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Record W3203793860 · doi:10.1111/oik.08884

Exposure order effects of consecutive stressors on communities: the role of co‐tolerance

2021· article· en· W3203793860 on OpenAlexaff
Megan M. MacLennan, Rolf D. Vinebrooke

Bibliographic record

VenueOikos · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStressorEcologyBiologyPsychologyNeuroscience

Abstract

fetched live from OpenAlex

Cumulative impacts of multiple extreme and novel environmental changes on communities are often the result of asynchronous rather than simultaneous exposures to such stressors. Yet, the importance of temporal dynamics remains a major knowledge gap in multiple stressor ecology, lacking theory or evidence. We provide a conceptual template for predicting the ecological importance of the order in which consecutive stressors occur (i.e. an exposure order effect) based on correlated species responses. Negative correlation of species responses is hypothesized to increase, while positive correlation is expected to reduce, exposure order effects of consecutive stressors on communities. Towards a proof of concept, we experimentally exposed planktonic communities from fishless mountain lakes to different temporal sequences of two stressors, namely invasive sportfish and elevated water temperatures. Both stressors suppressed the same resident top predator and large grazers while eliciting positive responses from smaller tolerant taxa, attesting to their interchangeable effects across species based on size selection. As a result, reversal of the order of exposure to the two stressors did not alter their combined effects on community composition and function. Our findings highlight how the order in which consecutive stressors occur may not matter to a community if ecological memory of either stressor induces tolerance of the other.

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 score0.100
Threshold uncertainty score0.882

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)0.0010.000

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 head0.005
GPT teacher head0.209
Teacher spread0.204 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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