Using Ecopath Modeling to Describe Historical Conditions for a Large, Boreal Lake Ecosystem prior to European Settlement
Bibliographic record
Abstract
Abstract To help guide restoration efforts at a large-lake ecosystem (Lac la Biche) in Alberta, Canada, I used Ecopath modeling software to create an energetically plausible model representing the system prior to European settlement. Over the last 200 years, Lac la Biche has shifted from a system dominated by predatory fish (e.g., Walleyes Sander vitreus and Northern Pike Esox lucius) to one dominated by forage fish and double-crested cormorants Phalacrocorax auritus. In 2005, the Fisheries Management Branch of Alberta Environment and Sustainable Resource Development initiated a fisheries restoration program focused on increasing the abundance of Walleyes and average fish size in Lac la Biche; restoration would target a nondescript “historical” ecosystem configuration based on contemporary assumptions of what the system might have looked like. I used Ecopath to organize the main assumptions and information on the historical Lac la Biche ecosystem into an energetically plausible representation for the year 1800. From the modeling process, I learned that most of the assumptions regarding the trends in species biomass between contemporary and historical systems were appropriate guides for model balancing; however, the magnitude of the expected differences between 1800 and the present day were often larger than predicted, especially for infrequently sampled species (e.g., Yellow Perch Perca flavescens, White Suckers Catostomus commersonii, Spottail Shiners Notropis hudsonius, and Burbot Lota lota). Testing the validity of single-species assumptions in an ecosystem context is important for improving our understanding of ecosystem structure and function. In a historical context, model creation based on assumptions and available information is important for developing ecosystem baselines that can provide context to and guide future fisheries management. Historical baselines are also important for highlighting ecosystem potential and productive capacity and for counteracting the effects of the shifting baseline syndrome. Received February 13, 2012; accepted August 2, 2013 Published online December 23, 2013
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".