A multidisciplinary approach to physical-biological interactions in early life history of marine populations

Traditionally, the quantitative analysis of ecological processes considered separately the temporal population dynamics, under the assumption of homogenous mixing over space, and the spatial distributions of individuals, as a “snapshot” of the species displacement. However, when dealing with populations distributed along a heterogeneous space with fluxes of individuals among separated groups, it is necessary to couple both the spatial and temporal dimensions. A new paradigm for Movement Ecology has been recently proposed renewing the challenge to investigate the driving forces and the ultimate result of movement integrating biological information about interacting species with the mathematical modelling of the organism’s movement. Many models are devoted to the Movement Ecology approach, but the study of species’ early life history in marine ecology has found in the individual-based coupled physical-biological models the specific tool to investigate the dispersal and movement of juveniles. In this thesis some new individual-based coupled physical-biological models for larval dispersal are proposed and analysed. These models are built reproducing the most important biological features of the species considering also the interactions with the external environment. In particular, this work tries to integrate experimental observations and laboratory data to build reliable models through validation and tuning, a not much common practice in this field of modelling. The first part of the thesis presents two dispersal models coupled with a genetic analysis for two species (the European green crab and the white sea bream) in the Adriatic Sea. These models are used with an explanatory approach generating patterns of larval distribution. Larval retention, spill-over and level of connectivity among different places are evaluated. The model results are compared with the results of the genetic analysis. The consistency between the two approaches points out the role of the ocean currents and temperatures in determining the separation or the homogeneity among the analysed groups. The second part of the thesis is devoted to the study of the larval migration of the European eel in the North Atlantic Ocean. An individual- based coupled physical-biological model is developed using alternative scenarios to quantitatively compare the outputs with observed field data. This inferential approach allows to characterise the species biological features, namely body growth, mortality and active locomotion, and to investigate, with the most likely scenario, the inter-annual variation (1960- 2000) of juveniles arriving on the European shelf. This application is used to generate hypothesis that could explain the recruitment collapse observed during the 1980s

SmartDissolve User Guide

SmartDissolve is a polygon aggregation tool developed in the frame of the Global Human Settlement Layer (GHSL) project and it is being used to dissolve polygons setting an areal threshold. SmartDissolve is a tool that handles minimum mapping unit, resolution mismatch between layers, or spatial uncertainty problems in GISc. This tool automatically dissolves polygons below a threshold area, updating fields’ values. The toolbox allows to select the ordering of polygon analysis (i.e. from the smallest to the largest area, vice versa, or order of IDs), different dissolve rules (i.e. with smallest, largest, or maximum-border-share adjacent polygon, minimum total perimeter or maximum compactness) and different field updating operations (i.e. sum, mean or text concatenation). The software is available as toolbox for ArcGIS 10.X, a standalone command line tool or a MATLAB function. More details about the algorithm, the method and its actual applications can be found in the paper in the Reference section. This guide provides instructions about installing and using the SmartDissolve toolbox for ArcGIS and the SmartDissolve command line tool on a Windows computer and the SmartDissolve MATLAB function in a MATLAB environment.JRC.E.1-Disaster Risk Managemen

Megacities Spatiotemporal Dynamics Monitored with the Global Human Settlement Layer

Megacities are urban agglomerations hosting at least 10 million inhabitants. The rise in number, population size, and spatial extent of megacities are among the most prominent manifestations of the process of urbanisation taking place in the contemporary urban age. Until recently, urban growth has been quantified with data derived from satellites mainly for single megacities or for a limited subset of them. With the current advances in Remote Sensing and data processing, the integration of satellite data with other datasets could become a key contributor to the data revolution and support more complete urban studies and better informed policymaking. Although many remote sensing-derived products exist, few are open and free and possess the adequate resolution, information and contents to monitor the process of urban expansion. This research article builds on the premier open and free geospatial information contained in the Global Human Settlements Layer (GHSL) data package (produced at the European Commission - Joint Research Centre). This research takes advantage of existing GHSL data to identify megacities and to analyse their spatial and demographic change over the last 25 years (between 1990 and 2015). This paper quantifies how much and how fast megacities have expanded in spatial and demographic terms, and we provide graphical examples of the different manifestations of growth across megacities. The main findings of our research reveal an average demographic growth in megacities exceeding 2% a year between 1990 and 2000, and of 1.9% a year between 2000 and 2015. In the first period (1990 to 2000), megacities have expanded faster than the global average and more than the average of other urban centres. In the second period, global urban population increase has been greater than that of megacities. The comparative analysis of megacities however, reveals swift population growth in several cases: in seven cities population more than doubled between 1990 and 2015, and in six the average annual population growth exceeded 4% a year. Spatial expansion of megacities tends to occur at rates slower than that of population. In 27 cities built-up per capita has decreased over 25 years, by more than 10% in 17 cities. Megacities also differ in population density (in 2015), which in five is above 10,000 inhabitants per square kilometre, while in others, especially the ones in high-income countries, density remains around half this figure. Results highlight the value of new remote sensing-based data and methods for mapping and characterizing global urbanisation processes, in a consistent and comparable manner across space and time. The provision of open and free data ensures methods and findings can be audited and analyses extended to other cities, while the temporal dimension enables monitoring urbanisation and intergovernmental policies on sustainable urban development

Detecting spatial pattern of inequality from remote sensing

Spatial inequalities across the globe are not easy to detect and satellite data have shown to be of use in this task. Earth Observation (EO) data combined with other information sources can provide complementary information to those derived from traditional methods. This research shows patterns of inequalities emerging by combining global night lights measured from Earth Observation, population density and built-up in 2015. The focus of the paper is to describe the spatial patterns that emerge by combing the three variables. This work focuses on processing EO data to derive information products, and in combining built-up- and population density with nighttime emission. The built-up surface was derived entirely from remote sensing archives using artificial intelligence and pattern recognition techniques. The built-up was combined with population census data to derive population density. Also the nighttime emission data were available from EO satellite sensors. The three layers are subsequently combined as three colour compositions based on the three primary colours (i.e. red green and blue) to display the “human settlement spatial pattern” maps. These GHSL nightlights provide insights in inequalities across the globe. Many patterns seem to be associated with countries income. Typically, high income countries are very well lit at night, low income countries are poorly lit at night. All larger cities of the world are lit at night, those in low-income countries are often less well lit than cites in high-income countries. There are also important differences in nightlights emission in conflict areas, or along borders of countries. This report provides a selected number of patterns that are described at the regional, national and local scale. However, in depth analysis would be required to assess more precisely that relation between wealth access to energy and countries GDP, for example. This work also addresses regional inequality in GHSL nightlights in Slovakia. The country was selected to address the deprivation of the Roma minority community. The work aims to relate the information from the GHSL nightlights with that collected from field survey and census information conducted at the national level. Socio-economic data available at subnational level was correlated with nightlight. The analysis shows that despite the potential of GHSL nightlights in identifying deprived areas, the measurement scale of satellite derived nightlights at 375 x 375 m and 750 x 750 m pixel size is too coarse to capture the inequalities of deprived communities that occur at finer scale. In addition, in the European context the gradient of inequality is not strong enough to produce strong evidence. Although there is a specific pattern of GHSL nightlights in settlements with high Roma presence, this cannot be used to identify such areas among the others. This work is part of the exploratory data analysis conducted within the GHSL team. The exploratory analysis will be followed by more quantitative assessments that will be available in future work.JRC.E.1-Disaster Risk Managemen

GHS-SmartDissolve User Guide: Documentation Version 2.0

The GHS-SmartDissolve Tool– version 2.0 is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to conduct smart and flexible aggregation of adjacent and complex polygons storing quantitative data. GHS-SmartDissolve is a tool that handles minimum mapping unit, resolution mismatch between layers, or spatial uncertainty problems in GISc. This tool automatically dissolves polygons below a threshold area or a threshold attribute value, updating fields’ values to meet a minimum target area or a minimum attribute value. This flexible framework allows to select the ordering of polygon analysis, different dissolve rules, and different field updating operations. The GHS-SmartDissolve is available as toolbox for ArcGIS 10.X. This document contains the description of the GHS-SmartDissolve Tool use, with details and description of the different settings and output. The GHS-SmartDissolve, as all GHSL Tools, is issued with an end-user licence agreement, included in the download package.JRC.E.1-Disaster Risk Managemen

Sensing global patterns of inequality from space

The combination of Earth Observation and population data produces new information that describes inequalities across the globe in an original, objective and spatially distinct way.The new information contributes to a better understanding of the spatial distribution of wealth and poverty around the globe.The approach has potential for the monitoring and detection of changes in spatial patterns of inequality.JRC.E.1-Disaster Risk Managemen

GHSL-OECD Functional Urban Areas

Function Urban Areas (FUAs), as defined by the OECD and the European Union, are sets of contiguous local (administrative) units composed of a ‘city’ and its surrounding, less densely populated local units that are part of the city’s labour market (‘commuting zone’). To be included in the commuting zone, local units should at least 15% of their working population to the city. This definition is limited to the OECD countries and it is subject to both availability of commuting flows data at local level and to the definition of administrative unit boundaries. In the context of international comparability of urban-related statistics and indicators the aim of this task is to propose a FUA definition that does not depend on arbitrary and not harmonized administrative units and scale it to the globe. To pursue this goal it is proposed an automated classification procedure of FUAs based on objective characteristics (distance from the Urban Centre, area and population of the Urban Centre, local population and GDP per capita at national level), to classify areas within and outside FUAs. The automated classification of FUA is done in collaboration with the OECD and supported by DG REGIO. This document describes the public release of the GHSL-OECD Functional Urban Areas 2019 (GHS-FUA).JRC.E.1-Disaster Risk Managemen

GHS-DUG User Guide: Degree of Urbanisation Grid User Guide Version 4

The Degree of Urbanisation Grid (GHS-DUG) Tool (– version 4) is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce geospatial grids to map settlement classes and extract related statistics. The settlement classes are derived from the “Degree of Urbanisation” method and ported to the GHSL environment through the GHSL Settlement Mode (GHSL SMOD). The GHS-DUG 4 is designed as a scalable tool allowing the application of the GHSL Settlement Model to the input data available to the user or to data made available in the GHSL Data Package 2019. This document contains the description of the GHS-DUG Tool use, the rationale of the differentiation between settlement classes and the comprehensive description of the outputs. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort for the uptake of the Degree of Urbanisation, the people-based harmonised definition of cities and settlements recommended by the 51st Session of the United Nations Statistical Commission as the method to delineate cities and rural areas for international statistical comparison. The GHS-DUG, as all GHSL Tools, is issued with an end-user licence agreement, included in the download package.JRC.E.1-Disaster Risk Managemen

GHS-DU-TUC User Guide: Degree of Urbanisation Territorial Units Classifier User Guide Version 1

The Degree of Urbanisation Territorial Units Classifier (GHS-DU-TUC) Tool (– version 1) is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce the classification of territorial units based on the Degree of Urbanisation and extract related statistics. The tool classifies territorial units by Degree of Urbanisation at Level 1 (3 classes) and Level 2 (7 classes) based on population majority by settlement classes derived from the “Degree of Urbanisation” method and ported to the GHSL environment through the GHSL Settlement Model (GHSL SMOD). The GHS-DU-TUC 1 is designed as an operational tool to perform the second step required to apply the Degree of Urbanisation released as standalone tool and as ArcGIS Toolbox. Once the first step produces the settlement classification grid (i.e. with the GHS-DUG Tool), the user runs the GHS-DU-TUC that requires this settlement classification grid, the population grid used to produce the settlement classification grid (i.e. produced with the GHS-POP2G Tool) and a geometry of territorial units to be classified by Degree of Urbanisation. This tool is conceptualised to be deployed after the application of the GHSL tools GHS-POP2G and GHS-DUG but it accepts in input population grids produced by means of any other procedure respecting the described constrains. This document contains the description of the GHS-DU-TUC Tool use, the rationale for the second step to apply the Degree of Urbanisation (the classification of territorial units) and the comprehensive description of the outputs. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort for the uptake of the Degree of Urbanisation, the people-based harmonised definition of cities and settlements recommended by the 51st Session of the United Nations Statistical Commission as the method to delineate cities and rural areas for international statistical comparison. The GHS-DU-TUC, as all GHSL Tools, is issued with an end-user licence agreement, included in the download package.JRC.E.1-Disaster Risk Managemen

DUG User Guide

The Degree of Urbanisation Grid (DUG) Tool (– version 3.0) is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce geospatial grids to map settlement classes and extract related statistics. The settlement classes are derived from the “Degree of Urbanisation” method and ported to the GHSL environment through the GHSL Settlement Mode (GHSL SMOD)l. The DUG 3.0 is designed as a scalable tool allowing the application of the GHSL Settlement Model to the input data available to the user or to data made available in the GHSL Data Package 2019. This document contains the description of the DUG Tool use, the rationale of the differentiation between settlement classes and the comprehensive description of the outputs. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort to develop a people-based harmonised definition of cities and settlements that helps the assessment of the feasibility of applying a global definition of cities/urban areas in support of global monitoring of SDGs and the New Urban Agenda urban targetsJRC.E.1-Disaster Risk Managemen