Seasonal forecasting of tuna habitat in the Great Australian Bight

Eveson, J. Paige; Hobday, Alistair J.; Hartog, Jason R.; Spillman, Claire M.; Rough, Kirsten

doi:10.1016/j.fishres.2015.05.008

Cited by 96 publications

(84 citation statements)

References 32 publications

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“…For example, to predict the spatiotemporal overlap between protected and targeted species, empirical statistical relationships are used to link marine species to their preferred environment, and then predict the distribution of those species based on more widely available oceanographic observations or predictions. In Australian waters this approach has been extended to operational seasonal forecasts using dynamical climate forecast systems, with notable examples for coral reef stress on the Great Barrier Reef (Spillman et al 2013), bycatch reduction for Southern Bluefin Tuna off east Australia (Hobday et al 2011), and improved efficiency of the Southern Bluefin Tuna fishery in the Great Australian Bight (Eveson et al 2015). While similar efforts have not yet been operationalized in the CCS, Kaplan et al (2016) demonstrate one potential application using a sea surface temperature (SST) based habitat model in conjunction with downscaled seasonal forecasts to predict Pacific sardine distributions in the northern CCS.…”

Section: Introductionmentioning

confidence: 99%

On the skill of seasonal sea surface temperature forecasts in the California Current System and its connection to ENSO variability

et al. 2017

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underlie SST predictability. A simple persistence forecast provides considerable skill for lead times up to ~4 months, while skill above persistence is mostly confined to forecasts of late winter/spring and derives primarily from predictable evolution of ENSO-related variability. Specifically, anomalously weak (strong) equatorward winds are skillfully forecast during El Niño (La Niña) events, and drive negative (positive) upwelling anomalies and consequently warm (cold) temperature anomalies. This mechanism prevails during moderate to strong ENSO events, while years of ENSO-neutral conditions are not associated with significant forecast skill in the wind or significant skill above persistence in SST. We find also a strong latitudinal gradient in predictability within the CCS; SST forecast skill is highest off the Washington/Oregon coast and lowest off southern California, consistent with variable wind forcing being the dominant driver of SST predictability. These findings have direct implications for regional downscaling of seasonal forecasts and for short-term management of living marine resources.

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Section: Introductionmentioning

confidence: 99%

On the skill of seasonal sea surface temperature forecasts in the California Current System and its connection to ENSO variability

et al. 2017

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“…Previous work has shown that provision of seasonal forecasts to seafood businesses have led operators to make different decisions on the basis of the forecasts. For example, tuna fishers in the Great Australia Bight adjusted the timing of their fishing activity based on forecasts of environmental conditions in the upcoming season (Eveson et al, 2015), while prawn farmers in Queensland changed their stocking times based on seasonal rainfall forecasts . Thus, seasonal forecasting will be most useful to businesses after the environmental conditions first exceed a threshold (first exceedance time) and before conditions are permanently unsuitable (permanent exceedance time; Figure 3).…”

Section: Benefit Of Seasonal Forecasting As the Climate Changesmentioning

confidence: 99%

“…For example, the southern bluefin tuna (SBT) fishery in the Great Australia Bight currently utilizes seasonal forecasts to plan the timing and location of fishing operations, workforce management, and equipment deployment (Eveson et al, 2015). This fishery is more site-attached than most in that captured fish are towed back to farm sites near Port Lincoln, South Australia, for grow-out in cages (Ellis and Kiessling, 2016).…”

Section: Risk Management For Climate-exposed Seafood Businessesmentioning

confidence: 99%

“…Seasonal forecasting applications in Australia and elsewhere have been developed for a range of marine resource segments, including salmon and prawn aquaculture (Spillman and Hobday, 2014;Spillman et al, 2015), commercial tuna Eveson et al, 2015) and sardine fisheries (Kaplan et al, 2016), and recreational fisheries (Brodie et al, 2017). Depending on the application, these forecasting applications have delivered information on both environmental conditions, such as water temperature, rainfall, and air temperature, and habitat distribution, at lead times of up to 3 months , helping managers and fishers to plan activities based on predicted conditions (Eveson et al, 2015;Spillman et al, 2015). These marine industries are thus in a position to make improved management decisions and perform better than those without information about the future environment.…”

Section: Introductionmentioning

confidence: 99%

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A Framework for Combining Seasonal Forecasts and Climate Projections to Aid Risk Management for Fisheries and Aquaculture

et al. 2018

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A changing climate, in particular a warming ocean, is likely to impact marine industries in a variety of ways. For example, aquaculture businesses may not be able to maintain production in their current location into the future, or area-restricted fisheries may need to follow the fish as they change distribution. Preparation for these potential climate impacts can be improved with information about the future. Such information can support a risk-based management strategy for industries exposed to both short-term environmental variability and long-term change. In southern Australia, adverse climate impacts on valuable seafood industries have occurred, and they are now seeking advice about future environmental conditions. We introduce a decision tree to explain the potential use of long-term climate projections and seasonal forecasts by these industries. Climate projections provide insight into the likely time in the future when current locations will no longer be suitable for growing or catching particular species. Until this time, seasonal forecasting is beneficial in helping industries plan ahead to reduce impacts in poor years and maximize opportunities in good years. Use of seasonal forecasting can extend the period of time in which industries can cope in a location as environmental suitability declines due to climate change. While a range of short-term forecasting approaches exist, including persistence and climatological forecasts, only dynamic model forecasts provide a viable option for managing environmental risk for marine industries in regions where climate change is reducing environmental suitability and creating novel conditions.

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“…Recent advances in environmental forecast model accuracy and species distribution modeling have facilitated a shift in dynamic ocean management techniques, from "reactive" systems, where catches are regularly summarized and reported back to vessels, to "proactive" forecasting systems (Hobday and Hartmann, 2006;Manderson et al, 2011;O'Keefe et al, 2013;Eveson et al, 2015;Lewison et al, 2015). Species distribution models provide the foundation for most proactive dynamic management systems, as species distributions are directly or indirectly related to environmental conditions (Mann, 1993).…”

Section: Introductionmentioning

confidence: 99%

Cooperative Research to Evaluate an Incidental Catch Distribution Forecast

et al. 2017

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Concern over incidental catches in commercial fisheries has been increasing, and while simple mitigation strategies have been effective, few effective mitigation strategies have been established for more complex species interactions. Incidental catches of alewife (Alosa pseudoharengus) and blueback herring (A. aestivalis) in the commercial Atlantic herring (Clupea harengus) fishery have received substantial attention on the Northeast U.S. continental shelf, despite an existing bycatch avoidance program. This study evaluates the utility of existing species distribution forecasts to predict river herring catches in the southern New England small mesh bottom trawl Atlantic herring fishery, with the ultimate goal of incorporating incidental catch forecasts into the bycatch avoidance program. Commercial Atlantic herring bottom trawl vessels assisted with field-based evaluation of alewife, blueback herring, and Atlantic herring species distribution forecast models. Vessels were equipped with conductivity, temperature, and depth probes, and sampling occurred throughout the fishery season (January-March). Locations of expected low and high forecasted incidental catches were sampled, as well as locations the captain expected to find low and high incidental catches. This allowed us to sample within the spatial area the fishery occurs, and to evaluate the forecasted conditions, and predictions, at the spatial scale of the fishery. Catch differences between high and low probability stations were small and variable, as were differences in modeled probability of species presence. No differences were observed between observations at model-predicted stations and captain-selected stations. The sampling provided a better understanding of the potential effectiveness of distribution forecasts for further reducing incidental catches. Existing models have limited use at the spatial scale of this fishery, but could be improved by developing models with fishery-dependent data. Collaborations between researchers, managers, and the Atlantic herring commercial fleet have improved relationships between the groups, and continued collaboration in the development and evaluation of incidental catch reduction tools is key for further reducing incidental catches.

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Seasonal forecasting of tuna habitat in the Great Australian Bight

Cited by 96 publications

References 32 publications

On the skill of seasonal sea surface temperature forecasts in the California Current System and its connection to ENSO variability

On the skill of seasonal sea surface temperature forecasts in the California Current System and its connection to ENSO variability

A Framework for Combining Seasonal Forecasts and Climate Projections to Aid Risk Management for Fisheries and Aquaculture

Cooperative Research to Evaluate an Incidental Catch Distribution Forecast

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