IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i21p9461-d1510902.html
   My bibliography  Save this article

Identifying Landscape Characteristics That Maximize Ecosystem Services Provision

Author

Listed:
  • Yanina Benedetti

    (Department of Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, CZ-165 00 Prague, Czech Republic)

  • Federico Morelli

    (Department of Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, CZ-165 00 Prague, Czech Republic
    Institute of Biological Sciences, University of Zielona Góra, Prof. Z. Szafrana St. 1, PL-65-516 Zielona Góra, Poland)

  • Marek Svitok

    (Faculty of Ecology and Environmental Sciences, Technical University in Zvolen, T. G. Masaryka 24, SK-960 01 Zvolen, Slovakia
    Department of Forest Ecology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, CZ-165 00 Prague, Czech Republic
    Slovak Academy of Sciences, Plant Science and Biodiversity Center, Institute of Botany, Dúbravská Cesta 9, SK-845 23 Bratislava, Slovakia)

  • Riccardo Santolini

    (Department of Humanities, Università degli Studi di Urbino Carlo Bo, 61029 Urbino, Italy)

  • Petra Kadlecová

    (Department of Landscape Architecture, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamýcká 129, CZ-165 00 Prague, Czech Republic)

  • Alice Cavalli

    (Italian National Institute for Environmental Protection and Research (ISPRA), Department of Geological Survey of Italy, Via V. Brancati 48, 00144 Rome, Italy)

  • Andrea Strollo

    (Italian National Institute for Environmental Protection and Research (ISPRA), Department of Geological Survey of Italy, Via V. Brancati 48, 00144 Rome, Italy)

  • Michele Munafò

    (Italian National Institute for Environmental Protection and Research (ISPRA), Department of Geological Survey of Italy, Via V. Brancati 48, 00144 Rome, Italy)

Abstract

Given global changes and the loss of ecosystem services, it is crucial to assess the effects of landscape characteristics on ecosystem service distribution for sustainable territory management. Italy’s diverse landscapes present an opportunity to study this effect. This study identified optimal elevation and landscape heterogeneity ranges that optimize four ecosystem service provisions across Italy. We mapped ecosystem services across Italy using generalized additive models (GAM) to assess their spatial relationships with landscape characteristics, such as elevation and heterogeneity, and specifically, we identified their optimal values concerning elevation and landscape heterogeneity. In Italy, agricultural production is concentrated at low altitudes, like the Po Valley, while the pre-Alps and Apennines regions at intermediate altitudes provide ecosystem services like timber production and carbon storage. However, elevation gradient and landscape heterogeneity significantly influence trade-offs between agricultural production and these services. The optimal altitude for timber production, carbon storage, and habitat quality is around 1500 m above sea level, while agricultural production peaks at the lowest and highest elevations. Our study shows landscape features’ significant role in supporting specific ecosystem services. This information is crucial for guiding land use planning and management decisions, especially under global land use and climate change.

Suggested Citation

  • Yanina Benedetti & Federico Morelli & Marek Svitok & Riccardo Santolini & Petra Kadlecová & Alice Cavalli & Andrea Strollo & Michele Munafò, 2024. "Identifying Landscape Characteristics That Maximize Ecosystem Services Provision," Sustainability, MDPI, vol. 16(21), pages 1-14, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:21:p:9461-:d:1510902
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/21/9461/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/21/9461/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Salvatore Di Falco & Jean-Paul Chavas, 2008. "Rainfall Shocks, Resilience, and the Effects of Crop Biodiversity on Agroecosystem Productivity," Land Economics, University of Wisconsin Press, vol. 84(1), pages 83-96.
    2. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    3. Schmidt, Katja & Martín-López, Berta & Phillips, Peter M. & Julius, Eike & Makan, Neville & Walz, Ariane, 2019. "Key landscape features in the provision of ecosystem services: Insights for management," Land Use Policy, Elsevier, vol. 82(C), pages 353-366.
    4. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Belitz, Christiane & Lang, Stefan, 2008. "Simultaneous selection of variables and smoothing parameters in structured additive regression models," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 61-81, September.
    2. Kneib, Thomas & Silbersdorff, Alexander & Säfken, Benjamin, 2023. "Rage Against the Mean – A Review of Distributional Regression Approaches," Econometrics and Statistics, Elsevier, vol. 26(C), pages 99-123.
    3. Cornelius Fritz & Göran Kauermann, 2022. "On the interplay of regional mobility, social connectedness and the spread of COVID‐19 in Germany," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 400-424, January.
    4. Stefan Lang & Nikolaus Umlauf & Peter Wechselberger & Kenneth Harttgen & Thomas Kneib, 2012. "Multilevel structured additive regression," Working Papers 2012-07, Faculty of Economics and Statistics, Universität Innsbruck.
    5. Muschinski, Thomas & Mayr, Georg J. & Simon, Thorsten & Umlauf, Nikolaus & Zeileis, Achim, 2024. "Cholesky-based multivariate Gaussian regression," Econometrics and Statistics, Elsevier, vol. 29(C), pages 261-281.
    6. Stefan Sperlich & Raoul Theler, 2015. "Modeling heterogeneity: a praise for varying-coefficient models in causal analysis," Computational Statistics, Springer, vol. 30(3), pages 693-718, September.
    7. Nadja Klein & Michel Denuit & Stefan Lang & Thomas Kneib, 2013. "Nonlife Ratemaking and Risk Management with Bayesian Additive Models for Location, Scale and Shape," Working Papers 2013-24, Faculty of Economics and Statistics, Universität Innsbruck.
    8. Simon N. Wood, 2020. "Inference and computation with generalized additive models and their extensions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 307-339, June.
    9. Thomas Kneib & Nikolaus Umlauf, 2017. "A Primer on Bayesian Distributional Regression," Working Papers 2017-13, Faculty of Economics and Statistics, Universität Innsbruck.
    10. Klein, Nadja & Denuit, Michel & Lang, Stefan & Kneib, Thomas, 2013. "Nonlife Ratemaking and Risk Management with Bayesian Additive Models for Location, Scale and Shape," LIDAM Discussion Papers ISBA 2013045, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Citores, L. & Ibaibarriaga, L. & Lee, D.-J. & Brewer, M.J. & Santos, M. & Chust, G., 2020. "Modelling species presence–absence in the ecological niche theory framework using shape-constrained generalized additive models," Ecological Modelling, Elsevier, vol. 418(C).
    12. M.L. Nores & M.P. Díaz, 2016. "Bootstrap hypothesis testing in generalized additive models for comparing curves of treatments in longitudinal studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(5), pages 810-826, April.
    13. Yixuan Wang & Jianzhu Li & Ping Feng & Rong Hu, 2015. "A Time-Dependent Drought Index for Non-Stationary Precipitation Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5631-5647, December.
    14. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," Working Papers hal-02790523, HAL.
    15. Nathaniel Geiger & Bryan McLaughlin & John Velez, 2021. "Not all boomers: temporal orientation explains inter- and intra-cultural variability in the link between age and climate engagement," Climatic Change, Springer, vol. 166(1), pages 1-20, May.
    16. Panayi, Efstathios & Peters, Gareth W. & Danielsson, Jon & Zigrand, Jean-Pierre, 2018. "Designating market maker behaviour in limit order book markets," Econometrics and Statistics, Elsevier, vol. 5(C), pages 20-44.
    17. Gauss Cordeiro & Josemar Rodrigues & Mário Castro, 2012. "The exponential COM-Poisson distribution," Statistical Papers, Springer, vol. 53(3), pages 653-664, August.
    18. Fang, Lei & Härdle, Wolfgang Karl, 2015. "Stochastic population analysis: A functional data approach," SFB 649 Discussion Papers 2015-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    19. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    20. Iñaki Galán & Lorena Simón & Elena Boldo & Cristina Ortiz & Rafael Fernández-Cuenca & Cristina Linares & María José Medrano & Roberto Pastor-Barriuso, 2017. "Changes in hospitalizations for chronic respiratory diseases after two successive smoking bans in Spain," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:21:p:9461-:d:1510902. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.