Browsing by Author "Karenyi, Natasha"

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    Open Access
    Assessing the utility of open-source data in exploring benthic biodiversity in mining concessions off the South African coast
    (2022) Lennox, Emma; Karenyi, Natasha
    Extractive activity in the marine realm is a well-recognised pressure on the marine environment, particularly for the preservation of biodiversity (Majiedt et al. 2019). Data that are openly available, from repositories, guides and within other studies, are a growing resource available to researchers, with the benefits including accessibility, cost effectiveness, and access to long-term data. Data were sourced from the Ocean Biodiversity Information System (OBIS), Offshore Invertebrate Field Guide (Atkinson and Sink) and mining impact datasets (Steffani and Pulfrich 2008, Cook 1995, 1996, 1997 and 1999) to explore the utility of openly available data in exploring benthic biodiversity within two mining concessions on the west and south coasts of South Africa. Lists of benthic taxa were generated, and biodiversity quantified using quantitative measures (species richness, Shannon-Wiener index) and multivariate analyses, where possible. Each dataset provided a different aspect of the benthic biota within the concessions, including taxonomic data (i.e., species, genera, class, phylum) that are easily quantified within a particular region. Long-term data available from OBIS allowed for patterns to be visualised over time, although this was constrained by data gaps, differences in methodology and lack of metadata, for instance. For the purposes of assessing how anthropogenic pressure impacts biodiversity, the utility of open-source data was limited to datasets that considered the impacts of mining in particular. To consider the impacts different types of extractive activity have on biodiversity at a finer scale, in-situ sampling of the proposed area is substantially more beneficial than open-source data in assessing the particular ways ecosystems are impacted by anthropogenic activity.
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    Open Access
    Climate change and small-scale fishing in South Africa: a community scale social vulnerability assessment for the southern Cape handline fishery
    (2022) Andra, Kayla; Jarre, Astrid; Karenyi, Natasha
    Climate change is majorly affecting the quality and quantity of marine organisms, as well as people's livelihoods. Coastal communities, small-scale fishers, and fishing-reliant individuals are especially vulnerable to climate change impacts (and other stressors) that alter the state and availability of ocean resources. Therefore, implementing integrated management approaches (such as the Ecosystem Approach to Fisheries (EAF)) is crucial to address these vulnerabilities. This study concerns the vulnerability to the impacts of climate change of fishers who act as crew members in the southern Cape commercial handline fishery. The southern Cape is a rural, peri-urban, and urban region characterised by agriculture, fishing, tourism, and retirement services as major economic activities. Aside from their documented social, governance, and economic stressors, small-scale fishers in the southern Cape also face biophysical stress (i.e., changes in wind, rainfall, and sea state). The Global learning for understanding local solutions (GULLS) survey instrument and a social vulnerability framework were created to assess vulnerability and its comprising concepts (sensitivity, exposure, and adaptive capacity) for coastal communities experiencing notable climate change. This study presents the first quantitative analysis of the data collected in the southern Cape in 2014-15. Univariate and multivariate analyses were performed to answer four key questions to investigate whether social vulnerability (as well as sensitivity, exposure, and adaptive capacity) differed among the communities, how variable vulnerability is within the communities, what drives the differences among the communities and to gain insight on the implications for issues of scales. The fishing communities differed significantly in their overall social vulnerability scores. Sensitivity and exposure were similar between the fishing communities. Sensitivity was the main driver of vulnerability for all fishers due to their attachment to fishing as an occupation, low self-sufficiency, and attachment to their communities. There was no significant difference in the dispersion (homogeneity) of the fishers' responses within the communities. The results also corroborate previous qualitative research, showing that variations between the communities are driven by adaptive capacity. The “component” scale (the second scale of the four-scale GULLS framework) yielded the most beneficial results and is recommended for future analyses. In addition, recommendations are made for future surveys to address uneven weighting, fundamental system changes (such as COVID-19), and questions irrelevant to the southern Cape small-scale fishers. Overall, with these recommendations, an improved survey offers a quicker methodology that can easily be communicated with various decision-makers and paves the way for consistent temporal comparisons that stimulate a long-term understanding of vulnerability. Most importantly, these recommendations and methods can contribute to the Ecosystem Approach to Fisheries (EAF) implementation in the southern Cape and the sustainability of this marine social-ecological system.
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    Open Access
    Marine ecosystem classification and conservation targets within the Agulhas ecoregion, South Africa
    (2022) Nefdt, Leila; Karenyi, Natasha; Griffiths, Charles; Sink, Kerry; Atkinson, Lara
    Deep-sea benthic ecosystems remain poorly studied in South Africa, limiting understanding of community biodiversity patterns and their environmental drivers. This is one of the first studies to (i) visually investigate marine epifaunal community patterns and their environmental drivers along the Agulhas ecoregion outer shelf, shelf edge and upper slope to support marine ecosystem classification and mapping, and (ii) to determine the conservation targets for selected national marine ecosystem types to inform improved management of the marine environment, through Marine Spatial Planning processes. Visual surveys of the seabed were conducted to quantify epifauna during the ACEP Deep Secrets Cruise in 2016, using a towed benthic camera system. Twenty-nine sites were sampled, ranging from 120-700 m in depth and spanning the shelf-slope transition from the western edge of the Agulhas Bank to offshore of the Kei River mouth. A total of 855 seabed images were processed, and 173 benthic taxa quantified. Corresponding environmental variables were used to determine potential drivers of observed biodiversity patterns. Data were analysed using multivariate analyses, including CLUSTER, MDS and DistLM, in PRIMER v6 with PERMANOVA. Ten different epifaunal communities were classified and described with key characteristic taxa identified. Communities found in habitats that comprised mostly hard rocky substrata generally exhibited higher in species richness and were most commonly characterized by stalked crinoids, various corals and bryozoans, whereas communities found in habitats comprising unconsolidated sediment were lower in species richness and commonly characterized by polychaetes, cerianthids and brittle stars. Communities found in habitats comprising both hard and soft substrata had a mix of the above-mentioned epifauna. The distribution of these communities was mostly influenced by substratum type, longitude, trawling intensity, depth, and presence of visible particulate organic matter. The combined interactions of topography, substratum and the unique hydrodynamic conditions along the Agulhas ecoregion shelf-slope transition are likely responsible for the observed patterns. The observed community patterns were also compared to the existing classification of marine ecosystem types from the 2018 National Biodiversity Assessment. Fine-scale heterogeneity was revealed within the examined marine ecosystem types, particularly with substratum type and associated community variability and should be recognized and incorporated into future iterations of the national marine ecosystem classification and map. Species-area curves were used to calculate conservation targets for three ecosystem types, defined by the 2018 National Biodiversity Assessment, namely the Agulhas Coarse Sediment Shelf Edge, South West Indian Upper Slope, and the Agulhas Rocky Shelf Edge. Considering the epifaunal species richness (using the bootstrap estimator) and area, per image and per ecosystem type, the rate of accumulation of species was calculated and used to estimate the percentage of species expected to be represented by any given percentage of protected ecosystem type area. Between 20 and 30% of the area within these ecosystem types will need to be protected to represent 80% of the species. This study has shown that an integration of environmental parameters together with biodiversity measures to better understand and classify offshore benthic ecosystems has worked well. However, to improve the resolution of the national marine ecosystem classification and map, there needs to be greater input of fine-scale biological and environmental sampling and mapping of substratum types across the Agulhas ecoregion shelf-slope transition zone. This work is contributing to improvements in the national marine ecosystem classification and map and hence the spatial assessment and planning processes that rely on these products.
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    Open Access
    Patterns and potential environmental drivers of mesophotic communities of the warm temperate shelf of the Amathole Region, South Africa
    (2021) Adams, Luther A; Karenyi, Natasha; Parker, Denham
    Foundational biodiversity research has seen a recent shift from the collection of epibenthic data using destructive methods to less destructive methods that include visual surveys- using underwater camera platforms to explore the seabed. South African mesophotic ecosystems are under-sampled compared to both their shallower and deeper counterparts. The Amathole offshore region, considered as a transition zone between the Agulhas and Natal ecoregions, is a historically unexplored region of the South African coastline. This thesis aimed to define and describe the benthic communities and identify the processes driving their distribution on the temperate shelf in the Amathole offshore region, using a Remotely Operated Vehicle (ROV). This study piloted the application of the Australian developed CATAMI classification scheme to annotate images collected by ROV in South Africa. Data were collected on the ACEP: Imida Frontiers project on board the RV Phakisa during January and May 2017 off the Kei River, Amathole offshore region. This survey combined 14 sites comprising 215 images from remotely operated vehicle imagery and nine environmental variables from 30 to 100 m water depth. Multivariate analyses (multidimensional scaling and a cluster dendrogram) of image data produced nine benthic communities. Similarly, multivariate analyses (principal component analyses, distancebased linear model and distance-based redundancy analysis and constrained binary divisive clustering analysis) of the environmental data revealed that substratum type and correlates of depth to be the main variables likely responsible for the observed biodiversity patterns. Additionally, the LINKTREE analysis revealed a depth break at 74 m which established the boundary between the upper and lower mesophotic zone in this region. Rhodolith bed communities were discovered in the upper mesophotic and are a welcomed novel ecosystem type for South African benthic ecologists. The upper mesophotic zone was also characterised by communities of dense brittle star aggregations and reefs dominated by macroalgae. The lower mesophotic zone was characterised by animal forests consisting of communities dominated by sponge gardens with diverse growth forms and dense stands of canopy forming gorgonians. This thesis provides recommendations for future research and guidelines for future ROV field sampling. It also highlights the need for greater sampling effort by ROV on unconsolidated substratum and at depths greater than 74 m. The use of morphospecies in image classification to define macrobenthic communities on an unexplored continental shelf was effective despite limited knowledge of species. Similarly, the environmental variables that structure these temperate shelf communities were identified. Information from this study contributed to the foundational biodiversity information needed to inform marine spatial planning and spatial management efforts for the newly proclaimed Amathole Offshore Marine Protected Area and the greater Amathole offshore region.
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    Quantifying phytoplankton biomass and sediment in river plumes along the Agulhas Bank using remotely sensed data with deep learning techniques
    (2023) Pillay, Humeshni; Karenyi, Natasha
    River plumes play a major role in land-sea connectivity by providing essential nutrients, sediment, and organic matter to marine environments, and maintaining ecosystem function and habitat structure. Phytoplankton communities' reliance on riverine nutrient deposition to build up their biomass and suspended sediments released from terrestrial systems have been identified as major products of riverine outputs that impact pelagic and benthic ecosystems. Quantifying their concentrations in river plumes could improve our understanding of how anthropogenic activities, seasonality and climate change affect their concentrations in marine habitats and ecosystems over time. The need to monitor phytoplankton biomass and sediment loads in coastal systems has encouraged the use of remotely sensed data obtained from satellites. Remote sensing capabilities have advanced over the past few decades, along with increased computational efficiency, and sizeable open-access data pipelines. However, current satellite data products are not always appropriate for highly turbid, optically complex waters, along coastal regions where Atmospheric Correction (AC) is known to be challenging. The aim of this project was to use four rivers along the Agulhas Bank of South Africa to test satellite-derived Total Suspended Matter (TSM) and chlorophyll-a concentration ([Chl-a]) data products using a suite of available AC options against in situ [Chl-a] and TSM. The in situ data were collected through filtrations for fluorometric ([Chl-a]) and gravimetric (TSM) analyses. The performances of geophysical algorithms for TSM and [Chl-a] were assessed using remote sensing reflectance (Rrs) derived from standard Sentinel-3 Level-2 files, as well as two alternative approaches to AC, namely the Case-2 Regional CoastColour (C2RCC) processor, and the POLYnomial-based for MERIS (POLYMER). Thereafter, a regionally parameterised deep learning neural network (Multi-Layer Perceptron; MLP) model for retrieving TSM and [Chl-a] was developed and evaluated in the context of its application to Sentinel-3 satellite data. The MLP model is trained on a synthetic dataset of Rrs parameterised using the in situ ranges of TSM and [Chl-a]. The MLP model was evaluated using the three mentioned ACs as the model requires Rrs uncontaminated by the atmosphere. The regional MLP model was separated into a model containing the full range of TSM in situ values and a constrained version. POLYMER's [Chl-a] and C2RCC's TSM were the best geophysical products over the region when assessing the accuracy of retrievals to the match-up in situ dataset. However, AC is a major concern in regions where land-based fynbos biogenic burning is common. As a result, the current [Chl-a] satellite data product performances were not optimal, especially when mapped over the open ocean region. For the developed [Chl-a] MLP algorithm, the constrained and full MLP models were similar, with C2RCC's AC producing the best results. However, the constrained TSM MLP model's results vastly outperformed the full MLP model, with no clear delineation for the best-performing AC. The use of deep learning models shows promising results over the optically complex Agulhas Bank River plumes, however, the effectiveness of the MLP technique may be dependent on the variability of in situ data (i.e. phytoplankton size) used to create the training synthetic dataset, as well as the quality of Rrs applied to the MLP models. Thus, more in situ data is required to develop flexible, regional algorithms for highly turbid waters along South Africa's atmospherically variable coastlines.
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    Open Access
    The influence of sampling method on detecting benthic biodiversity patterns at the ecoregion scale on the South African west coast
    (2022) Brandt, Silke; Karenyi, Natasha; Atkinson, Lara
    Long-term monitoring of marine benthic communities provides data which are essential for effective ocean management. However, long-term monitoring is limited by the difficulty and expense of sampling deep seafloor areas comprehensively enough to represent the whole benthic community. This has led to the development of a wide array of seafloor sampling methods. Consequently, the integration and prioritisation of data collected using different methods remains an area of concern. Demersal research trawling and grab sampling are two methods employed to sample the marine benthos in South Africa, targeting different habitats and fauna, at different scales and with different sampling efficiencies. Both datasets inform the national marine ecosystem classification, yet the consistency between biodiversity patterns detected by each sampling method has not yet been explored. The aim of this thesis is to determine the influence of sampling method on the detection of benthic biodiversity patterns. This was explored using demersal trawl and grab datasets collected from twenty-four pairs of stations within the Southern Benguela Shelf ecoregion on the west coast of South Africa (70 – 600 m) between the years 2009 and 2020. Differences in benthic structure, univariate diversity indices, and multivariate assemblage structure were compared between demersal trawl and grab datasets using both taxonomic and trait-based approaches. This study utilised the rarely applied co-correspondence analysis (CoCA) to test for congruency in multivariate assemblage patterns sampled by trawls and grabs. Furthermore, the use of Biological Traits Analysis (BTA) allowed for the assessment of functional diversity patterns which is often a missing link when measuring biodiversity relative to ecosystem functioning. The epifaunal community was dominated by Asteroidea, Decapoda and Gastropoda, whereas Polychaeta, Amphipoda and Bivalvia dominated infaunal communities. BTA found trawl samples to be dominated by epifauna exhibiting large sizes, dorso-ventrally flattened body forms, free-living and surface-crawling life habits, moderate mobility, predatory feeding, planktotrophic larval development and medium-long lifespans. Grab samples were dominated by infauna with small sizes, vermiform or laterally flattened body forms, low mobility, surface deposit feeding strategies, planktotrophic larval development and short lifespans. Demersal trawl and grab sampling detected significantly similar patterns in species abundance, species richness, species diversity, and functional richness values across the west coast. Species evenness, functional evenness, functional diversity, and functional redundancy gave no evidence of a significant relationship between the two sampling methods. CoCA found infaunal assemblage patterns to be highly correlated with epifaunal assemblage patterns across the study area using both taxonomic and trait-based approaches. Environmental and spatial gradients, including depth, longitude, and sediment characteristics, played key roles in structuring broad scale biodiversity patterns. The results of this thesis have implications for how biological datasets from trawl and grab surveys should be prioritised or weighted at different ecological scales when incorporated in the South African marine ecosystem classification and mapping process. This is the first step in transitioning the current ecosystem classification from a data informed, expert driven approach to an expert informed, data driven approach through the use of quantitative multivariate statistical techniques. Furthermore, multi-method biodiversity studies are crucial to represent the entire benthic community and understanding the extent to which the choice of sampling method affects the biodiversity patterns detected is an integral component of accurate ecosystem delineation. The findings from this thesis can be applied to future assessments of South Africa's marine ecosystem classification, increasing its accuracy, and therefore contributing to improved ecosystem-based sea-use management.
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    Towards informing a data-driven approach to marine bioregionalization in South Africa: a case study using benthic epifaunal datasets from the southern Benguela shelf
    (2023) Wozniak, Donia; Karenyi, Natasha
    Marine bioregional, ecosystem and habitat classifications and maps are important for understanding and managing the marine environment. Benthic epifaunal assemblages often inform marine ecosystem classifications and maps, being recognized as good surrogates for broad benthic biodiversity patterns. In South Africa, ecosystem classification and mapping follow an expert-derived data-informed hierarchical approach. A move towards employing data-driven approaches to bioregionalization using quantitative biological and environmental datasets is underway. However, quantitative datasets collected with different sampling methods cannot easily be combined in analyses. It is also unknown whether available biological and environmental datasets can sufficiently define bioregions using existing datadriven approaches. As a case study, this research focuses on the southern Benguela shelf, located on the western margin of South Africa, where research trawl and towed camera sampling methods regularly collect quantitative data on epifaunal assemblages. This research therefore aims to 1) quantify congruency of epifaunal abundance patterns detected by a research trawl and a towed camera, so that their datasets can be appropriately weighted or prioritised in data-driven approaches and to 2) classify and predict epifaunal bioregions by applying a data-driven approach to bioregionalization using abundance data collected by research trawling. Various univariate and multivariate analyses were used to compare differences in species composition, diversity and assemblage structure at 18 sites (50–700 m) collected between 2017 and 2020 by towed camera and research trawl sampling methods. To quantify congruency in multivariate assemblage patterns between sampling methods, a symmetric cocorrespondence analysis (Co-CA) was used on the log+1 transformed abundance matrices. Univariate patterns of diversity were not significantly correlated between sampling methods, which detected mostly different subsets of epifaunal assemblages. The towed camera detected small and patchily distributed epifauna (e.g. the small brittle star Ophiura trimeni) and Anthozoans better than the trawl, while the trawl captured patterns of larger, highly motile Decapoda (e.g. the hermit crab Sympagurus dimorphous) and burrowing Asteroidea better than the towed camera. Though broad similarities in assemblage structure were evident between sampling methods, with high correlations found between important Co-CA axes (r = 0.93, 0.93, 0.79, 0.80), patterns were not significantly similar (p > 0.05). To statistically determine epifaunal bioregions across the study area, Regions of Common Profile (RCP) models were applied, using abundance data collected by research trawling between 2017 and 2020 from 325 sites. An RCP modelling approach was selected as a potential data-driven method for marine bioregionalization in South Africa, since classification and prediction are performed simultaneously, thereby quantifying uncertainty in estimated bioregions. Research trawl datasets were used due to their systematic sampling design which covers a greater spatial and temporal extent than other sampling methods across the study area. Rare species and collinear predictors were removed prior to modelling, resulting in 46 species and three environmental predictors (bottom temperature, dissolved oxygen and slope) used in the final model. Five bioregions were identified, based on lowest Bayesian Information Criterion (BIC). Low values of dissolved oxygen (< 0.3 ml/l) and low bottom temperature (3.76– 5.24 ˚C) were important predictors for bioregions which aligned with the inner shelf (RCP 5, < 150 m) and upper slope (RCP 1, > 500 m) respectively. These bioregions were associated with the highest confidence in spatial predictions. The highest uncertainty was attributed to bioregions across intermediate depths (RCP 2, 3 and 4, ~150–500 m) where species richness was highest. This study recommends the use of both towed camera and research trawl datasets to describe epifaunal assemblages holistically, though consideration should be given to the strengths and limitations of each dataset for specific applications. A low sample size of sites available for comparison between sampling methods may have influenced findings to be inconclusive, and further comparisons are recommended. However, findings suggest the strength of congruence between sampling methods is dependent on the species, habitat and spatial scale (resolution) of interest. Bioregions defined in this study aligned with broad depth breaks and known biogeographical patterns, though further effort is required to source and test relevant environmental predictors for species distributed across intermediate depth ranges (~150– 500 m). Data-driven approaches to bioregionalization which can quantify uncertainty in spatial predictions of bioregions and utilise quantitative datasets provide more information for management applications. As such, RCP models informed by research trawl datasets could be a viable option when delineating marine bioregions for South Africa, though validation with independent datasets is strongly recommended. This study highlights the importance of context, method and spatial resolution when detecting ecological patterns. When collecting data, different sampling methods may detect patterns with varying degrees of congruence depending on location, species sampled or the spatial scale of interest. When analysing data, the type and quantity of environmental predictors and species used to inform data-driven approaches likely influence bioregional patterns produced. The importance of long-term monitoring using a variety of sampling methods is emphasised to reliably compare and quantify bioregional patterns. Rigorous comparisons between datasets and analytical approaches are encouraged to improve understanding of their advantages and limitations. This study contributes methodological advances towards informing a data-driven approach to marine bioregionalization in South Africa.