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Record W2939367231 · doi:10.1111/fwb.13278

Nutrient management and structural shifts in fish assemblages: Lessons learned from an Area of Concern in Lake Ontario

2019· article· en· W2939367231 on OpenAlexafffundabout
Monir Hossain, George B. Arhonditsis, James A. Hoyle, Robert G. Randall, Marten A. Koops

Bibliographic record

VenueFreshwater Biology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsMinistry of Natural Resources and ForestryUniversity of TorontoFisheries and Oceans Canada
FundersNatural Sciences and Engineering Research Council of CanadaDental Foundation of Oregon
KeywordsBayTrophic levelEutrophicationBiomass (ecology)Environmental scienceFood webEcologyEcosystemFisheryLake ecosystemProductivityZooplanktonNutrientOceanographyBiology

Abstract

fetched live from OpenAlex

Abstract While total phosphorus ( TP ) is a critical determinant of freshwater ecosystem productivity, multiple stressors can induce shifts in energy pathways, with profound implications for ecosystem and fishery restoration. The Bay of Quinte (Lake Ontario, Canada) is a Great Lakes nearshore ecosystem that has been historically subjected to a variety of environmental perturbations: cultural eutrophication, low dissolved oxygen, reduced fisheries, climatic extremes, phosphorus (P) abatement, and aquatic invasive species. We used the Bay of Quinte to study how trophic state alterations affect fish assemblages in Great Lakes nearshore environments by examining the response of fish biomass to TP concentration variability in the presence of multiple stressors. Our analysis is based on a 42‐year (1972–2013) dataset from the Bay of Quinte for water quality through the food web to fishes. We employed a series of statistical tools that can offer insights into the structural changes induced by the events examined. We first used dynamic linear modelling to detect temporal trends in fish biomass, while accounting for year‐to‐year TP variability over three spatial segments of the bay. We then developed piecewise regression models to assess the extent to which specific ecological events induced distinct shifts in the fish assemblage. Multiple regression modelling was used to quantify the relative importance of TP , zooplankton, and surface water temperature on fish biomass. Based on gillnets, there were consistent fish biomass changes across the bay with increased biomass before P control (1972–1977), declines after P control followed by the establishment of a steady state or modest increase (1978–1994), and a declining trajectory during the recent period (1995–2013). Even when accounting for the role of water temperature and zooplankton, TP still had a significant effect on fish biomass. However, the strength and nature of the relationship varied among fish groups, and overall, the effect of TP on fish biomass has weakened in recent years. Our models show that fish biomass in the Bay of Quinte is shaped by the year‐to‐year TP variability. However, the relationship between P and fish abundance has been modulated by various ecological events with the consequence that the Bay of Quinte fish assemblage has changed and the food web now produces less fish biomass per unit of TP . A projected reduction of mean ambient TP levels from 30 to 25 μg/L, is expected to induce a 24% decline in total fish biomass, and further shift the fish assemblage with the biomass of planktivores and walleye declining by >60% and 30%, respectively. Recreational fishing provides important economic benefits in the Bay of Quinte, through tourism and other local business operations. Recognising the economic importance of fishing, our analysis provides critical insights regarding the on‐going management efforts to reduce external nutrient loadings (point and non‐point sources, urban storm water) and further lower ambient TP levels and primary productivity. The potential implications of already low nutrient concentrations for fish productivity represent a major challenge in effectively balancing water quality with fisheries management in the area.

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 score0.925
Threshold uncertainty score0.988

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)0.0120.000

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 head0.038
GPT teacher head0.272
Teacher spread0.234 · 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

Citations26
Published2019
Admission routes3
Has abstractyes

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