Search Results (19)

Nuclear microreactors are a potential technology to provide heat and electricity for remote microgrids. There is potential for the microgrid on the island of El Hierro to use a microreactor, within an integrated energy system (IES), to generate electricity and provide desalinated water. This work proposes a workflow for optimizing and analyzing IESs for microgrids. In this study, an IES incorporating a microreactor, thermal energy storage (TES) system, combined heat and power plant, and a thermal desalination plant was designed, optimized, and analyzed using Idaho National Laboratory’s Framework for Optimization of Resources and Economics (FORCE) toolset. The optimization tool, Holistic Energy Resource Optimization Network (HERON), was used to determine the optimal capacity sizes and dispatch for the reactor and thermal energy storage systems to meet demand. The optimized reactor and TES sizes were found to be 11.61 MW_th and 58.47 MWh_th, respectively, when optimizing the IES to replace 95% of the island’s existing diesel generation needs. A dynamic model of the system was created in the Modelica language, using models from the HYBRID repository, to analyze and verify the dispatch from the optimizer. The dynamic model was able to meet the ramp rates while maintaining reactor power with minimal control adjustments. Full article

The increasing prevalence of species that are detrimental to biodiversity is a major concern, particularly for managers of national parks. To develop effective programmes for controlling weeds, it is essential to have a thorough understanding of the extent and severity of infestations, as well as the contributing factors such as temperature, rainfall, and disturbance. Predicting these factors on a regional scale requires models that can incorporate a wide range of variables in a quantifiable manner, while also assisting with on-ground operations. In this study, we present two Bayesian Network models specifically designed for six significant weed species found along the southern coast of Australia. Our models are based on empirical data collected during a coastal weed survey conducted in 2015 and repeated in 2016. We applied these models to the coastal national parks in the isolated and pristine East Gippsland region. Importantly, the prediction models were developed at two different spatial scales that directly corresponded to the scale of the observations. Our findings indicate that coastal habitats, with their vulnerable environments and prevalence of open dune systems, are particularly susceptible to weed infestations. Moreover, adjacent regions also have the potential for colonization if these infestations are not effectively controlled. Climate-related factors play a role in moderating the potential for colonization, which is a significant concern for weed control efforts in the context of global climate change. Full article

Comprehending the spatial distribution of human fishing endeavors holds significant importance in the context of monitoring fishery resources and implementing spatial management measures. To gain insights into the spatial arrangement of tuna longline activities within the exclusive economic zones of Tonga and their correlation with the marine environment, this study utilizes data from the Tonga Tuna Longline Fisheries spanning from 2002 to 2018. The data are employed to extract information about the spatial distribution of fishing efforts and coupled with 15 marine environmental variables covering both sea surface and subsurface conditions. This study employs boosted regression trees (BRT) and general additive models (GAM) to establish the non-linear relationships between the distribution of fishing effort and marine environmental factors. Furthermore, it examines and analyzes the ecological niche occupied by tuna longline vessels in high-sea environments. The outcomes of the factor analysis indicate that the most important factors influencing the fishing efforts of tuna longliners are the dissolved oxygen content at the sea surface and latitude. These two factors contribute significantly, accounting for 19.06% and 18.62% of the fishing efforts of vessels, respectively, followed by distance to ports, longitude, and dissolved oxygen at 100 m depth, contributing 10.77%, 7.07%, and 6.30%, respectively. The sea surface chlorophyll, ocean current at 100 m depth, and mixed layer depth contributed the least, 3.63%, 2.13%, and 1.72, respectively. In terms of space and time, tuna longliners are more likely to operate in the 18–22° S latitudinal and 172–178° W longitudinal region, and fishing efforts increased in the months from March to August. The spatial distribution of the fishing efforts modeled for fishing vessels in 2018 is predicted to have good spatial distribution with the actual fishing efforts of these vessels. This research aids in comprehending the environmental impacts resulting from shifts in the spatial distribution of tuna longline vessels, offering valuable insights for the effective management of tuna longline fisheries in Tonga. Full article

The Tongan fisheries targeting the species of albacore (Thunnus alalunga), bigeye (Thunnus obesus), skipjack (Katsuwonus pelamis), and yellowfin tuna (Thunnus albacares), comprising the main tuna catch landed, within the EEZ of Tonga is critical to the economy of Tonga. Thus, it is crucial to study the spatiotemporal pattern of their catch and the influence of environmental and physical variables, in addition to the month and year of the catch. To this end, sets of eight generalized additive models were applied to model the distribution of these four species. Selection among competing models was carried out based on k-fold cross-validation, using RMSPE prediction error as a measure of model predictive performance. The following sets of predictors were considered; sea surface temperature, sea surface chlorophyll, bottom depth, month, and year. In addition, to assess the influence of fronts, gradients in SST and Chl-a were computed and used as predictors. Catch year was the most important variable for all, except Albacore tuna, for which month was the important variable. The third most important variable was SST for albacore and bigeye tuna, whereas bottom depth was the most important variable for skipjack and yellowfin tuna. A standardized index of CPUE indicates mostly inter-annual variation in CPUE for albacore and bigeye tuna, whereas a it indicates a general increase in CPUE for skipjack and yellowfin tuna. Hotspots of albacore tuna catches are around the northern and southern edges of the exclusive economic zone and typically during the months of June to August. The bigeye tuna hotspots were concentrated on the eastern side of the islands, in waters overlying trenches; this was most obvious during the months of January to June. Skipjack tuna hotspots were near the edges of the exclusive economic zone, although it is caught in smaller amounts to the three tuna species considered and higher catch rates were observed only after 2014. For yellowfin tuna, the highest catch rates were concentrated around the islands and descending towards the southern edge of the EEZ. As part of the initiative of this study to support national optimal resource management, this study generated standardized CPUE (indices of abundance), an important input in stock assessment, and also looked into the potential influence of environmental and physical variables on the CPUE of these valuable tuna stocks within the EEZ of Tonga. Full article

Despite the crucial role played by international and regional tuna fisheries in facilitating the successful implementation of the ecosystem approach to fisheries management, there exist disparities in viewpoints among these stakeholders, resulting in gaps between regional fisheries management and local communities. Nevertheless, the Tongan government, under the Ministry of Fisheries, is dedicated to the efficient management of its tuna resources, aiming to establish it as the preferred and optimal approach for ensuring the long-term sustainability of its tuna fisheries and the ecosystem services they provide to the community. Recognizing that an appropriate legal, policy and institutional framework is in place for sustainable management of tuna, the first part of this paper presents a review of current Tonga fisheries laws and policies for its tuna fisheries. This review reflects the implementation of an information-based management framework, namely the Tonga National Tuna Fishery Management and Development Plan. The tuna fisheries in Tonga mainly catch albacore (Thunnus alalunga), bigeye (Thunnus obesus), skipjack (Katsuwonus pelamis), and yellowfin (Thunnus albacares) tuna. These tuna species are caught within Tonga’s exclusive economic zones and play a crucial role in the country’s economy; hence, it is crucial to examine the spatio-temporal distributions of their catch in relation to their environmental conditions. In pursuit of this goal, the tasks of mapping (i) the spatio-temporal distribution of catch landed at ports and (ii) the spatio-temporal of environmental conditions were performed. The study utilizes longline catch per unit effort data spanning from 2002 to 2018 for albacore, bigeye, skipjack, and yellowfin tuna. It also incorporates data on environmental conditions, including sea surface temperature, sea surface chlorophyll, sea surface current, and sea surface salinity. Additionally, the El Nino Southern Oscillation Index is mapped in relation to catch data to examine the potential effects of climate change on the tuna catch. Results show that bigeye, skipjack, and yellowfin CPUE show a central–northernmost distribution and are primarily caught between latitudes 14° S–22° S, while albacore, shows a central–southern distribution. The highest CPUE for all species are in latitudes 15.5° S–22.5° S and longitudes 172.5° W–176.5° W. The data indicate that sea surface current velocities range from −0.03 to 0.04 ms⁻¹, sea surface salinity ranges from 34.8 to 35.6 PSU, sea surface chlorophyll concentration varies from 0.03 to 0.1 mg m⁻³, and sea surface temperature fluctuates seasonally, ranging from 18 °C to 30 °C. Mapping also reveals that times of reduced catches in Tonga coincide with periods of moderate to strong El Nino events from 2002 to 2018. Full article

The potential impacts of climate change on the distribution of tuna in Pacific Island countries’ exclusive economic zones have yet to be investigated rigorously and so their persistence and abundance in these areas remain uncertain. Here, we estimate optimal fisheries areas for four tuna species: albacore (Thunnus alalunga), bigeye (Thunnus obesus), skipjack (Katsuwonus pelamis), and yellowfin (Thunnus albacares). We consider different climate change scenarios, RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5, within a set of tuna catch records in the exclusive economic zone of Tonga. Using environmental and CPUE datasets, species distribution modelling estimated and predicted these fisheries areas in the current and future climatic scenarios. Our projections indicate an expansion in area and a shift of productive areas to the southern part of this exclusive economic zone of Tonga. This is an indication that future climatic scenarios might be suitable for the species under study; however, changes in trophic layers, ocean currents, and ocean chemistry might alter this finding. The information provided here will be relevant in planning future national actions towards the proper management of these species. Full article

Achieving coastal and shallow-water bathymetry is essential for understanding the marine environment and for coastal management. Bathymetric data in shallow sea areas can currently be obtained using SDB (satellite-derived bathymetry) with multispectral satellites based on depth inversion models. In situ bathymetric data are crucial for validating empirical models but are currently limited in remote and unapproachable areas. In this paper, instead of using the measured water depth data, ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) ATL03 bathymetric points at different acquisition dates and multispectral imagery from Sentinel-2/GeoEye-1 were used to train and evaluate water depth inversion empirical models in two study regions: Shanhu Island in the South China Sea, and Heron Island in the Great Barrier Reef (GBR) in Australia. However, different sediment types also influenced the SDB results. Therefore, three types of sediments (sand, reef, and coral/algae) were analyzed for Heron Island, and four types of sediments (sand, reef, rubble and coral/algae) were analyzed for Shanhu Island. The results show that accuracy generally improved when sediment classification information was considered in both study areas. For Heron Island, the sand sediments showed the best performance in both models compared to the other sediments, with mean R² and RMSE values of 0.90 and 1.52 m, respectively, representing a 5.6% improvement of the latter metric. For Shanhu Island, the rubble sediments showed the best accuracy in both models, and the average R² and RMSE values were 0.97 and 0.65 m, respectively, indicating an RMSE improvement of 15.5%. Finally, bathymetric maps were generated in two regions based on the sediment classification results. Full article

Underwater image restoration is of significant importance in unveiling the underwater world. Numerous techniques and algorithms have been developed in recent decades. However, due to fundamental difficulties associated with imaging/sensing, lighting, and refractive geometric distortions in capturing clear underwater images, no comprehensive evaluations have been conducted with regard to underwater image restoration. To address this gap, we constructed a large-scale real underwater image dataset, dubbed Heron Island Coral Reef Dataset (‘HICRD’), for the purpose of benchmarking existing methods and supporting the development of new deep-learning based methods. We employed an accurate water parameter (diffuse attenuation coefficient) to generate the reference images. There are 2000 reference restored images and 6003 original underwater images in the unpaired training set. Furthermore, we present a novel method for underwater image restoration based on an unsupervised image-to-image translation framework. Our proposed method leveraged contrastive learning and generative adversarial networks to maximize the mutual information between raw and restored images. Extensive experiments with comparisons to recent approaches further demonstrate the superiority of our proposed method. Our code and dataset are both publicly available. Full article

The bio-optical properties of coral reef waters were examined across coral reef ecosystems not influenced by land-derived run-off, in the Great Barrier Reef lagoon (Heron Island) and the Coral Sea (the Coringa-Herald and Lihou Reefs). The aim was to determine whether the absorption properties, the concentration-specific absorption properties, and the phytoplankton and non-algal pigmented particle (NAP) absorption concentrations varied from the ocean waters flushing onto the reef at high tide to those waters on the reef or flushing off the reef at low tide. The optical and biogeochemical properties of on-reef waters systematically differed from the surrounding ocean waters. The chl a concentration values varied up to 7-fold and the NAP concentrations up to 29-fold; for the reef samples, the chl a values were on average 2 to 3 times lower than for the oceans whilst the NAP values were slightly higher on the reefs. The spectral absorption values of the chl a, NAP, and colored dissolved organic matter (CDOM) varied up to 6-fold for reef waters and up to 15-fold for ocean waters. The spectral absorption for chl a was up to 3-fold lower on the reef waters, the absorption by the CDOM was up to 2-fold higher and the NAP absorption was 1.6-fold higher on the reef waters. The concentration-specific absorption coefficients for chl a and NAP varied up to 9-fold in reef waters and up to 30-fold in ocean waters. In the case of Heron Island and Coringa-Herald cays, this concentration-specific absorption was on average 1.3 to 1.7-fold higher for chl a and up to 2-fold lower for NAP on the reefs. The Lihou Reef measurements were more ambiguous between the reef waters and ocean waters due to the complex nature and size of this reef. Based on our results, the assumption that the optical properties of on-reef waters and the adjacent ocean waters are the same was shown to be invalid. Ocean waters flowing on to the reef are higher in phytoplankton, whilst waters on the reef or flowing off the reefs are higher in CDOM and NAP. We found differences in the pico,- nano-, and microplankton distributions as well as in the ratios of photosynthetic to photoprotective pigments. The variability in the bio-optical properties between the reef waters and adjacent ocean waters has implications for the estimations of sunlight absorption along the water column, the UV penetration depth, the temperature distributions, and the nutrient and carbon fluxes in coral reef ecosystems. As Earth observation algorithms require proper parameterization for the water column effects when estimating benthic cover, the actual optical properties need to be used. These results will improve the use of Earth observation to systematically map the differences in the water quality between reefs and the adjacent ocean. Full article

The genus Acaryochloris is unique among phototrophic organisms due to the dominance of chlorophyll d in its photosynthetic reaction centres and light-harvesting proteins. This allows Acaryochloris to capture light energy for photosynthesis over an extended spectrum of up to ~760 nm in the near infra-red (NIR) spectrum. Acaryochloris sp. has been reported in a variety of ecological niches, ranging from polar to tropical shallow aquatic sites. Here, we report a new Acarychloris strain isolated from an NIR-enriched stratified microbial layer 4–6 mm under the surface of stromatolite mats located in the Hamelin Pool of Shark Bay, Western Australia. Pigment analysis by spectrometry/fluorometry, flow cytometry and spectral confocal microscopy identifies unique patterns in pigment content that likely reflect niche adaption. For example, unlike the original A. marina species (type strain MBIC11017), this new strain, Acarychloris LARK001, shows little change in the chlorophyll d/a ratio in response to changes in light wavelength, displays a different Fv/Fm response and lacks detectable levels of phycocyanin. Indeed, 16S rRNA analysis supports the identity of the A. marina LARK001 strain as close to but distinct from from the A. marina HICR111A strain first isolated from Heron Island and previously found on the Great Barrier Reef under coral rubble on the reef flat. Taken together, A. marina LARK001 is a new cyanobacterial strain adapted to the stromatolite mats in Shark Bay. Full article

Fisheries management has historically focused on the population elasticity of target fish based primarily on demographic modeling, with the key assumptions of stability in environmental conditions and static trophic relationships. The predictive capacity of this fisheries framework is poor, especially in closed systems where the benthic diversity and boundary effects are important and the stock levels are low. Here, we present a probabilistic model that couples key fish populations with a complex suite of trophic, environmental, and geomorphological factors. Using 41 years of observations we model the changes in eastern Baltic cod (Gadus morhua), herring (Clupea harengus), and Baltic sprat (Sprattus sprattus balticus) for the Baltic Sea within a Bayesian network. The model predictions are spatially explicit and show the changes of the central Baltic Sea from cod- to sprat-dominated ecology over the 41 years. This also highlights how the years 2004 to 2014 deviate in terms of the typical cod–environment relationship, with environmental factors such as salinity being less influential on cod population abundance than in previous periods. The role of macrozoobenthos abundance, biotopic rugosity, and flatfish biomass showed an increased influence in predicting cod biomass in the last decade of the study. Fisheries management that is able to accommodate shifting ecological and environmental conditions relevant to biotopic information will be more effective and realistic. Non-stationary modelling for all of the homogeneous biotope regions, while acknowledging that each has a specific ecology relevant to understanding the fish population dynamics, is essential for fisheries science and sustainable management of fish stocks. Full article

Surveying the breeding population of a given species can be difficult for many logistic reasons. Marine turtles are a challenging taxon for the study of reproductive ecology and breeding strategies, because turtles aggregate off-shore and males remain exclusively at sea. For successful management of sea turtle populations, determining operational sex ratios (OSRs) on a continuing basis is critical for determining long-term population viability, particularly in the context of changing hatchling sex ratios due to temperature-dependent sex determination in a warming climate. To understand how survey technique and stage of the breeding season might influence the ability to detect turtles and determine OSRs, we surveyed the presence and identified the sex of adult male and female green sea turtles (Chelonia mydas) using a boat and small commercial unoccupied/unmanned aerial vehicle (UAV), at the start (October) and peak (December) of a nesting season at an important breeding site at Heron Island, Great Barrier Reef, Australia. The ratio of males to females within the breeding ground detected by both survey methods changed from being male-biased in October to heavily female-biased in December, indicating that most males cease their reproductive effort and depart before the peak of the nesting season. Surveying with a UAV more than doubled the rate of turtles seen per minute of survey effort compared with surveying solely from the boat and allowed surveys to be conducted at times and/or places unsafe or inaccessible for boats. The sex of a slightly greater proportion of turtles seen could not be identified by observers using a UAV versus a boat, although more turtles were detected using the UAV. The departure of many males during the peak of the nesting season is likely due to an increasing biological cost of residency in the area because males encounter fewer receptive females as the season progresses and the limited foraging opportunity is insufficient to support the number of males present. Overall, we found that UAVs are an effective tool for studying important but difficult to observe aspects of sea turtle biology. Full article

Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed an automated bathymetry mapping approach based on the Sentinel-2 surface reflectance dataset in Google Earth Engine. We created a new method for generating a clean-water mosaic and a tailored automatic bathymetric estimation algorithm. We then evaluated the performance of the models at six globally diverse sites (Heron Island, Australia; West Coast of Hawaiʻi Island, Hawaiʻi; Saona Island, Dominican Republic; Punta Cana, Dominican Republic; St. Croix, United States Virgin Islands; and The Grenadines) using 113,520 field bathymetry sampling points. Our approach derived accurate bathymetry maps in shallow waters, with Root Mean Square Error (RMSE) values ranging from 1.2 to 1.9 m. This automatic, efficient, and robust method was applied to map shallow water bathymetry at the global scale, especially in areas which have high biodiversity (i.e., coral reefs). Full article

Remote sensing of anthropogenic light has substantial potential to quantify light pollution levels and understand its impact on a wide range of taxa. Currently, the use of space-borne night-time sensors for measuring the actual light pollution that animals experience is limited. This is because most night-time satellite imagery and space-borne sensors measure the light that is emitted or reflected upwards, rather than horizontally, which is often the light that is primarily perceived by animals. Therefore, there is an important need for developing and testing ground-based remote sensing techniques and methods. In this study, we aimed to address this gap by examining the potential of ground photography to quantify the actual light pollution perceived by animals, using sea turtles as a case study. We conducted detailed ground measurements of night-time brightness around the coast of Heron Island, a coral cay in the southern Great Barrier Reef of Australia, and an important sea turtle rookery, using a calibrated DSLR Canon camera with an 8 mm fish-eye lens. The resulting hemispheric photographs were processed using the newly developed Sky Quality Camera (SQC) software to extract brightness metrics. Furthermore, we quantified the factors determining the spatial and temporal variation in night-time brightness as a function of environmental factors (e.g., moon light, cloud cover, and land cover) and anthropogenic features (e.g., artificial light sources and built-up areas). We found that over 80% of the variation in night-time brightness was explained by the percentage of the moon illuminated, moon altitude, as well as cloud cover. Anthropogenic and geographic factors (e.g., artificial lighting and the percentage of visible sky) were especially important in explaining the remaining variation in measured brightness under moonless conditions. Night-time brightness variables, land cover, and rock presence together explained over 60% of the variation in sea turtle nest locations along the coastline of Heron Island, with more nests found in areas of lower light pollution. The methods we developed enabled us to overcome the limitations of commonly used ground/space borne remote sensing techniques, which are not well suited for measuring the light pollution to which animals are exposed. The findings of this study demonstrate the applicability of ground-based remote sensing techniques in accurately and efficiently measuring night-time brightness to enhance our understanding of ecological light pollution. Full article

Determining the small-scale movement patterns of marine vertebrates usually requires invasive active acoustic tagging or in-water monitoring, with the inherent behavioural impacts of those techniques. In addition, these techniques rarely allow direct continuous behavioural assessments or the recording of environmental interactions, especially for highly mobile species. Here, we trial a novel method of assessing small-scale movement patterns of marine vertebrates using an unmanned aerial vehicle that could complement longer-term tracking approaches. This approach is unlikely to have behavioural impacts and provides high accuracy and high frequency location data (10 Hz), while subsequently allowing quantitative trajectory analysis. Unmanned aerial vehicle tracking is also relatively low cost compared to single-use acoustic and GPS tags. We tracked 14 sharks for up to 10 min in a shallow lagoon of Heron Island, Australia. Trajectory analysis revealed that Epaulette sharks (Hemiscyllium ocellatum) displayed sinusoidal movement patterns, while Blacktip Reef Sharks (Carcharhinus melanopterus) had more linear trajectories that were similar to those of a Lemon shark (Negaprion acutidens). Individual shark trajectory patterns and movement speeds were highly variable. Results indicate that Epaulette sharks may be more mobile during diurnal low tides than previously thought. The approach presented here allows the movements and behaviours of marine vertebrates to be analysed at resolutions not previously possible without complex and expensive acoustic arrays. This method would be useful to assess the habitat use and behaviours of sharks and rays in shallow water environments, where they are most likely to interact with humans. Full article