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Environmental stability and lake zooplankton diversity – contrasting effects of chemical and thermal variability

2010· article· en· W2166683581 on OpenAlexaff
Jonathan B. Shurin, Monika Winder, Rita Adrian, Wendel Keller, Blake Matthews, Andrew M. Paterson, Michael J. Paterson, Bernadette Pinel‐Alloul, James A. Rusak, Norman D. Yan

Bibliographic record

VenueEcology Letters · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsLaurentian UniversityUniversité de MontréalFisheries and Oceans CanadaMinistry of the Environment, Conservation and ParksYork UniversityUniversity of British Columbia
Fundersnot available
KeywordsSpecies richnessZooplanktonEcologyBiodiversityEnvironmental scienceTemperate climateBiology

Abstract

fetched live from OpenAlex

Environmental variability in space and time is a primary mechanism allowing species that share resources to coexist. Fluctuating conditions are a double edged sword for diversity, either promoting coexistence through temporal niche partitioning or excluding species by stochastic extinctions. The net effect of environmental variation on diversity is largely unknown. We examined the association between zooplankton species richness in lakes and environmental variability on interannual, seasonal and shorter time scales, as well as long-term average conditions. We analyzed data on physical, chemical and biological limnology in 53 temperate zone lakes in North America and Europe sampled over a combined 1042 years. Large fluctuations in pH, phosphorus and dissolved organic carbon concentration on different time scales were associated with reduced zooplankton species richness. More species were found in lakes that showed greater temperature variation on all time scales. Environmental variability on different time scales showed similar or, in some cases, stronger associations with zooplankton species richness compared with long-term average conditions. Our results suggest that temporal fluctuations in the chemical environment tend to exclude zooplankton species while temperature variability promotes greater richness. The results indicate that anthropogenic increases in temporal variability of future climates may have profound effects on biodiversity.

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 score0.297
Threshold uncertainty score0.707

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.002
GPT teacher head0.157
Teacher spread0.155 · 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

Citations146
Published2010
Admission routes1
Has abstractyes

Explore more

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