Region-based MTI: resolving geographic expansion in the Marine Trophic Index
Bibliographic record
Abstract
MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 512:185-199 (2014) - DOI: https://doi.org/10.3354/meps10949 Region-based MTI: resolving geographic expansion in the Marine Trophic Index K. Kleisner1,3,*, H. Mansour2,3, D. Pauly1 1Sea Around Us Project, Fisheries Centre, University of British Columbia, 2202 Main Mall, Vancouver, BC V6T 1Z4, Canada 2Earth and Ocean Sciences, University of British Columbia, 2207 Main Mall, Vancouver, BC V6T 1Z4, Canada 3Present address: NOAA, Northeast Fisheries Science Center, 166 Water St., Woods Hole, MA 02543, USA *Corresponding author: kristin.kleisner@noaa.gov ABSTRACT: The Marine Trophic Index (MTI), which tracks the mean trophic level of fishery catches from an ecosystem, generally, but not always, tracks changes in mean trophic level of an ensemble of exploited species in response to fishing pressure. However, one of the disadvantages of this indicator is that declines in trophic level can be masked by geographic expansion and/or the development of offshore fisheries, where higher trophic levels of newly accessed resources can overwhelm fishing-down effects closer inshore. Here, we show that the MTI should not be used without accounting for changes in the spatial and bathymetric reach of the fishing fleet, and we develop a new index that accounts for the potential geographic expansion of fisheries, called the region-based MTI (RMTI). To calculate the RMTI, the potential catch that can be obtained given the observed trophic structure of the actual catch is used to assess the fisheries in an initial (usually coastal) region. When the actual catch exceeds the potential catch, this is indicative of a new fishing region being exploited. The MTI of the new region can then be calculated, and subsequent regions are determined in a sequential manner. This method improves upon the use of the Fishing-in-Balance (FiB) index in conjunction with the original MTI calculated over the whole time series because assumptions of fleet and stock stationarity over the entire time series and geographic area are removed. We illustrate the utility of the RMTI with simulated examples and actual data, and generalize these results. KEY WORDS: Mean trophic level · Indicators · Expansion of fishing · Fishing-in-Balance Index Full text in pdf format PreviousNextCite this article as: Kleisner K, Mansour H, Pauly D (2014) Region-based MTI: resolving geographic expansion in the Marine Trophic Index. Mar Ecol Prog Ser 512:185-199. https://doi.org/10.3354/meps10949 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 512. Online publication date: October 09, 2014 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2014 Inter-Research.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".