Stefano Balbi

This review article about applications of collaborative intelligence is relevant and timely. I really enjoyed reading it: the article is well written and I think it represents a significant scientific contribution. However, I have a major critique, w...

This article describes the adaptation of a non-spatial model of pastureland dynamics, including vegetation life cycle, livestock management and nitrogen cycle, for use in a spatially explicit and modular modelling platform (k.LAB) dedicated to make data and models more interoperable. The aim is to showcase to the social-ecological modelling community the delivery of an existing, monolithic model, into a more modular, transparent and accessible approach to potential end users, regional managers, farmers and other stakeholders. This also allows better usability and adaptability of the model beyond its originally intended geographical scope (the Cantabrian Region in the North of Spain). The original code base (written in R in 1,491 lines of code divided into 13 files) combines several algorithms drawn from the literature in an opaque fashion due to lack of modularity, non-semantic variable naming and implicit assumptions. The spatiotemporal rewrite is structured around a set of 10 namespaces called PaL (Pasture and Livestock), which includes 198 interoperable and independent models. The end user chooses the spatial and temporal context of the analysis through an intuitive web-based user interface called k.Explorer. Each model can be called individually or in conjunction with the others, by querying any PaL-related concepts in a search bar. A scientific dataflow and a provenance diagram are produced in conjunction with the model results for full transparency. We argue that this work demonstrates key steps needed to create more Findable, Accessible, Interoperable and Reusable (FAIR) models beyond the selected example. This is particularly essential in environments as complex as agricultural systems, where multidisciplinary knowledge needs to be integrated across diverse spatial and temporal scales in order to understand complex and changing problems.

In this experimental study, different components are computed for three different ecosystem services (ES). Specifically, supply, demand and use are estimated for pollination service, flood risk regulation service and nature-based tourism. These are analysed and assessed in 2012 and 2018 for the Italian context, in order to estimate the evolution over this period and to allow a significant comparison of results. The same methodology and models are applied for the selected accounting years and accounting tables and tend to reflect as closely as possible the System of Environmental-Economic Accounting-Ecosystem Accounting (SEEA EA), which is the international standard endorsed by the United Nations to compile Natural Capital Accounting in 2021. Both biophysical and monetary assessments are performed using the ARIES technology, an integrated modelling platform providing automatic and flexible integration of data and models, via its semantic modelling nature. Models have been run adjusting the components of the global modelling approach to the Italian context and, whenever available, prioritising the use of local data to carry out the study. This approach is particularly useful to analyse trends over time, as potentially biased components of models and data are substantially mitigated when the same biases is constant over time. This study finds an increase in benefits over the period analysed for the ES examined. The main contribution of this pioneering work is to support the idea that ES accounting or Natural Capital Accounting can provide a very useful tool to improve economic and environmental information at national and regional level. This can support processes to provide the necessary incentives to steer policy-making towards preventative rather than corrective actions, which are usually much less effective and more costly, both at environmental and economic levels. Nevertheless, particular attention must be paid to the meaning of the estimates and the drivers of these values to derive a direct or indirect relationship between the benefits observable and the actual Italian ecosystems condition.

Progress in key social-ecological challenges of the global environmental agenda (e.g., climate change, biodiversity conservation, Sustainable Development Goals) is hampered by a lack of integration and synthesis of existing scientific evidence. Facing a fast-increasing volume of data, information remains compartmentalized to pre-defined scales and fields, rarely building its way up to collective knowledge. Today's distributed corpus of human intelligence, including the scientific publication system, cannot be exploited with the efficiency needed to meet current evidence synthesis challenges; computer-based intelligence could assist this task. Artificial Intelligence (AI)-based approaches underlain by semantics and machine reasoning offer a constructive way forward, but depend on greater understanding of these technologies by the science and policy communities and coordination of their use. By labelling web-based scientific information to become readable by both humans and computers, machines can search, organize, reuse, combine and synthesize information quickly and in novel ways. Modern open science infrastructure—i.e., public data and model repositories—is a useful starting point, but without shared semantics and common standards for machine actionable data and models, our collective ability to build, grow, and share a collective knowledge base will remain limited. The application of semantic and machine reasoning technologies by a broad community of scientists and decision makers will favour open synthesis to contribute and reuse knowledge and apply it toward decision making.

Analysis of climate change risks in support of policymakers to set effective adaptation policies requires an innovative yet rigorous approach towards integrated modelling (IM) of social-ecological systems (SES). Despite continuous advances, IM still faces various challenges that span through both unresolved methodological issues as well as data requirements. On the methodological side, significant improvements have been made for better understanding the dynamics of complex social and ecological systems, but still, the literature and proposed solutions are fragmented. This paper explores available modelling approaches suitable for long-term analysis of SES for supporting climate change adaptation (CCA). It proposes their classification into seven groups, identifies their main strengths and limitations, and lists current data sources of greatest interest. Upon that synthesis, the paper identifies directions for orienting the development of innovative IM, for improved analysis and management of socio-economic systems, thus providing better foundations for effective CCA.

This article describes the adaptation of a non-spatial model of pastureland dynamics, including vegetation life cycle, livestock management and nitrogen cycle, for use in a spatially explicit and modular modelling platform (k.LAB) dedicated to make data and models more interoperable. The aim is to deliver an existing, locally successful monolithic model, into a more modular, transparent and accessible approach to potential end users, regional managers, farmers and other stakeholders. This allows better usability and adaptability of the model beyond its originally intended geographical scope (the Cantabrian Region in the North of Spain). The original model, named Puerto, is developed in the R language and includes 1,491 lines of code divided into 13 script files and linked to 19 input tables. The spatiotemporal rewrite is structured around a set of 10 namespaces called PaL (Pasture and Livestock), which includes 198 interoperable but independent models. The end user chooses the spatial and temporal context of the analysis through an intuitive web-based user interface called k.Explorer. Each model can be called individually or in conjunction with the others, by querying any PaL-related concepts in a search bar. A scientific workflow is built as a response, which is run to produce result datasets and a report with information on the data sources and modelling processes used, delivering results with full transparency. We argue that this work demonstrates key steps needed to create more Findable, Accessible, Interoperable and Reusable (FAIR) models. This is particularly essential in environments as complex as agricultural systems, where multidisciplinary knowledge needs to be integrated across diverse spatial and temporal scales in order to understand complex and changing problems.

Enhancing the governance of social-ecological systems for more equitable and sustainable development is hindered by inadequate knowledge about how different social groups and communities rely on natural resources. We used openly accessible national survey data to develop a metric of overall dependence on natural resources. These data contain information about households' sources of water, energy, building materials and food. We used these data in combination with Bayesian learning to model observed patterns of dependence using demographic variables that included: gender of household head, household size, income, house ownership, formality status of settlement, population density, and in-migration rate to the area. We show that a small number of factors-in particular population density and informality of settlements-can explain a significant amount of the observed variation with regards to the use of natural resources. Subsequently, we test the validity of these predictions using alternative, open access data in the eThekwini and Cape Town metropolitan areas of South Africa. We discuss the advantages of using a selection of predictors which could be supplied through remotely sensed and open access data, in terms of opportunities and challenges to produce meaningful results in data-poor areas. With data availability being a common limiting factor in modelling and monitoring exercises, access to inexpensive, up-to-date and free to use data can significantly improve how we monitor progress towards sustainability targets. A small selection of openly accessible demographic variables can predict household's dependence on local natural resources.