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Record W2681829666 · doi:10.1139/cjfas-2016-0526

Suburbanization alters small pond ecosystems: shifts in nitrogen and food web dynamics

2017· article· en· W2681829666 on OpenAlexvenueno aff
Meredith A. Holgerson, Max R. Lambert, L. Kealoha Freidenburg, David K. Skelly

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
FundersInstitute for Biospheric Studies, Yale UniversityYale UniversityNational Science Foundation
KeywordsSuburbanizationEcologyFood webEcosystemEnvironmental sciencePeriphytonPlant litterNutrientNutrient cycleBiologyGeography

Abstract

fetched live from OpenAlex

Small ponds often survive the transition from forested to suburban land cover and provide habitat for many species, yet little is known about how suburbanization affects pond ecosystems. We surveyed 18 small ponds across a forest-to-suburban land cover gradient and compared how physical and chemical changes altered biological and ecosystem properties, such as nutrient and food web dynamics. Suburbanization decreased canopy cover, increased water temperatures, and increased periphyton chlorophyll a, but was associated with only weak increases in total nutrients. Yet, stable isotope analysis indicated that suburbanization altered nitrogen dynamics and resource use in the food web. We observed increases in δ 15 N in algae, biofilm, and frog larvae across the suburban gradient, indicative of wastewater intrusion. Suburbanization also shifted the energy and nutrient source of a dominant consumer (Rana sylvatica; = Lithobates sylvaticus) from leaf litter to algae. Overall, we identified cryptic changes to suburban pond ecosystems, highlighting that suburbanization can profoundly impact nutrients and food web resources. As residential land use increases globally, we may expect substantial shifts in nutrient dynamics and food web pathways.

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 score0.831
Threshold uncertainty score0.998

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 studies0.0000.001
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 head0.014
GPT teacher head0.201
Teacher spread0.187 · 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

Citations52
Published2017
Admission routes1
Has abstractyes

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