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Warming, eutrophication, and predator loss amplify subsidies between aquatic and terrestrial ecosystems

2011· article· en· W2143061396 on OpenAlexaff
Hamish S. Greig, Pavel Kratina, Patrick L. Thompson, Wendy J. Palen, John S. Richardson, Jonathan B. Shurin

Bibliographic record

VenueGlobal Change Biology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsSimon Fraser UniversityMcGill UniversityUniversity of British Columbia
Fundersnot available
KeywordsEcosystemTerrestrial ecosystemEnvironmental scienceFood webAquatic ecosystemEcologyEutrophicationMesocosmBiomass (ecology)DetritusFreshwater ecosystemNutrient cycleNutrientBiology

Abstract

fetched live from OpenAlex

Abstract The exchange of organisms and energy among ecosystems has major impacts on food web structure and dynamics, yet little is known about how climate warming combines with other pervasive anthropogenic perturbations to affect such exchanges. We used an outdoor freshwater mesocosm experiment to investigate the interactive effects of warming, eutrophication, and changes in top predators on the flux of biomass between aquatic and terrestrial ecosystems. We demonstrated that predatory fish decoupled aquatic and terrestrial ecosystems by reducing the emergence of aquatic organisms and suppressing the decomposition of terrestrial plant detritus. In contrast, warming and nutrients enhanced cross‐ecosystem exchanges by increasing emergence and decomposition, and these effects were strongest in the absence of predators. Furthermore, we found that warming advanced while predators delayed the phenology of insect emergence. Our results demonstrate that anthropogenic perturbations may extend well beyond ecosystem boundaries by influencing cross‐ecosystem subsidies. We find that these changes are sufficient to substantially impact recipient communities and potentially alter the carbon balance between aquatic and terrestrial ecosystems and the atmosphere.

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 score0.010
Threshold uncertainty score0.508

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)0.0000.000

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 head0.060
GPT teacher head0.273
Teacher spread0.213 · 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

Citations165
Published2011
Admission routes1
Has abstractyes

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