Geographic information systems for ichnofabric analysis: modelling a modern lagoon (Grado, Italy) with the IchnoGIS method
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Data di Pubblicazione:
2011
Citazione:
Geographic information systems for ichnofabric analysis: modelling a modern lagoon (Grado, Italy) with the IchnoGIS method / A. Baucon, F. Felletti, G. Muttoni. ((Intervento presentato al 11. convegno International Ichnofabric Workshop tenutosi a Colugna nel 2011.
Abstract:
1. Introduction
The Grado-Marano lagoon is one of the major transitional systems of the Adriatic Sea, consisting of a barrier island system extended for over 30 km (Baucon, 2008a; Turri, 1999). Characterized by significant biodiversity and heterogeneous environments, this area provide optimal conditions to assess the ichnologic and sedimentary features of siliciclastic, marginal-marine settings.
The complex relationships that exist among ichnological, physical, and environmental proprieties require advanced, integrated analysis techniques to visualize spatial patterns and determine the factors controlling trace distribution. For these reasons, a new method for quantitative ichnosedimentological analysis (IchnoGIS) has been developed.
The goal of this work is to discuss a quantitative ichnological model of the external margin of the Grado lagoon and test the application of the IchnoGIS method for ichnofabric analysis.
2. Geographical and geological setting
The study area (Fig.1) is located on the external margin of the Grado basin, between Grado town and the locality Pineta. Tides, which are the main driving forces of the lagoon hydrodynamics, created a composite mosaic of marginal marine environments, among which vast siliciclastic intertidal flats. A very peculiar environment is represented by microbial-related settings: large sections of the tidal flat are colonized by microbial mats, which are presenting a diverse ichnofauna, preliminary described by Baucon (2008a) and discussed in this study.
Fig. 1 – Study area. Modified from Baucon, 2008a.
3. Method and approach
Similarly to a geographic information system, the proposed approach integrates hardware, software, and data for capturing, managing, analyzing, and displaying geographically referenced ichnological data. For this reason, the method has been named ‘IchnoGIS’. Its development derived from previous work on the application of GPS and GIS techniques to neoichnology (Baucon, 2008b; 2008a). IchnoGIS is an orderly procedure consisting of 6 steps:
a. Survey design: The starting point is defining the objects of interest and the sampling size.
b. Sampling: If we want to know how traces are distributed in a particular habitat, it is usually impossible to count each and every one present. For this reason, the second step of IchnoGIS is based on quadrat sampling, a method widely used in the interpretation of large ecological data sets with environmental gradients (McIntyre & Eleftheriou, 2005). It consists of characterizing ichnological, sedimentological, environmental attributes (i.e. number of Arenicolites, grain size, salinity) contained in a square frame (in this study: 0.25 m2; Fig.2).
c. Significance test: In the simplest case, the result of the sampling process is a spreadsheet including X Y coordinates, facies type and abundance of each structure (Fig. 3). For this reason, nearest neighbour analysis (Borradaile, 2003) is an efficient method to assess the sampling quality.
d. Descriptive statistics. One of the primary goals is to describe the influence of the sedimentological features on the numbers and types of traces. This aim can be achieved by cross-tabulating frequency counts of ichnotaxa respect to facies. Another possibility is to provide a measure of central tendency (i.e. Fig.5A) and/or distribution.
e. Ichnoassemblage analysis. Ichnoassemblages are verified by cross tabulating the abundance of a trace in relation to that of another trace.
f. Spatial analysis. Spatial analysis is performed through (a) classed post maps (b) geostatistical interpolation techniques (i.e. Fig.4B, 5B). Classed post maps are simpler to implement, but interpolation can estimate the value of a variable (facies type, number of traces) in unsampled positions, deliveri
Tipologia IRIS:
14 - Intervento a convegno non pubblicato
Elenco autori:
A. Baucon, F. Felletti, G. Muttoni
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