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Record W2105507495 · doi:10.1890/10-1846.1

Differential effects of energy and mass influx on the landscape synchrony of lake ecosystems

2011· article· en· W2105507495 on OpenAlexaff
Richard J. Vogt, James A. Rusak, Alain Patoine, Peter R. Leavitt

Bibliographic record

VenueEcology · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsMinistry of the Environment, Conservation and ParksUniversité de MonctonUniversity of Regina
Fundersnot available
KeywordsEcologyEcosystemDifferential (mechanical device)GeographyEnvironmental scienceBiologyPhysics

Abstract

fetched live from OpenAlex

Interannual variation of 45 annually resolved time series of environmental, limnological, and biotic parameters was quantified (1994-2009) in six lakes within 52,000 km2 to test the hypothesis that influx of energy (E; as irradiance, heat, wind) varies synchronously among sites and induces temporal coherence in lakes and their food webs, whereas influx of mass (m; as water, solutes, particles) reduces synchrony because local catchments uniquely modify hydrologic inputs. Overall, 82% of parameters exhibited significant (P < 0.05) synchrony (S) estimated as mean pair-wise correlation of Z-transformed time series. Influx of E as atmospheric heat and irradiance was both more highly synchronous and less temporally variable (months-to-decades) than influx of m as summer precipitation, snow, or river discharge. Similarly, S of limnological parameters varied from 0.08 to 0.85, with variables known to be regulated by E influx (ice melt, gas solubility) up to twofold more coherent than those regulated by m inputs (organic solutes). Pairs of variables linked by simple direct mechanisms exhibited similar S values (air temperature and ice melt, nutrients and algae), whereas the coherence of other parameters (water temperature, mixing) was intermediate to that of multiple regulatory agents. Overall, aggregate measures of plankton density varied more coherently among lakes than did constituent taxa. These findings suggest that environmental variability is transmitted to most levels of aquatic ecosystems, but that the precise effects depend on whether E or m fluxes predominate, the coherence of each forcing mechanism, and the strength of linkages between exogenous forcing and lake response.

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 score0.074
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.159
Teacher spread0.153 · 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

Citations66
Published2011
Admission routes1
Has abstractyes

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