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Ensemble models, statistical analysis and machine learning (ML) can be used to predict novel conditions in a rapidly changing ocean. Traditionally, ML has been understood as a purely data-driven approach. Recently, success has been reported in training ML on both observational and model data to forecast Sea Surface Temperature (SST) anomalies. Here we use ML trained only on climate model simulations to predict regional SST variations, thereby suggesting a novel role for ML as an ensemble model interpolator. We propose a measure of the predictability provided by different ML implementations as well as by standard time series analysis methods. Weighting each forecast by this predictability measure computed on model data only, provides a significant improvement in forecast skill. We demonstrate the performance of this approach for regions around Australia, the Nino3.4 region (central-eastern equatorial Pacific) and in the eastern equatorial Pacific and discuss the implications for SST predictability as a function of geographical location, area size, seasonality, proximity to the coast and model data quality.

Risks to deepwater chondrichthyans (sharks, rays, and chimaeras) from fishing are poorly understood, particularly in areas beyond national jurisdiction. We adapted productivity–susceptibility analysis (PSA) and sustainability assessment for fishing effects (SAFE) to assess the vulnerability of 173 deepwater chondrichthyans to various demersal fishing gears in the Southern Indian and South Pacific Oceans. Several species were categorized as being at high or extreme vulnerability, including some deepwater shark species in the Southern Indian Ocean that are reported to be commercially targeted. There was good concurrence between PSA and SAFE results for species categorized as being at high or extreme vulnerability by the SAFE, but as expected there was an overall greater number assessed to be as higher vulnerability using PSA due to its precautionary nature. Our results indicate probable misclassifications in the PSA relative vulnerability rankings, highlighting the value of applying more quantitative tools, such as SAFE, when adequate data are available. Our findings indicate that better catch, effort, and biological information are needed to inform the assessment and management of deepwater chondrichthyans. If targeted fishing of deepwater shark species continues in the Southern Indian Ocean, improved assessments and estimates of sustainable yields are urgently required to mitigate the risk of overexploitation.

The highly dynamic nature of the marine environment can have a substantial influence on the foraging behaviour and spatial distribution of marine predators, particularly in pelagic marine systems. However, knowledge of the susceptibility of benthic marine predators to environmental variability is limited. This study investigated the influence of local-scale environmental conditions and large-scale climate indices on the spatial distribution and habitat use in the benthic foraging Australian fur seal (Arctocephalus pusillus doriferus; AUFS). Female AUFS provisioning pups were instrumented with GPS or ARGOS platform terminal transmitter tags during the austral winters of 2001–2019 at Kanowna Island, south-eastern Australia. Individuals were most susceptible to changes in the Southern Oscillation Index that measures the strength of the El Niño Southern Oscillation, with larger foraging ranges, greater distances travelled and more dispersed movement associated with 1-yr lagged La Niña-like conditions. Additionally, the total distance travelled was negatively correlated with the current year sea surface temperature and 1-yr lagged Indian Ocean Dipole, and positively correlated with 1-yr lagged chlorophyll-aconcentration. These results suggest that environmental variation may influence the spatial distribution and availability of prey, even within benthic marine systems.

Demonstration of fishery sustainability has expanded from a relatively narrow biological focus to one that includes a wide range of issues in response to environmental legislation, social factors, and demands from markets and consumers. The Healthcheck for Australian Fisheries Sustainability (Healthcheck) is a new initiative designed to be comprehensive with regard to ecological, economic, social and governance aspects, presenting available information about a fishery for easy access and use. Here we report on the framework development process, including engagement with fishery managers, environmental non-government organisations, and fishery participants. All participants emphasized the need for a broad sustainability assessment with timely reporting, easy availability, and wider coverage of seafood sustainability information than is currently accessible, and expressed the importance of trustworthy and transparent information. Differences were found when comparing sustainability issues generally reported and issues of main concern to stakeholders. Subsequent refinement of the Healthcheck extended coverage into issues that are on the horizon for fishery reporting, but may soon be of interest to a wide range of stakeholders.

Understanding the factors which influence foraging behaviour and success in marine mammals is crucial to predicting how their populations may respond to environmental change. The Australian fur seal (Arctocephalus pusillus doriferus, AUFS) is a predominantly benthic forager on the shallow continental shelf of Bass Strait, and represents the greatest biomass of marine predators in south-eastern Australia. The south-east Australian region is experiencing rapid oceanic warming, predicted to lead to substantial alterations in prey diversity, distribution and abundance. In the present study, foraging effort and indices of foraging success and efficiency were investigated in 138 adult female AUFS (970 foraging trips) during the winters of 1998–2019. Large scale climate conditions had a strong influence on foraging effort, foraging success and efficiency. Foraging effort and foraging success were also strongly influenced by winter chlorophyll-a concentrations and sea-surface height anomalies in Bass Strait. The results suggest increasing foraging effort and decreasing foraging success and efficiency under anticipated environmental conditions, which may have population-level impacts.

Large numbers of juvenile southern bluefin tuna (SBT; Thunnus maccoyii) migrate into the warm shelf waters of the Great Australian Bight (GAB) each austral summer. Whilst in the GAB, they aggregate in schools that spend substantial periods in the surface layer of the water column. In this study we investigate biological, temporal and environmental factors influencing this surfacing phenomena using an extensive archival tagging dataset collected between 1998 and 2011. High frequency data on the vertical movement of SBT collected by these tags were used to calculate the proportion of time fish spent in the shallowest 20 m during each day and night period. Estimates of fish location derived from light sensor data on the tags allowed us to investigate the influence that local environmental conditions had on a fish's surfacing behaviour. Although there is high variability in surfacing behaviour within and between individuals, some general patterns emerge. There are clear diel differences in surfacing, with the proportion of time fish spend at the surface tending to be high during most days and either very high (>90%) or very low (

Large scale migrations are a key component of the life history of many marine species. We quantified the annual migration cycle of juvenile southern bluefin tuna (Thunnus maccoyii; SBT) and spatiotemporal variability in this cycle, based on a multi-decadal electronic tagging dataset. Behaviour-switching models allowed for the identification of cohesive areas of residency and classified the temporal sequence of movements within a migration cycle from austral summer foraging grounds in the Great Australian Bight (GAB) to winter foraging grounds in the Indian Ocean and Tasman Sea and back to the GAB. Although specific regions within the Indian Ocean were frequented, individuals did not always return to the same area in consecutive years. Outward migrations from the GAB were typically longer than return migrations back to the GAB. The timing of individual arrivals to the GAB, which may be driven by seasonality in prey availability, was more cohesive than the timing of departures from the GAB, which may be subject to the physiological condition of SBT. A valuable fishery for SBT operates in the GAB, as do a number of scientific research programs designed to monitor SBT for management purposes; thus, understanding SBT migration to and from the area is of high importance to a number of stakeholders.

While ecologists have long recognized the influence of spatial resolution on species distribution models (SDMs), they have given relatively little attention to the influence of temporal resolution. Considering temporal resolutions is critical in distribution modelling of highly mobile marine animals, as they interact with dynamic oceanographic processes that vary at time-scales from seconds to decades. We guide ecologists in selecting temporal resolutions that best match ecological questions and ecosystems, and managers in applying these models. We group the temporal resolutions of environmental variables used in SDMs into three classes: instantaneous, contemporaneous and climatological. We posit that animal associations with fine-scale and ephemeral features are best modelled with instantaneous covariates. Associations with large scale and persistent oceanographic features are best modelled with climatological covariates. Associations with mesoscale features are best modelled with instantaneous or contemporaneous covariates if ephemeral processes are present or interannual variability occurs, and climatological covariates if seasonal processes dominate and interannual variability is weak.

Seasonal forecasting of environmental conditions and marine species distribution has been used as a decision support tool in commercial and aquaculture fisheries. These tools may also be applicable to species targeted by the recreational fisheries sector, a sector that is increasing its use of marine resources, and making important economic and social contributions to coastal communities around the world. Here, a seasonal forecast of the habitat and density of dolphinfish (Coryphaena hippurus), based on sea surface temperatures, was developed for the east coast of New South Wales (NSW), Australia. Two prototype forecast products were created; geographic spatial forecasts of dolphinfish habitat and a latitudinal summary identifying the location of fish density peaks. The less detailed latitudinal summary was created to limit the resolution of habitat information to prevent potential resource over-exploitation by fishers in the absence of total catch controls. The forecast dolphinfish habitat model was accurate at the start of the annual dolphinfish migration in NSW (December) but other months (January - May) showed poor performance due to spatial and temporal variability in the catch data used in model validation. Habitat forecasts for December were useful up to five months ahead, with performance decreasing as forecast were made further into the future. The continued development and sound application of seasonal forecasts will help fishery industries cope with future uncertainty and promote dynamic and sustainable marine resource management.

Advances in ocean observing technologies and modeling provide the capacity to revolutionize the management of living marine resources. While traditional fisheries management approaches like single-species stock assessments are still common, a global effort is underway to adopt ecosystem-based fisheries management (EBFM) approaches. These approaches consider changes in the physical environment and interactions between ecosystem elements, including human uses, holistically. For example, integrated ecosystem assessments aim to synthesize a suite of observations (physical, biological, socioeconomic) and modeling platforms [ocean circulation models, ecological models, short-term forecasts, management strategy evaluations (MSEs)] to assess the current status and recent and future trends of ecosystem components. This information provides guidance for better management strategies. A common thread in EBFM approaches is the need for high-quality observations of ocean conditions, at scales that resolve critical physical-biological processes and are timely for management needs. Here we explore options for a future observing system that meets the needs of EBFM by (i) identifying observing needs for different user groups, (ii) reviewing relevant datasets and existing technologies, (iii) showcasing regional case studies, and (iv) recommending observational approaches required to implement EBFM. We recommend linking ocean observing within the context of Global Ocean Observing System (GOOS) and other regional ocean observing efforts with fisheries observations, new forecasting methods, and capacity development, in a comprehensive ocean observing framework.

Summary Electronic tags have revolutionised animal movement studies, but the reliability of subsequent ecological inference hinges on being able to quantify uncertainty in location estimates. Light-based geolocation, which uses the time series of light intensity during twilight events, remains the only viable technology for many species (e.g. fish and small birds) despite its limited accuracy. Modern approaches to movement modelling, such as Kalman filters and gridded hidden Markov models, require a valid likelihood for each observed twilight. It is difficult to directly construct such a likelihood (i.e. the probability density of the light data during a twilight period) given any location on the globe, because of complicated autocorrelation structures and non-standard statistical distributions. We therefore use data from moored tags at known locations to construct a transformation that turns a simple one-dimensional statistic into a quantity with the properties of a log-likelihood. The result is a set of calibration splines that can be used with light data from a similar tag deployed on a real animal: for each twilight, the one-dimensional statistic is calculated for any location (e.g. on a grid of, or all possible, locations) and then transformed into a likelihood using the calibration splines. The likelihoods can then be input to any state-space model to estimate a track. We show an example track from a grid-based hidden Markov model applied to light data from a tag deployed on a southern bluefin tuna. This approach to light-based geolocation provides the flexibility to integrate movement and behaviour modelling in a novel way. The likelihood surfaces from our approach can be used in any state-space model of animal movement and behaviour, irrespective of whether estimation is by maximum-likelihood or Bayesian methods. Our approach is primarily aimed at users interested in developing and fitting their own state-space models to explore biological hypotheses about animal behaviour.

Changes over the scale of decades in oceanic environments present a range of challenges for management and utilisation of ocean resources. Here we investigate sources of global temporal variation in Sea Surface Temperature (SST) and Ocean Colour (Chl-a) and their co-variation, over a 14 year period using statistical methodologies that partition sources of variation into inter-annual and annual components and explicitly account for daily auto-correlation. The variation in SST shows bands of increasing variability with increasing latitude, while the analysis of annual variability in Chl-a shows mostly mid-latitude high variability bands. Covariation patterns of SST and Chl-a suggests several different mechanisms impacting Chl-a change and variance. Our high spatial resolution analysis indicates these are likely to be operating at relatively small spatial scales. There are large regions showing warming and rising of Chl-a, contrasting with regions that show warming and decreasing Chl-a. The covariation pattern in annual variation in SST and Chl-a reveals broad latitudinal bands. On smaller scales there are significant regional anomalies where upwellings are known to occur. Over decadal time scales both trend and variation in SST, Chl-a and their covariance is highly spatially heterogeneous, indicating that monitoring and resource management must be regionally appropriate.

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.

Commercial fishing involves locating fish in a variable environment, and a fisher's historical experience with environmental conditions and the influence on fish distribution underpins their economic efficiency. However, in many regions, changing environments are reducing the utility of this experience. In the Great Australian Bight, recent environmental changes have modified the summer distribution of southern bluefin tuna (SBT, Thunnus maccoyii). This has affected the timing and location of fishing activity and contributed to economic impacts, at the same time as international competition is lowering value of the catch. The SBT purse-seine fishery is managed under a strict quota, so catching more fish is not an option to reduce fixed costs; instead fish must be caught more efficiently in a changing environment. Following discussion with industry stakeholders, we developed a seasonal forecast system based on a three stage process. We first assessed needs through discussions with industry. We then developed a SBT habitat forecast system based on a seasonal environmental forecasting model (POAMA: the Predictive Ocean Atmosphere Model for Australia) coupled with a habitat preference model for SBT (developed using data from tagged fish). Based on a historical evaluation of the environmental forecasting model and the habitat model, we expect temperature-based habitat forecasts to have useful skill up to two months into the future during the months of interest. The final stage involved forecast delivery via an industry-specific website and engagement with stakeholders, which led to improved presentation and contextualization of the forecasts. The forecasts, which are updated daily, are now being used by SBT fishers and have proven a useful aid in their decision-making.

A wide range of aquaculture industries are exposed to environmental conditions and variability which can impact on production. Information about future climate conditions on a range of time scales can improve risk management, buffer production against unfavourable environmental conditions, and allow maximised production during opportune times. Seasonal forecasting, providing information beyond weather forecasting up to several months into the future, can aid such decision making. In north-east Australian coastal pond-based prawn farms environmental stresses influence timing of farming periods, animal growth and survival. Here we describe the development, packaging and provision of regional and local forecast products, derived from the Predictive Ocean Atmosphere Model for Australia (POAMA; version 2), to the Queensland prawn industry. Product development followed a three-stage process; (i) assessment of management needs and critical timescales, (ii) development and evaluation of the forecast products, and (iii) forecast implementation and refinement of the forecast products. Prawn farm managers identified the critical forecast variables as minimum and maximum air temperatures and rainfall at lead times of up to 2–3 months. The POAMA forecast skill for all three variables was evaluated, with three spatially averaged regional environmental indexes derived from POAMA showing a strong relationship with large-scale observed conditions. Forecast accuracy was assessed using model hindcast data together with historical observations, and was similar in all three regions, higher for temperature than rainfall, and declined with lead time in all cases. Forecast indices were then scaled using local weather station information for a subset of prawn farms in the study. Discussion with prawn farm managers helped refine the format and visualisation of the forecasts. Tailored forecast packages were then delivered through a web-based system and directly by email. These forecasts could aid a range of management decisions, and user feedback led to further refinement. This approach has great potential to be extended to other coastal aquaculture industries using these and other environmental variables. Information about future conditions, such as provided by seasonal forecasting, can assist aquaculture managers in development of production plans that will be more robust to short-term environmental variability and represents the first adaptation step on a pathway to coping with longer term climate change.

Identifying essential biological variables in marine ecosystems is harder than essential ocean variables because choices about the latter are guided by the needs of global oceanic models, and the number of candidate variables to choose from is much smaller. We present a process designed to assist managers identify biological indicators and essential variables for marine ecosystems, and demonstrate its application to Australia's Exclusive Economic Zone. The process begins with a spatially explicit description of key ecological systems and predicts how these systems are impacted by anthropogenic pressures. The process does not require experts to agree on the system's structure or the activities that threaten the ecosystem. Rather it defines a suite of pressure scenarios that accommodate uncertainty in these aspects, and seeks to identify indicators that are predicted to respond in a consistent fashion across these scenarios. When the process is applied at national or regional scales, essential biological variables emerge as the set of consistent indicators that are common to similar but spatially distinct systems.

Dynamic ocean management, or management that uses near real-time data to guide the spatial distribution of commercial activities, is an emerging approach to balance ocean resource use and conservation. Employing a wide range of data types, dynamic ocean management can be used to meet multiple objectives—for example, managing target quota, bycatch reduction, and reducing interactions with species of conservation concern. Here, we present several prominent examples of dynamic ocean management that highlight the utility, achievements, challenges, and potential of this approach. Regulatory frameworks and incentive structures, stakeholder participation, and technological applications that align with user capabilities are identified as key ingredients to support successful implementation. By addressing the variability inherent in ocean systems, dynamic ocean management represents a new approach to tackle the pressing challenges of managing a fluid and complex environment.

Primary environmental variables, such as sea surface temperature, wind speed, and chlorophyll, have been used widely in a variety of studies by biological oceanographers to explore the relationship between "physics" and, say, distribution and abundance of marine organisms. Fisheries scientists in particular have explored a range of relationships between physics and catch data to understand fish distribution and fishing impacts. The explanatory power of models based on such primary variables is typically limited and may not lead to insight into mechanisms behind the environmental associations. Variables that are more direct measures of habitat, such as thermal fronts, upwelling zones, eddies, and water column descriptors (e.g., mixed layer depth), may yield additional explanatory power. We have developed a suite of these derived variables and demonstrate their utility using examples from Australian fisheries and marine spatial planning. Refinement and access to derived variables may be useful in a range of applications, including catch standardization, habitat prediction, ecosystem models, spatial management, and harvest strategies, and will play an important role in the emerging area of dynamic ocean management.

Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.

Capture of the target, bycatch, and protected species in fisheries is often regulated through spatial measures that partition fishing effort, including areal closures. In eastern Australian waters, southern bluefin tuna (SBT, Thunnus maccoyii ) are a quota-limited species in a multispecies longline fishery; minimizing capture by nonquota holders is an important management concern. A habitat preference model (conditioned with electronic tag data) coupled with ocean reanalysis data has been used since 2003 to generate real-time predicted maps of SBT distribution (nowcasts). These maps are used by fishery managers to restrict fisher access to areas with high predicted SBT distribution. Here we use the coupled ocean–atmosphere model, POAMA (predictive ocean atmosphere model for Australia), and a habitat model to forecast SBT distribution at lead times of up to 4 months. These forecasts are comparable with nowcasts derived from the operational system, and show skill in predicting SBT habitat boundaries out to lead-times of 3–4 months. For this fishery, seasonal forecasts can provide managers and fishers with valuable insights into future habitat distributions for the upcoming months, to better inform operational decisions.

Southern bluefin tuna (SBT) are presently a quota-managed species in the multi-species eastern Australian tuna and billfish longline fishery (ETBF). Capture of SBT is regulated by quota, as is access to regions likely to contain SBT. A habitat prediction model combining data from an ocean model and pop-up satellite archival tags is used to define habitat zones based on the probability of SBT occurrence. These habitat zones are used by fishery managers to restrict access by ETBF fishers to SBT habitat during a May-November management season. The zones display a distinct seasonal cycle driven by the seasonal southward expansion and northward contraction of the East Australia Current (EAC) and as a result access by fishers to particular ocean regions changes seasonally. This species also overlaps with the commercially valuable yellowfin tuna (YFT), thus, we modified the SBT model to generate YFT habitat predictions in order to investigate habitat overlap between SBT and YFT. There is seasonal variation in the overlap of the core habitat between these two species, with overlap early (May-Jul) in the management season and habitat separation occurring towards the end (Aug-Nov). The EAC is one of the fastest warming ocean regions in the southern hemisphere. To consider the future change in distribution of these two species compared to the present and to explore the potential impact on fishers and managers of the future, we use future ocean predictions from the CSIRO Bluelink ocean model for the year 2064 to generate habitat predictions. As the ocean warms on the east coast of Australia and the EAC extends southward, our model predicts the suitable habitat for SBT and YFT will move further south. There was an increase in the overlap of SBT and YFT habitat throughout the management season, due to regional variation of each species' habitat. These results illustrate that a management tradeoff exists between restricting fisher access to SBT habitat and allowing access to YFT habitat. We suggest that some options to address this tradeoff are possible by identifying the seasonal variability of the overlap.

The poleward flowing East Australian Current (EAC) is characterised by its separation from the coast, 100-200 nautical miles north of Sydney, to form the eastward flowing Tasman Front and a southward flowing eddy field. The separation zone greatly influences coastal ecosystems for the relatively narrow continental shelf (only 15-50. km wide), particularly between 32-34°S. In this region the continental shelf has a marked shift in the seasonal temperature-salinity relationship and elevated surface nitrate concentrations. This current parallels the portion of the coast where Australia's population is concentrated and has a long history of scientific research. However, understanding of physical and biological processes driven by the EAC, particularly in linking circulation to ecosystems, is limited. In this special issue of 16 papers on the EAC, we examine the effects of climatic wind-stress forced ocean dynamics on EAC transport variability and coastal sea level, from ENSO to multi-decadal time scales; eddy formation and structure; fine scale connectivity and larval retention. Comparisons with the poleward-flowing Leeuwin Current on Australia's west coast show differences in ecosystem productivity that can be attributed to the underlying physics in each region. On average there is double the chlorophyll a concentration on the east coast than the west. In comparison to the Leeuwin, the EAC may have less local retention of larvae and act as a partial barrier to onshore transport, which may also be related to the local spawning and early life history of small pelagic fish on each coast. Inter-annual variations in the EAC transport produce a detectable sea-level signal in Sydney Harbour, which could provide a useful fisheries index as does the Fremantle sea level and Leeuwin Current relationship. The EAC's eddy structure and formation by the EAC are examined. A particular cold-core eddy is shown to have a "tilt" towards the coast, and that during a rotation the flow of particles may rise up to the euphotic zone and then down beneath. In a warm-core eddy, surface flooding is shown to produce a new shallower surface mixed layer and promote algal growth. An assessment of plankton data from 1938-1942 showed that the local, synoptic conditions had to be incorporated before any comparison with the present. There are useful relationships of water mass characteristics in the Tasman Sea and separation zone with larval fish diversity and abundance, as well as with long-line fisheries. These fisheries-pelagic habitat relationships are invaluable for fisheries management, as well as for climate change assessments. There is further need to examine the EAC influence on rainfall, storm activity, dust deposition, and on the movements by fish, sharks and whales. The Australian Integrated Marine Observing System (IMOS) has provided new infrastructure to determine the changing behaviour of the EAC and its bio-physical interaction with the coasts and estuaries. The forecasting and hindcasting capability developed under the Bluelink project has provided a new tool for data synthesis and dynamical analysis. The impact of a strengthening EAC and how it influences the livelihoods of over half the Australian population, from Brisbane to Sydney, Hobart and Melbourne, is just being realised. © 2010.

Reduction of bycatch through spatial management is one possible approach in fisheries management. One requirement is description of the habitat of the species in question, and this is now possible for a range of species using data collected from electronic tags. Here we report on a spatial management approach used to limit unwanted capture of southern bluefin tuna (SBT, Thunnus maccoyii) in the eastern Australia longline fishery. The approach combines a habitat model conditioned with temperature preference data from 56 pop-up satellite archival tags deployed on SBT, and an ocean model to produce near real-time habitat predictions utilized by fishery managers during the fishing season. Three habitat types based on probability of SBT occurrence are identified in the fishing region, and spatial zoning by managers is used to regulate fisher access to these zones. This management approach has been used since 2003, and despite dynamic spatial management often being resisted by fishers or managers because of perceived implementation complexity, there has been an increase in complexity of the management actions over the 6-yr period. Managers now request more habitat predictions each season, update the spatial zoning more frequently, and apply more complicated management lines dividing the habitat types. This has resulted in an improved fit to the modeled habitat predictions, and as catch per unit effort (CPUE) of SBT is related to the habitat type, has contributed to the overall success of the management approach. This implemented management approach illustrates that managers and fishers can successfully implement complex dynamic spatial zoning.

Recent advances in electronic tag technology have resulted in an explosion of data for marine biologists. Providing descriptions of data management systems and discussion of their strengths and weaknesses will be important in promoting a dialogue with the ultimate goal of building better systems to support research. The importance of this will only increase as large multinational, multi-institutional studies become more common. Modern memory components permit a single archival tag to collect many megabytes of data. Effective handling of the volume of data generated by multiple tag deployments is a major challenge, and an essential step before data analysis can be performed. Tags are deployed on multiple species and a single animal may carry several different tag types. Data handling systems must be flexible enough to accommodate the variety of ways tags are used, as well as the changes to tag specifications over time. This paper describes a relational database system that has been developed to meet these requirements. This database stores data from a variety of electronic tags from different manufacturers, including archival tags, satellite tags and acoustic tags. The data are downloaded, processed and stored automatically where possible. Centralising data storage allows flexibility in data access, quality control, exploration and analysis. Software programs allow access to the data from local servers or via the internet and include initial visualization of animal tracks via a mapping system. A suite of environmental data and products can also be matched to the selected track(s), which aids initial analyses and development and refinement of scientific hypotheses.

We tested the ability of archival tags and their associated algorithms to estimate geographical position based on ambient light intensity by attaching six tags (three tags each from Northwest Marine Technologies [NMT] and Wildlife Computers [WC]) at different depths to a stationary mooring line in the Pacific Ocean (approx. 166°42′W, 24°00′N), for approximately one year (29-Aug-98 to 16-Aug-99). Upon retrieval, one tag each from the two vendors had malfunctioned: from these no data (NMT) or only partial data (WC) could be downloaded. An algorithm onboard the NMT tag automatically calculated geographical positions. For the WC tags, three different algorithms were used to estimate geographical positions from the recorded light intensity data. Estimates of longitude from all tags were significantly less variable than those for latitude. The mean absolute error for longitude estimates from the NMT tags ranged from 0.29 to 0.35°, and for the WC tags from 0.13 to 0.25°. The mean absolute error in latitude estimates from the NMT tags ranged from 1.5 to 5.5°, and for the WC tags from 0.78 to 3.50°. Ambient weather conditions and water clarity will obviously introduce errors into any geoposition algorithm based on light intensity. We show that by applying objective criteria to light level data, outliers can be removed and the variability of geographical position estimates reduced. We conclude that, although archival tags are suitable for questions of ocean basin-scale movements, they are not well suited for studies of daily fine scale movement patterns because of the likely magnitude of position estimate errors. For studies of fine scale movements in relation to specific oceanographic conditions, forage densities and distance scales of 100 km or less, other methods (e.g. acoustic tracking) remain the tool of choice.

Using archival tags implanted in the body cavity of juvenile southern bluefin tuna (SBT) being held in fattening cages, we examine the effect of varying ration (of clupeoid bait fish) on the patterns and extent of warming in the viscera. Our experiments were run in summer and winter to examine the effect of ambient water temperature on the relationship between ration and visceral warming. In both seasons, we found strong relationships between the amount of food eaten and the extent of visceral warming. For clupeoid prey and the size of fish we worked with, the relationships make it possible to predict ration and intake from various measures of visceral warming calculated from archival tag data. The time taken for the viscera to reach maximum heat increment seems the easiest and most robust measure for prediction. Our three year old SBT maintained a constant differential of 2–3°C between their basal visceral temperature and ambient water temperature throughout a 6–7°C seasonal change in water temperature. For a given ration, the extent of warming of the viscera during digestion relative to the basal visceral temperature was significantly higher in winter than in summer, indicating either improved heat conservation and/or higher aerobic metabolic rates in winter. Despite the greater warming, viscera were still significantly cooler during digestion in winter than in summer. Even though visceral temperatures were cooler in winter than in summer, rates of digestion were very similar in the two seasons. Lipid content of the food varied between seasons and may account for a slowing of digestion in summer. However, it is unlikely that this accounts for the apparent lack of a thermal effect on digestion rates. We hypothesise that this is facilitated through positive thermal compensation during winter, through the activation of cold adapted isozymes in the digestive system.