Background Non-communicable diseases (NCDs) are a major threat to health and development and account for 75% of deaths in the Pacific Islands Countries and Territories (PICTs). Childhood obesity has been identified as a main risk factor for NCDs later in life. This review compiled overweight and obesity (OWOB) prevalence (anthropometric data) for children aged six to 12 years old living in the Pacific region and identified possible related causes. Methods We conducted a systematic search using PubMed, Google Scholar and ScienceDirect for articles published between January 1980 and August 2022. We also searched for technical reports from Ministries of Health. Guided by the eligibility criteria, two authors independently read the selected articles and reports to extract and summarise relevant information related to overweight and obesity. Results We selected 25 articles, two worldwide analyses of population-based studies and four national reports. Information revealed that childhood OWOB prevalence reached 55% in some PICTs. This review also indicated that age, gender and ethnicity were linked to children’s weight status, while dietary practices, sleep time and level of physical activity played a role in OWOB development, as well as the living environment (socio-economic status and food availability), parenting practices and education level. Conclusion This review highlighted that anthropometric data are limited and that comparisons are difficult due to the paucity of surveys and non-standardized methodology. Main causes of overweight and obesity are attributed to individual characteristics of children and behavioural patterns, children’s socio-economic environment, parenting practices and educational level. Reinforcement of surveillance with standardised tools and metrics adapted to the Pacific region is crucial and further research is warranted to better understand root causes of childhood OWOB in the Pacific islands. More robust and standardized anthropometric data would enable improvements in national strategies, multisectoral responses and innovative interventions to prevent and control NCDs.
Background: Cardiovascular disease (CVD) and diabetes mellitus are major health issues in Tonga and other Pacific countries, although mortality levels and trends are unclear. We assess the impacts of cause-of-death certification on coding of CVD and diabetes as underlying causes of death (UCoD). Methods: Tongan records containing cause-of-death data (2001–2018), including medical certificates of cause-of-death (MCCD), were assigned an UCoD using International Classification of Diseases 10th revision (ICD-10) coding. Deaths without recorded cause were included to ascertain total mortality. Alternative UCoD were assigned following reallocation of diabetes and hypertension from Part 1 of the MCCD (direct cause) to Part 2 (contributory cause) if potentially fatal complications were not recorded. CVD and diabetes mortality estimated using alternative certification was compared with estimates using unaltered certification. Results: ver 2001–18, in ages 35-59 years, alternative CVD mortality was higher than unaltered CVD mortality in men (p=0.043) and women (p=0.15); for 2010-18, alternative versus unaltered measures in men were 3.3/103 (95%CI: 3.0-3.7/10³) versus 2.9/10³ (95%CI: 2.6-3.2/10³), and in women were 1.1/10³ (95%CI: 0.9-1.3/10³) versus 0.9/10³ (95%CI: 0.8-1.1/10³). Conversely, alternative diabetes mortality rates were significantly lower than the unaltered rates over 2001–18 in men (p<0.0001) and women (p=0.013); for 2010-18, these measures in men were 1.3/10³ (95%CI: 1.1-1.5/10³) versus 1.9/10³ (95%CI: 1.6-2.2/10³), and in women were 1.4/10³ (95%CI: 1.2-1.7/10³) versus 1.7/10³ (95%CI: 1.5-2.0/10³). Diabetes mortality rates increased significantly over 2001–18 in men (unaltered: p<0.0001; alternative: p=0.0007) and increased overall in women (unaltered: p=0.0015; alternative: p=0.014). Conclusions: Diabetes reporting in Part 1 of the MCCD, without potentially fatal diabetes complications, has led to over-estimation of diabetes, and under-estimation of CVD, as UCoD in Tonga. This indicates the importance of controlling various modifiable risks for atherosclerotic CVD (including stroke) including hypertension, tobacco use, and saturated fat intake, besides obesity and diabetes. Accurate certification of diabetes as a direct cause of death (Part 1) or contributory factor (Part 2) is needed to ensure that valid UCoD are assigned. Examination of multiple cause-of-death data can improve understanding of the underlying causes of premature mortality to better inform health planning.
Robust prediction of population responses to changing environments requires the integration of factors controlling population dynamics with processes affecting distribution. This is true everywhere but especially in polar pelagic environments. Biological cycles for many polar species are synchronised to extreme seasonality, while their distributions may be influenced by both the prevailing oceanic circulation and sea-ice distribution. Antarctic krill (krill, Euphausia superba) is one such species exhibiting a complex life history that is finely tuned to the extreme seasonality of the Southern Ocean. Dependencies on the timing of optimal seasonal conditions have led to concerns over the effects of future climate on krill’s population status, particularly given the species’ important role within Southern Ocean ecosystems. Under a changing climate, established correlations between environment and species may breakdown. Developing the capacity for predicting krill responses to climate change therefore requires methods that can explicitly consider the interplay between life history, biological conditions, and transport. The Spatial Ecosystem And Population Dynamics Model (SEAPODYM) is one such framework that integrates population and general circulation modelling to simulate the spatial dynamics of key organisms. Here, we describe a modification to SEAPODYM, creating a novel model – KRILLPODYM – that generates spatially resolved estimates of krill biomass and demographics. This new model consists of three major components: (1) an age-structured population consisting of five key life stages, each with multiple age classes, which undergo age-dependent growth and mortality, (2) six key habitats that mediate the production of larvae and life stage survival, and (3) spatial dynamics driven by both the underlying circulation of ocean currents and advection of sea-ice. We present the first results of KRILLPODYM, using published deterministic functions of population processes and habitat suitability rules. Initialising from a non-informative uniform density across the Southern Ocean our model independently develops a circumpolar population distribution of krill that approximates observations. The model framework lends itself to applied experiments aimed at resolving key population parameters, life-stage specific habitat requirements, and dominant transport regimes, ultimately informing sustainable fishery management.
Minimising the unintended capture of fish, marine mammals, reptiles, seabirds and other marine organisms is an important component of responsible fisheries management and for stabilising declines and rebuilding populations of threatened species. The analyses presented were designed to establish the first quantitative baseline of historical catches, catch rates and species composition for the dominant tuna fisheries operating in the western and central Pacific, the world's largest in terms of tuna catch. Using records from 612,148 fishing events collected by independent ‘at sea’ observers, estimates for finfish, billfish, elasmobranchs, marine mammals and sea turtles show that the composition and magnitude of catches varied considerably by fishery type and practice for the period 2003–2019. Simulations indicated that precision in longline estimates would be improved by monitoring a proportion of fishing sets from all fishing trips rather than full coverage from a proportion of all fishing trips. While attributing reasons for temporal trends in estimated bycatch was difficult due to the confounding impacts of changing abundances and fishing practices, the trends identified the nature of potential relationships for species that are not accurately quantified, or not covered, by fishing vessel logbooks. The trends in catch estimates, and the catch rate models, have utility in identifying species which may require targeted additional analyses and management interventions, including species of conservation interest (either due to their threatened status or vulnerability to fishing) such as elasmobranchs and sea turtles. Moreover, the estimates should support future evaluations of the impact of these industrial‐scale fisheries on bycatch species.
Waves are one of the main causes of coastal flooding and shoreline change in low‐lying atoll nations like Tuvalu. A detailed understanding of Tuvalu’s wave climate is therefore critical for decision‐makers, coastal engineers, and disaster‐risk managers. Here, we investigate Tuvalu’s wind‐wave climate, changes due to large‐scale climate variability, and long‐term trends. A 44‐year (1979–2022) high‐resolution wave hindcast was developed using the unstructured version of the wave model Simulating Waves Nearshore (SWAN). The model resolution varied between 20 km offshore in deep water and 300 m close to shore. The model was forfed with the European Centre for Medium‐Range Weather Forecasts Reanalysis v5 (ERA5) wind and boundary wave conditions. Northern and Southern Hemisphere winter months produced the largest and most powerful waves. Through the analysis of the directional wave energy spectra, we identified three main wave energy sources: (a) extratropical storms in the Southern Ocean; (b) extratropical storms in the North Pacific; and (c) easterly trade winds. Slightly positive trends in high‐frequency (∼10 s) wave energy from the east and low‐frequency (∼15 s) wave energy from the southwest were linked to an intensification of trade winds and an intensification and poleward displacement of the Southern Ocean storm belt over recent decades. The interannual variability of Tuvalu’s wave climate was strongly linked to large‐scale climate modes such as El Niño Southern Oscillation, Pacific Decadal Oscillation, and Arctic and Antarctic Oscillation. The study builds on previous research and significantly enhances the understanding of Tuvalu’s wave climate variability.
The Pacific region is experiencing a non-communicable disease epidemic largely driven by an ongoing nutrition transition from nutrient rich whole foods to imported staples and highly processed foods. Food trade is a major driver of this transition. We characterize regional and sub-regional trade from 1995 to 2018 for 18 Pacific Island Countries and Territories using the Pacific Food Trade Database. The analysis shows the growing dependence on imports of rice from South-East Asia and wheat from Australia, and recent growth in imports of meat from Australia, New Zealand and USA, and highly processed foods from South-East Asia. Findings are discussed in terms of policy and trade agreements, and global shocks including COVID-19 and the war in Ukraine.
1. Substantial global population declines in pelagic sharks have led to the introduction of management and conservation measures, including gear restrictions and no-retention policies, to curb declines and encourage stock recovery. As the rate of discarding sharks increases, there is a growing need to understand prognostic factors that influence their post-release survival (PRS) outcomes. 2. PRS was measured with survival pop-up satellite archival tags attached to shortfin mako (Isurus oxyrinchus) and silky sharks (Carcharhinus falciformis) released or discarded from pelagic tuna longline fishing vessels operating in the Western and Central Pacific Fisheries Commission Convention Area. In total, 117 tags were deployed on 60 mako and 57 silky sharks captured as bycatch during commercial pelagic longline fishing trips in New Zealand (n = 35), Fiji (n = 58), New Caledonia (n = 10) and the Republic of the Marshall Islands (n = 14). 3. Mako engaged in long-distance movements between New Zealand, Australia, Fiji and New Caledonia, while silky sharks tagged in the Marshall Islands showed evidence of seasonal movements eastward. 4. PRS was determined for 110 sharks (57 mako, 53 silky sharks). Most tagged sharks of both species were uninjured (89%) at capture and most sharks (88%) survived post-release until tag loss or the programmed pop-up date (60 days). However, when considering a complete fishing interaction (haulback, handling, release), PRS estimates were markedly reduced to 48.6% and 52.3% for mako and silky sharks, respectively. For both species, survivorship was greater in large (>150 cm fork length) uninjured sharks and sharks released with low shark length to trailing branchline ratios. 5. While these findings suggest that retention bans offer sharks an increased chance of survival, continued efforts should be made to improve handling and release practices, reduce trailing gear and minimize pelagic shark bycatch.
Sample cross-contamination remains a pervasive issue in genetics and genomics. With growing reliance on molecular methods for managing marine resources, the need to ensure the integrity of tissue samples that underpin these methods has never been more pressing. We conducted an experiment on wild-caught bigeye tuna (Thunnus obesus) to assess cross-contamination risk under seven at-sea and laboratory-based tissue sampling treatments. The six at-sea treatments (T1–T6) differ in sampling equipment, cleaning, and storage procedures. Combining observed heterozygosity (Ho) and relatedness coefficients (r) to flag cross-contamination, treatments T2–T6 proved effective at mitigating contamination risk. Each exhibited significantly smaller mean Ho and less Ho variability compared with intentionally contaminated samples in the T1 treatment. In T2-T6, no samples flagged as contaminated based on Ho outlier thresholds and elevated r were traced to the point of sampling at sea. Laboratory-based subsampling of T1 tissue (T7) also led to significantly smaller, less variable Ho values compared to T1, suggesting that recovery of samples contaminated onboard, or those of unknown provenance, is possible. We show that simple adjustments to current tissue sampling protocols dramatically reduce cross-contamination risk for downstream genetic analyses on tunas and potentially on other species and fisheries.
Modelling marine predator foraging habitats is a widespread research approach for projecting species responses to a rapidly changing Southern Ocean. Yet a key remaining challenge is to understand how changing prey biomass within foraging habitats could affect predator foraging success. Quantifying this using observed prey information is challenging given a paucity of synoptic data. Here, we investigated whether prey biomass from a mechanistic model, could provide useful predictions of pre-breeding arrival body mass of macaroni penguins (Eudyptes chrysolophus) from Marion Island, a standard metric of predator foraging success, measured over a 20-year period. In testing this, we used a spatially iterative correlation approach between predicted prey biomass and observed penguin arrival body mass, allowing likely foraging areas to emerge in regions most frequently associated with significant correlations. We then considered whether the distribution of these emergent foraging areas is consistent with tracking-derived foraging distributions for this species and island. Our results indicated emergent foraging areas where prey biomass was most often correlated with arrival body mass were located within expected and observed foraging ranges. Further, variability in prey biomass, within these emergent foraging areas provided reasonable predictions of annual penguin arrival body mass and outperformed metrics of primary production within these foraging areas. Our findings demonstrate that mechanistic models can provide biologically meaningful representations of difficult-to-observe prey, and can predict predator foraging success. This work could improve understanding of predator responses in a changing habitat.
The Pacific Guidelines for Healthy Living recommend consuming a minimum of five servings of fruit and/or non-starchy vegetables each day, however, diets in Solomon Islands stray from the regional and global trend of healthy diets high in fresh fruit and vegetables. Our study drew on multiple sources of data and a food systems framework to show a country-wide picture of the role and benefits offered by fruit and non-starchy vegetables in Solomon Islands. First, we analysed data on fruit and non-starchy vegetable consumption and matched this to the data on supply. Second, we used a policy documentary analysis to highlight opportunities for the roles of fruit and non-starchy vegetables in the Solomon Island food system to advance progress in multiple Sustainable Development Goals. Key findings related to supply were the findings that domestic production of fruit and non-starchy vegetables is insufficient to meet per capita requirements, which coupled with our finding that per capita national level supply through imports is inconsequential, thus highlighting important undersupply issues for the nation. The food environment analysis indicated multiple further challenges hampering fruit and non-starchy vegetable consumption. Integrated with our analysis of policy, these revealed several opportunities, including improving affordability of this healthy commodity, enhancing livelihood equitability of supply chains, and strengthening environmentally sustainable agricultural practices that support increased production.
Large-scale, no-take marine protected areas (MPAs) have been established in several locations in the Pacific and expansion of such areas to reach 30% of the ocean area is actively promoted in some quarters. Justification for the establishment of large oceanic MPAs often includes the conservation benefits that they would bring for tuna stocks, which are the subject of important commercial fisheries in the Pacific. The aim of this paper was to evaluate the conservation efficacy of an existing MPA, the Phoenix Islands Protected Area (PIPA) and a series of large hypothetical MPAs each constituting approximately 33% of the western and central Pacific Ocean, for two important and contrasting tuna species, skipjack and bigeye tuna. The evaluation was conducted by comparing control and counterfactual simulations in which the estimated population and fishery dynamics of the species were modelled using a high-resolution modelling framework known as SEAPODYM (Spatial Ecosystem And Population DYnamics Model). We found that stock-wide conservation benefits of the PIPA for these species, assuming that total fishing effort is maintained, to be weak to non-existent, and only modest increases in spawning biomass of both species occur within and in the near vicinity of the PIPA itself. For the larger 33% hypothetical MPAs, changes in stock-wide spawning biomass were estimated to be -0.1% to +5.8% for skipjack tuna and +4.8% to +12.0% for bigeye tuna. Conservation efficacy of MPAs for species such as tropical tunas is limited by their wide larval dispersal and high mobility of later life stages, which spatially dissipate the protective effects of MPAs. Also, the displacement of fishing effort from MPAs to areas remaining open can have negative consequences for stocks and fisheries performance in those areas. We conclude that large oceanic MPAs are not likely to be effective frontline management tools for tropical tunas and other species having similar life history characteristics.
Natural mortality (M) is one of the most influential parameters in fisheries stock assessment and management. It relates directly to stock productivity and reference points used for fisheries management advice. Unfortunately, M is also very difficult to estimate, and hence very uncertain. Representing the uncertainty in M and how this influences estimates of management quantities is therefore an important component of conducting stock assessments. This paper outlines the range of methods available to estimate M for use in stock assessment. The methods include those based on maximum age, life history theory, relationships between “well-known” values for M (those found in the literature and based on data for the stock being assessed) and covariates, use of tagging data and catch curve analysis, and estimation within a single- or multi-species stock assessment model. All methods are likely subject to bias and imprecision due to incorrect assumptions and incomplete data. Furthermore, M is generally assumed to be constant over time, age, and sex - assumptions that are unlikely to be true for any stock. Based on our review, there is an obvious benefit to directly estimating M using data and within a stock assessment while assigning a prior based on empirical methods. This approach effectively uses all the available information while also representing the uncertainty. Carefully examining diagnostics and checking for model misspecification is required to ensure that the available data and stock assessment model assumptions are appropriately informative about M when it is estimated during the model fitting process. For situations where direct estimation is not possible (a condition found in data-limited to data-rich stock assessments), the use of multiple methods with robust sensitivity exploration is recommended. Even when direct data are integrated into a stock assessment, we recommend using other methods to estimate M and analysing the direct data outside the stock assessment model as diagnostic tools.
Catch and distribution of tuna in the ocean are typically investigated with ocean basin-scale models. Due to their large scale, such models must greatly simplify tuna behaviour occurring at a scale below ∼100 km, despite interactions at this level potentially being important to both catch and distribution of tuna. For example, the associative behaviour of tuna with man-made floating objects, that are deployed by fishers to improve their catch rates (Fish Aggregating Devices; FADs), are usually ignored or simplified. Here we present a model that can be used to investigate the influence of tuna dynamics below the ∼100 km scale on larger scales. It is an Individual-Based Model (IBM) of a hypothetical, tuna-like species, that includes their interactions with each other, free-floating FADs and prey. In this IBM, both tuna and FADs are represented by Lagrangian particles that are advected by an ocean flow field, with tuna also exhibiting active swimming based on internal states such as stomach fullness. We apply the IBM in multiple configurations of idealized flow and prey fields, alongside differing interaction strengths between agents. When tuna swimming behaviour is influenced equally by prey and FADs, we find that the model simulations compare well with observations at the ≲ 100 km scale. For instance, compared to observations, tuna particles have a similar stomach fullness when associated or non-associated to a FAD, tuna colonize at similar timescales at FADs after their deployment and tuna particles exhibit similar variations in continuous residence times. However, we find large differences in emergent dynamics such as residence and catch among different flow configurations, because the flow determines the time scale at which tuna encounter FADs. These findings are discussed in the context of directing future research, and an improved interpretation of tuna catch and other data for the sustainable management of these economically important species.
A variety of density-dependent and -independent processes have been proposed to influence natural mortality rates, potentially leading to variation through time. Processes of natural mortalities are rarely directly observed, making estimation of natural mortality rates difficult. Mark-recapture data allow estimation of total mortality rates, which can be separated into natural and fishing mortality with information on rates of tag reporting, tag shedding and tag-induced mortality. We fitted attrition models and length-based Brownie models to four decades of mark-recapture data from skipjack and yellowfin tuna in the Western and Central Pacific Ocean, a period representing a sustained expansion of associated fisheries in the region as well as rapid changes to the marine environment. The modelled dataset included c. 250,000 skipjack and 100,000 yellowfin tag releases, with 45,000 and 17,000 recoveries of skipjack and yellowfin respectively, released from 1977 to 2017. Increases in fishing mortality were detected over this time for both skipjack and yellowfin, with evidence of temporal changes in selectivity for yellowfin. Estimates of natural mortality were highest for the smallest size class and generally lower for larger sizes, though there was large uncertainty in the largest size groups due to lower sample sizes of tagged fish. There was no clear evidence of temporal changes in natural mortality rates for either species, though there was some evidence of changes in natural mortality for the smaller yellowfin size classes (< 61 cm). However, there was likely insufficient statistical power to test for plausible changes in natural mortality rates for yellowfin due to low precision of estimates during the earlier years of the tag dataset.
Integrated stock assessments consist of fitting several sources of catch, abundance, and auxiliary biological information to estimate parameters of equations that describe the population dynamics of fish stocks. Stock assessments are subject to uncertainty, and it is a common practice to characterize uncertainty using alternative hypotheses and assumptions within an ensemble of models to develop scientific advice for fisheries management. In this context, there is the need to assign levels of plausibility to each of the combinations of factors that ultimately reflect the uncertainty on different biological and fishery processes. In this study, we describe and apply a model diagnostic to identify trends in process error in recruitment deviation estimates within ensembles of integrated assessment models of tropical tunas. We demonstrate that assessment model ensembles for tropical tunas contain distinct scenarios with significant trends in process error that are overlooked, with the associated implications for fisheries management. Using the Indian Ocean yellowfin as a case study, we found that trends in recruitment deviates are linked to extreme productivity scenarios which strongly diverged in scale from deterministic models fitted without recruitment deviates. This indicates that when recruitment deviates show an increasing trend, these can compensate for the loss of biomass in periods of high catch beyond the surplus production. In these cases, variation in recruitment is not a random process, but rather takes the function of a compensatory, systematic driver in productivity. Significant trends in recruitment were positively correlated with increased standard deviations and auto-correlation coefficient, non-random residual pattern in fits to abundance indices, and particularly poor performance of the Age-Structured Production Model (ASPM) diagnostic. We suggest that trends in recruitment deviates can be caused by misspecification of the biological parameters used as fixed values in integrated assessment models. The process error diagnostic described here can provide a statistical criterion in support for hypotheses and assumptions when using ensembles of models to develop fisheries management advice.
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