~50% relative abundance in the ~1970s, but subsequently declined to background levels within the following decade. We show that cladoceran assemblages have undergone marked shifts during a period of rapid climate change in this region, but unlike comparable work on algal indictors, the response has been more complicated. We conclude that climate is likely affecting these keystone aquatic invertebrates, and may begin to impact higher level predators such as fish, which often rely on cladocerans as a food source.

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)0.002
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 score0.025
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.080
GPT teacher head0.335
Teacher spread0.255 · 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

Citations21
Published2016
Admission routes1
Has abstractyes

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