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Marine Institute Open Access Repository
Welcome to the Marine Institute Open Access Repository
The Marine Institute Open Access Repository facilitates full text access to the publications of the Marine Institute in accordance with copyright permissions. The aim of the Repository is to collect, preserve and provide open access to the publications of the Marine Institute, including the research publications supported by National and European funded marine research programmes.
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Marine Environmental Characterisation of Irish Inshore Aquaculture RegionsUnderstanding the hydrography and ocean dynamics of Ireland's inshore regions is crucial for assessing how these physical processes impact the aquaculture or fisheries industries. This study examines temperature, salinity, and stratification patterns, along with extreme climatic events such as marine heatwaves (MHWs) and marine cold spells (MCSs), which significantly impact marine ecosystems and aquaculture productivity. Using a combination of observational data, numerical ocean models, and literature sources, the study provides insights into physical environmental characteristics per region, both contemporary and historical. Distributions of temperature and salinity within regional bays and harbours, as shown here, highlight the growing threats climate change poses to Irish coastal waters, particularly through increasing sea surface temperatures (SSTs) and MHWs. Results indicate potential for harsher growing conditions and more frequent harmful algal blooms (HABs). Although a decline in marine cold spells (MCSs) at most studied sites offers some respite, the rise in the frequency of cold spells in Bantry Bay highlights the need for continued monitoring and adaptive strategies. Existing, planned and potential long-term observations at key Marine Institute sites, aim to bridge gaps in our understanding of marine environmental trends, aiding mitigation and adaptive efforts, which are evermore required in warming regional seas
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Data-Driven Modelling for Assessing Trophic Status in Marine Ecosystems Using Machine Learning ApproachesAssessing eutrophication in coastal and transitional waters is of utmost importance, yet existing Trophic Status Index (TSI) models face challenges like multicollinearity, data redundancy, inappropriate aggregation methods, and complex classification schemes. To tackle these issues, we developed a novel tool that harnesses machine learning (ML) and artificial intelligence (AI), enhancing the reliability and accuracy of trophic status assessments. Our research introduces an improved data-driven methodology specifically tailored for transitional and coastal (TrC) waters, with a focus on Cork Harbour, Ireland, as a case study. Our innovative approach, named the Assessment Trophic Status Index (ATSI) model, comprises three main components: the selection of pertinent water quality indicators, the computation of ATSI scores, and the implementation of a new classification scheme. To optimize input data and minimize redundancy, we employed ML techniques, including advanced deep learning methods. Specifically, we developed a CHL prediction model utilizing ten algorithms, among which XGBoost demonstrated exceptional performance, showcasing minimal errors during both training (RMSE = 0.0, MSE = 0.0, MAE = 0.01) and testing (RMSE = 0.0, MSE = 0.0, MAE = 0.01) phases. Utilizing a novel linear rescaling interpolation function, we calculated ATSI scores and evaluated the model's sensitivity and efficiency across diverse application domains, employing metrics such as R2, the Nash-Sutcliffe efficiency (NSE), and the model efficiency factor (MEF). The results consistently revealed heightened sensitivity and efficiency across all application domains. Additionally, we introduced a brand new classification scheme for ranking the trophic status of transitional and coastal waters. To assess spatial sensitivity, we applied the ATSI model to four distinct waterbodies in Ireland, comparing trophic assessment outcomes with the Assessment of Trophic Status of Estuaries and Bays in Ireland (ATSEBI) System. Remarkably, significant disparities between the ATSI and ATSEBI System were evident in all domains, except for Mulroy Bay. Overall, our research significantly enhances the accuracy of trophic status assessments in marine ecosystems. The ATSI model, combined with cutting-edge ML techniques and our new classification scheme, represents a promising avenue for evaluating and monitoring trophic conditions in TrC waters. The study also demonstrated the effectiveness of ATSI in assessing trophic status across various waterbodies, including lakes, rivers, and more. These findings make substantial contributions to the field of marine ecosystem management and conservation.
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Standardising long-term eel (Anguilla anguilla) fyke net survey data reveals covariate effects and improves estimates of declining relative abundanceEel recruitment and stock have been in decline since at least the mid 1980s. So far, much research has focussed on conservation objectives and estimating silver eel production in response to the EU Regulation (EC 1100/2007) and most surveys have been on waters subject to exploitation and/or stocking. The aim of this study is to examine a 35-year yellow eel survey dataset to determine relative abundance trends while accounting for survey design, and important physical and operational covariates. Chains of ten, or sometimes five, fyke nets were fished at fixed sites in four lakes on a catchment in the west of Ireland not impacted on by exploitation or stocking. Captured eels (10,474) were counted for each trap, and eel weight was recorded for each chain of nets (5515 net nights fished). Data were analysed using a generalised additive model (GAM) that included smoothed trends over time and covariates day-of-year, depth, gradient, site and net chain. Yearly trends and between-chain variability were significant for all locations with trap depth and gradient being important for some lakes. Standardised trends by number and mass declined markedly in both tidal areas − 84.7% and − 89.5% by count and − 93.5% and − 89.5% by weight; significant declines (− 39.2% by count and − 54.1% by weight) were found in one of the freshwater lakes with the other, the one farthest upstream, having had changes over time but no significant difference between the start and end. This study provides a framework for analysing long-term fyke net catch data for eel, which may be useful for the international analysis of eel survey data.
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Climate-Driven Oceanic Range Shift of Saint John River Atlantic Salmon Revealed by Multidecadal Stable Isotope TrendsMarine migration patterns of unique or regional stocks of Salmo salar (Atlantic Salmon) are poorly known. Atlantic Salmon (AS) utilizing the North Atlantic Subpolar Gyre (NASpG) for foraging and growth consist of stocks from the United States (41°N) to Greenland (64°N) in the western Atlantic and from Spain (41°N) to Norway (70°N) in the eastern Atlantic. Wide latitudinal range and unique riverine characteristics probably result in stocks selecting differing suites of marine sea-surface temperatures (SST) within the species' known selection range (i.e., their Grinnellian niche). We analyzed trends in stable carbon isotope (δ13C) composition from archived scale tissues in relation to long-term variation of SST for the North Atlantic Ocean to identify marine feeding regions used by adult AS successfully returning to the Saint John River, Canada, during the period 1982–2011. Marine foraging regions were determined for individuals that spent 1 winter (1SW) or multiple winters (MSW) at sea. During 1982–1991 and 1992–2001, AS which successfully returned, exploited more southern regions of the NASpG, while returning adults from 2002–2011 exploited more northern regions. Historically presumed marine feeding regions, which were identified by the isotopic model as probable during 1982–2001, have experienced a warming trend, and data suggests that AS returning to the Saint John River have been migrating further north in the NASpG in response to recent global climate change. The northward range shift as the North Atlantic warmed is consistent with recent biogeographical changes found among other marine species also following their Grinnellian niche.
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Newport Research Facility, Annual Report No. 5, 1960This report is a predecessor to our current Newport Research Facility, Annual Reports. Prior to the Marine Institute, this work was carried out by the Salmon Research Trust of Ireland, and later the Salmon Research Agency of Ireland. For consistency the titles remain as 'Newport Research Facility, Annual Reports'. The scientific aspects of the Annual Reports published by the Salmon Research Trust of Ireland are now integrated into the Fisheries Ecosystem Advisory Services Group (FEAS) of the Marine Institute. The data presented creates a unique record of fish rearing and wild fish census data. These data are an essential component in the local, regional and national management of salmon, sea trout and eel and are becoming ever more valuable in the light of increasing pressures on natural stocks, such as exploitation, habitat degradation and global climate change scenarios. The fish monitoring facilities in Newport, along with the reared and ranched salmon stocks held in Burrishoole, are also essential for supporting projects such as development of novel enhancement techniques, alternative stocks and ranching and evaluation of interactions between farmed, ranched and wild strains. An expanding programme in the Burrishoole system is including ecological and genetics research into eel, sticklebacks and stock dynamics of juvenile salmonids and eels.
