Geospatial Data analysis promises a potential transformation of coastal management and planning

24 gener 2018
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Category blog
24 gener 2018, Comments 0

By: Francisco Sacramento Gutierres, Ph.D (web:, twitter:@FRSGutierres)

Why defend the coastal sand dunes environments using innovative tools?

There are a number of reasons for protecting the coast. Coast lines tend to be very heavily populated. They are areas of high economic value due to tourism. Coastlines are particularly susceptible to flooding and anthropogenic impacts. Therefore, they are fragile ecosystems which take a long time to recover if they are damaged.
In 1975, 16 Mediterranean countries and the European Community adopted the Mediterranean Action Plan (MAP), presented as the first-ever Regional Seas Programme under United Nations Environment Programme (UNEP) authority.
In 1995, the Action Plan for the Protection of the Marine Environment and the Sustainable Development of the Coastal Areas of the Mediterranean (MAP Phase II) was adopted by the Contracting Parties to replace the Mediterranean Action Plan of 1975.
In the recent years, the Barcelona Convention for the Protection of the Marine Environment and the Coastal Region of the Mediterranean (adopted in 1995) and MAP are more active than ever. The Contracting Parties are now 22, and they are determined to protect the Mediterranean marine and coastal environment while boosting regional and national plans to reach a sustainable development.
In this sense, the recent Springer book (Coastal Research Library), titled “Beach Management Tools – Concepts, Methodologies and Case Studies”, gives an exemplary account of the many and diverse data analytics that can be used in regional and national coastal management plans. Since in the scope of the Ecosystem Management, Geomorphology, Risk Management, Governance, Environmental Quality, User’s Perception until Innovative Tools.

Species distribution modelling (SDM): Data analysis in ecology of coastal areas

In recent years, Species Distribution Models (SDM) are used to discover how the occurrence of a species or (vegetation) communities are associated to the environment, and how a species might respond to variations or to find the potential distribution in its environment. This modelling approach can help discovery new sites where a rare or invasive species might be found, or understand better the potential threats to a species due to tourism-related pressures on coastal zones, urban encroachment, temporal changes in land use and land cover, climate change, or other causes.
Methodology for assembling SDM is a stimulating interdisciplinary research front in which contributions are being made by data scientists and ecologists. Species Distribution Models (SDM) are seen as numerical tools that associate observations of species occurrence or abundance (can be of the type presence or presence/absence) with environmental/independent variables (to see what environments the species tends to be associated with). Species distribution models (SDM) methods are based on different type of model objects (usually they have the same designation as the modeling method used such as Generalized Linear Model (GLM), Maximum Entropy Method (Maxent), Support Vector Machines (SVM), Artificial Neural Networks (ANN), Ecological Niche Factor Analysis (ENFA), and others). All of these model objects, regardless of their class of mathematical model (regression, machine learning and rule-based models), can be used to with the predict function to perform predictions of species occurrence’s for any combination of values of the environmental/independent variables.
Preceding to the application of SDM it is recommended to develop an Exploratory Data Analysis (EDA) to reduce errors in the model, especially those derived by the spatial autocorrelation of presence or presence/absence data or the multi-collinearity of the selected environmental/independent variables.
Therefore, using SDM, we can investigate the distribution of species or (vegetation) communities along environmental gradients (called SDM response curves).

Spatially Explicit Models in Local Dynamics Analysis: The Potential Natural Vegetation (PNV) as a Tool for Beach and Coastal Management
One of the most advanced innovative techniques developed for scientists and beach managers from around the world is related with the integration of Spatially Explicit Models in Local Dynamics Analysis. This topic is discussed by Gutierres et al. (2017) in the above mentioned publication as an Ecosystem Management Tools focused on the application of the Modelling of the Potential Natural Vegetation (PNV) based on Species Distribution Models (SDM) as a tool for Beach and Coastal Management.

Thus, the concept of Potential Natural Vegetation (PNV) and its mapping through the application of SDM have become extremely important within the scope of habitat restoration in almost every European country. The aim of the PNV models is to predict the PNV, in Natura 2000 protected habitats in coastal areas, based on the vegetation series and the main environmental variables.
The SDM modelling approach based on several statistical model-fitting techniques are applied to the survey data (biological data) and environmental/independent variables. The Geographic Information System (GIS) allows the spatial integration of Species Distribution Models (SDM) and consequently mapping the PNV.
Often, conservation planning and biodiversity resource management in coastal regions, such as Barcelona, can be carried out at more detailed scales, where SDM allows the integration of vegetation in-situ observations and improve our interpretation of PNV local distributions along environmental gradients (by SDM response curves) in beach and coastal sand dunes environments.
Therefore, the usage of PNV maps distribution as a baseline for a quantitative comparison with Actual Vegetation (ACV) distribution represents a first step towards a general model for the evaluation of the effects of disturbance on vegetation patterns and diversity of the beach and coastal ecosystems. As observed here, the PNV models and maps can be used as an Ecosystem Management tool for defining restoration goals and evaluating the success of restoration efforts in Coastal Areas of the Mediterranean.

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