Dragonfly Data Science (New Zealand)

Highly pathogenic avian influenza (HPAI) poses a major threat to the world economy and human health; hence, rapid and accurate identification of avian influenza virus (AIV) subtypes is required for effective surveillance and outbreak prevention, particularly in remote or resource-limited settings. Here, we present a portable laboratory system designed to streamline the characterisation of AIV subtypes. This system integrates sample processing, rapid sequencing, and easy to use bioinformatics pipelines to enable characterisation of AIV subtypes, directly from the field. The approach also reduces the chances of samples being degraded, delayed or mixed-up during transport to the laboratory. Equipment for the portable system was first assessed in the laboratory, using AIV isolates with known subtypes, then with swabs collected from several locations in New Zealand. The system was then deployed in the field in Antarctica in December 2024, where it identified HPAI H5N1 in a skua, demonstrating its ability to characterise AIV subtypes rapidly and accurately in situ. This highlights the potential of our portable field system to enhance AIV surveillance in challenging environments and to mitigate the impact of its spread.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-32771-8.

In a state-space framework, temporal variations in fishery-dependent processes can be modeled as random effects. This modeling flexibility makes state-space models (SSMs) powerful tools for data-limited assessments. Though SSMs enable the model-based inference of the unobserved processes, their flexibility can lead to overfitting and non-identifiability issues. To address these challenges, we developed a suite of state-space length-based age-structured models and applied them to the Korean chub mackerel (Scomber japonicus) stock. Our research demonstrated that incorporating temporal variations in fishery-dependent processes can rectify model mis-specification but may compromise robustness, which can be diagnosed through a series of model checking processes. To tackle non-identifiability, we used a non-degenerate estimator, implementing a gamma distribution as a penalty for the standard deviation parameters of observation errors. This penalty function enabled the simultaneous estimation of both process and observation error variances with minimal bias, a notably challenging task in SSMs. These results highlight the importance of model checking and the effectiveness of the penalized approach in estimating SSMs. Additionally, we discussed novel assessment outcomes for the mackerel stock.

Increasing temperatures under climate change are thought to affect individual physiology of fish and other ectotherms through increases in metabolic demands, leading to changes in species performance with concomitant effects on species ecology. Although intuitively appealing, the driving mechanism behind thermal performance is contested; thermal performance (e.g. growth) appears correlated with metabolic scope (i.e. oxygen availability for activity) for a number of species, but a substantial number of datasets do not support oxygen limitation of long-term performance. Whether or not oxygen limitations via the metabolic scope, or a lack thereof, have major ecological consequences remains a highly contested question. size and trait-based model of energy and oxygen budgets to determine the relative influence of metabolic rates, oxygen limitation and environmental conditions on ectotherm performance. We show that oxygen limitation is not necessary to explain performance variation with temperature. Oxygen can drastically limit performance and fitness, especially at temperature extremes, but changes in thermal performance are primarily driven by the interplay between changing metabolic rates and species ecology. Furthermore, our model reveals that fitness trends with temperature can oppose trends in growth, suggesting a potential explanation for the paradox that species often occur at lower temperatures than their growth optimum. Our model provides a mechanistic underpinning that can provide general and realistic predictions about temperature impacts on the performance of fish and other ectotherms and function as a null model for contrasting temperature impacts on species with different metabolic and ecological traits.

Quantitative analysis of stable isotopes (SI) and, more recently, fatty acid profiles (FAP) are useful and complementary tools for estimating the relative contribution of different prey items in the diet of a predator. The combination of these two approaches, however, has thus far been limited and qualitative. We propose a mixing model for FAP that follows the Bayesian machinery employed in state-of-the-art mixing models for SI. This framework provides both point estimates and probability distributions for individual and population level diet proportions. Where fat content and conversion coefficients are available, they can be used to improve diet estimates. This model can be explicitly integrated with analogous models for SI to increase resolution and clarify predator-prey relationships. We apply our model to simulated data and an experimental dataset that allows us to illustrate modeling strategies and demonstrate model performance. Our methods are provided as an open source software package for the statistical computing environment R.

Quantitative analysis of stable isotopes (SI) and, more recently, fatty acid profiles (FAP) are useful and complementary tools for estimating the relative contribution of different prey items in the diet of a predator. The combination of these two approaches, however, has thus far been limited and qualitative. We propose a mixing model for FAP that follows the Bayesian machinery employed in state-of-the-art mixing models for SI. This framework provides both point estimates and probability distributions for individual and population level diet proportions. Where fat content and conversion coefficients are available, they can be used to improve diet estimates. This model can be explicitly integrated with analogous models for SI to increase resolution and clarify predator-prey relationships. We apply our model to simulated data and an experimental dataset that allows us to illustrate modeling strategies and demonstrate model performance. Our methods are provided as an open source software package for the statistical computing environment R.

Quantitative analysis of stable isotopes (SI) and, more recently, fatty acid profiles (FAP) are useful and complementary tools for estimating the relative contribution of different prey items in the diet of a predator. The combination of these two approaches, however, has thus far been limited and qualitative. We propose a mixing model for FAP that follows the Bayesian machinery employed in state-of-the-art mixing models for SI. This framework provides both point estimates and probability distributions for individual and population level diet proportions. Where fat content and conversion coefficients are available, they can be used to improve diet estimates. This model can be explicitly integrated with analogous models for SI to increase resolution and clarify predator-prey relationships. We apply our model to simulated data and an experimental dataset that allows us to illustrate modeling strategies and demonstrate model performance. Our methods are provided as an open source software package for the statistical computing environment R.

Catch-per-unit-effort (CPUE) is commonly used as an index of abundance in fishery stock assessments, but CPUE may be misleading, as a number of global fishery collapses have been attributed to a hyper-stable CPUE. In abalone (Halitidae family) fisheries, CPUE at large spatial scales may be hyper-stable because of aggregating behaviour and serial-depletion, whereby fishers sequentially fish areas with no corresponding decline in CPUE. Obtaining detailed spatial information in abalone fisheries might mitigate this problem, allowing CPUE to be used more confidently in these fisheries. Here, we report on the use of newly-developed high-resolution Global Positioning System (GPS) data loggers in New Zealand's blacklip abalone (pāua, Haliotis iris) fisheries. Using these data loggers, we tested, via a fish-down experiment, if CPUE is a reliable indicator of abundance at a small spatial scale and over a period of months. In the experiment, hyper-stability at small spatial scales occurred at high abundance, but CPUE reflected the estimated depletion level at the end of experimental fishing. This experiment suggests that the GPS data loggers provide a promising avenue to track CPUE at a small spatial scale, and to assess spatial resource use in New Zealand's pāua fisheries.

Quantitative analysis of stable isotopes (SI) and, more recently, fatty acid profiles (FAP) are useful and complementary tools for estimating the relative contribution of different prey items in the diet of a predator. The combination of these two approaches, however, has thus far been limited and qualitative. We propose a mixing model for FAP that follows the Bayesian machinery employed in state-of-the-art mixing models for SI. This framework provides both point estimates and probability distributions for individual and population level diet proportions. Where fat content and conversion coefficients are available, they can be used to improve diet estimates. This model can be explicitly integrated with analogous models for SI to increase resolution and clarify predator-prey relationships. We apply our model to simulated data and an experimental dataset that allows us to illustrate modeling strategies and demonstrate model performance. Our methods are provided as an open source software package for the statistical computing environment R.

Population monitoring programmes are used to gain knowledge about system functioning or about the response of a system's state to management measures. The current study provided an assessment of comprehensive monitoring data of intertidal bivalve populations in northern New Zealand. In this region, a number of bivalve species are targeted in recreational and customary fisheries, including cockles (Austrovenus stutchburyi) that are one of the main target species. To monitor the sustainability of cockle populations, regular population surveys have been conducted since the 1990s at different northern New Zealand sites, although the sampling frequency has varied greatly across sites. Furthermore, a general lack of fishing data for the target populations prevents a systematic assessment of management measures and population trends within a fishery assessment framework. In view of these shortcomings, the current study provided a systematic phenomenological assessment of the survey data to make general recommendations about the effectiveness of management and for guiding prioritisation and design for future surveys. The assessment was based on a Bayesian state-space model that simultaneously analysed monitoring data from 21 incomplete survey time-series between 1999-2000 and 2014-15. The model showed that the predictability of estimated large-size cockle densities was highly site-specific. Across all sites, the predictability of density trends could be used to guide the prioritisation of sites to be included in the survey, or to adjust the sampling frequency at individual sites. Based on the current analysis, existing management measures in the form of fishery closures at some of the sites appear to be effective in allowing recovery of depleted cockle populations. Only those sites that had management measures in place showed an increase in large cockle densities. In the absence of fishing data, adaptive management and targeted intervention could be used to further assess the efficacy of management measures.