Table Of ContentLANDSCAPE PATTERN ANALYSIS FOR
ASSESSING ECOSYSTEM CONDITION
Environmental and Ecological Statistics
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Center for Statistical Ecology and Environmental Statistics
Department of Statistics
The Pennsylvania State University
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LANDSCAPE PATTERN ANALYSIS FOR
ASSESSING ECOSYSTEM CONDITION
by
Glen D. Johnson
New York State Department of Health and
University at Albany, State University of New York, USA
and
Ganapati P. Patil
Center for Statistical Ecology and Environmental Statistics
The Pennsylvania State University, USA
^ Sprriin ger
Library of Congress Control Number: 2006930744
ISBN-10: 0-387-37684-4 e-ISBN-10: 0-387-37685-2
ISBN-13: 978-0-387-37684-4
Printed on acid-free paper.
© 2006 Springer Science+Business Media, LLC
All rights reserved. This work may not be translated or copied in whole or in part without the
written permission of the publisher (Springer Science-l-Business Media, LLC, 233 Spring Street,
New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly
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Printed in the United States of America.
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Contents
List of Figures ix
List of Tables xiii
Preface xv
Acknowledgments xvii
1. INTRODUCTION 1
1 Loss of Biodiversity through Excessive
Habitat Fragmentation 2
2 Land Use Pattern and Surface Water Quality 5
3 Quantifying Landscape Pattern 5
4 Application to Watershed-Delineated Landscapes
in Pennsylvania 7
4.1 Land Cover Grids 8
4.2 Watersheds based on the
State Water Management Plan 9
2. METHODS FOR QUANTITATIVE CHARACTERIZATION
OF LANDSCAPE PATTERN 13
1 Single-resolution Measurements 13
2 The Conditional Entropy Profile 16
2.1 Computing Conditional Entropy of Expected
Frequencies based on Single -Resolution Maps 18
3. ILLUSTRATIONS 23
1 Example 1: Checkerboard Map 23
2 Example 2: Irregular Black and White Map 26
3 Example 3: Illustration with Actual Landscapes 36
vi LANDSCAPE PATTERN ANALYSIS
4. CLASSIFYING PENNSYLVANIA WATERSHEDS ON
THE BASIS OF LANDSCAPE CHARACTERISTICS 41
1 Introduction 41
2 Clustering Watersheds into Common Groups 43
3 Comparison to Conditional Entropy Profiles 53
5. PREDICTABILITY OF SURFACE WATER POLLUTION
IN PENNSYLVANIA USING WATERSHED-BASED
LANDSCAPE MEASUREMENTS 57
1 Introduction 57
2 Surface Water Pollution Assessment 57
2.1 Nitrogen Loading 57
2.2 Pollution Potential Index 60
3 Selecting an Initial Set of Landscape Pattern Variables 63
4 Linear Models for Relating Water Pollution Loading to
Landscape Variables 66
4.1 Predicting In-Stream Nitrogen Loading 68
4.2 Predicting a Pollution Potential Index 70
5 Interpretation 72
6. PREDICTABILITY OF BIRD COMMUNITY-BASED
ECOLOGICAL INTEGRITY USING LANDSCAPE
VARIABLES 79
1 Introduction 79
2 Assessing Ecological Integrity using Songbird Community
Composition 80
2.1 Background 80
2.2 Application to Breeding Bird Data 85
2.3 Block-Level BCI Values 85
2.4 Watershed-level Ecological Integrity 86
3 Relating Landscape Attributes to the Songbird-based
Assessments of Watershed-wide Ecological Integrity 91
3.1 Linear Regression Modehng 91
3.2 Clustering 95
3.3 Conditional Entropy Profiles 97
4 Ecological Integrity based on All Species in the Breeding
Bird Atlas 100
4.1 Relation to Landscape Attributes 101
5 Comparison of the Different Methods for Computing a
Bird Community Index 104
Contents vii
6 Summary 106
7. SUMMARY AND FUTURE DIRECTIONS 109
8. REFERENCES 115
Index 129
List of Figures
1.1 A hypothetical response of vertebrate species rich
ness to increasing forest fragmentation. 4
1.2 Location of the state of Pennsylvania within the
United States and watersheds based on the state
water plan along with major physiographic provinces. 10
1.3 Land cover based on LANDS AT TM images. 11
2.1 Hierarchical nesting of pixels. 17
3.1 Hypothetical checkerboard map at the floor reso
lution and results from scaling up with the random
filter. 24
3.2 Conditional entropy profile corresponding to Ex
ample 1, a simple checkerboard pattern. 26
3.3 Hypothetical binary landscape map at the floor
resolution and results from scaling up with the ran
dom filter. 27
3.4 Conditional entropy profile corresponding to Ex
ample 2, a somewhat random pattern dominated
by black pixels. 36
3.5 Demonstration watersheds exhibiting increasing for
est fragmentation. 38
3.6 Conditional entropy profiles obtained for the three
watersheds in Figure 3.5. 39
4.1 Example conditional entropy profile and associated
parameters. 42
4.2 Pairwise scatterplots of all watersheds for the land
scape pattern variables. 44
LANDSCAPE PATTERN ANALYSIS
4.3 Pairwise scatterplots of all watersheds for an ap
proximately orthogonal subset of pattern variables,
along with the entropy profile variables A, B and
C, and the land cover proportions. 45
4.4 Cluster dendrogram of the Appalachian Plateaus
watersheds. 46
4.5 Cluster dendrogram of the Ridge and Valley watersheds. 47
4.6 Cluster dendrogram of the Piedmont Plateau watersheds. 48
4.7 Mapping of the clusters depicted in the dendro
grams of Figures 4.4 to 4.6. 49
4.8 Conditional entropy profiles of watersheds in the
Appalachian Plateaus physiographic province. 53
4.9 Conditional entropy profiles of watersheds in the
Ridge and Valley physiographic province. 54
4.10 Conditional entropy profiles of watersheds in the
Piedmont physiographic province. 55
5.1 The 85 watersheds from the NPS study and the
primary set of watersheds, based on the state water
plan, for which landscape pattern measurements
are available. 59
5.2 Matching of watershed delineations among the Non-
Point Source (NPS) study and the State Water Plan. 60
5.3 Total Phosphorus vs Total Nitrogen (kg/ha). 61
5.4 Nitrogen loading for 30 watersheds. 62
5.5 Thematic presentation of Pollution Potential Rank
ing. 63
5.6 Variance contributed by the first ten principal com
ponents. 64
5.7 Pairwise scatterplots of the final set of potential
predictor variables along with the natural loga
rithm of total nitrogen per hectare for 30 watersheds. 67
5.8 Pairwise scatterplots of the set of potential predic
tor variables along with the inverse of the pollution
potential index. 68
5.9 Diagnostic plots corresponding to the regression
model defined in Table 5.2. 71
5.10 Partial residual plots for the predictors listed in
Table 5.2. 72
List of Figures xi
5.11 Diagnostic plots corresponding to the regression
model defined in Table 5.3. 74
5.12 Partial residual plots for the quantitative predic
tors hsted in Table 5.3. 75
5.13 Conditional entropy profiles for watersheds con
taining the top 3, middle 3 and bottom 3 nitrogen
loadings. 77
5.14 Conditional entropy profiles for watersheds con
taining the top 3, middle 3 and bottom 3 PPI values. 78
6.1 Breeding Bird Atlas blocks and State Water Plan-
based watersheds. 80
6.2 The Mid-Atlantic Highlands Assessment Area. 81
6.3 Breeding Bird Atlas blocks coded according to cat
egories of ecological integrity. 87
6.4 Dendrogram of watershed clusters in the Pennsyl
vania MAHA region. 88
6.5 Watersheds in the MAHA region coded according
to general ecological integrity. 89
6.6 Box plots of the proportions of "low", "medium",
"high" and "highest" integrity land. 90
6.7 Box plots of the landscape pattern variables for
each of the seven clusters identified in Figure 6.4. 92
6.8 Box plots of the land cover proportions for each of
the seven clusters identified in Figure 6.4. 93
6.9 Partial residual plots from regressing the "propor
tion of low integrity BBA blocks" within each MAHA
Pennsylvania watershed on the optimal set of land
scape variables. 95
6.10 Partial residual plots from regressing the songbird-
based bird community index on landscape vari
ables within each MAHA watershed. 97
6.11 Conditional entropy profiles of watersheds in the
MAHA section of Pennsylvania that were labeled
as having "highest", "medium" or "lowest" general
ecological integrity, as assessed from the songbird
community. 99
6.12 Species richness-based bird community index. 102
6.13 Guild potential-based bird community index. 103
