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6. List of contributors

Author

Alex, Nimusiima, primary, Balbi, Stefano, additional, Balica, S.F., additional, Ballesteros-Cánovas, Juan A., additional, Bamutaze, Yazidhi, additional, Bates, Paul D., additional, Benito, Gerardo, additional, Biscaro, Claudio, additional, Bogaard, Thom, additional, Bohms, S., additional, Budde, M., additional, Dabrowa, Amy, additional, Di Baldassarre, Giuliano, additional, Díez-Herrero, Andrés, additional, Dinh, Q., additional, Elena, Ridolfi, additional, Farnaghi, Mahdi, additional, Gain, Animesh K., additional, George, Oriangi, additional, Giupponi, Carlo, additional, Jonas, Ardö, additional, Manalili, Michael, additional, Mansourian, Ali, additional, Maskey, Shreedhar, additional, Matchanov, Muzaffar, additional, Mojtahed, Vahid, additional, Mondino, Elena, additional, Mukolwe, Micah, additional, Neal, Jeffrey C., additional, Pilesjö, Petter, additional, Popescu, I., additional, Prades, Lara, additional, Rogger, Magdalena, additional, Rowland, J., additional, Schumann, Guy, additional, Senay, G.B., additional, Solomatine, Dimitri P., additional, Trambauer, Patricia, additional, Velpuri, N.M., additional, Verdin, J.P., additional, Viglione, Alberto, additional, Yan, Kun, additional, and Young, C., additional

Published
2023
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9. A Continental-Scale Validation of Ecosystem Service Models

Author

Willcock, Simon, Hooftman, Danny A. P., Balbi, Stefano, Blanchard, Ryan, Dawson, Terence P., O’Farrell, Patrick J., Hickler, Thomas, Hudson, Malcolm D., Lindeskog, Mats, Martinez-Lopez, Javier, Mulligan, Mark, Reyers, Belinda, Shackleton, Charlie, Sitas, Nadia, Villa, Ferdinando, Watts, Sophie M., Eigenbrod, Felix, and Bullock, James M.

Published
2019

11. Pollination supply models from a local to global scale

Author

Giménez-García, Angel, primary, Allen-Perkins, Alfonso, additional, Bartomeus, Ignasi, additional, Balbi, Stefano, additional, Knapp, Jessica L., additional, Hevia, Violeta, additional, Woodcock, Ben Alex, additional, Smagghe, Guy, additional, Miñarro, Marcos, additional, Eeraerts, Maxime, additional, Colville, Jonathan F., additional, Hipólito, Juliana, additional, Cavigliasso, Pablo, additional, Nates-Parra, Guiomar, additional, Herrera, José M., additional, Cusser, Sarah, additional, Simmons, Benno I., additional, Wolters, Volkmar, additional, Jha, Shalene, additional, Freitas, Breno M., additional, Horgan, Finbarr G., additional, Artz, Derek R., additional, Sidhu, C. Sheena, additional, Otieno, Mark, additional, Boreux, Virginie, additional, Biddinger, David J., additional, Klein, Alexandra-Maria, additional, Joshi, Neelendra K., additional, Stewart, Rebecca I. A., additional, Albrecht, Matthias, additional, Nicholson, Charlie C., additional, O'Reilly, Alison D., additional, Crowder, David William, additional, Burns, Katherine L. W., additional, Nabaes Jodar, Diego Nicolás, additional, Garibaldi, Lucas Alejandro, additional, Sutter, Louis, additional, Dupont, Yoko L., additional, Dalsgaard, Bo, additional, da Encarnação Coutinho, Jeferson Gabriel, additional, Lázaro, Amparo, additional, Andersson, Georg K. S., additional, Raine, Nigel E., additional, Krishnan, Smitha, additional, Dainese, Matteo, additional, van der Werf, Wopke, additional, Smith, Henrik G., additional, and Magrach, Ainhoa, additional

Published
2023
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12. Design of an Empirical Agent-Based Model to Explore Rural Household Food Security Within a Developing Country Context

Author

Dobbie, Samantha, Balbi, Stefano, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Jager, Wander, editor, Verbrugge, Rineke, editor, Flache, Andreas, editor, de Roo, Gert, editor, Hoogduin, Lex, editor, and Hemelrijk, Charlotte, editor

Published
2017
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14. Machine learning for ecosystem services

Author

Willcock, Simon, Martínez-López, Javier, Hooftman, Danny A.P., Bagstad, Kenneth J., Balbi, Stefano, Marzo, Alessia, Prato, Carlo, Sciandrello, Saverio, Signorello, Giovanni, Voigt, Brian, Villa, Ferdinando, Bullock, James M., and Athanasiadis, Ioannis N.

Published
2018
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17. Pollination supply models from a local to global scale

Author

Giménez-García, Angel, Allen-Perkins, Alfonso, Bartomeus, Ignasi, Balbi, Stefano, Knapp, Jessica L., Hevia, Violeta, Woodcock, Ben Alex, Smagghe, Guy, Miñarro, Marcos, Eeraerts, Maxime, Colville, Jonathan F., Hipólito, Juliana, Cavigliasso, Pablo, Nates-Parra, Guiomar, Herrera, José M., Cusser, Sarah, Simmons, Benno I., Wolters, Volkmar, Jha, Shalene, Freitas, Breno M., Horgan, Finbarr G., Artz, Derek R., Sidhu, C. Sheena, Otieno, Mark, Boreux, Virginie, Biddinger, David J., Klein, Alexandra-Maria, Joshi, Neelendra K., Stewart, Rebecca I.A., Albrecht, Matthias, Nicholson, Charlie C., O'Reilly, Alison D., Crowder, David William, Burns, Katherine L.W., Nabaes Jodar, Diego Nicolás, Garibaldi, Lucas Alejandro, Sutter, Louis, Dupont, Yoko L., Dalsgaard, Bo, da Encarnação Coutinho, Jeferson Gabriel, Lázaro, Amparo, Andersson, Georg K.S., Raine, Nigel E., Krishnan, Smitha, Dainese, Matteo, van der Werf, Wopke, Smith, Henrik G., Magrach, Ainhoa, Giménez-García, Angel, Allen-Perkins, Alfonso, Bartomeus, Ignasi, Balbi, Stefano, Knapp, Jessica L., Hevia, Violeta, Woodcock, Ben Alex, Smagghe, Guy, Miñarro, Marcos, Eeraerts, Maxime, Colville, Jonathan F., Hipólito, Juliana, Cavigliasso, Pablo, Nates-Parra, Guiomar, Herrera, José M., Cusser, Sarah, Simmons, Benno I., Wolters, Volkmar, Jha, Shalene, Freitas, Breno M., Horgan, Finbarr G., Artz, Derek R., Sidhu, C. Sheena, Otieno, Mark, Boreux, Virginie, Biddinger, David J., Klein, Alexandra-Maria, Joshi, Neelendra K., Stewart, Rebecca I.A., Albrecht, Matthias, Nicholson, Charlie C., O'Reilly, Alison D., Crowder, David William, Burns, Katherine L.W., Nabaes Jodar, Diego Nicolás, Garibaldi, Lucas Alejandro, Sutter, Louis, Dupont, Yoko L., Dalsgaard, Bo, da Encarnação Coutinho, Jeferson Gabriel, Lázaro, Amparo, Andersson, Georg K.S., Raine, Nigel E., Krishnan, Smitha, Dainese, Matteo, van der Werf, Wopke, Smith, Henrik G., and Magrach, Ainhoa

Abstract

Ecological intensification has been embraced with great interest by the academic sector but is still rarely taken up by farmers because monitoring the state of different ecological functions is not straightforward. Modelling tools can represent a more accessible alternative of measuring ecological functions, which could help promote their use amongst farmers and other decision-makers. In the case of crop pollination, modelling has traditionally followed either a mechanistic or a data-driven approach. Mechanistic models simulate the habitat preferences and foraging behaviour of pollinators, while data-driven models associate georeferenced variables with real observations. Here, we test these two approaches to predict pollination supply and validate these predictions using data from a newly released global dataset on pollinator visitation rates to different crops. We use one of the most extensively used models for the mechanistic approach, while for the data-driven approach, we select from among a comprehensive set of state-of-the-art machine-learning models. Moreover, we explore a mixed approach, where data-derived inputs, rather than expert assessment, inform the mechanistic model. We find that, at a global scale, machine-learning models work best, offering a rank correlation coefficient between predictions and observations of pollinator visitation rates of 0.56. In turn, the mechanistic model works moderately well at a global scale for wild bees other than bumblebees. Biomes characterized by temperate or Mediterranean forests show a better agreement between mechanistic model predictions and observations, probably due to more comprehensive ecological knowledge and therefore better parameterization of input variables for these biomes. This study highlights the challenges of transferring input variables across multiple biomes, as expected given the different composition of species in different biomes. Our results provide clear guidance on which pollination supply mode

Published
2023

18. Pollination supply models from a local to global scale

Author

Agencia Estatal de Investigación (España), Eusko Jaurlaritza, Comunidad de Madrid, European Commission, Universidad Politécnica de Madrid, Research Foundation - Flanders, Ministerio de Economía y Competitividad (España), Ministerio de Universidades (España), Fundação para a Ciência e a Tecnologia (Portugal), Conselho Nacional de Desenvolvimento Científico e Tecnológico (Brasil), Department of Agriculture (US), German Research Foundation, Irish Research Council, Swedish Research Council, Giménez-García, Ángel, Allen-Perkins, Alfonso, Bartomeus, Ignasi, Balbi, Stefano, Knapp, Jessica L., Hevia, Violeta, Woodcock, Ben Alex, Smagghe, Guy, Miñarro, Marcos, Eeraerts, Maxime, Colville, Jonathan F., Hipólito, Juliana, Cavigliasso, Pablo, Nates-Parra, Guiomar, Herrera, José M., Cusser, Sarah, Simmons, Benno I., Wolters, Volkmar, Jha, Shalene, Freitas, Breno M., Horgan, Finbarr G., Artz, Derek R., Sidhu, C. Sheena, Otieno, Mark, Boreux, Virginie, Biddinger, David J., Klein, Alexandra Maria, Joshi, Neelendra K., Stewart, Rebecca I.A., Albrecht, Matthias, Nicholson, Charlie C., O'Reilly, Alison D., Crowder, David William, Burns, Katherine L.W., Nabaes Jodar, Diego Nicolás, Garibaldi, Lucas Alejandro, Sutter, Louis, Dupont, Yoko L., Dalsgaard, Bo, Da Encarnação Coutinho, Jeferson Gabriel, Lázaro, Amparo, Andersson, Georg K. S., Raine, Nigel E., Krishnan, Smitha, Dainese, Matteo, Van Der Werf, Wopke, Smith, Henrik G., Magrach, Ainhoa, Agencia Estatal de Investigación (España), Eusko Jaurlaritza, Comunidad de Madrid, European Commission, Universidad Politécnica de Madrid, Research Foundation - Flanders, Ministerio de Economía y Competitividad (España), Ministerio de Universidades (España), Fundação para a Ciência e a Tecnologia (Portugal), Conselho Nacional de Desenvolvimento Científico e Tecnológico (Brasil), Department of Agriculture (US), German Research Foundation, Irish Research Council, Swedish Research Council, Giménez-García, Ángel, Allen-Perkins, Alfonso, Bartomeus, Ignasi, Balbi, Stefano, Knapp, Jessica L., Hevia, Violeta, Woodcock, Ben Alex, Smagghe, Guy, Miñarro, Marcos, Eeraerts, Maxime, Colville, Jonathan F., Hipólito, Juliana, Cavigliasso, Pablo, Nates-Parra, Guiomar, Herrera, José M., Cusser, Sarah, Simmons, Benno I., Wolters, Volkmar, Jha, Shalene, Freitas, Breno M., Horgan, Finbarr G., Artz, Derek R., Sidhu, C. Sheena, Otieno, Mark, Boreux, Virginie, Biddinger, David J., Klein, Alexandra Maria, Joshi, Neelendra K., Stewart, Rebecca I.A., Albrecht, Matthias, Nicholson, Charlie C., O'Reilly, Alison D., Crowder, David William, Burns, Katherine L.W., Nabaes Jodar, Diego Nicolás, Garibaldi, Lucas Alejandro, Sutter, Louis, Dupont, Yoko L., Dalsgaard, Bo, Da Encarnação Coutinho, Jeferson Gabriel, Lázaro, Amparo, Andersson, Georg K. S., Raine, Nigel E., Krishnan, Smitha, Dainese, Matteo, Van Der Werf, Wopke, Smith, Henrik G., and Magrach, Ainhoa

Abstract

Ecological intensification has been embraced with great interest by the academic sector but is still rarely taken up by farmers because monitoring the state of different ecological functions is not straightforward. Modelling tools can represent a more accessible alternative of measuring ecological functions, which could help promote their use amongst farmers and other decision-makers. In the case of crop pollination, modelling has traditionally followed either a mechanistic or a data-driven approach. Mechanistic models simulate the habitat preferences and foraging behaviour of pollinators, while data-driven models associate georeferenced variables with real observations. Here, we test these two approaches to predict pollination supply and validate these predictions using data from a newly released global dataset on pollinator visitation rates to different crops. We use one of the most extensively used models for the mechanistic approach, while for the data-driven approach, we select from among a comprehensive set of state-of-The-Art machine-learning models. Moreover, we explore a mixed approach, where data-derived inputs, rather than expert assessment, inform the mechanistic model. We find that, at a global scale, machine-learning models work best, offering a rank correlation coefficient between predictions and observations of pollinator visitation rates of 0.56. In turn, the mechanistic model works moderately well at a global scale for wild bees other than bumblebees. Biomes characterized by temperate or Mediterranean forests show a better agreement between mechanistic model predictions and observations, probably due to more comprehensive ecological knowledge and therefore better parameterization of input variables for these biomes. This study highlights the challenges of transferring input variables across multiple biomes, as expected given the different composition of species in different biomes. Our results provide clear guidance on which pollination supply mode

Published
2023

19. Fire risk modeling: an integrated and data-driven approach applied to Sicily.

Author

Marquez Torres, Alba, Signorello, Giovanni, Kumar, Sudeshna, Adamo, Greta, Villa, Ferdinando, and Balbi, Stefano

Subjects
FIRE risk assessment, GEOGRAPHIC information systems, BAYESIAN analysis, WILDFIRE prevention, DATA integration, REMOTE sensing, FIRE management
Abstract

Wildfires are key not only to landscape transformation and vegetation succession, but also to socio-ecological values loss. Fire risk mapping can help to manage the most vulnerable and relevant ecosystems impacted by wildfires. However, few studies provide accessible daily dynamic results at different spatio-temporal scales. We develop a fire risk model for Sicily (Italy), an iconic case of the Mediterranean Basin, integrating a fire hazard model with an exposure and vulnerability analysis under present and future conditions. The integrated model is data-driven but can run dynamically at a daily time step, providing spatially and temporally explicit results through the k.LAB (Knowledge Laboratory) software. This software provides an environment for input data integration, combining methods and data such as geographic information systems, remote sensing and Bayesian network algorithms. All data and models are semantically annotated, open and downloadable in agreement with the FAIR principles (findable, accessible, interoperable and reusable). The fire risk analysis reveals that 45 % of vulnerable areas of Sicily have a high probability of fire occurrence in 2050. The risk model outputs also include qualitative risk indexes, which can make the results more understandable for non-technical stakeholders. We argue that this approach is well suited to aiding in landscape and fire risk management, under both current and climate change conditions. [ABSTRACT FROM AUTHOR]

Published
2023
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24. European blue and green infrastructure network strategy vs. the common agricultural policy. Insights from an integrated case study (Couesnon, Brittany)

Author

Houet, Thomas, Palka, Gaetan, Rigo, Roberta, Boussard, Hugues, Baudry, Jacques, Poux, Xavier, Narcy, Jean-Baptiste, Alvarez Martinez, José Manuel, Balbi, Stefano, Mony, Cendrine, Lecoq, Lucie, Beganton, Johanna, Barquin, José, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes ), Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Nantes Université (Nantes Univ)-Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (NantesUniv – IGARUN), Nantes Université - pôle Humanités, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Humanités, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (NantesUniv – IGARUN), Nantes Université (Nantes Univ), LTSER Zone Atelier Armorique (LTSER), Knowledge Learning and Information Modelling (LABISEN-KLAIM), Laboratoire ISEN (L@BISEN), Institut supérieur de l'électronique et du numérique (ISEN)-YNCREA OUEST (YO)-Institut supérieur de l'électronique et du numérique (ISEN)-YNCREA OUEST (YO), Biodiversité agroécologie et aménagement du paysage (UMR BAGAP), Ecole supérieure d'Agricultures d'Angers (ESA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Bureau d'Etudes ASCA, IHCantabria - Instituto de Hidráulica Ambiental de La Universidad de Cantabria, Santander, Basque Centre for Climate Change (BC3), Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Aménagement des Usages des Ressources et des Espaces marins et littoraux - Centre de droit et d'économie de la mer (AMURE), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Brest (UBO)-Université de Rennes 2 (UR2)-Nantes Université (Nantes Univ)-Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (Nantes Univ - IGARUN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (Nantes Univ - IGARUN), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), and Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SHS.ENVIR]Humanities and Social Sciences/Environmental studies, Participatory approach, Geography, Planning and Development, Modeling, Environmental policies, Forestry, Biodiversity, [SHS.GEO]Humanities and Social Sciences/Geography, Management, Monitoring, Policy and Law, [SDE.ES]Environmental Sciences/Environmental and Society, Landscape connectivity, Land use and cover changes, Nature and Landscape Conservation
Abstract

International audience; Urbanization and agricultural intensification are the main drivers of biodiversity losses through multiple stressors, especially habitat fragmentation, isolation and loss. Designing Blue and Green Infrastructure Networks (BGIN) has been recommended as a potential tool for land-use planning to increase ecosystem services while preserving biodiversity. All municipalities in France are required to perform BGIN planning. This article focuses on the Couesnon watershed (Brittany, France) and the participatory process used to define and analyze five possible pathways of future land-use and land-cover changes that included implementation of BGINs. Impacts on biodiversity were estimated by quantifying the change in landscape connectivity of woodlands, grasslands and wetlands. The effectiveness of BGIN policies was assessed by comparing current landscape connectivity (2018) to those in possible futures. Landscape connectivity referred to functional connectivity for three indicator species (Abax parallelepipedus, Maniola jurtina and Arvicola sapidus) across three landscape features: woodlands, grasslands and wetlands, respectively. Results allowed impacts of urban and agricultural land-use changes to be identified in terms of extent and quality. If BGIN policies were applied effectively to control the expansion of gray infrastructure, they would help increase the area and the quality of grassland and woodland connectivity by no more than 2%. Agricultural land-use and land-cover changes could have more impact on the extent of grassland (− 82% to +38%) and wetland (− 49% to +47%) connectivity. Current and future trends for hedgerows implied a decrease in woodland connectivity of 9.8-33.8%. Impacts on the quality of landscape connectivity is not proportional with the extent, as a decrease of the latter can have relatively more negative impacts on the former, and inversely. The study highlights that the BGIN strategy can preserve landscape connectivity effectively in urban ecosystems, where human density is higher, but can be threatened by agricultural intensification.

Published
2022

25. Supplementary material 1 from: Capriolo A, Boschetto RG, Mascolo RA, Bulckaen A, Balbi S, Villla F (2022) How regulating and cultural services of ecosystems have changed over time in Italy. One Ecosystem 7: e83214. https://doi.org/10.3897/oneeco.7.e83214

Author

Capriolo, Alessio, primary, Boschetto, Riccardo Giuseppe, additional, Mascolo, Rosa Anna, additional, Bulckaen, Alessio, additional, Balbi, Stefano, additional, and Villla, Ferdinando, additional

Published
2022
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27. Supplementary material 2 from: Capriolo A, Boschetto RG, Mascolo RA, Bulckaen A, Balbi S, Villla F (2022) How regulating and cultural services of ecosystems have changed over time in Italy. One Ecosystem 7: e83214. https://doi.org/10.3897/oneeco.7.e83214

Author

Capriolo, Alessio, primary, Boschetto, Riccardo Giuseppe, additional, Mascolo, Rosa Anna, additional, Bulckaen, Alessio, additional, Balbi, Stefano, additional, and Villla, Ferdinando, additional

Published
2022
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28. Supplementary material 3 from: Capriolo A, Boschetto RG, Mascolo RA, Bulckaen A, Balbi S, Villla F (2022) How regulating and cultural services of ecosystems have changed over time in Italy. One Ecosystem 7: e83214. https://doi.org/10.3897/oneeco.7.e83214

Author

Capriolo, Alessio, primary, Boschetto, Riccardo Giuseppe, additional, Mascolo, Rosa Anna, additional, Bulckaen, Alessio, additional, Balbi, Stefano, additional, and Villla, Ferdinando, additional

Published
2022
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37. Integrated modelling of social-ecological systems for climate change adaptation

Author

Giupponi, Carlo, primary, Ausseil, Anne-Gaelle, additional, Balbi, Stefano, additional, Cian, Fabio, additional, Fekete, Alexander, additional, Gain, Animesh K., additional, Essenfelder, Arthur Hrast, additional, Martínez-López, Javier, additional, Mojtahed, Vahid, additional, Norf, Celia, additional, Relvas, Hélder, additional, and Villa, Ferdinando, additional

Published
2022
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38. Fire risk: an integrated modelling approach.

Author

Torres, Alba Marquez, Signorello, Giovanni, Kumar, Sudeshna, Adamo, Greta, Villa, Ferdinando, and Balbi, Stefano

Subjects
FIRE risk assessment, GEOGRAPHIC information systems, WILDFIRE prevention, BAYESIAN analysis, DATA integration, SOLAR stills, FIRE management
Abstract

Wildfires are key to landscape transformation and vegetation succession, but also to socio-ecological values loss. Fire risk mapping can help to manage the most vulnerable and relevant ecosystems impacted by fires. However, few studies provide accessible daily dynamic results at different spatio-temporal scales. We develop a fire risk model for Sicily (Italy), an iconic case of the Mediterranean basin, integrating a fire hazard model with an exposure and vulnerability analysis under present and future conditions. The integrated model is data-driven but can run dynamically at a daily time-step, providing spatially and temporally explicit fashion results through the k.LAB platform. K.LAB provides an environment for input data integration, employing modeling methods such as Geographic Information System, Remote Sensing and Bayesian Network algorithms. All data and models are semantically annotated, open and downloadable in agreement with the FAIR principles (Findable, Accessible, Interoperable and Reusable). The fire risk analysis reveals that 45 % of vulnerable areas of Sicily are at high probability of danger in 2050. The risk model outputs also include qualitative risk indexes, which can make the results more understandable for non-technical stakeholders. We argue that this approach is well suited to aid in landscape management and preventing wildfires due to climate change. [ABSTRACT FROM AUTHOR]

Published
2023
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40. Contributors

Author

Andreadis, Konstantinos M., primary, Balbi, Stefano, additional, Balica, S.F., additional, Bates, Paul D., additional, Benito, Gerardo, additional, Biscaro, Claudio, additional, Bogaard, Thom, additional, Bohms, S., additional, Budde, M., additional, Dabrowa, Amy, additional, Di Baldassarre, Giuliano, additional, Díez-Herrero, Andrés, additional, Dinh, Q., additional, Gain, Animesh K., additional, Giupponi, Carlo, additional, Maskey, Shreedhar, additional, Mojtahed, Vahid, additional, Mukolwe, Micah, additional, Neal, Jeffrey C., additional, Popescu, I., additional, Rogger, Magdalena, additional, Rowland, J., additional, Schumann, Guy J.-P., additional, Senay, G.B., additional, Solomatine, Dimitri P., additional, Trambauer, Patricia, additional, Velpuri, N.M., additional, Verdin, J.P., additional, Viglione, Alberto, additional, Yan, Kun, additional, and Young, C., additional

Published
2014
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46. Project ALICE: Integrated modelling to link terrestrial and riverine interfaces accounting for biodiversity and ecosystem services

Author

Cabecinha, Edna, Fonseca, Andre Ribeiro, Santos, João A, Cabral, João Alexandre, Màrio Gabriel Santiago Santos, Lourenço, Martinho, Aranha, Jose Tadeu Marques, Cortes, Rui, Varandas, Simone, Monteiro, Sandra Mariza, Fernandes, Luís Filipe Sanches, Queirós, Vanessa, Houet, Thomas, Balbi, Stefano, Villa, Ferdinando, Mony, Cendrine, Hynes, Stephen, Álvarez-Martínez, Jose Manuel, and Barquín, Pepe

Published
2019
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48. Human dependence on natural resources in rapidly urbanising South African regions

Author

Balbi, Stefano, Selomane, Odirilwe, Sitas, Nadia, Blanchard, Ryan, Kotzee, Ilse, O'Farrell, Patrick, Villa, Ferdinando, Balbi, Stefano, Selomane, Odirilwe, Sitas, Nadia, Blanchard, Ryan, Kotzee, Ilse, O'Farrell, Patrick, and Villa, Ferdinando

Abstract

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.

Published
2019
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49. Towards globally customizable ecosystem service models

Author

Martínez-López, Javier, Bagstad, Kenneth J., Balbi, Stefano, Magrach, Ainhoa, Voigt, Brian, Athanasiadis, Ioannis, Pascual, Marta, Willcock, Simon, Villa, Ferdinando, Martínez-López, Javier, Bagstad, Kenneth J., Balbi, Stefano, Magrach, Ainhoa, Voigt, Brian, Athanasiadis, Ioannis, Pascual, Marta, Willcock, Simon, and Villa, Ferdinando

Abstract

Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The ARtificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multi-criteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed.

Published
2019

50. Improving biophysical models to link terrestrial, riverine and coastal interfaces accounting for biodiversity and ES

Author

Cabecinha, Edna, Hugges, Samantha, Cortes, Rui, Cabral, João, Santos, Mário, Varandas, Simone, Sanches, Luis Filipe, Pacheco, Fernando, Santos, João, Fonseca, André, Aranha, José, Lopes, Domingos, Moura, João Paulo, Burgess, Diane, Houet, Thomas, Balbi, Stefano, Villa, Ferdinando, Milagaia, Mariana, Magalhães, Marco, Ballé-Béganton, Johanna, Alvarez-Martinez, Jose, Barquin, Jose, Universidade de Trás-os-Montes e Alto Douro (UTAD), Universidade do Trás-os-Montes e Alto Douro, Agri Food & Biosciences Institute - AFBI, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Basque Centre for Climate Change (BC3), Aménagement des Usages des Ressources et des Espaces marins et littoraux - Centre de droit et d'économie de la mer (AMURE), Centre National de la Recherche Scientifique (CNRS)-Université de Brest (UBO)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Instituto de Hidráulica Ambiental, Houet, Thomas, Institut Universitaire Européen de la Mer (IUEM), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SHS.ENVIR] Humanities and Social Sciences/Environmental studies, [SHS.GEO] Humanities and Social Sciences/Geography, [SHS.ENVIR]Humanities and Social Sciences/Environmental studies, [SHS] Humanities and Social Sciences, [SDE.ES] Environmental Sciences/Environmental and Society, [SHS.GEO]Humanities and Social Sciences/Geography, [SDE.ES]Environmental Sciences/Environmental and Society, ComputingMilieux_MISCELLANEOUS, [SHS]Humanities and Social Sciences
Abstract

International audience

Published
2018

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