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Record W7133277885

Estimating impacts and offsets for death of fish

2022· other· en· W7133277885 on OpenAlexfundno aff
Fisheries and Oceans Canada, Pêches et Océans Canada

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersFisheries and Oceans Canada
KeywordsHabitatFish <Actinopterygii>Selection (genetic algorithm)HarmResidualOffset (computer science)FishingFish mortality
DOInot available

Abstract

fetched live from OpenAlex

This Science Advisory Report (SAR) summarizes a review of methods that can be used to quantify residual mortality (i.e. the death of fish by means other than fishing) resulting from a Work, Undertaking or Activity (WUA) for fish populations and communities and measures to offset this residual mortality. This advice has focused on decision making for the Fish and Fish Habitat Protection Program (FFHPP) related to fish mortality and productivity. Many of these methods can also be used in mortality situations where there are species at risk, however, the selection of methods and risk profile in these situations may be different. Population-level methods that evaluate the equivalency between the quantification of fish mortality resulting from a WUA, and productivity increases from any associated offsetting (including potential habitat offsets) were reviewed. When data exist, the ‘total biomass lost’ method is recommended for use as it best aligns with the ‘productivity’ considerations of the Fisheries Act. All methods have advantages and limitations, and the selection of an appropriate approach may be case dependent, based on considerations such as data availability, existing models, and the overall objective of the analysis. Consideration of jeopardy under the SARA may require the application of different methods. The calculation of equivalence should acknowledge, assess, and as fully as possible manage, all the sources of uncertainty including uncertainty about impact prediction, the effectiveness of offsetting, and future states of aquatic ecosystems. The potential time-lags and uncertainty related to the delivery and functionality of an offset, as well as uncertainty in the magnitude of harm and efficacy of proposed offsets, can be included within the calculations using time-lag and uncertainty compensation ratios. Compensation ratios in the literature generally range between 1:1.5 and 1:5. Mortality applied to a greater proportion of species in a community and/or an ecosystem context results in a higher likelihood that species in the network will exhibit a negative outcome. Ultimately, the outcome for any species depends on the direct and indirect effects in the network. A series of community-level methods is presented and these should be used in scenario testing when mortality is expected to impact multiple fish species. Simple community models suggest that the productivity of higher trophic-level species tend to be negatively affected by mortality acting at lower trophic levels. These higher trophic level species are often valued fisheries species. When assessing the effects of fish mortality on populations and communities the following factors should be considered: species’ life-history, the size and dynamics of impacted populations, the community and ecosystem composition, the timing, duration, scale, magnitude and mechanism of mortality, and interactions with other mortality sources. The precautionary approach frameworks used to manage fisheries could be used to support risk-based decisions related to WUA-related fish mortality and associated offsets. The use of a common framework by fisheries and fish and fish habitat managers would allow for the leveraging of data and information across different decision-making approaches. Precautionary approach frameworks can be applied in data rich or data limited scenarios. A systematic review of offsetting options and methods for fish mortality events indicated that the current literature is limited on this topic but examples of offsets based on habitat creation, habitat restoration and enhancement, and biological and chemical manipulation (including stocking) have been used in different circumstances. All offsetting methods reviewed have potential benefits and challenges with respect to their implementation and detailed advice on these methods exists elsewhere. With respect to fish mortality, habitat creation has mostly been studied for Salmonid species and generally has been focused on spawning/egg, larval and juvenile life stages. Habitat restoration and enhancement are the most commonly used offsets in cases of fish mortality. Restoration measures often provide benefits to the entire fish community while enhancement measures may be more likely to provide species-specific benefits. Habitat enhancement and restoration provides the most benefits for early life stages. Stocking and nutrient addition have been used to offset for fish mortality in freshwater ecosystems. While there are inherent difficulties with both approaches, they can be used in specific circumstances. Stocking can be used when the fish mortality is direct and not linked to indirect sources. Nutrient enrichment can be an interim tool to offset nutrient deficits and increase overall ecosystem productivity. The reviewed literature indicates no matter what method was used, offsetting projects that had a pre-impact assessment and longer monitoring programs were associated with better assessment of the effectiveness of the offset. This suggests the importance of offset planning and monitoring programs, detailed advice for offsetting program design and monitoring exists. In general, the scale of the models and analysis presented in this advice is usually set at the population level, in applicable situations the scale of analysis can be at a sub-population or local level.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.015
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.003
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2022
Admission routes1
Has abstractyes

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