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Thematic analysis is a highly popular technique among qualitative researchers for analyzing qualitative data, which usually comprises thick descriptive data. However, the application and use of thematic analysis has also involved complications due to confusion regarding the final outcome’s presentation as a conceptual model. This paper develops a systematic thematic analysis process for creating a conceptual model from qualitative research findings. It explores the adaptability of the proposed process across various research methodologies, including constructivist methodologies, positivist methodologies, grounded theory, and interpretive phenomenology, and justifies their application. The paper distinguishes between inductive and deductive coding approaches and emphasizes the merits of each. It suggests that the derived systematic thematic analysis model is valuable across multiple disciplines, particularly in grounded theory, ethnographic approaches, and narrative approaches, while also being adaptable to more descriptive, positivist-based methodologies. By providing a methodological roadmap, this study enhances the rigor and replicability of thematic analysis and offers a comprehensive strategy for theoretical conceptualization in qualitative research. The contribution of this paper is a systematic six-step thematic analysis process that leads to the development of a conceptual model; each step is described in detail and examples are given.

The concept of saturation in qualitative research is a widely debated topic. Saturation refers to the point at which no new data or themes are emerging from the data set, which indicates that the data have been fully explored. It is considered an important concept as it helps to ensure that the findings are robust and that the data are being used to their full potential to achieve the research aim. Saturation, or the point at which further observation of data will not lead to the discovery of more information related to the research questions, is an important aspect of qualitative research. However, there is some mystification and semantic debate surrounding the term saturation, and it is not always clear how many rounds of research are needed to reach saturation or what criteria are used to make that determination during the thematic analysis process. This paper focuses on the actualisation of saturation in the context of thematic analysis and develops a systematic approach to using data to justify the contribution of research. Consequently, we introduce a distinct model to help researchers reach saturation through refining or expanding existing quotations, codes, themes and concepts as necessary.

As global temperatures continue to rise, shallow coral reef bleaching has become more intense and widespread. Mesophotic coral ecosystems reside in deeper (30–150 m), cooler water and were thought to offer a refuge to shallow-water reefs. Studies now show that mesophotic coral ecosystems instead have limited connectivity with shallow corals but host diverse endemic communities. Given their extensive distribution and high biodiversity, understanding their susceptibility to warming oceans is imperative. In this multidisciplinary study of an atoll in the Chagos Archipelago in the central Indian Ocean, we show evidence of coral bleaching at 90 m, despite the absence of shallow-water bleaching. We also show that the bleaching was associated with sustained thermocline deepening driven by the Indian Ocean Dipole, which might be further enhanced by internal waves whose influence varied at a sub-atoll scale. Our results demonstrate the potential vulnerability of mesophotic coral ecosystems to thermal stress and highlight the need for oceanographic knowledge to predict bleaching susceptibility and heterogeneity. Rising global temperatures cause widespread bleaching of shallow coral reefs but mesophotic reefs at depths over 30 metres are thought to be sheltered by cooler waters. Here, at sites in the Chagos Archipelago, the authors show bleaching of corals at depths of 90 metres, which might be due to warm surface waters being pushed deeper by the ocean’s response to the Indian Ocean Dipole.

Anthropogenic litter is present in all marine habitats, from beaches to the most remote points in the oceans. On the seafloor, marine litter, particularly plastic, can accumulate in high densities with deleterious consequences for its inhabitants. Yet, because of the high cost involved with sampling the seafloor, no large-scale assessment of distribution patterns was available to date. Here, we present data on litter distribution and density collected during 588 video and trawl surveys across 32 sites in European waters. We found litter to be present in the deepest areas and at locations as remote from land as the Charlie-Gibbs Fracture Zone across the Mid-Atlantic Ridge. The highest litter density occurs in submarine canyons, whilst the lowest density can be found on continental shelves and on ocean ridges. Plastic was the most prevalent litter item found on the seafloor. Litter from fishing activities (derelict fishing lines and nets) was particularly common on seamounts, banks, mounds and ocean ridges. Our results highlight the extent of the problem and the need for action to prevent increasing accumulation of litter in marine environments.

In 2009 the NW and SE flanks of Anton Dohrn Seamount were surveyed using multibeam echosounder and video ground-truthing to characterise megabenthic biological assemblages (biotopes) and assess those which clearly adhere to the definition of Vulnerable Marine Ecosystems, for use in habitat mapping. A combination of multivariate analysis of still imagery and video ground-truthing defined 13 comprehensive descriptions of biotopes that function as mapping units in an applied context. The data reveals that the NW and SE sides of Anton Dohrn Seamount (ADS) are topographically complex and harbour diverse biological assemblages, some of which agree with current definitions of 'listed' habitats of conservation concern. Ten of these biotopes could easily be considered Vulnerable Marine Ecosystems; three coral gardens, four cold-water coral reefs, two xenophyophore communities and one sponge dominated community, with remaining biotopes requiring more detailed assessment. Coral gardens were only found on positive geomorphic features, namely parasitic cones and radial ridges, found both sides of the seamount over a depth of 1311-1740 m. Two cold-water coral reefs (equivalent to summit reef) were mapped on the NW side of the seamount; Lophelia pertusa reef associated with the cliff top mounds at a depth of 747-791 m and Solenosmilia variabilis reef on a radial ridge at a depth of 1318-1351 m. Xenophyophore communities were mapped from both sides of the seamount at a depth of 1099-1770 m and were either associated with geomorphic features or were in close proximity (< 100 m) to them. The sponge dominated community was found on the steep escarpment either side of the seamount over at a depth of 854-1345 m. Multivariate diversity revealed the xenophyophore biotopes to be the least diverse, and a hard substratum biotope characterised by serpulids and the sessile holothurian, Psolus squamatus, as the most diverse.

Benthic ecosystems are chronically undersampled, particularly in environments >50 m depth. Yet a rising level of anthropogenic threats makes data collection ever more urgent. Currently, modern underwater sampling tools, particularly autonomous underwater vehicles (AUVs), are able to collect vast image data, but cannot bypass the bottleneck formed by manual image annotation. Computer vision (CV) offers a faster, more consistent, cost effective and sharable alternative to manual annotation. We used TensorFlow to evaluate the performance of the Inception V3 model with different numbers of training images, as well as assessing how many different classes (taxa) it could distinguish. Classifiers (models) were trained with increasing amounts of data (20 to 1000 images of each taxa) and increasing numbers of taxa (7 to 52). Maximum performance (0.78 sensitivity, 0.75 precision) was achieved using the maximum number of training images but little was gained in performance beyond 200 training images. Performance was also highest with the least classes in training. None of the classifiers had average performances high enough to be a suitable alternative to manual annotation. However, some classifiers performed well for individual taxa (0.95 sensitivity, 0.94 precision). Our results suggest this technology is currently best applied to specific taxa that can be reliably identified and where 200 training images offers a good compromise between performance and annotation effort. This demonstrates that CV could be routinely employed as a tool to study benthic ecology by non-specialists, which could lead to a major increase in data availability for conservation research and biodiversity management.

Modelling approaches have the potential to significantly contribute to the spatial management of the deep-sea ecosystem in a cost effective manner. However, we currently have little understanding of the accuracy of such models, developed using limited data, of varying resolution. The aim of this study was to investigate the performance of predictive models constructed using non-simulated (real world) data of different resolution. Predicted distribution maps for three deep-sea habitats were constructed using MaxEnt modelling methods using high resolution multibeam bathymetric data and associated terrain derived variables as predictors. Model performance was evaluated using repeated 75/25 training/test data partitions using AUC and threshold-dependent assessment methods. The overall extent and distribution of each habitat, and the percentage contained within an existing MPA network were quantified and compared to results from low resolution GEBCO models. Predicted spatial extent for scleractinian coral reef and Syringammina fragilissima aggregations decreased with an increase in model resolution, whereas Pheronema carpenteri total suitable area increased. Distinct differences in predicted habitat distribution were observed for all three habitats. Estimates of habitat extent contained within the MPA network all increased when modelled at fine scale. High resolution models performed better than low resolution models according to threshold-dependent evaluation. We recommend the use of high resolution multibeam bathymetry data over low resolution bathymetry data for use in modelling approaches. We do not recommend the use of predictive models to produce absolute values of habitat extent, but likely areas of suitable habitat. Assessments of MPA network effectiveness based on calculations of percentage area protection (policy driven conservation targets) from low resolution models are likely to be fit for purpose.

Aim: To demonstrate the application of predictive species distribution modelling methods to habitat mapping and assessment of percentage area-based conservation targets. Location: The NE Atlantic deep sea (UK and Irish extended continental shelf limits). Methods: MaxEnt modelling of three listed habitats (Lophelia pertusa (Linnaeus, 1758) reef (LpReef), Pheronema carpenteri (WyvilleThomson, 1869) aggregations (PcAggs) and Syringammina fragilissima (Brady, 1883) aggregations (SfAggs)), with some pre-selection of variables by generalized additive modelling. Models are validated using repeated 70/30 build/test data splits using AUC and threshold-dependent assessment methods. Predicted distribution maps are used to assess the adequacy of existing area closures for the protection of listed habitats and to assess percentage representation of each community within existing MPA networks. Results: Model performances are rated as fair (LpReef), excellent (PcAggs) and good (SfAggs). Current closures are focused on the protection of cold-water coral reef and incidentally capture some SfAggs suitable environments, but largely fail to protect PcAggs. Considering the wider network of MPAs in the study region, approximately 23% (LpReef), 2% (PcAggs) and 6% (SfAggs) of the area predicted as suitable for each habitat respectively is contained within an MPA. Main conclusions: To date, decisions on area closures for the protection of 'listed' deep-sea habitats have been based on maps of recorded presence of species that are taken as being indicative of that habitat. Predictive habitat modelling may provide a useful method of better estimating the extent of listed habitats, providing direction for future MPA establishment and a means of assessing MPA network effectiveness against politically set percentage targets. Given the coarse resolution of the model, percentages should be taken as maximal figures, with habitat occurrence likely to be less prevalent in reality.

AimResearch on mesophotic coral ecosystems (MCEs) has increased exponentially in recent decades, and the significance of this ecosystem has been recognised both in terms of biodiversity and distribution. However, this research has mostly focussed on corals and is globally sporadic, with the Indian Ocean remaining largely unexplored and overall MCEs under protected. Hence, baseline data on MCE benthic communities is lacking, but nonetheless essential for developing adequate management strategies. Here, we assess the variation in diversity and community structure of MCEs along the depth gradient and among sites in the Indian Ocean and the environmental parameters that are potentially driving these differences.LocationEgmont Atoll, Chagos Archipelago, central Indian Ocean.Time PeriodPresent.Major Taxa StudiedMarine benthic invertebrates, plants.MethodsUsing a remotely operated vehicle, we collected video transects between 15 and 160 m. We analysed the diversity and composition of benthic communities and used DistLM analysis to determine the environmental drivers of the community structure over the depth gradient and between sites.ResultsWe observed distinct benthic communities along the depth gradient, with a strong community break observed at ~60 m. We also identified a clear zonation of the benthic taxa with depth. This zonation was primarily driven by downward irradiance and temperature, with additional environmental processes playing a minor role in structuring the benthic communities.Main ConclusionsWe show that MCEs in the Chagos Archipelago are distinct communities, and their distribution is driven primarily by irradiance and temperature. Our results highlight the variability in benthic community structure of MCEs, both along the depth gradient and at local geographical scales for the study region. This study showcases the uniqueness of MCEs and will aid in predicting their distribution, potential refugia for shallow reefs, and in developing evidence-based protection for MCEs, to maintain overall marine biodiversity.

Approaches to measuring marine biological parameters remain almost as diverse as the researchers who measure them. However, understanding the patterns of diversity in ocean life over different temporal and geographic scales requires consistent data and information on the potential environmental drivers. As a group of marine scientists from different disciplines, we suggest a formalized, consistent framework of 20 biological, chemical, physical, and socioeconomic parameters that we consider the most important for describing environmental and biological variability. We call our proposed framework the General Ocean Survey and Sampling Iterative Protocol (GOSSIP). We hope that this framework will establish a consistent approach to data collection, enabling further collaboration between marine scientists from different disciplines to advance knowledge of the ocean (deep-sea and mesophotic coral ecosystems).