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Identifying priority sites for whale shark ship collision management globally
No full textThe expansion of the world's merchant fleet poses a great threat to the ocean's biodiversity. Collisions between ships and marine megafauna can have population-level consequences for vulnerable species. The Endangered whale shark (Rhincodon typus), shares a circumglobal distribution with this expanding fleet and tracking of movement pathways has shown that large vessel collisions pose a major threat to the species. However, it is not yet known whether they are also at risk within aggregation sites, where up to 400 individuals can gather to feed on seasonal bursts of planktonic productivity. These “constellation” sites are of significant ecological, socio-economic and cultural value. Here, through expert elicitation, we gathered information from most known constellation sites for this species across the world (>50 constellations and >13,000 individual whale sharks). We defined the spatial boundaries of these sites and their overlap with shipping traffic. Sites were then ranked based on relative levels of potential collision danger posed to whale sharks in the area. Our results showed that researchers and resource managers may underestimate the threat posed by large ship collisions due to a lack of direct evidence, such as injuries or witness accounts, which are available for other, sub-lethal threat categories. We found that constellations in the Arabian Sea and adjacent waters, the Gulf of Mexico, the Gulf of California, and Southeast and East Asia, had the greatest level of vessel collision threat. We also identified 39 sites where peaks in shipping activity coincided with peak seasonal occurrences of whale sharks, sometimes across several months. Simulated potential collision mitigation options estimated a minimal impact to industry, as most whale shark core habitat areas were relatively small. Given the threat posed by vessel collisions, a coordinated, multi-national approach to collision mitigation is needed within priority whale shark habitats to ensure collision protection for the specie
Modelling pollutants transport scenarios based on the X-Press Pearl disaster
Get PDFThe MV X-Press Pearl accident near Sri Lanka in May 2021 released several pollutants into the ocean, including
1843.3 t of urea, raising concerns about the impact on the region. This study uses a coupled ocean (NEMO)–
biogeochemistry (ERSEM) model to simulate urea dispersion under various scenarios. While it doesn't directly
reflect the real accident, it provides insights into the potential impact of similar chemical spills. By adjusting
tracer release rates and timing, we assessed their impact on the distribution of the chemical plume. Findings
show slower release rates prolong higher urea concentrations, potentially causing phytoplankton blooms, while
monsoon conditions significantly affect dispersal patterns. Due to a lack of publicly available urea observations,
we used particle tracking experiments validated with data on plastic nurdle beaching. This research shows how a
simpler, affordable scenario approach could inform the management of chemical spills without a fully developed
operational oceanographic system
Efficient plastic detection in coastal areas with selected spectral bands
Get PDFMarine plastic pollution poses significant ecological, economic, and social challenges, necessitating innovative detection, management, and mitigation solutions. Spectral imaging and optical remote sensing have proven valuable tools in detecting and characterizing macroplastics in aquatic environments. Despite numerous studies focusing on bands of interest in the shortwave infrared spectrum, the high cost of sensors in this range makes it difficult to mass-produce them for long-term and large-scale applications. Therefore, we present the assessment and transfer of various machine learning models across four datasets to identify the key bands for detecting and classifying the most prevalent plastics in the marine environment within the visible and near-infrared (VNIR) range. Our study uses four different databases ranging from virgin plastics under laboratory conditions to weather plastics under field conditions. We used Sequential Feature Selection (SFS) and Random Forest (RF) models for the optimal band selection. The significance of homogeneous backgrounds for accurate detection is highlighted by a 97 % accuracy, and successful band transfers between datasets (87 %–91 %) suggest the feasibility of a sensor applicable across various scenarios. However, the model transfer requires further training for each specific dataset to achieve optimal accuracy. The results underscore the potential for broader application with continued refinement and expanded training datasets. Our findings provide valuable information for developing compelling and affordable detection sensors to address plastic pollution in coastal areas. This work paves the way towards enhancing the accuracy of marine litter detection and reduction globally, contributing to a sustainable future for our oceans
Research Impact Spotlight Event Plymouth Sound & the Tamar Catchment
Get PDFEvent report and next steps - A joint event by Plymouth Marine Laboratory (PML), Tamar Estuary Consultative Forum (TECF) and Plymouth Sound National Marine Park (PSNMP
A machine learning model-based satellite data record of dissolved organic carbon concentration in surface waters of the global open ocean
Get PDFDissolved Organic Carbon (DOC) is the largest organic carbon pool in the ocean. Considering the biotic and abiotic factors controlling DOC processes, indirect satellite methods for open ocean DOC estimation can be developed, using conceptual, empirical or statistical models, driven by multiple satellite products. In this study, we infer a time series of global DOC from data of the European Space Agency’s (ESA) Ocean Colour Climate Change Initiative (OC-CCI) in combination with a global database of in situ DOC observations. We tested empirical machine learning modelling approaches in which the available in situ data are used to train the models and to find empirical relationships between DOC and variables available from remote sensing. Of the tested methods, a random forest regression showed the best results, and the details of this model are further reported here. We present a time series of global open ocean DOC concentrations between 2010–2018 that is made freely available through the archive of the UK Centre for Environmental Data Analysis (CEDA)
Impacts of droughts and human activities on water quantity and quality: Remote sensing observations of Lake Qadisiyah, Iraq
Get PDFWater quantity and quality in lakes are closely linked to the compounding effects of climate change and human activities in their catchments, especially for lakes located in semi-arid and arid regions where water resources are scarce. Whilst knowledge gaps exist for these effects in semi-arid and arid region lakes due mainly to the lack of long-term in situ monitoring data. By using satellite remote sensing data, this study firstly investigated the variations of water level, chlorophyll-a concentration (Chl-a) and turbidity in Lake Qadisiyah, Iraq between 2000 and 2019. Results showed that the average water level was 138.3 m in 2000–2019, it decreased clearly in 2001, 2009, 2015 and 2018 with the lowest value of 120 m in July 2015. The mean Chl-a was 6.3 mg/m3 and it showed an overall increasing trend during 2000 and 2019. Turbidity showed extremely high values (>10 NTU) in 2009 and 2017–2018 compared to the mean value of 3.6 NTU in 2000–2019. The boosted regression tree (BRT) was then used to explore the relationship between those variations and El Niño-Southern Oscillation, droughts, meteorological factors and land use land cover changes in the catchment. Results revealed that water level declines were mainly associated with droughts led by La Niña events. Chl-a increase in the lake were mainly explained by built-up area increase and water area decrease in the catchment, with a relative contribution of 29.2 % and 28.6 % respectively. Water area changes in the catchment were the main factor influencing turbidity explaining 55.3 % of the variation. An exception water level decline in 2014–2016 was also observed when there was no drought, which was most likely caused by the cut off of water flow upstream and the release of water from the dam during periods of war. The findings in this study underscored the impacts of climate and human activities on water quantity and quality in semi-arid region lakes. Actions such as improving water use efficiency, establishing water storages, and enhancing cross-border cooperation are therefore recommended to deal with extreme events. Pollution control measures in the catchment are also suggested to prevent water quality deterioration in the lake
Global impacts of marine heatwaves on coastal foundation species
No full textWith increasingly intense marine heatwaves affecting nearshore regions, foundation species are coming under increasing stress. To better understand their impacts, we examine responses of critical, habitat-forming foundation species (macroalgae, seagrass, corals) to marine heatwaves in 1322 shallow coastal areas located across 85 marine ecoregions. We find compelling evidence that intense, summer marine heatwaves play a significant role in the decline of foundation species globally. Critically, detrimental effects increase towards species warm-range edges and over time. We also identify several ecoregions where foundation species don’t respond to marine heatwaves, suggestive of some resilience to warming events. Cumulative marine heatwave intensity, absolute temperature, and location within a species’ range are key factors mediating impacts. Our results suggest many coastal ecosystems are losing foundation species, potentially impacting associated biodiversity, ecological function, and ecosystem services provision. Understanding relationships between marine heatwaves and foundation species offers the potential to predict impacts that are critical for developing management and adaptation approache
The relative impact of co-occurring stressors on the abundance of benthic species examined with three-way correspondence analysis
Get PDFThis paper presents a novel application of three-way correspondence analysis as a technique to analyse three-way
contingency tables with abundance scores of several species. The example data analysis presented was taken from a previous mesocosm experiment and consists of a two-factor experimental design with physical disturbance and organic enrichment as factors, applied to sediment collected from the Oslofjørd, Norway. The focus of the original research was to evaluate the influence of the two factors and their interactions on the abundance of the species present in the sediment. In the current paper we demonstrate that by using a three-way correspon�dence approach it is possible to undertake simultaneous analysis of the species, identifying and evaluating their relative sensitivity to the environmental factors thus adding additional insight than was possible in the original analysis. In particular, this new approach allowed even relatively scarce species to be included in the analysis and evaluated together with abundant species. This paper demonstrates how three-way correspondence analysis can be a useful analytical tool in teasing out effects and interactions from multi-factorial studies
Using semi-automated classification algorithms in the context of an ecosystem service assessment applied to a temperate atlantic estuary
Get PDFThe growing anthropogenic pressure near estuarine areas is evidence of the relevance of these systems to human well-being, especially because of their delivery of essential ecosystem services and benefits. Estuaries are composed of a rich large selection of habitats frequently organised in complex patterns. Mapping and further understanding of these habitats can contribute significantly to environmental management and conservation. The main goal of this study was to integrate different data sources to perform a supervised image classification, using remote-sensing products with different spatial resolutions and features. It was focused on the Sado Estuary, located on the Portuguese Atlantic coast. Considering the limitation of using free satellite images to map estuary habitats (i.e. limited spectral range and spatial resolution), this study uses a semi-automated supervised and pixel-based classification to overcome some of the derived classification problems. Support Vector Machine classifier was used to map the estuary for future evaluation of ecosystem services provided by each habitat. High-resolution remote sensing data (i.e., Planet Scope satellite images, aerial photographs) with different spectral and spatial features (3 m and 20 cm resolution, respectively) were used with ground truthing data to train the classifier and validate the derived maps. The first step of the classification identified broader classes of habitats in the satellite images based on visual interpretation of ground-truth data. From this output, aerial images were classified into detailed classes, the same procedure was hindered on the satellite images due to spatial resolution constraints. The sand class had the best overall accuracy (96%), due to its contrasts with surrounding objects. While the vegetation (i.e., pioneer saltmarshes) and algae classes had lower accuracy values (49.6–89.0%), possibly due to being still damp or covered in fine sediment This is a common challenge in transitional systems across land-water interfaces, such as wetlands, where the abiotic conditions (e.g. solar exposure, tides) fluctuate heterogeneously over time and space. The findings presented herein revealed the considerable success of this approach. For the purpose of local decision-making, these are relevant outputs that can be replicated in other regions worldwide
A framework for optimising opportunistic collaborative syntheses to propel ecological conservation
No full textEcological data are being opportunistically synthesised at unprecedented scales in response to the global biodiversity and climate crises. Such syntheses are often only possible through large-scale, international, multidisciplinary collaborations and provide important pathways for addressing urgent conservation questions. Although large collaborative data syntheses can lead to high-impact successes, they can also be plagued with difficulties. Challenges include the standardisation of data originally collected for different purposes, integration and interpretation of knowledge sourced across different disciplines and spatio-temporal scales, and management of differing perspectives from contributors with distinct academic and cultural backgrounds. Here, we use the collective expertise of a global team of conservation ecologists and practitioners to highlight common benefits and hurdles that arise with the development of opportunistic collaborative syntheses. We outline a framework of “best practice” for developing such collaborations, encompassing the design, implementation, and deliverable phases. Our framework addresses common challenges, highlighting key actions for successful collaboration and emphasizing the support requirements. We identify funding as a major constraint to sustaining the large, international, multidisciplinary teams required to advance collaborative syntheses in a just, equitable, diverse, and inclusive way. We further advocate for thinking strategically from the outset and highlight the need for reshaping funding agendas to prioritize the structures required to propel global scientific networks. Our framework will advance the science needed for ecological conservation and the sustainable use of global natural resources by supporting proto-groups initiating new syntheses, leaders and participants of ongoing projects, and funders who want to facilitate such collaborations in the futur