Disentangling the climate change impacts on productivity and availability for Macruronus species
Bibliographic record
Abstract
Fish populations are impacted by a variety of processes including physical (temperature, salinity, currents), biological (growth, mortality) and human impacts (harvest). Most fish species exhibit a pre-recruitment or larval phase that is impacted by stochastic environmental factors which results in year to year variability in biomass. Importantly overlying annual recruitment can be long term trends, such as climate change, which require different approaches to management. Understanding the drivers for recruitment variability, especially the larval phase, provides an opportunity to build these relationships into traditional fisheries models to improve stock assessment and provide greater confidence to future projections. A crucial need for the appropriate management of marine resources is to have projections of sustainable harvests. For fisheries management, information to determine the limit and target catch levels are necessary to ensure a sustainable fish stock and industry. This thesis investigated recruitment variability in fisheries. The first data chapter (Chapter 2) explores commonality in recruitment patterns in a range of Southern Hemisphere commercially fished species to determine any common recruitment signals. We tested three climate indices: the Inter-decadal Pacific Oscillation (IPO), Southern Annular Mode (SAM) and Southern Oscillation Index (SOI) to explore their relationship with fish stock recruitment patterns. The time-series of IPO and SOI showed the strongest correlation with New Zealand hoki (blue grenadier) and Australian jackass morwong (r= 0.50 and r= -0.50), and SAM was positively related to Australian Macquarie Island Patagonian toothfish (r= 0.49). This chapter has been published in the Canadian Journal of Marine and Fisheries Science as Coincident recruitment patterns of southern hemisphere fishes‚ÄövÑvp (73(2): 270-278, 10.1139/cjfas 2015-0069). In Chapter 3 I focused on fine scale recruitment patterns at the level of a single species focusing on the Patagonian grenadier fishery. The Patagonian grenadier stock in Chile appears to have undergone rapid biomass depletion related to changes in recruitment and thus provided an ideal case study to investigate fine scale recruitment changes in a fishery. To be able to evaluate the impact of future recruitment scenarios on future catches of Patagonian grenadier, I developed an agestructured Stock Synthesis assessment model using data from 1985 to 2013 (Chapter 3). The Stock Synthesis assessment modelling platform allows the use of all the data available for the fishery including different fleets operating across different spatial regions and during different years. The model was validated against the existing stock assessment model and sensitivity testing demonstrate it is suitable in describing the status of the stock. The stock synthesis model was then used to evaluate changes in recruitment that had been documented in the fishery. I compared two scenarios: Scenario 1 is the standard assessment approach that uses the entire recruitment data from 1985 and treats recruitment as a stationary variable that assumes recruitment varies around an equilibrium trend. Scenario 2 is a two-phased stock recruitment curve that splits the recruitment time series into two distinct periods with differing productivity, associated with a climate change induced regime shift in the late 1990s. Using the assessment model developed in Chapter 3, I evaluated different management strategy scenarios for future sustainability of the Chilean Patagonian grenadier fishery and compared different future outcomes based on whether a shift in stock recruitment had or had not occurred (Chapter 4). A Management Strategy Evaluation (MSE) procedure was implemented to examine the consequences of incorporating a different model structure for recruitment values in the assessment which underpins the harvest strategy used to set the annual total allowable catch (TAC). A management strategy that does not consider a shift in recruitment resulted in average TAC values of approximately 125,000 tonnes, substantially above the sustainable yield of 45,000 tonnes when the recruitment shift was incorporated. A TAC based upon No Shift in recruitment would lead to unsustainable catches with significant impact on the ecosystem as well as the industry and coastal communities reliant on the industry if there was an actual shift in recruitment. The history of the Patagonian grenadier fishery demonstrates the benefits of taking a precautionary approach that accounts for the change in fish productivity (whether climate-driven or otherwise). However, there can be considerable delays before a regime shift is observed in the recruitment data or an assessment model mis-specification is detected.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".