The Role of Environmental Characteristics on Fish Community Structure and Food Web Interactions In Lake Ontario Embayments
Bibliographic record
Abstract
THE ROLE OF ENVIRONMENTAL CHARACTERISTICS ON FISH COMMUNITY STRUCTURE AND FOOD WEB INTERACTIONS IN LAKE ONTARIO EMBAYMENTS Kristin K. Arend, Ph. D. Cornell University 2008 Aquatic ecosystems are influenced by physical, chemical, and biological processes operating at multiple spatial scales, from landscape through microhabitats. Processes operating at the landscape level, such as watershed land use or precipitation, are external factors that influence an aquatic ecosystem. Internal factors are processes operating on an aquatic ecosystem from within the system, such as habitat. I explored how external and internal factors influenced fish community structure and function in Lake Ontario embayments. With my research, I aimed to address the following questions: (1) which internal and external factors influence how much and where biomass is distributed in the fish community (i.e., structure); (2) which factors influence energy sources utilized by the fish communities (i.e., function); (3) are structural and functional responses related to each other? Structural characteristics responded to both external and internal factors. Biomass increased with phosphorus loading (external factor) and area (internal factor), whereas abundance increased and size structure decreased with percent vegetation (internal factor). Similarly, both external and internal factors influenced energy sources incorporated by the fish communities, including connectivity to adjacent habitats (external factor), depth profile (internal factor), and vegetation (internal factor). Fish communities in embayments with stronger connections to their watersheds (versus Lake Ontario) incorporated greater energy and nutrients from the watershed, and vice versa. Fish communities in deep embayments relied primarily on energy sources from pelagic habitat; fish communities in shallow embayments utilized energy sources from both pelagic and littoral habitats. Finally, structural and functional responses appeared to be related through their effects on trophic interactions, as indicated by a study of yellow perch (Perca flavescens) populations. A comparison of observed yellow perch growth versus energy budget model predictions suggested that embayment morphometry could influence the relative importance of trophic interactions. Yellow perch populations in shallow, littoral embayments, where vegetation provides protection from predation, were sensitive to prey availability and composition. In contrast, yellow perch growth and size structure in deep, pelagic embayments might have been influenced to a greater extent by predation. Overall, internal factors influenced fish communities to a greater extent than external factors, primarily by influencing trophic interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".