Spatially Explicit Models in Local Dynamics Analysis: The Potential Natural Vegetation (PNV) as a Tool for Beach and Coastal Management

  • Francisco GutierresEmail author
  • Pedro Gomes
  • Jorge Rocha
  • Ana Cláudia Teodoro
Part of the Coastal Research Library book series (COASTALRL, volume 24)


The concept of Potential Natural Vegetation (PNV) and its mapping have become extremely important within the scope of habitat restoration in almost every European country. The aim of this study is to predict the PNV in the sites of Natura 2000 Network ‘Sado Estuary’ and ‘Comporta-Galé’ based on the vegetation series and the main environmental variables. The modelling approach is based on the distribution of communities referred to as classification then modelling.

Subsequently, several statistical model-fitting techniques, such as regression models, machine learning and rule-based, were successfully applied to the survey data (9 vegetation series; and 7 environmental/predictor variables). The spatial database was organized as a Geographic Information System (GIS) and was also used to perform the Species Distribution Models (SDM) at community level. The results show a high correspondence between the vegetation series and the environmental gradients. The predicted PNV maps based on the Maximum Entropy Model were validated with the official map of the PNV of the sites of Natura 2000 Network ‘Sado Estuary’ and ‘Comporta-Galé’, and presented an overall accuracy of 86%. Often, conservation planning and biodiversity resource management is carried out at more detailed scales, where SDM allows integration of community direct observations and improve our interpretation of PNV local distributions along environmental gradients VNP in beach and coastal sand dunes environments.


Potential natural vegetation Predictive modelling Species distribution models Habitat restoration Beach and coastal management 



The authors would like to thank Foundation for Science and Technology (FCT) (PhD Project «Structure and dynamics of habitats and landscapes of Sado Estuary and Comporta/Galé Places»/SFRH/BD/45147/2008) for their funding of this research.


  1. Blasi C, Capotorti G, Frondoni R (2005) Defining and mapping typological models at the landscape scale. Plant Biosyst 139(2):155–163CrossRefGoogle Scholar
  2. Bohn U, Gollub G, Hettwer C, Neuhäuslová Z, Schlüter H, Weber H (2003) Map of the natural vegetation of Europe 1–3. Federal Agency for Nature Conservation, Bonn-Bad GodesbergGoogle Scholar
  3. Braun-Blanquet J (1932) Plant sociology – the study of plant communities. Trans. George D. Fuller and Henry S. Conrad. McGraw-Hill Book Company, Inc, New YorkGoogle Scholar
  4. Cancela d’Abreu A, Pinto Correia T, Oliveira R (2004) Contributos para a identificação e caracterização da paisagem em portugal continental (Vol. IV). Universidade de Évora, DGOTDU, ÉvoraGoogle Scholar
  5. Capelo J (2007) Nemorum transtaganae descriptio. Sintaxonomia numérica das comunidades florestais e Pré-florestais do baixo alentejo. Docetoral dissertation, Universidade Técnica de Lisboa, Instituto Superior de Agronomia, LisboaGoogle Scholar
  6. Costa JC, Arsénio P, Monteiro-Henriques T et al (2009) Finding the boundary between eurosiberian and mediterranean salt marshes. In: Silva CP (ed) ICS2009, conducted at the 10th international coastal symposium, 2009 Apr Lisboa, J Coastal Res, Special Issue 2009, 56:1340–1344Google Scholar
  7. Costa JC, Neto C, Aguiar C et al (2012) Vascular plant communities in Portugal (Continental, the Azores and Madeira). Global Geobotany 2:1–180Google Scholar
  8. Ferrier S, Drielsma M, Manion G, Watson G (2002) Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modeling. Biodivers Conserv 11:2309–2338CrossRefGoogle Scholar
  9. Franklin J (2009) Mapping species distributions: spatial inference and prediction. Cambridge University Press, LondonGoogle Scholar
  10. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135(2–3):147–186CrossRefGoogle Scholar
  11. Gutierres F (2014) Estrutura e dinâmica dos habitats e da paisagem dos sítios estuário do sado e comporta/galé – um contributo para a gestão e restauro ecológico. Doctoral dissertation, Institute of Geography and Spatial Planning, Universidade de LisboaGoogle Scholar
  12. Gutierres F, Gabriel L, Emídio A, Mendes P, Neto C, Reis E (2015) Modeling potential natural vegetation (PNV) of the loures council (Lisbon, Portugal). Finisterra L 99:31–62Google Scholar
  13. Gutierres F, Gil A, Reis E, Lobo A, Neto C, Calado H, Costa JC (2011) Acacia saligna (Labill.) H. Wendl in the sesimbra council: invaded habitats and potential distribution modeling. J Coastal Res SI 64:403–407Google Scholar
  14. Jiménez-Valverde A, Lobo JM, Hortal J (2009) The effect of prevalence and its interaction with sample size on the reliability of species distribution models. Community Ecol 10(2):196–205CrossRefGoogle Scholar
  15. Kleinbaum DG, Kupper LL, Nizam A, Muller KE (eds) (2007) Applied regression analysis and other multivariable methods, Duxbury applied series, 4th edn. Cengage Learning, BostonGoogle Scholar
  16. Loidi J, Fernandéz-González F (2012) Potential natural vegetation: reburying or reboring? J Veg Sci 23(3):596–604CrossRefGoogle Scholar
  17. Mucina L (2010) Floristic-phytosociological approach, potential natural vegetation, and survival of prejudice. Lazaroa 31:173–182CrossRefGoogle Scholar
  18. Neto C (2002) A flora e a vegetação do superdistrito sadense (Portugal). Guineana 8:1–269Google Scholar
  19. Neto C, Moreira MESA, Caraça RM (2005) Landscape ecology of the sado river estuary (Portugal). Quercetea 7:43–64Google Scholar
  20. Neto C, Pereira E, Reis E, Costa JC, Capelo J, Henriques C (2008) Carta da vegetação natural potencial de caldas da rainha. Finisterra 43(86):31–56Google Scholar
  21. Ricotta C, Carranza ML, Avena G, Blasi C (2002) Are potential natural vegetation maps a meaningful alternative to neutral landscape models? Appl Veg Sci 5:271–275CrossRefGoogle Scholar
  22. Ricotta C, Carranza ML, Avena G, Blasi C (2000) Quantitative comparison of the diversity of landscapes with actual vs. potential natural vegetation. Appl Veg Sci 3:157–162CrossRefGoogle Scholar
  23. Rivas-Martínez S (1987) Memória del mapa de séries de vegetación de España 1: 400.000. ICONA, MadridGoogle Scholar
  24. Tüxen R (1956) Die heutige potentielle natürliche vegetation als gegenstand der vegetationskartierung. Angew Pflanzensoz (Stolzenau) 13:5–42Google Scholar
  25. Westhoff V, van der Maarel E (1978) The Braun-blanquet approach. In: Whittaker RH (ed) Classification of plant communities. Junk, The Hague, pp 287–374CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Francisco Gutierres
    • 1
    Email author
  • Pedro Gomes
    • 2
  • Jorge Rocha
    • 3
  • Ana Cláudia Teodoro
    • 4
  1. 1.Big Data Analytics UnitEurecat – Technology Centre of CataloniaBarcelonaSpain
  2. 2.Department of Environment and AgricultureNational Statistics InstituteLisbonPortugal
  3. 3.Institute of Geography and Spatial PlanningUniversidade de LisboaLisboaPortugal
  4. 4.Earth Sciences Institute (ICT) and Department of Geosciences, Environment and Land Planning, Faculty of SciencesUniversity of PortoPortoPortugal

Personalised recommendations