UNIVERSITÀ DEGLI STUDI DI ROMA "TOR VERGATA" FACOLTÀ DI INGEGNERIA DOTTORATO DI RICERCA IN GEOINFORMAZIONE XXI CICLO DEL CORSO DI DOTTORATO Geoinformatic Methodologies and Quantitative Tools for Detecting Hotspots and for Multicriteria Ranking and Prioritization: Application on Biodiversity Monitoring and Conservation ANGELO PECCI Docente Guida: Prof. Maria Ioannilli Tutor: Prof. Orazio Rossi Coordinatore: Prof. Domenico Solimini A.A. 2009/2010 “Anyone who has never made a mistake has never tried anything new”. Acknowledgements I would like to acknowledge first of all Ing. Maria Ioannilli, my PhD tutor and researcher at University of Rome "Tor Vergata" for her scientific and human help since my Degree Thesis in 2003 and Orazio Rossi Professor of Multivariate Statistic and Quantitative Ecology at University of Parma to whom any word, adjective or thanks would be insufficient for all the learnings, help and availability during these years. I wish to thank warmly G. P. Patil Distinguished Professor of Mathematical Statistics and Director of the Center for Statistical Ecology and Environmental Statistics at Pennsylvania State University, W. Myers Professor Emeritus of Forest Biometrics at Pennsylvania State University and Dr. Rainer Brüggemann of the Leibniz-Institute of Freshwater Ecology and Inland Fisheries in Berlin. Thanks are also due to Sharad Joshi and Shuhua Mao for their assistance with some software work during my stay in Pennsylvania State University. A special thanks to Dr. Pierfrancesca Rossi and all the people of the Environmental Sciences Department at University of Parma. I am grateful to the Institute for Environmental Protection and Research. (ISPRA), ex- Italian Environment Protection and Technical Services Agency (APAT) for providing all the data and information needed to describe the ecological aspects of the habitats. CONTENTS 1 THE PROBLEM : STATE OF THE ART 1 1.1 Introduction and Objectives 1 1.2 The International and National background: The “Natura 2000” network, the Map of Italian Nature Project and their effects on the Environmental Planning. 3 1.3 Landscape Ecology as methodological and essential interpretative support for environmental evaluation processes 7 1.3.1 The Ecological Value and the Ecological Sensitivity of a natural habitat 9 1.3.2 The Ecological Attention and the Ecological Fragility of a natural habitat 9 1.4 Relationships between demographic structure of the territory and environmental conservation policies 12 1.5 Methodological statistical tools: State of the Art 16 1.5.1 HotSpot Detection methodologies 17 1.5.2 Objects Ranking and Prioritization methods 22 1.5.2.1 Total Ranking Theory and Methods (Additive Aggregation methods) 24 1.5.2.2 Pros and cons (arguments for and against) of composite indicators 30 1.5.2.3 Partial Ranking Theory and Methods 32 1.5.3 Other useful statistical tools 35 1.5.3.1 Principal Component Analysis 35 1.5.3.2 Cluster Analysis 36 1.5.3.3 Multiple Discriminant Analysis 39 1.5.3.4 Fuzzy Partial Order 40 1.5.3.5 Partially Ordered Scalogram Analysis with Coordinates (POSAC) 43 1.6 Other available instruments 45 1.6.1 The Geographical Information Systems (GIS) 45 1.6.2 The Ecological Networks 45 2 STUDY AREAS AND MATERIALS 48 2.1 Study Area “A”: Baganza Valley (Parma) 48 2.2 Study Area “B”: Oltrepò Pavese and Ligurian-Emilian Apennine 51 2.3 The Ecological Indicators 54 2.3.1 Ecological Value indicators 54 2.3.2 Ecological Sensitivity indicators 57 2.4 Demographical indicators 59 3 METHODS 61 3.1 Ranking and Prioritization Methods utilized 61 I 3.1.1 Ideal Vector Distance 61 3.1.2 Hasse Diagram Theory (HDT): Basic concepts, Linear Extensions and Posets Linearization 63 3.1.3 Salience and Primacy 76 3.2 Upper Level Set Scan Statistic for HotSpot Detection 82 3.3 Systematic Conservation Planning 90 4 RESULTS AND DISCUSSION 96 4.1 Natural Habitat Level Analysis: Individuation of Habitat of Ecological Attention 96 4.1.1 Redundancy degree of the proposed ecological indicators 96 4.1.2 Habitat ranking: Ideal Vector Vs Salience 100 4.2 Planning and analysing a habitat Network with some optimal ecological characteristics 115 4.3 Administrative Level Analysis: Communes 130 4.3.1 Demographical analysis of Communes 132 4.3.2 Deriving possible guidelines for the Hotspots of Ecological Attention (HSEAs) management 138 4.3.3 Providing a ranked list of Communes with Highest Funding Preference for biodiversity conservation purposes 143 4.3.3.1 Method “A”: Hotspot Detection method 144 4.3.3.2 Method “B”: Partial Order Method using Hasse Diagram 169 4.3.3.3 Method “C”: Salience and Primacy method 176 5 GENERAL CONCLUSIONS 185 6 REFERENCES 192 II INDEX OF FIGURES Figure 1-1 Conceptual model of the effect of Ecological Sensitivity on Anthropic Pressure results on Ecological Fragility (Rossi, 2001). 11 Figure 1-2a and 1-2b Flow-chart for determining the appropriate test 22 Figure 1-3 Representation of the four quadrants in a two-dimensional criterion space around the point P. 26 Figure 1-4. With two indicators, each object a divides indicator space into four quadrants. Objects in the second and fourth quadrants are ambiguous in making comparisons with a. 33 Figure 1-5. Contour of index H passing through object a. A linear index is shown on the left and a non-linear index on the right. 33 Figure 1-6. The top two diagrams depict valid contours while the bottom two diagrams depict invalid contours. 34 Figure 1-7 Two dimensional ordering 44 Figure 2-1 Geographical and administrative location of the Baganza Valley. The protected areas are highlighted. 48 Figure 2-2 Spatial distribution of the 47 types of CORINE Biotope habitats within the study area “A”. 49 Figure 2-3 Histogram of frequencies of the 47 types of CORINE Biotope habitats within the study area “A”. 50 Figure 2-4 Geographical and administrative location of the study area “B”. The protected areas are highlighted. 51 Figure 2-5 Spatial distribution of the 34 types of CORINE Biotope habitats within the study area “B”. 52 Figure 2-6 Histogram of frequencies of the 34 types of CORINE Biotope habitats within the study area “B”. 53 Figure 3-1 Hasse diagram of a hypothetical poset (left), some linear extensions of that poset (middle), and a decision tree enumerating all 16 possible linear extensions (right). Links shown in dashed/red (called jumps) are not implied by the partial order. The six members of the poset can be arranged in 6!=720 different ways, but only 16 of these orderings are valid linear extensions. 66 Figure 3-2 Cumulative rank-frequency distributions for the poset of Figure 3-1. The curves are stacked one above the other giving a linear ordering of the elements: a > b > c > d > e > f . 67 Figure 3-3 (Left) Two iterations of the CRF operator are required to transform this partial order into a linear order. (Right) A poset for which the CRF operator produces ties. 68 Figure 3-4 Two principal down sets and one principal up set, taken from the poset (X, ≤) 70 Figure 3-5 Assignment of matchings m i to behaviour classes B i. 73 Figure 3-6 (a): Separated subsets in a schematic presentation of Hasse diagrams. (b) and (c): Examples for which the scheme (L.H.S.) may stand. 74 Figure 3-7 |AIB| = 2. A scatter plot of X 1 and X 2. 75 Figure 3-8 Scattered plot obtained coupling domination (horizontal axis) and subordination (vertical axis) views. 79 Figure 3-9 Scattered plot obtained coupling superior frequency (horizontal axis) and inferior frequency (vertical axis). 80 III Figure 3-10 An example of possible primacy plot. The cases are ordered showing the rank ranges as vertical lines along with the numbers of cases above A (inferior to current case) and below B (superior to current case). 81 Figure 3-11 Primacy plot obtained using reduced ranges as vertical lines (spanning second best to second worst). 82 Figure 3-12 Limitations of circular scanning windows. (Left) An irregularly shaped cluster- perhaps a cholera outbreak along a winding river foodplain. Small circles miss much of the outbreak and large circles include many unwanted cells. (Right) Circular windows may report a single irregularly shaped cluster as a series of small clusters. 85 Figure 3-13 Connectivity for tessellated regions. The collection of shaded cells on the left is connected and, therefore, constitutes a zone. The collection on the right is not connected. 86 Figure 3-14 Schematic response surface with two response levels, g and g'. The upper level set determined by g has three connected components, Z 1, Z 2 and Z 3; that determined by g' has Z 4, Z 5 and Z 6 as its connected components. The diagram also illustrates the three ways in which connectivity can change as the level drops from g to g': (i) zones Z 1 and Z 2 grow in size and eventually coalesce into a single zone Z 4, (ii) zone Z 3 simply grows to Z 5, and (iii) zone Z 6 is newly emergent. 87 Figure 3-15 ULS connectivity tree for the schematic surface displayed in Figure 3-14. The four leaf nodes correspond to surface peaks. The root node represents the entire region. Junction nodes (A, B and C) occur when two (or more) connected components coalesce into a single connected component. 88 Figure 3-16 A confidence set of hotspots on the ULS tree. The different connected components correspond to different hotspot loci while the nodes within a connected component correspond to different delineations of that hotspot - all at the appropriate confidence level.