Data-poor Areas Research Articles

Prevalence of sudden unexpected death of infants and its subcategories in low- or middle-income countries: a systematic review protocol.

Infant mortality in low or middle-income countries (LoMICs) is still triple that of high-income countries (HICs), and the high mortality burden regions are also weighed down by a triple or quadruple burden of disease such as HIV and tuberculosis; chronic illness; mental health; injury and violence; and maternal, neonatal and child mortality. Emerging data suggest that the sudden unexpected death in infancy (SUDI) burden in LoMICs is at least 10-fold that in HICs. While ending preventable deaths in the neonatal period has received some global attention, the postnatal period where SUDIs occur is a poorly understood and data-poor area in LoMICs. We propose conducting a systematic review to evaluate the burden and trends of SUDIs in LoMICs since 2004. We will systematically search PubMed, Web of Science, Scopus, African Index Medicus, EBSCOHost, Google Scholar, WHOIS and WHO database to identify studies published from July 2004 until October 2024. Two reviewers will screen titles and abstracts and select full-text articles independently for review. We will use the tool developed by the South African Medical Research Council-Burden of Disease Review Manager (BODRevMan)-to assess the risk of bias for each included study. Risk of bias will be assessed for each included study. Information on the prevalence and/or incidence of SUDI and its subcategories and case definitions will be extracted from each article. Where possible, data on prevalence, incidence and subcategories will be pooled using a random effects meta-analysis to account for variability between estimates. The I2 statistic will establish the level of heterogeneity due to variation in estimates rather than chance. Results will be presented in tables and graphs. The systematic review will be reported according to the PRISMA 2020 checklist. Ethical approval is not required as this is a protocol for a systematic review. Findings will be disseminated through peer-reviewed publications and conference presentations. CRD42023466162.

Understanding Groundwater Resource Vulnerability at Bryce Canyon National Park, Utah, Using Applied Geophysics at the Rubys Inn Thrust Fault

Abstract Recent development near Bryce Canyon National Park, Utah, could affect local groundwater usage, availability, and dependent resources. The National Park Service and Utah Geological Survey conducted a geophysical study targeting the Rubys Inn thrust fault. This fault lies between commonly targeted aquifers in Emery Valley and groundwater flow systems of the Paunsaugunt Plateau, which support springs and dependent ecosystems. Fault zone geometry and internal structure are complex, resulting in a heterogeneous permeability distribution that affects groundwater flow. The influence of fault zones on groundwater flow parallel and perpendicular to their planes is difficult to predict. Geophysical imaging can yield important information about subsurface fault geometry. We utilized electrical resistivity tomography (ERT) surveys to investigate the influence of the Rubys Inn fault on groundwater occurrence and movement along the southeast boundary of Emery Valley. We collected ERT data along three transects orthogonal to the mapped fault strike in May and September 2022. Where available, we used water-level and lithologic data to constrain ERT inversion model interpretations. The inversion models illustrate the complexity and variability of the Rubys Inn fault within a short distance along strike. Where the fault is concealed, results indicate that the actual and mapped locations differ by 70–100 m along the transects. Groundwater is well constrained in the hanging wall, but poorly constrained in the footwall, and some seasonal variation is discernible. Variable stratigraphy and structure are apparent in all transects. This study enables strategic placement of test wells that will further establish the influence of the Rubys Inn fault on the occurrence and movement of groundwater in and adjacent to the fault zone. The study demonstrates that ERT is a cost-effective and noninvasive tool for detecting the precise surface location and delineating subsurface fault geometry in otherwise data-poor areas with sensitive ecological or archaeological resources.

Questionnaire surveys to investigate marine mammal fisheries bycatch: systematic review and best practice

Bycatch is the most significant threat to marine mammals globally. There are increasing requirements for national governments to fulfil their obligations to international agreements and treaties to assess fisheries catch and bycatch of non-target species. Questionnaire surveys represent one low-cost method to collect data to estimate fisheries catch and bycatch of vulnerable species including marine mammals. Questionnaire surveys can be particularly advantageous when bycatch is being investigated on large spatial and temporal scales, or in data-poor areas. This review aims to provide the necessary guidance required to design and conduct questionnaire studies investigating marine mammal bycatch. To do so, a systematic review was conducted of the methods used in 91 peer-reviewed or grey literature questionnaire studies from 1990 to 2023 investigating marine mammal bycatch. Literature was searched, screened, and analysed following the RepOrting standards for Systematic Evidence Syntheses (ROSES) protocols. A narrative synthesis and critical evaluation of the methods used were conducted and best practice recommendations are proposed. The recommendations include suggestions for how to generate representative samples, the steps that should be followed when designing a questionnaire instrument, how to collect reliable data, how to reduce under-reporting and interviewer bias, and how weighting or model-based bycatch estimation techniques can be used to reduce sampling bias. The review’s guidance and best practice recommendations provide much-needed resources to develop and employ questionnaire studies that produce robust bycatch estimates for marine mammal populations where they are currently missing. Recommendations can be used by scientists and decision-makers across the globe. Whilst the focus of this review is on using questionnaires to investigate marine mammal bycatch, the information and recommendations will also be useful for those investigating bycatch of any other non-target species.

Geostatistical Simulation of Daily Rainfall Fields—Performance Assessment for Extremes in West Africa

Abstract The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.

Testing spatial transferability of species distribution models reveals differing habitat preferences for an endangered delphinid (Cephalorhynchus hectori) in Aotearoa, New Zealand.

Species distribution models (SDMs) can be used to predict distributions in novel times or space (termed transferability) and fill knowledge gaps for areas that are data poor. In conservation, this can be used to determine the extent of spatial protection required. To understand how well a model transfers spatially, it needs to be independently tested, using data from novel habitats. Here, we test the transferability of SDMs for Hector's dolphin (Cephalorhynchus hectori), a culturally important (taonga) and endangered, coastal delphinid, endemic to Aotearoa New Zealand. We collected summer distribution data from three populations from 2021 to 2023. Using Generalised Additive Models, we built presence/absence SDMs for each population and validated the predictive ability of the top models (with TSS and AUC). Then, we tested the transferability of each top model by predicting the distribution of the remaining two populations. SDMs for two populations showed useful performance within their respective areas (Banks Peninsula and Otago), but when used to predict the two areas outside the models' source data, performance declined markedly. SDMs from the third area (Timaru) performed poorly, both for prediction within the source area and when transferred spatially. When data for model building were combined from two areas, results were mixed. Model interpolation was better when presence/absence data from Otago, an area of low density, were combined with data from areas of higher density, but was otherwise poor. The overall poor transferability of SDMs suggests that habitat preferences of Hector's dolphins vary between areas. For these dolphins, population-specific distribution data should be used for conservation planning. More generally, we demonstrate that a one model fits all approach is not always suitable. When SDMs are used to predict distribution in data-poor areas an assessment of performance in the new habitat is required, and results should be interpreted with caution.

Using social media image to identify threatened elasmobranch species caught by a small-scale fishery in a data-poor area.

Sharing fishing activity via social media attracts millions of views. The data from these shares includes key information for a fisheries database such as the species present in the catches and in many cases the number of specimens caught. Many countries in the Global South do not have official information about basic data on fishing activity, especially small-scale activity. In many of such countries like Brazil, overfishing poses a significant threat to numerous threatened species, including elasmobranchs, due to the frailty of fisheries management and law enforcement. The lack of a national (Brazil) data collection program makes sharing data on social media a potential source of fisheries related information. Hence, this study aimed to verify the potential of social media data to describe artisanal fishing and identify elasmobranch species caught by a small-scale fleet in a data-poor Equatorial Southwest Atlantic region. The identification of elasmobranch species was made based on 250 videos shared by local fishers publicly on social media (Youtube™). Among these, sharks and rays were observed in 19 (8%) and 42 (17%) videos, respectively. A total of 98 specimens were recorded, of which 80% could be identified to a species level. Most of the records comprised the stingrays Hypanus berthalutzae and Hypanus guttatus, while Ginglymostoma cirratum was the most recorded shark. More than half of the elasmobranch species recorded are listed at some level of threat according to the IUCN Red List. According to the Brazilian legislation, at least 20% of the species identified in these videos are considered Endangered (EN) or Critically Endangered (CR). Therefore, fishing activity shared via social media can be considered effective in identifying threatened elasmobranch species, as well as providing data on species occurrence, number of specimens, or rare sightings. It is suggested that fishing activity shared via social media be used in parallel with traditional methods (e.g. on-board data and landing observations) to obtain fisheries information and data about endangered species in data poor areas.

Construction Cost Estimation in Data-Poor Areas Using Grasshopper Optimization Algorithm-Guided Multi-Layer Perceptron and Transfer Learning

Construction Cost Estimation in Data-Poor Areas Using Grasshopper Optimization Algorithm-Guided Multi-Layer Perceptron and Transfer Learning

Fish community composition in the tropical archipelago of São Tomé and Príncipe.

Understanding species distribution across habitats and environmental variables is important to inform area-based management. However, observational data are often lacking, particularly from developing countries, hindering effective conservation design. One such data-poor area is the Gulf of Guinea, an understudied and biodiverse region where coastal waters play a critical role in coastal livelihoods. Here, we describe the results of the largest national-scale Baited Remote Underwater Video Systems (BRUVS) survey in the region, aiming to understand the effects of several environmental variables on fish community composition and diversity. From 2018 to 2020, we successfully deployed 417 benthic BRUVS in the coastal waters of the São Tomé and Príncipe (STP) archipelago. Species richness and relative abundance were higher in deeper waters, on steeper slopes, and in rocky reef habitats. Nevertheless, maerl and sand habitats also hosted unique, and economically important species. Our results potentially indicate historical impacts of fishing in the archipelago, especially in São Tomé Island, where observed fishing effort is higher. Indeed, abundance of large predatory fish was low in both islands and abundance of species targeted by artisanal fisheries was lower in São Tomé than in Príncipe. Our results provide crucial information supporting the designation and future monitoring of marine protected areas in STP.

Landslide mapping based on a hybrid CNN-transformer network and deep transfer learning using remote sensing images with topographic and spectral features

Landslides frequently cause serious property damage and casualties. Therefore, it is crucial to have rapid and accurate landslide mapping (LM) to support post-earthquake landslide damage assessment and emergency rescue efforts. Many studies have been conducted in recent years on the application of automatic LM methods using remote sensing images (RSIs). However, existing methods face challenges in accurately distinguishing landslides due to the problems of large differences in features and scales among landslides, as well as similarities among different ground objects in optical RSIs. Here, we propose a semantic segmentation model called SCDUNet++, which combines the advantages of convolutional neural network (CNN) and transformer to enhance the discrimination and extraction of landslide features. Then, we constructed a multi-channel landslide dataset in the Luding and Jiuzhaigou earthquake areas using Sentinel-2 and NASADEM data. We evaluated the performance of SCDUNet++ on this dataset. The results showed that SCDUNet++ can extract and fuse spectral and topographic information more effectively. Compared with other state-of-the-art models, SCDUNet++ achieved the highest IoU and F1 score in all four test areas. In addition, the models achieved significant improvements in mapping the landslides of the Jiuzhaigou area after knowledge transfer and fine-tuning. Compared with direct prediction, eight models, namely DeepLabv3+, Segformer, TransUNet, SwinUNet, STUNet, UNet, UNet++, and SCDUNet++, demonstrated improvements in IoU ranging from 8.33% to 27.5% and F1 from 6.58% to 23.67% after implementing deep transfer learning (DTL). This finding highlights the significant practicality of using DTL for cross-domain LM in data-poor areas.

Drought Assessment Using Gridded Data Sources in Data-Poor Areas with Different Aridity Conditions

Drought Assessment Using Gridded Data Sources in Data-Poor Areas with Different Aridity Conditions

Subtidal seagrass detector: development of a deep learning seagrass detection and classification model for seagrass presence and density in diverse habitats from underwater photoquadrats

This paper presents the development and evaluation of a Subtidal Seagrass Detector (the Detector). Deep learning models were used to detect most forms of seagrass occurring in a diversity of habitats across the northeast Australian seascape from underwater images and classify them based on how much the cover of seagrass was present. Images were collected by scientists and trained contributors undertaking routine monitoring using drop-cameras mounted over a 50 x 50 cm quadrat. The Detector is composed of three separate models able to perform the specific tasks of: detecting the presence of seagrass (Model #1); classify the seagrass present into three broad cover classes (low, medium, high) (Model #2); and classify the substrate or image complexity (simple of complex) (Model #3). We were able to successfully train the three models to achieve high level accuracies with 97%, 80.7% and 97.9%, respectively. With the ability to further refine and train these models with newly acquired images from different locations and from different sources (e.g. Automated Underwater Vehicles), we are confident that our ability to detect seagrass will improve over time. With this Detector we will be able rapidly assess a large number of images collected by a diversity of contributors, and the data will provide invaluable insights about the extent and condition of subtidal seagrass, particularly in data-poor areas.

Local ecological knowledge can support improved management of small-scale fisheries in the Bay of Bengal

Fishers’ local ecological knowledge (LEK) can be used to reconstruct or supplement long-term trends in heavily exploited population or poorly assessed species with low biomass. We used historical memories of small-scale fishers to understand their perceptions of changes in catch trends in marine fisheries over the last 20 years. The study aimed at evaluating how fishers could provide consistent and reliable data on major fish species/groups comparable with official catch data and to explore the potential of increasing their participation in fishery management. We conducted focus group discussions (FGDs) and key informant interviews with experienced fishers and stakeholders, using a structured template related to the catch and effort data. Using FGD data, we systematically compared changes in fish catch rates and effort over time and space. Data were collected on major groups of species that had reasonable landing over time. Overall, the analysis revealed changes in catch rate, monthly landings per landing center, factors that likely influence the catch trends, and the spatial expansion of fisheries. Our study provides insight into species’ abundance over time. Fishers’ LEK shows declining catch rates for major species and groups, but monthly harvests at landing centers have increased over two decades because of increased fishing efforts. Small-scale fishers are catching more fish from deeper waters over time, indicating a geographical expansion and/or development of fisheries beyond traditionally exploited areas. Such expansions of nearshore fisheries may result from the overfishing of nearby areas. On the contrary, this could be viewed as a positive indication of the potential for growth and development of small-scale fisheries in the region, especially in the context of the blue economy. The agreement between official statistics and fishers’ data on species catch trends over time suggests that fishers have a good understanding of their fishing system. This indicates that fishers’ knowledge could be invaluable, especially in data-poor areas. LEK integration into policy and management is thus expected to facilitate the efficient management of small-scale fisheries.

Perceptions towards governance of billfish resource use: The lens of artisanal fishers in Kenya

Multiple fisher groups target billfish species, each with different motivations and experiences, which can influence the effectiveness and sustainability of governance approaches. However, limited studies underscore the perceptions of billfish resource users in defining and implementing governance in the Western Indian Ocean region. We conducted 211 semi-structured qualitative interviews between December 2020 and September 2021, to explore how artisanal fishers perceive the performance and sustainability of governance approaches in Kenya, with a focus on billfish. Our findings show that artisanal fishers have adequate knowledge of fishing laws and regulations, as well as governing institutions and their performance. Further, artisanal fishers had a positive attitude and support for fishing rules, managing institutions, and effectiveness of governance intervention. Specifically, the fishers rated Beach Management Units (BMUs) as highly effective in implementing fisheries rules, indicating the involvement of fishers in co-management of fisheries and tendency for governance success and sustainability. This highlights the need to strengthen and support BMUs as an effective governance tool in the co-management of fisheries. We draw attention to our first-time study of the contribution of artisanal billfish fishers to governance of shared fisheries resources. We show that involvement of resource users promotes a bottom-up approach to the co-management of billfish which compliments the current regional and national efforts that have largely focused on commercial fisheries. Our research adds to the scientific body of knowledge on the importance of perceptions in the formation of natural resource governance interventions at varying scales, especially for transboundary species in data-poor areas.

Evaluation of overland flow modelling hypotheses with a multi-objective calibration using discharge and sediment data.

Conceptual hydrological models can move towards process-oriented modelling when addressing broader issues than discharge modelling alone. For instance, water quality modelling generally requires understanding of both pathways and travel times which might not be easily identified because observations at the outlet aggregate all processes at the catchment scale. In this study we tested if adding a second kind of observation, specifically sediment data, can help distinguish overland flow from total discharge. We applied a multi-objective calibration on both discharge and suspended sediment concentration simulation performance to the World-Wide Hydrological Predictions for the Environment (HYPE) model for 111 catchments spread over the USA. Results show that in comparison to two calibrations made one after the other, the multi-objective calibration leads to a significant improvement on the simulation performance of suspended sediments without a significant impact on the performance of discharge. New modelling hypotheses for overland flow calculations are proposed and resulted in similar discharge performances as the original one but with fewer parameters, which reduces equifinality and can prevent unwarranted model complexity in data-poor areas.

Methodological approaches to assess climate vulnerability and cumulative impacts on coastal landscapes

Natural and human-induced hazards and climatic risks threaten marine and coastal ecosystems worldwide, with severe consequences for these socio-ecological systems. Therefore, assessing climate vulnerability (exposure, sensitivity and adaptive capacity) and the cumulative environmental impacts of multiple hazards are essential in coastal planning and management. In this article, we review some approaches used in climate vulnerability assessment and marine and coastal cumulative environmental impacts to learn about state-of-the-art on the subject. Besides, we qualitatively evaluated the climatic vulnerability of five coastal regions of Venezuela using the IPCC concept of Reasons for Concern (RFCs) to determine their level of climatic exposure. We also assessed the cumulative environmental impact of multiple stressors on marine and terrestrial ecosystems using a well-known impact assessment method partially modified to explore the feasibility of this model in data-poor areas. However, we found no standardization of the methodologies used in evaluating Coastal Climate Vulnerability or Cumulative Environmental Impacts in coastal landscapes or frameworks that operationally link them with socio-ecological systems. Most studied coastal regions are at risk from at least three RFCs, loss of unique ecosystems (RFC1), risks associated with extreme events (RFC2) and risks associated with global aggregate impacts (RFC4). Furthermore, the assessment showed that areas with accumulated impact cover about 10 percent ranging from moderate to high in urban areas, growth zones, industrial oil settlements, port areas and aquaculture areas with fishing activity. Moreover, areas with moderate to low cumulative impact cover half of the study area, dominated by uninhabited regions and vegetation of the thorny scrub and coastal grassland types. Therefore, we consider it essential to implement regional climate risk management that incorporates these assessments into the ordinance in countries that are particularly vulnerable to climate change, such as Venezuela, which has an extensive line of low-lying coastlines (where 60% of the country's population lives) and coastal regions with harsh climates and poor economic conditions. Finally, we present the scope and limitations of implementing these evaluations and highlight the importance of incorporating them into regional strategies for adaptation to climate change.

Identifying conservation priorities for a widespread dugong population in the Red Sea: Megaherbivore grazing patterns inform management planning

Extensive home ranges of marine megafauna present a challenge for systematic conservation planning because they exceed spatial scales of conventional management. For elusive species like dugongs, their management is additionally hampered by a paucity of basic distributional information across much of their range. The Red Sea is home to a wide-spread, globally important but data-poor population of dugongs. We surveyed the north-eastern Red Sea in the waters of NEOM, Kingdom of Saudi Arabia, to locate feeding sites and determine priority areas for dugong conservation. We conducted large-scale in-water surveys of dugong feeding trails across 27 seagrass meadows that span 0.7 degree of latitude and recorded nine seagrass species and 13 dugong feeding sites. Spread over ∼4‚061 km2 of nearshore and offshore waters, many of these sites clustered around five main core feeding areas. Dugong feeding trails were mostly recorded at sites dominated by the fast-growing pioneer seagrasses Halodule uninervis, Halophila ovalis and/or H. stipulacea. Multispecific meadows with pioneer seagrasses tended to be sheltered and shallow, reflecting a similar spatial pattern to the identified dugong feeding sites. Often close to hotels and fishing harbours, these high-use dugong areas are subject to high boat traffic, fishing, and coastal development which places considerable pressures on this vulnerable mammal and its seagrass habitat. The rapidly accelerating coastal development in the northern Red Sea directly threatens the future of its dugong population. Although our sampling focuses on feeding signs in early successional seagrasses, the results are valuable to spatial conservation planning as they will trigger overdue conservation interventions for a globally threatened species in a data-poor area. Urgent dugong conservation management actions in the northern Red Sea should focus on shallow waters sheltered by coastal lagoons, bays and the lee of large islands.

Identifying and quantifying key pressures in a data poor region: Coastal Spatial Planning in Heraklion Prefecture, Greece

Coastal regions are among the most valuable ecosystems due to the socioeconomic and ecological benefits and services they provide. Pressures, leading to multiple adverse effects on coastal habitats, seriously threaten these benefits. Coastal Spatial Planning (CSP) aims to mitigate these pressures in parallel with the E.U. Marine Strategy Framework Directive. A key aspect of ecosystem-based CSP is mapping the spatial distribution of pertinent pressures, and identifying Key Environmental Indicators (KEIs). This objective however, is difficult to achieve in data-poor regions leading to variable sources of uncertainty. Furthermore, a conceptual framework to homogenize spatially explicit data of relevant pressures is lacking. This study proposes a methodology for developing and integrating spatial pressure datasets in a Geographical Information Systems (GIS) environment. This methodology was applied to a case study location in the northern and southern coastal area of Heraklion Prefecture, in Crete (Greece). The pressure datasets, used, were developed from a synthesis of proxy and satellite data, including abrasion, chlorophyll-a, commercial fish catches, invasive species, light pollution, microplastics, noise pollution, ocean acidification and organic load. These nine Potential Environmental Indicators (PEIs) were subjected to Principal Component Analysis (PCA) in order to reduce the number of parameters, and identify their relationships. The shortlisted 7 KEIs were organic load, chlorophyll-a, microplastics, commercial fish catches, and noise pollution, indicating that this methodology can be of management relevance in the initial steps of CSP implementation, especially in data poor areas. The proposed spatial integration of disparate pressure datasets as part of the flowchart presented constitutes a compositional approach to the spatial illustration of pressures in a coastal area. The patterns of KEI connectivity and disturbance highlighted herein through statistical analysis, could provide a feasible, economic and less biased procedure leading to effective marine resource management, also allowing for a regional and European implementation.

Site suitability analysis of indigenous rainwater harvesting systems in arid and data-poor environments: a case study of Sana’a Basin, Yemen

Indigenous rainwater harvesting (RWH) is a promising tool for increasing water availability and conserving groundwater in arid environments. This study aimed to develop a multicriteria analysis (MCA) framework to identify and rank suitable sites for different indigenous RWH systems in data-poor areas taking Sana’a Basin, Yemen, as a case study. Unlike previous research, this study focused only on indigenous RWH systems and, for the first time, considered the site suitability analysis of spate irrigation systems. Five biophysical and six socioeconomic criteria were used to select, prioritize, and map suitable zones for indigenous RWH systems. This study, the first to be conducted in Yemen, found that more than 30% of the case area has a high or very high level of suitability for each of the studied RWH systems. Only 11% to 17% of the area is not suitable for RWH due to socioeconomic activities. Twenty-four percent of the area was considered moderately suitable, and 28% had a low level of suitability for RWH. These results are promising, and the preliminary identification process of suitable areas for indigenous RWH systems is a useful guide for future water resource and land management programs.Graphical abstract

Applying Acoustic Scattering Layer Descriptors to Depict Mid-Trophic Pelagic Organisation: The Case of Atlantic African Large Marine Ecosystems Continental Shelf

To identify key nonspecific organisational characteristics of the mid-trophic pelagic communities, which remain a challenge, we work with sound scattering layers (SSLs). Application was tested in the three African Atlantic Large Marine Ecosystems (AALMEs) to assess the utility of adapting and developing new acoustic variables. Our methodology allowed comparison between Large Marine Ecosystems (LMEs) based on 14 morphological, spatial and acoustic variables to characterize SSLs over time. These original variables were effective in monitoring and comparing the LMEs, and even allowed us to discriminate different organisations inside the Canary Current LME. Common traits identified for all AALMEs included the importance of the shallowest SSL. However, we identified an unexpected night-time pattern in SSL distributions in the Benguela Current LME which reflect a major difference in diel vertical migration mechanisms relative to other AALMEs. We also highlighted the predominance of elementary sampling unit (ESUs) with a single SSL and an unoccupied tiny layer close to the bottom, even if some ESUs presented up to six SSLs. Inter-annual changes in SSL organisation are highlighted by adaptation of original variables as the proportion of the water column occupied by SSLs and the relative importance of the shallowest SSL. SSL variables have been used mostly in deep water; here, we applied them on the continental shelf. SSL variables can be used to standardize the monitoring of marine ecosystems and capture change in spatial structure and function of mid-trophic pelagic marine ecosystems worldwide, even in data-poor areas where species identification of lower-trophic pelagic organism remains a challenge.

Spotting areas critical to storm waves and surge impacts on coasts with data scarcity: a case study in Santa Catarina, Brazil

The impacts of severe storms on the coastal zone, combined with rapid population growth in this area, have made coastal risk management an urgent need. However, integrated risk assessment can be a challenging task for many locations worldwide, as it normally requires the use of a large amount of data. The Coastal Risk Assessment Framework phase one (CRAF1) is a recently proposed analytical scheme based on empirical models and spatial analysis that combines different indicators to identify storm-induced hotspots. With a high degree of flexibility, the methodology was originally designed to be of broad use. Still, there is little information about the tool applicability in data scarcity conditions. In this study, we show that this approach can be applied, with some simplifications, on data-poor areas, allowing the identification of hotspots considering one or multiple hazards. Here, the coastal risk was assessed for erosion and coastal flooding events with return periods of 10 and 50 years on the Santa Catarina Central Coast. The study area is characterized by the occurrence of storm-induced impacts that historically cause disruption and damage to local communities. Although the components of risk have been assessed using various methods along this sector, to date, no integrated risk analysis has been presented in probabilistic terms. Predicted scenarios for the Santa Catarina Central Coast suggest that extreme episodes may cause several impacts, exposing urban settlements as well local road systems, especially in the municipalities of Tijucas and Florianópolis. The results show that the CRAF1 is an appropriate approach for a first-level risk analysis, even when implemented with poor data resolution, as it effectively points to some of the most vulnerable stretches detected in the study area.