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Record W2028482297 · doi:10.1139/b01-018

Macrophyte succession in Minnesota beaver ponds

2001· article· en· W2028482297 on OpenAlexvenueno aff
Andrew M. Ray, Alan J. Rebertus, Heather L. Ray

Bibliographic record

VenueCanadian Journal of Botany · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
FundersNorthern Michigan University
KeywordsMacrophyteBeaverEcological successionEcologySpecies richnessKeystone speciesWetlandBiological dispersalEcosystem engineerHabitatWaterfowlBiomass (ecology)Environmental scienceBiologyPopulation

Abstract

fetched live from OpenAlex

Beavers (Castor canadensis Kuhl.) are keystone species that dramatically alter nutrient cycles and food webs in aquatic and terrestrial ecosystems by modifying their hydrology and selectively removing riparian trees. We documented macrophyte succession in 36 beaver ponds ranging from 4 to over 40 years old. We used impounded bogs, because they were isolated from other waterbodies and less prone to washout. Richness and diversity increased linearly in ponds during the first four decades. Pond age and the product of pond size and the number of neighboring ponds in a 0.25-km radius explained 64% (p = 0.001) of the variation in richness. Compositional trends were best explained by propagule dispersal traits. Initial colonists included vagile species, like free-floating macrophytes and desirable foods for waterfowl, like the narrow-leaved pondweeds. Ponds of intermediate age (11–40 years) had the highest diversity, with both floating-leaved and submersed life forms represented. Two community types were identified in older ponds: one characterized by dense lily pad cover and the other characterized by a rich assemblage of Potamogeton. By developing a predictive model for macrophyte succession in beaver ponds, we provide a basis for studying a variety of ecological processes and organisms that depend on macrophytes in these regionally abundant habitats.Key words: macrophyte, succession, beaver, bogs, Minnesota, pond.

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 score0.188
Threshold uncertainty score0.999

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)0.0020.000

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 head0.008
GPT teacher head0.193
Teacher spread0.184 · 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

Citations71
Published2001
Admission routes1
Has abstractyes

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