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Record W2074585320 · doi:10.1139/f06-037

Whole-lake effects of invasive crayfish (<i>Orconectes</i> spp.) and the potential for restoration

2006· article· en· W2074585320 on OpenAlexvenueno aff
Sadie K Rosenthal, Samantha S Stevens, David M. Lodge

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsnot available
FundersU.S. Forest ServiceMichigan Department of Natural ResourcesUniversity of Notre Dame
KeywordsMacrophyteCrayfishEcologySpecies richnessInvasive speciesIntroduced speciesInvertebrateBenthic zoneBiologyFisheryEnvironmental science

Abstract

fetched live from OpenAlex

Effects of invasive species are often extrapolated to whole systems based on small-scale, short-term, and (or) single-system studies. For example, previous laboratory studies and in-lake cage experiments suggest that invasive crayfish Orconectes rusticus and O. propinquus reduce macrophyte and snail abundance in north temperate lakes, and snapshot lake surveys provide supporting evidence. Still, these impacts have not been demonstrated in multiple whole lakes over time. Thus, in summer of 2003, we resurveyed benthic invertebrates and macrophytes in lakes originally surveyed by the Michigan Department of Natural Resources in the late 1930s. Our multilake survey supports the macrophyte results from small-scale and comparative studies: macrophyte species richness and abundance declined significantly in invaded lakes relative to uninvaded lakes. We next conducted a laboratory seed-bank study to examine the potential for macrophyte restoration in a lake occupied by rusty crayfish for at least 15 years. Only two macrophyte species (Najas flexilis and Chara spp.) germinated from sediments from the invaded lake compared with eight species from reference sediments. This suggests that invaded lakes may have depauperate seed banks and that restoration of invaded macrophyte communities may require manual planting, even if crayfish could be removed.

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)0.001
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 score0.124
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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

Citations75
Published2006
Admission routes1
Has abstractyes

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