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Abstract:In the last few decades the magnitude and impacts of planetary urban transformations have become increasingly evident to scientists and policymakers. The ability to understand these processes remained limited in terms of territorial scope and comparative capacity for a long time: data availability and harmonization were among the main constraints. Contemporary technological assets, such as remote sensing and machine learning, allow for analyzing global changes in the settlement process with unprecedented detail. The Global Human Settlement Layer (GHSL) project set out to produce detailed datasets to analyze and monitor the spatial footprint of human settlements and their population, which are key indicators for the global policy commitments of the 2030 Development Agenda. In the GHSL, Earth Observation plays a key role in the detection of built-up areas from the Landsat imagery upon which population distribution is modelled. The combination of remote sensing imagery and population modelling allows for generating globally consistent and detailed information about the spatial distribution of built-up areas and population. The GHSL data facilitate a multi-temporal analysis of human settlements with global coverage. The results presented in this article focus on the patterns of development of built-up areas, population and settlements. The article reports about the present status of global urbanization (2015) and its evolution since 1990 by applying to the GHSL the Degree of Urbanisation definition of the European Commission Directorate General for Regional and Urban Policy (DG-Regio) and the Statistical Office of the European Communities (EUROSTAT). The analysis portrays urbanization dynamics at a regional level and per country income classes to show disparities and inequalities. This study analyzes how the 6.1 billion urban dwellers are distributed worldwide. Results show the degree of global urbanization (which reached 85% in 2015), the more than 100 countries in which urbanization has increased between 1990 and 2015, and the tens of countries in which urbanization is today above the global average and where urbanization grows the fastest. The paper sheds light on the key role of urban areas for development, on the patterns of urban development across the regions of the world and on the role of a new generation of data to advance urbanization theory and reporting.

Aim: To explore the patterns of connectivity between marine protected areas (MPAs) and neighbouring non-protected areas and the scale at which the benefits of MPAs are expected using the white sea bream as model species. Location: Marine protected area of Torre Guaceto (TGMPA, Italy) in the south Adriatic Sea. Methods: A multidisciplinary approach was used combining (1) a genetic survey using 12 highly informative microsatellite loci of samples collected within the MPA and in several locations up to 100 km from the MPA; and (2) larval trajectories using Lagrangian simulations based on an oceanographical model of the region that includes specific data on early life-history traits. Both genetic and simulation studies were temporarily replicated during two consecutive years. Results: The overall genetic homogeneity observed indicates that the TGMPA is not isolated and that there is genetic connectivity among locations across a scale of at least ~200 km. A high degree of connectivity between the TGMPA and neighbouring areas is in agreement with Lagrangian simulations, which indicate that white sea bream larvae can be transported over large distances up to about 300 km. Trajectories released from the TGMPA showed that on average, 12.75% of the larvae remains within the TGMPA, while the rest travel south towards non-protected areas. Main conclusions: Our findings highlight the potential benefits of effectively enforced MPAs for neighbouring or relatively distant non-protected fishing areas and the potential connection with other MPAs at regional scale. Combining genetics and modelling can provide a general framework to investigate the role of connectivity in MPA design that can be easily extended to other species and geographical areas

Seascape connectivity critically affects the spatiotemporal dynamics of marine metacommunities. Understanding how connectivity patterns emerge from physically and biologically-mediated interactions is therefore crucial to conserve marine ecosystem functions and biodiversity. Here, we develop a set of biophysical models to explore connectivity in assemblages of species belonging to a typical Mediterranean community (Posidonia oceanica meadows) and characterized by different dispersing traits. We propose a novel methodological framework to synthesize species-specific results into a set of community connectivity metrics and show that spatiotemporal variation in magnitude and direction of the connections, as well as interspecific differences in dispersing traits, are key factors structuring community connectivity. We eventually demonstrate how these metrics can be used to characterize the functional role of each marine area in determining patterns of community connectivity at the basin level and to support marine conservation planning.

Investigating the interactions between the physical environment and early life history is crucial to understand the mechanisms that shape the genetic structure of marine populations. Here, we assessed the genetic differentiation in a species with larval dispersal, the Mediterranean shore crab (Carcinus aestuarii) in the Adriatic Sea (central Mediterranean), and we investigated the role of oceanic circulation in shaping population structure. To this end, we screened 11 polymorphic microsatellite loci from 431 individuals collected at eight different sites. We found a weak, yet significant, genetic structure into three major clusters: a northern Adriatic group, a central Adriatic group and one group including samples from southern Adriatic and Ionian seas. Genetic analyses were compared, under a seascape genetics approach, with estimates of potential larval connectivity obtained with a coupled physical-biological model that integrates a water circulation model and a description of biological traits affecting dispersal. The cross-validation of the results of the two approaches supported the view that genetic differentiation reflects an oceanographic subdivision of the Adriatic Sea into three subbasins, with circulation patterns allowing the exchange of larvae through permanent connections linking north Adriatic sites and ephemeral connections like those linking the central Adriatic with northern and southern locations.

Data on global population distribution are a strategic resource currently in high demand in an age of new Development Agendas that call for universal inclusiveness of people. However, quality, detail, and age of census data varies significantly by country and suffers from shortcomings that propagate to derived population grids and their applications. In this work, the improved capabilities of recent remote sensing-derived global settlement data to detect and mitigate major discrepancies with census data is explored. Open layers mapping builtup presence were used to revise census units deemed as 'unpopulated' and to harmonize population distribution along coastlines. Automated procedures to detect and mitigate these anomalies, while minimizing changes to census geometry, preserving the regional distribution of population, and the overall counts were developed, tested, and applied. The two procedures employed for the detection of deficiencies in global census data obtained high rates of true positives, after verification and validation. Results also show that the targeted anomalies were significantly mitigated and are encouraging for further uses of free and open geospatial data derived from remote sensing in complementing and improving conventional sources of fundamental population statistics.

Geo-information on settlements from Earth Observation offers a base for objective and scalable monitoring of the evolution of cities and settlements, including their location, extent and other attributes. In this work, we deploy the best available global knowledge on the presence of human settlements and built-up structures derived from Earth Observation to advance the understanding of the human presence on Earth. We start from a concept of Generalised Settlement Area to identify the Earth surface within which any built-up structure is present. We further characterise the resulted map by using an agreement map among the state of the art of remote sensing products mapping built-up areas or other strictly related semantic abstractions as urban areas or artificial surfaces. The agreement map is formed by a grid of 1 km 2 , where each cell is classified according to the number of EO-derived products reporting any positive occurrence of the abstractions related to the presence of built-up structures. The paper describes the characteristics of the Generalised Settlement Area, the differences in the agreement map across geographic regions of the world, and outlines the implications for potential users of the EO-derived products used in this study.