Future Marine Zooplankton Research- A Perspective

During the Second Marine Zooplankton Colloquium (MZC2) 3 issues were added to those developed 11 yr ago during the First Marine Zooplankton Colloquium (MZC1). First, we focused on hot spots, i.e., locations where zooplankton occur in higher than regular abundance and/or operate at higher rates. We should be able to determine the processes leading to such aggregations and rates, and quantify their persistence. Second, information on the level of individual species, even of highly abundant ones, is limited. Concerted efforts should be undertaken with highly abundant to dominant species or genera (e.g., Oithona spp., Calanus spp., Oikopleura spp., Euphausia superba) to determine what governs their abundance and its variability. Third, zooplankton clearly influence biogeochemical cycling in the ocean, but our knowledge of the underlying processes remains fragmentary. Therefore a thorough assessment of variables that still need to be quantified is required to obtain an understanding of zooplankton contributions to biogeochemical cycling. Combining studies on the 7 issues from MZC1 with the 3 from MZC2 should eventually lead to a comprehensive understanding of (1) the mechanisms governing the abundance and existence of dominant zooplankton taxa, and (2) the control of biodiversity and biocomplexity, for example, in the tropical ocean where diversity is high. These recommendations come from an assemblage of chemical, physical and biological oceanographers with experience in major interdisciplinary studies, including modeling. These recommendations are intended to stimulate efforts within the oceanographic community to facilitate the development of predictive capabilities for major biological processes in the ocean

Modelling and mapping the intra-urban spatial distribution of Plasmodium falciparum parasite rate using very-high-resolution satellite derived indicators

BACKGROUND: The rapid and often uncontrolled rural-urban migration in Sub-Saharan Africa is transforming urban landscapes expected to provide shelter for more than 50% of Africa's population by 2030. Consequently, the burden of malaria is increasingly affecting the urban population, while socio-economic inequalities within the urban settings are intensified. Few studies, relying mostly on moderate to high resolution datasets and standard predictive variables such as building and vegetation density, have tackled the topic of modeling intra-urban malaria at the city extent. In this research, we investigate the contribution of very-high-resolution satellite-derived land-use, land-cover and population information for modeling the spatial distribution of urban malaria prevalence across large spatial extents. As case studies, we apply our methods to two Sub-Saharan African cities, Kampala and Dar es Salaam. METHODS: Openly accessible land-cover, land-use, population and OpenStreetMap data were employed to spatially model Plasmodium falciparum parasite rate standardized to the age group 2-10 years (PfPR2-10) in the two cities through the use of a Random Forest (RF) regressor. The RF models integrated physical and socio-economic information to predict PfPR2-10 across the urban landscape. Intra-urban population distribution maps were used to adjust the estimates according to the underlying population. RESULTS: The results suggest that the spatial distribution of PfPR2-10 in both cities is diverse and highly variable across the urban fabric. Dense informal settlements exhibit a positive relationship with PfPR2-10 and hotspots of malaria prevalence were found near suitable vector breeding sites such as wetlands, marshes and riparian vegetation. In both cities, there is a clear separation of higher risk in informal settlements and lower risk in the more affluent neighborhoods. Additionally, areas associated with urban agriculture exhibit higher malaria prevalence values. CONCLUSIONS: The outcome of this research highlights that populations living in informal settlements show higher malaria prevalence compared to those in planned residential neighborhoods. This is due to (i) increased human exposure to vectors, (ii) increased vector density and (iii) a reduced capacity to cope with malaria burden. Since informal settlements are rapidly expanding every year and often house large parts of the urban population, this emphasizes the need for systematic and consistent malaria surveys in such areas. Finally, this study demonstrates the importance of remote sensing as an epidemiological tool for mapping urban malaria variations at large spatial extents, and for promoting evidence-based policy making and control efforts.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Enhancing station level Direct-Demand models with Multi-Scalar accessibility indicators

Direct-demand models (DDM) are increasingly being used for a diversity of transit research and practice purposes. Yet few station-level DDM studies have explored the use of composite indicators of metropolitan accessibility in predicting demand. After all, provision of access to metropolitan destinations is one of the main goals of rapid-transit systems. Furthermore, to this author’s knowledge no study has explored potential interactions with local-level accessibility indicators that are typically included in station level transit DDMs. This study explores these possibilities and uses Los Angeles multimodal rapid-transit network as a representative case study of a system that operates in a dispersed agglomeration where multiple sub-centers are linked. Multi-level generalized linear models were implemented where key predictors, including stations\u27 metropolitan- and a local-accessibility indicators are regressed onto average weekday boardings. Furthermore, more general accessibility constructs were developed via EFA and implemented in models; and parameters non-stationarity was assessed via geographically weighted regressions. Results indicate that nodal metropolitan accessibility is a significant predictor of patronage in LA’s rapid-transit network, and that its interaction with local-accessibility amplifies boardings and improves DDM models’ explanatory power. More general constructs of accessibility at metropolitan and local-scale were derived via EFA and these resulted in a more parsimonious model with equal predictive power. Land-use and transit planners would benefit from including an accessibility lens in their DDM modeling. Practical applications of these type of models include TOD scenario planning, comparative route alignment studies, system expansion studies, and for didactic purposes given the ability of accessibility measures to capture land-use/transportation interactions

Digital mapping of GlobalSoilMap soil properties at a broad scale: a review

Soils are essential for supporting food production and providing ecosystem services but are under pressure due to population growth, higher food demand, and land use competition. Because of the effort to ensure the sustainable use of soil resources, demand for current, updatable soil information capable of supporting decisions across scales is increasing. Digital soil mapping (DSM) addresses the drawbacks of conventional soil mapping and has been increasingly used for delivering soil information in a time- and cost-efficient manner with higher spatial resolution, better map accuracy, and quantified uncertainty estimates. We reviewed 244 articles published between January 2003 and July 2021 and then summarised the progress in broad-scale (spatial extent >10,000 km2) DSM, focusing on the 12 mandatory soil properties for GlobalSoilMap. We observed that DSM publications continued to increase exponentially; however, the majority (74.6%) focused on applications rather than methodology development. China, France, Australia, and the United States were the most active countries, and Africa and South America lacked country-based DSM products. Approximately 78% of articles focused on mapping soil organic matter/carbon content and soil organic carbon stocks because of their significant role in food security and climate regulation. Half the articles focused on soil information in topsoil only (<30 cm), and studies on deep soil (100–200 cm) were less represented (21.7%). Relief, organisms, and climate were the three most frequently used environmental covariates in DSM. Nonlinear models (i.e. machine learning) have been increasingly used in DSM for their capacity to manage complex interactions between soil information and environmental covariates. Soil pH was the best predicted soil property (average R2 of 0.60, 0.63, and 0.56 at 0–30, 30–100, and 100–200 cm). Other relatively well-predicted soil properties were clay, silt, sand, soil organic carbon (SOC), soil organic matter (SOM), SOC stocks, and bulk density, and coarse fragments and soil depth were poorly predicted (R2 < 0.28). In addition, decreasing model performance with deeper depth intervals was found for most soil properties. Further research should pursue rescuing legacy data, sampling new data guided by well-designed sampling schemas, collecting representative environmental covariates, improving the performance and interpretability of advanced spatial predictive models, relating performance indicators such as accuracy and precision to cost-benefit and risk assessment analysis for improving decision support; moving from static DSM to dynamic DSM; and providing high-quality, fine-resolution digital soil maps to address global challenges related to soil resources

Modelação geográfica da fragmentação e conectividade de habitats: casos de estudo nos padrões de distribuição local de espécies selvagens

Habitat fragmentation and the resultant reduction in connectivity are process of major importance in the persistence and patterns distribution of wildlife species. This thesis focuses on habitat fragmentation and connectivity, assessing their consequences on the local patterns distribution of wildlife species. The cases studies were published and conducted with monitoring data systematized using a common database. The case studies were located in the Alentejo region between the years of 1995 and 2005. The case studies are supported by examples of local impacts of fragmentation on the habitat connectivity of birds and reptile species patterns distribution. The observed pattern-process interactions are assessing by geographic modeling techniques. Methodologies were developed based on the innovative application of spatial statistical and networks analysis. The results show that the geographic modeling represents an added value to the understanding pattern-process interactions. The findings show how much the local distribution patterns of individuals are affected by habitat disturbances; RESUMO: A fragmentação dos habitats e a conectividade são processos de importância maior na persistência e nos padrões de distribuição das espécies selvagens. Esta tese centra -se na avaliação da fragmentação e conectividade dos habitats nos padrões locais de distribuição de espécies selvagens. Para tal foram realizados casos de estudo, com dados relativos a monitorizações efectuadas no Alentejo entre os anos de 1995 e 2005 e sistematizados numa base dados. Os casos de estudo foram publicados e são suportados por exemplos de impactes locais no padrão de distribuição de espécies de aves e réptil. Foram utilizadas técnicas de modelação geográfica na descrição e avaliação dos processos e padrões observados. Aplicadas e desenvolvidas metodologias inovadoras, com o suporte de técnicas de estatística espacial e análise de redes. Os resultados mostram que a modelação geográfica representa uma maisvalia para a compreensão da dinâmica entre padrões-processos. Os resultados revelam o quanto, os padrões de distribuição local dos indivíduos são afectados pelas alterações nos habitats

Network Centralities in Polycentric Urban Regions: Methods for the Measurement of Spatial Metrics

The primary aim of this thesis is to explain the complex spatial organisations of polycentric urban regions (PURs). PURs are a form of regional morphology that often evolves from post-industrial structures and describe a subnational area featuring a plurality of urban centres. As of today, the analysis of the spatial organisation of PURs constitutes a hitherto uncharted territory. This is due to PURs’ inherent complexity that poses challenges for their conceptualisation. In this context, this thesis reviews theories on the spatial organisation of regions and cities and seeks to make a foundational methodological contribution by joining space syntax and central place theory in the conceptualisation of polycentric urban regions. It takes into account human agency embedded in the physical space, as well as the reciprocal effect of the spatial organisation for the emergence of centralities and demonstrates how these concepts can give insights into the fundamental regional functioning. The thesis scrutinises the role that the spatial organisation plays in such regions, in terms of organising flows of goods and people, ordering locational occupation and fostering centres of commercial activity. It proposes a series of novel measurements and techniques to analyse large and messy datasets. This includes a method for the application of large-scale volunteered geographic information in street network analysis. This is done, in the context of two post-industrial regions: the German Ruhr Valley and the British Nottinghamshire, Derbyshire and Yorkshire region. The thesis’ contribution to the understanding of regional spatial organisation and the study of regional morphology lies in the identification of spatial structural features of socio-economic potentials of regions and particular areas within them. It constitutes the first comparative study of comprehensive large-scale regional spatial networks and presents a framework for the analysis of regions and the evaluation of the predictive potential of spatial networks for socio-economic patterns and the location of centres in regional contexts

Review of macroeconomic approaches to modelling Wellbeing, Inclusion, and Sustainability

In response to the urgent global challenges of climate change and rising inequality, the need to re-evaluate our traditional economic models and adopt new approaches focused on sustainability, wellbeing, and inclusion has become evident. The current economic paradigms, based on equilibrium thinking and GDP-centric measurements, have proven inadequate in addressing the intricate interplay between economic, social, and environmental dimensions. As we embark on a transformative journey towards a sustainable and equitable future, it is crucial to adopt diverse modelling approaches to provide policymakers and stakeholders with informed decision-making tools. This report delves into the analysis of five different macroeconomic model types (general equilibrium models, macro-econometric & input-output models, stockflow-consistent models, integrated assessment models, and system dynamics models), evaluating their respective strengths and weaknesses to propose an integrated framework that encompasses the multifaceted nature of our world. A key recommendation is to improve existing models by enhancing their dynamics and feedback loops between dimensions and systems, thus better reflecting the interactions and effects of different social and economic policies. Striking a balance between complexity and transparency is essential, ensuring that models remain flexible and capable of linking with models with greater detail but narrower focus. The report emphasizes the incorporation of WISE accounts (detailed data on Wellbeing, Inclusion, Sustainability, and Economy that will be collected and harmonized during the project) into macroeconomic models as an opportunity to overcome the challenge of data availability, which poses a significant obstacle in modelling endeavours. Robust and reliable data sources are crucial to the success of any model and require continual improvement in data collection processes. To broaden our understanding of the dynamics of WISE dimensions and the potential impacts of policies, integrating alternative perspectives, such as heterodox economics, can offer valuable insights. Co-creating quantitative analysis with stakeholders enhances ownership and uptake of the models and may help with bridging the gap between research and policy implementation. Furthermore, an integrated modelling framework that accounts for the non-linear interactions between human and earth systems is necessary to properly assess policies tackling 21st century challenges in the context of WISE dimensions. This integrated model should draw upon the data of WISE accounts and synergize elements of Input-Output models, System-Dynamics, and Stock-Flow consistent models to provide a structured tool for policymakers and researchers in shaping a sustainable and inclusive future

Evaluation of an evaluation list for model complexity

This study (‘WOt-werkdocument’) builds on the project ‘Evaluation model complexity’, in which a list has been developed to assess the ‘equilibrium’ of a model or database. This list compares the complexity of a model or database with the availability and quality of data and the application area. A model or database is said to be in equilibrium if the uncertainty in the predictions by the model or database is appropriately small for the intended application, while the data availability supports this complexity. In this study the prototype of the list is reviewed and tested by applying it to test cases. The review has been performed by modelling experts from within and outside Wageningen University & Research centre (Wageningen UR). The test cases have been selected form the scientific literature in order to evaluate the various elements of the list. The results are used to update the list to a new version