1. Atlantic Slop Consortium: All-Hands Meeting Harpers Ferry, WV 15-17 September 2004 Analytical Needs What is Available What is Needed G.P. Patil

2. Overall Goals and Objectives Barbara Levinson International Journal of Remote Sensing Special Issue on Remote Sensing and GIS for Ecosystem Analysis EPA ORD Ecosystem Protection Program EPA ORD Ecosystem Protection Program Provide scientific understanding to measure, model, maintain, and/or restore, at multiple scales, Provide scientific understanding to measure, model, maintain, and/or restore, at multiple scales, the integrity and sustainability of highly valued ecosystems, now and in the future the integrity and sustainability of highly valued ecosystems, now and in the future EPA STAR EaGLe Program EPA STAR EaGLe Program Develop new widely applicable indicators of integrity and sustainability for specific ecological entities and then test their applicability across regions Develop new widely applicable indicators of integrity and sustainability for specific ecological entities and then test their applicability across regions Incorporation of these indicators into long-term monitoring programs is the desired outcome. Incorporation of these indicators into long-term monitoring programs is the desired outcome.

3. Barbara Levinson – Continued Develop indicators/ procedures useful for evaluating the health/condition for important coastal natural resources, e.g., lakes, streams, coastal wetlands, etc. Develop indicators/ procedures useful for evaluating the health/condition for important coastal natural resources, e.g., lakes, streams, coastal wetlands, etc. Develop indicators, indices, procedures for evaluating the integrated condition of multiple resources/ ecosystem types, e.g., bird community index reflecting the combined terrestrial and aquatic community within a watershed Develop indicators, indices, procedures for evaluating the integrated condition of multiple resources/ ecosystem types, e.g., bird community index reflecting the combined terrestrial and aquatic community within a watershed Develop landscape measures that characterize landscape attributes and serve as quantitative indicators of a range of environmental endpoints, such as, water quality or watershed quality. Develop landscape measures that characterize landscape attributes and serve as quantitative indicators of a range of environmental endpoints, such as, water quality or watershed quality. Develop nested suites of indicators that can both quantify the health/ condition of a resource and identify its primary stressors across a range of scales Develop nested suites of indicators that can both quantify the health/ condition of a resource and identify its primary stressors across a range of scales

4. Atlantic Slope Consortium: A Vision Statement Robert Brooks and Denice Wardrop Using a universe of watersheds, covering a range of social choices, we ask two questions: Using a universe of watersheds, covering a range of social choices, we ask two questions:  How “ good ” can the environment be, given those social choices?  What is the intellectual model of condition within those choices, i.e., what are the causes of condition and what are the steps for improvement? Accomplish with the group dynamics spirit of : form, storm, reform, norm, perform, and inform (Kent Thornton). Accomplish with the group dynamics spirit of : form, storm, reform, norm, perform, and inform (Kent Thornton).

5. Ecohealth Special Issue On Wetland Indicators Guest Editors: Rob Brooks and G. P. Patil -A Vision For The Special Issue- Investigators developing and testing ecological indicators on the condition of coastal or inland wetlands are invited to submit original manuscripts. Papers fostering the integration of condition assessments across various types of aquatic habitats or “waters”, and addressing biological, chemical, and physical dimensions, are encouraged. With this issue, we wish to demonstrate the next generation of ecological indicators is ready for use in wetlands and related habitats. Investigators developing and testing ecological indicators on the condition of coastal or inland wetlands are invited to submit original manuscripts. Papers fostering the integration of condition assessments across various types of aquatic habitats or “waters”, and addressing biological, chemical, and physical dimensions, are encouraged. With this issue, we wish to demonstrate the next generation of ecological indicators is ready for use in wetlands and related habitats. This issue will make a significant contribution to the literature. This issue will make a significant contribution to the literature. Final date for receipt of manuscripts-Sep 30 2004-peer review and editing Oct-Dec 2004, publication in 2005. Final date for receipt of manuscripts-Sep 30 2004-peer review and editing Oct-Dec 2004, publication in 2005.

6. What’s the question? How do we develop a useful, relevant, and defensible set of indicators for the Atlantic Slope? How do we develop a useful, relevant, and defensible set of indicators for the Atlantic Slope? We know how to do “defensible” We know how to do “defensible” This story is all about combining defensible ecology with “relevant” and “useful” in the Atlantic Slope This story is all about combining defensible ecology with “relevant” and “useful” in the Atlantic Slope Tools: watershed and estuarine segment classification for experimental design and identification of critical areas Tools: watershed and estuarine segment classification for experimental design and identification of critical areas

7. Use of Landscape and Land Use Parameters for Classification and Characterization of Watersheds in the Mid-Atlantic across Five Physiographic Provinces

8. Resulting Process Define Watershed Types Compile Frequency Distributions Designate Sampling Design Distribute Candidate Watersheds

9. Ternary Plot Description Require a fuller description of departure from reference than “disturbed” Require a fuller description of departure from reference than “disturbed” Describes land use via the three main land cover types Describes land use via the three main land cover types Can be used at any spatial extent (as can land cover %) Can be used at any spatial extent (as can land cover %)

10. Ternary plots of watersheds of Atlantic Slope by physiographic province.

11. The Concept of Nodes A circle with landscape properties attributable to a point A circle with landscape properties attributable to a point Where? At stream convergences Where? At stream convergences Incorporates description of stream complexity Incorporates description of stream complexity More circles in areas of complex drainage More circles in areas of complex drainage

13. Nodal Constellations and Variance Many ways to be a “moderately” disturbed watershed Many ways to be a “moderately” disturbed watershed Addresses disparity of land covers within a watershed; effective distances may be different Addresses disparity of land covers within a watershed; effective distances may be different Scaling issues can be explored at a future date Scaling issues can be explored at a future date

14. Median Slope Rough indicator of connection between land use and aquatic resources Rough indicator of connection between land use and aquatic resources Surrogate for ability to produce stress; e.g., steep slopes have more rapid runoff, minimal contact time for remediation and/or impact Surrogate for ability to produce stress; e.g., steep slopes have more rapid runoff, minimal contact time for remediation and/or impact Forested watersheds occur on both steep and low slopes, changing amount of stress produced, as well as susceptibility to regional stressors (e.g., atmospheric deposition) Forested watersheds occur on both steep and low slopes, changing amount of stress produced, as well as susceptibility to regional stressors (e.g., atmospheric deposition)

15. Factors for Classifying Watersheds % Land use (forested, agricultural, urban) in HUC 14 watershed % Land use (forested, agricultural, urban) in HUC 14 watershed % Land use (forested, agricultural, urban) in 1-km radius node % Land use (forested, agricultural, urban) in 1-km radius node Nodal variance Nodal variance Median slope Median slope

16. High Forest Low SlopeHigh Slope Low Forest, High Ag Moderate Forest High Urban Moderate Ag Low Slope Moderate Slope High Nodal VarLow Nodal Var

17. Results Six watershed classes identified: 1. Forested, high slope 2. Forested, low slope 3. Agricultural 4. Urban 5. Mixed, low nodal variance 6. Mixed, high nodal variance

19. Lessons Learned Be true to the project vision statement! Be true to the project vision statement! Future lines of investigation: Future lines of investigation: How do we articulate historical paths on ternary plots? How do we articulate historical paths on ternary plots? Are mixed land cover watersheds with high nodal variance indicative of a lack of planning efforts? Are mixed land cover watersheds with high nodal variance indicative of a lack of planning efforts?

20. Current Status Watershed classification provided a “social choice” basis for project design, and was informative Watershed classification provided a “social choice” basis for project design, and was informative Can we represent historic land use? Can we represent historic land use? How do we relate ecological condition (process) to landscapes (pattern) at different spatial scales? How do we relate ecological condition (process) to landscapes (pattern) at different spatial scales?

21. Critical area identification Ordinal map of entire project area is overwhelming Ordinal map of entire project area is overwhelming Identification of statistically-significant critical areas is needed Identification of statistically-significant critical areas is needed Explanatory factors can be applied Explanatory factors can be applied Target “non-resolvable” hot-spots for further investigation Target “non-resolvable” hot-spots for further investigation

24. The Spatial Scan Statistic Move a circular window across the map. Move a circular window across the map. Use a variable circle radius, from zero up Use a variable circle radius, from zero up to a maximum where 50 percent of the population is included.

25. A small sample of the circles used

26. Spatial Temporal Surveillance

27. Space-Time Poverty Hotspot Typology Federal Anti-Poverty Programs have had little success in eradicating pockets of persistent poverty Federal Anti-Poverty Programs have had little success in eradicating pockets of persistent poverty Can spatial-temporal patterns of poverty hotspots provide clues to the causes of poverty and lead to improved location-specific anti-poverty policy ? Can spatial-temporal patterns of poverty hotspots provide clues to the causes of poverty and lead to improved location-specific anti-poverty policy ?

28. Hotspot determination Percent of pixels (30m x 30m) in a 14-digit HUC that are both disturbed ( 3%) Percent of pixels (30m x 30m) in a 14-digit HUC that are both disturbed ( 3%) Percent of nodes that have less than 45% forested land cover Percent of nodes that have less than 45% forested land cover

30. The most likely cluster as found by the circular spatial scan statistic (left) and the elliptic spatial scan statistic (right) for an analysis of county-based breast cancer mortality in Northeastern United States, 1988–1992. For the circular-detected cluster, the relative risk is 1.07 and p=0.0001 (Kulldorff et al, 1997), while the elliptic-detected cluster has a relative risk of 1.08 and p=0.0001. Note that the elliptic-detected cluster is not connected, since the New York City area is not part of the cluster.

32. Circular spatial scan statistic zonation.

33. Upper Level Set (ULS) of Intensity Surface Hotspot zones at level g (Connected Components of upper level set)

34. Changing Connectivity of ULS as Level Drops

35. ULS Connectivity Tree Schematic intensity “surface” N.B. Intensity surface is cellular (piece-wise constant), with only finitely many levels A, B, C are junction nodes where multiple zones coalesce into a single zone A B C

36. Estimation Uncertainty in Hotspot Delineation Outer envelope Inner envelope MLE

37. LR Confidence Intervals CI at threshold t Disconnected CI

38. Confidence Region on ULS Tree MLE Junction Node Alternative Hotspot Locus Alternative Hotspot Delineation Tessellated Region R Nodes in Confidence Set Extremity Node

39. Hotspot Detection, Delineation, Prioritization Continuous Responses Both human health and environmental contexts Both human health and environmental contexts Simplest distributional model: Simplest distributional model: Additivity with respect to the index parameter k suggests that we model k as proportional to size: Additivity with respect to the index parameter k suggests that we model k as proportional to size: Other distribution models (e.g., lognormal) are possible but are computationally complex and applicable to only a single spatial scale Other distribution models (e.g., lognormal) are possible but are computationally complex and applicable to only a single spatial scale

40. Hotspot Detection, Delineation, Prioritization Examples of Continuous Responses Human Health Context: Human Health Context:  Blood pressure levels for spatial variation in hypertension  Estrodial levels in women for breast cancer and osteoporosis  Cancer survival (censoring issues) Environmental Context: Environmental Context:  Landscape metrics such as forest cover, fragmentation, etc.  Pollutant loadings  Animal abundance

42. Covariate Adjustment Known Covariate Effects (age, population size, etc.)

43. Covariate Adjustment Given Covariates, Unknown Effects

44. Incorporating Spatial Autocorrelation Ignoring autocorrelation typically results in:  under-assessment of variability  over-assessment of significance (H 0 rejected too frequently) How can we account for possible autocorrelation? GLMM (SAR) Model Y a = count in cell a Y a distributed as Poisson  a = log(E[Y a ]) Y a = count in cell a Y a distributed as Poisson  a = log(E[Y a ]) The Y a are conditionally independent given the  a The  a are jointly Gaussian with a Simultaneous AutoRegressive (SAR) specification

45. Incorporating Spatial Autocorrelation

47. Spatial Autocorrelation Plus Covariates

48. CAR Model The entire formulation is similar for Conditional AutoRegressive (CAR) specs except that the form of the variance-covariance matrix of  is changes.

49. Salford Systems  Robust Decision-Tree Tools CART Decision Tree Software CART Decision Tree Software MARS Predictive Modeling Software MARS Predictive Modeling Software TreeNet Stochastic Gradient Boosting Software TreeNet Stochastic Gradient Boosting Software LOGIT Software LOGIT Software RandomForests RandomForests  Application, Adaptation and Development Invasive Species Phragmites and Thresholds Invasive Species Phragmites and Thresholds

50. GIS Enhancement GIS Enhancement ClusterSeer, BoundarySeer ClusterSeer, BoundarySeer GeoSeer, SocioEconomics GeoSeer, SocioEconomics Wombling and Thresholds Identification Wombling and Thresholds Identification TerraSeer Software Space-time Intelligence Systems

51. Ecosystem State Stress Ecosystem State Stress Ecosystem State Stress Ecosystem State Stress F2F2 F1F1 F2F2 Schematic representation of four possible responses of ecosystems to stress. Reproduced from Panarchy, 2002, Island Press.

52. Application of the Scan Statistic to Wetland and Stream Monitoring: Testing the Method in the Upper Juniata Watershed, Pennsylvania, USA

53. This presentation will.. Provide background on the resource to be monitored (wetland condition and restoration) Provide background on the resource to be monitored (wetland condition and restoration) Provide a description of the data set (the Upper Juniata watershed) Provide a description of the data set (the Upper Juniata watershed) Illustrate how the scan statistic was applied Illustrate how the scan statistic was applied Recommend future enhancements of the scan statistic for environmental problem solving (surveillance and early warning) Recommend future enhancements of the scan statistic for environmental problem solving (surveillance and early warning)

54. The Environmental Problem

55. Deep Water ~ Lakes & Streams Upland Wetlands Transitional between the deep water and uplands Source: Tiner

56. Questions in Monitoring and Management How do we find the wetlands/streams? (Inventory) How do we find the wetlands/streams? (Inventory) How do we assess their ability to function? (Condition) How do we assess their ability to function? (Condition) How do we use this information to improve condition? (Restoration) How do we use this information to improve condition? (Restoration) InventoryConditionRestoration

57. How do we measure “condition”? Coeffficients of conservatism Coeffficients of conservatism Measure of specificity of habitat requirements Measure of specificity of habitat requirements Scale of 0 to 10 Scale of 0 to 10 Non-natives, aggressives receive 0 Non-natives, aggressives receive 0 Species with very specific requirements receive 10 Species with very specific requirements receive 10 Entire flora is given CoCs; CoCs are summed to provide the Floristic Quality Assessment Index (FQAI) Entire flora is given CoCs; CoCs are summed to provide the Floristic Quality Assessment Index (FQAI) High FQAI represents a diverse plant community with specific habitat requirements (intolerant of disturbance) High FQAI represents a diverse plant community with specific habitat requirements (intolerant of disturbance)

58. Necessary Context Wetlands are a valuable ecological resource Wetlands are a valuable ecological resource We are interested in their condition We are interested in their condition One way in which we assess condition is by the plant community One way in which we assess condition is by the plant community We seek to relate land cover characteristics to wetland condition (e.g., plant community) We seek to relate land cover characteristics to wetland condition (e.g., plant community) Since we cannot identify hotspots of condition, can we identify hotspots of stress? Since we cannot identify hotspots of condition, can we identify hotspots of stress?

59. The Upper Juniata Wetlands Monitoring Project One of three watersheds of the Juniata River watershed (3900 sq. miles), which is the second largest tributary to the Susquehanna R., in turn the largest tributary to the Chesapeake Bay One of three watersheds of the Juniata River watershed (3900 sq. miles), which is the second largest tributary to the Susquehanna R., in turn the largest tributary to the Chesapeake Bay Juniata watershed is 67% forested, 23% agriculture, 7% developed, plus mining, water, etc. Juniata watershed is 67% forested, 23% agriculture, 7% developed, plus mining, water, etc. Upper Juniata is 1300 sq. miles Upper Juniata is 1300 sq. miles 83 wetlands sampled according to a probabilistic survey design (USEPA-EMAP) 83 wetlands sampled according to a probabilistic survey design (USEPA-EMAP)

62. Why use the Scan Statistic? Identify critical areas of “stress” as an indicator of wetlands in potentially poor condition (condition assessment) Identify critical areas of “stress” as an indicator of wetlands in potentially poor condition (condition assessment) Target restoration areas (restoration) Target restoration areas (restoration) Narrow down an overwhelming management problem (1300 square miles) into a manageable one Narrow down an overwhelming management problem (1300 square miles) into a manageable one

63. Which factors are good predictors of condition? Exhaustive assessment of landscape factors shows that agricultural activity on steep slopes (>8%) is an important factor in determining physical and biological condition Exhaustive assessment of landscape factors shows that agricultural activity on steep slopes (>8%) is an important factor in determining physical and biological condition Management of practices on steep agricultural lands is feasible, and current restoration programs have targeted these areas (USDA) Management of practices on steep agricultural lands is feasible, and current restoration programs have targeted these areas (USDA)

65. Application of the Scan Statistic

66. Necessary Elements of the Scan Statistic Observed rate versus expected rate Observed rate versus expected rate Contiguous coverage of elements Contiguous coverage of elements Spatial independence Spatial independence Constraint on maximum hotspot size Constraint on maximum hotspot size

67. 2% Threshold: Map of hot spots with a p 2 blocks in the hot spot showing top five hotspots ranked by likelihood

69. 3% Threshold, Hotspot #2 >35<35 Out Hotspot17.0025.00 In Hotspot2.0011.00

70. Explanatory Factors

71. Potential advantages of critical areas as an indicator of stress Delineation of contributing areas is labor intensive Delineation of contributing areas is labor intensive Can critical areas give us a first cut as to wetlands in poor condition? Can critical areas give us a first cut as to wetlands in poor condition? Can they serve as an early warning? Can they serve as an early warning?

72. Are wetlands associated with Ag on HS threshold in poorer condition than those that are not? Ag on High Slope High FQAI Low FQAI <15% Ag on HS 1823 >15% Ag on HS 113

73. Are wetlands associated with hotspots in poorer condition than those that are not? 10% Size Constraint High FQAI Low FQAI Not Associated with Hotspot 1621 Associated with Hotspot 315

74. Are wetlands associated with hotspots in poorer condition than those that are not? 3% Size Constraint High FQAI Low FQAI Not Associated with Hotspot 1725 Associated with Hotspot 211

75. Does SaTScan improve explanatory power?

76. Did SaTScan “concentrate” agriculture on high slopes?

77. Restoration Targeting

78. Why on a watershed basis? Watersheds are more efficient unit financially, socially, ecologically Watersheds are more efficient unit financially, socially, ecologically Accounting Unit (AU) for Integrated 303(d)/305(b) Reporting Accounting Unit (AU) for Integrated 303(d)/305(b) Reporting Conceptually attractive for local managers Conceptually attractive for local managers

79. Restoration Targeting with SaTScan Can effectively target areas for restoration activities or best management practices Can effectively target areas for restoration activities or best management practices Can be scaled so that critical area matches management area Can be scaled so that critical area matches management area

80. Conclusions

81. Are there warnings? Agricultural Use Urbanization Mining Pollutant Trans. Eutrophication Turbidity Sedimentation Hydrologic Modif. Fragmentation Organic Matter Hydroperiod Macroinvertebrates Bird Community Amphibian Comm. Plant Community Acidification Soil Microtopography

82. Conclusions When using SaTScan, assess appropriateness of circular critical areas and size constraints When using SaTScan, assess appropriateness of circular critical areas and size constraints Can be used for condition assessment Can be used for condition assessment Highly useful for restoration targeting Highly useful for restoration targeting Use of scan statistic methods in an ecological context raises three future needs: Use of scan statistic methods in an ecological context raises three future needs: Non-circular critical areas Non-circular critical areas Ability to include spatial dependency (e.g., critical areas of condition, infectious disease) Ability to include spatial dependency (e.g., critical areas of condition, infectious disease) Continuous response variable (e.g., fertilizer loadings, livestock density) Continuous response variable (e.g., fertilizer loadings, livestock density)

84. Multiple Indicators Partial Orderings and Ranking and Prioritization without Combining Indicators

85. Two indicators of a person’s size: I 1 = Height I 2 = Weight I 1 = Height I 2 = Weight Scatter Plot: Is A bigger than B ? Is A bigger than B ? Multiple Indicators B A Height Weight

86. I 1 = Height I 2 = Weight I 1 = Height I 2 = Weight Size = (I 1 + I 2 )/2 Size = (I 1 + I 2 )/2 Combining Indicators Simple Average Bigger than A A Height Weight Smaller than A Contour of constant size

87. I 1 = Height I 2 = Weight I 1 = Height I 2 = Weight Size = w 1 I 1 + w 2 I 2, w 1 + w 2 = 1 Size = w 1 I 1 + w 2 I 2, w 1 + w 2 = 1 w 1 and w 2 reflect: w 1 and w 2 reflect: Units of measurement Units of measurement Relative importance of the two indicators Relative importance of the two indicators Combining Indicators Weighted Average A Height Weight Contour of constant size Slope determines tradeoff between Height and Weight

88. I 1 = Height I 2 = Weight I 1 = Height I 2 = Weight Size = F(I 1, I 2 ) Size = F(I 1, I 2 ) Combining Indicators Non-Linear Combination A Height Weight Contour of constant size Tradeoff varies

89. I 1 = Height I 2 = Weight I 1 = Height I 2 = Weight Partial Ordering A Height Weight Bigger than A Smaller than A Not comparable with A Not comparable with A

90. UNEP HEI National Land, Air, Water Indicators HEI with revised data: Land: undomesticated land to total land area Land: undomesticated land to total land area Air: (air indicator1 + air indicator2) / 2, where air indicator1 = renewable energy use to total energy use; air indicator2 = GDP per unit energy use, based on maximum and minimum concept Air: (air indicator1 + air indicator2) / 2, where air indicator1 = renewable energy use to total energy use; air indicator2 = GDP per unit energy use, based on maximum and minimum concept Water: (water indicator1 + water indicator2) / 2, where water indicator1 = ratio of water available after annual withdrawals to internal water resources; water indicator2 = ratio of people with access to an improved water source to total population Water: (water indicator1 + water indicator2) / 2, where water indicator1 = ratio of water available after annual withdrawals to internal water resources; water indicator2 = ratio of people with access to an improved water source to total population

91. UNEP HEI Data Matrix

92. Hasse Diagram---All Countries (labels are HEI ranks)

93. Hasse Diagram --- Western Europe

94. Ranking Partially Ordered Sets Linear extension decision tree a b dc e f a c e b b d ff d ed f e e f c f d ed f e e f d f e e f c f e e f c c f d ed f e e f d f e e f c b a b a d Jump Size: 1 3 3 2 3 5 4 3 3 2 4 3 4 4 2 2 Poset (Hasse Diagram)

95. Ranking Partially Ordered Sets Rank-Frequency Distributions Element a Element c Element e Element b Element d Element f Rank

96. Ranking Partially Ordered Sets 16 The curves are stacked one above the other and the result is a linear ordering of the elements: a > b > c > d > e > f

97. Cumulative Rank Frequency Operator –1 An example where  must be iterated Original Poset (Hasse Diagram) a f eb c g d h a f e b ad c h g a f e b ad c h g  2 2

98. More Indicators do not necessarily mean More Information and More Discrimination Suppose two indicator columns are exactly the same. Suppose two indicator columns are exactly the same. The second column does not add new information or discriminatory capability to that of the first column. The second column does not add new information or discriminatory capability to that of the first column. Likewise, there is little new information when there is a strong underlying (rank-)correlation between the two indicators. Likewise, there is little new information when there is a strong underlying (rank-)correlation between the two indicators. Landscape ecologists have discovered that some fifty landscape pattern metrics amount to essentially five to ten indicators. Landscape ecologists have discovered that some fifty landscape pattern metrics amount to essentially five to ten indicators.

99. Multiple Indicators can convey Redundant Information Combining Indicators (e.g., by averaging) typically fails to adjust for the Redundancy

101. Frontier/ Envelope Target (Virtual)

102. Urban Forest Agriculture Mixed

103. QOL Relative Efficiency QOL Planning History Relative Efficiency Planning History

104. Relative Efficiency Off-Farm Employment

105. Integration for ASC and the EAGLES —G. P. Patil— My participation has to do with data analysis, interpretation, and improvement, in the spirit of space age analysis of space age data. We need to appropriately develop, adapt, apply inferential geospatial informatics. We do not need to feel drowned in statistics, nor drowned without statistics. We do not need statistics to just sprinkle holy water, but to help water become holy! We face many important nonstandard problems, and several of them need nonstandard concepts, methods, and tools.

106. Environmental and Ecological Statistics Volume 12 Number 2 2005 Special Institutional Thematic Issue: Penn State Cross-Disciplinary Classroom in Statistical Ecology and Environmental Statistics Editorial PATIL, G. P. Editorial: Special Institutional Thematic Issue: Penn State Cross-Disciplinary Classroom in Statistical Ecology and Environmental Statistics Papers RATHBUN, S., and BLACK, B. Modeling and Spatial Prediction of Pre-Settlement Patterns of Forest Distribution Using Witness Tree Data RATHBUN, S., and FEI, S.. A Spatial Zero-Inflated Poisson Regression Model for Oak Regeneration MYERS, W.L., KURIHARA, K., PATIL, G. P., and VRANEY, R. Finding Upper-Level Sets in Cellular Surface Data Using Echelons and SaTScan MYERS, W. L., et a MidAtlantic Watersheds Classification and Prioritization for Protection and Restoration WARDROP, D. H., BISHOP, J. A., EASTERLING, M., HYCHKA, K., MYERS, W. L., PATIL, G. P., TAILLIE, C., and BROOKS, R. P. Characterization and classification of watersheds by landscape and land use parameters in five mid-Atlantic physiographic provinces. WARDROP, D., MYERS, W. L., PATIL, G. P., and TAILLIE Coupling Biological Impairment of Freshwater Streams and Human Dimensions

107. Environmetal and Ecological Statistics EPA EaGLes Special Issue Guest Editors --Invited Papers --Invited Papers --To Be Invited --To Be Invited --Suggestions Invited --Suggestions Invited

108. ASC ANALYTICAL NEEDS AND SYNERGIES Indicators and Data Analytical Exercises Indicators and Data Analytical Exercises Concepts, Methods, Tools, Softwares Concepts, Methods, Tools, Softwares Data Analyses and Interpretations Data Analyses and Interpretations Publishable Manuscripts Involving Substantive and/or Quantitative Innovations Publishable Manuscripts Involving Substantive and/or Quantitative Innovations Work plan Work plan

109. Myers, W. L., Bishop, J., Brooks, R., and Patil, G. P. (2001). Myers, W. L., Bishop, J., Brooks, R., and Patil, G. P. (2001). Composite spatial indexing of regional habitat importance. Composite spatial indexing of regional habitat importance. Community Ecology, 2(2), 213—220. Community Ecology, 2(2), 213—220.

110. Myers, W. L., and Patil, G. P. (2002). Myers, W. L., and Patil, G. P. (2002). Echelon analysis. Echelon analysis. In Encyclopedia of Environmetrics, Volume 2. In Encyclopedia of Environmetrics, Volume 2. A. El-Shaarawi and W. W. Piegorsch, eds. John Wiley & Sons, UK. pp. 583—586. A. El-Shaarawi and W. W. Piegorsch, eds. John Wiley & Sons, UK. pp. 583—586.

111. Johnson, G. D., Myers, W. L. Patil, G. P., O’Connell, T. J., and Brooks, R. P. (2001). Johnson, G. D., Myers, W. L. Patil, G. P., O’Connell, T. J., and Brooks, R. P. (2001). Predictability of bird community-based ecological integrity using landscape measurements. Predictability of bird community-based ecological integrity using landscape measurements. In Cartografia Multiscalare della Natura, O. Rossi, ed., In Cartografia Multiscalare della Natura, O. Rossi, ed., IX Congresso Nazaionale Della Societa Italiana di Ecologica, September 1999. pp. 79—104. IX Congresso Nazaionale Della Societa Italiana di Ecologica, September 1999. pp. 79—104. Also appears in Managing for Healthy Ecosystems, D. Rapport, W. Lasley, D. Rolston, O. Nielsen, C. Qualset, and A. Damania, eds. CRC Press/Lewis Publ. pp. 617—637 (2003). Also appears in Managing for Healthy Ecosystems, D. Rapport, W. Lasley, D. Rolston, O. Nielsen, C. Qualset, and A. Damania, eds. CRC Press/Lewis Publ. pp. 617—637 (2003).

112. Grossi, L., Patil, G. P., and Taillie, C. (2004). Grossi, L., Patil, G. P., and Taillie, C. (2004). Statistical selection of perimeter-area models for patch mosaics in multiscale landscape analysis. Statistical selection of perimeter-area models for patch mosaics in multiscale landscape analysis. Environmental and Ecological Statistics, 11, 165-181. Environmental and Ecological Statistics, 11, 165-181.

113. Johnson, G. D., Myers, W. L., Patil, G. P., and Taillie, C. (2001). Johnson, G. D., Myers, W. L., Patil, G. P., and Taillie, C. (2001). Predictability of surface water pollution loading in Pennsylvania using watershed- based landscape measurements. Predictability of surface water pollution loading in Pennsylvania using watershed- based landscape measurements. Journal of the American Water Resources Association, 37(4), 821—835. Journal of the American Water Resources Association, 37(4), 821—835.

114. Patil, G. P., Brooks, R. P., Myers, W. L., and Taillie, C. (2003). Patil, G. P., Brooks, R. P., Myers, W. L., and Taillie, C. (2003). Multiscale advanced raster map analysis system for measuring ecosystem health at landscape scale—A novel synergistic consortium initiative. Multiscale advanced raster map analysis system for measuring ecosystem health at landscape scale—A novel synergistic consortium initiative. In Managing for Healthy Ecosystems, In Managing for Healthy Ecosystems, D. Rapport, W. Lasley, D. Rolston, O. Nielsen, C. Qualset, and A. Damania, eds. D. Rapport, W. Lasley, D. Rolston, O. Nielsen, C. Qualset, and A. Damania, eds. CRC Press/Lewis Publ. pp. 567-576. CRC Press/Lewis Publ. pp. 567-576.

115. Patil, G. P., Brooks, R. P., Myers, W. L., Rapport, D. J., and Taillie, C. (2001). Patil, G. P., Brooks, R. P., Myers, W. L., Rapport, D. J., and Taillie, C. (2001). Ecosystem health and its measurement at landscape scale: Towards the next generation of quantitative assessments. Ecosystem health and its measurement at landscape scale: Towards the next generation of quantitative assessments. Ecosystem Health,7(4), 307-316. Ecosystem Health,7(4), 307-316.

116. Patil, G. P., Bishop, J., Myers, W. L., Taillie, C., Vraney, R., and Wardrop, D. H. (2004). Patil, G. P., Bishop, J., Myers, W. L., Taillie, C., Vraney, R., and Wardrop, D. H. (2004). Detection and delineation of critical areas using echelons and spatial scan statistics with synoptic cellular data. Detection and delineation of critical areas using echelons and spatial scan statistics with synoptic cellular data. Environmental and Ecological Statistics, 11(2), 139-164. Environmental and Ecological Statistics, 11(2), 139-164.

117. Patil, G. P., and Taillie, C. (2003). Patil, G. P., and Taillie, C. (2003). Modeling and interpreting the accuracy assessment error matrix for a doubly classified map. Modeling and interpreting the accuracy assessment error matrix for a doubly classified map. Environmental and Ecological Statistics, 10(3), 357-373. Environmental and Ecological Statistics, 10(3), 357-373.

118. Patil, G. P. Patil, G. P. Multiscale advanced raster map analysis system: Definition, design, and development. Multiscale advanced raster map analysis system: Definition, design, and development. In Novos Rumos em Estatistica, In Novos Rumos em Estatistica, L. Carvalho, F. Brilhante, and F. Rosado, eds. L. Carvalho, F. Brilhante, and F. Rosado, eds. Sociedade Portuguesa de Estatistica, pp. 21-54. Sociedade Portuguesa de Estatistica, pp. 21-54.

119. Patil, G. P. (2003). Patil, G. P. (2003). Classified raster map analysis for sustainable environment and development in the 21st Century. Classified raster map analysis for sustainable environment and development in the 21st Century. In Sustainable Environments: A Statistical Analysis In Sustainable Environments: A Statistical Analysis A. K. Ghosh, J. K. Ghosh and B.Mukhopadhyay, eds. A. K. Ghosh, J. K. Ghosh and B.Mukhopadhyay, eds. Oxford University Press, New Delhi. Pp. 148-174. Oxford University Press, New Delhi. Pp. 148-174.

120. Myers, W. L., Patil, G. P., Taillie, C., and Wardrop, D. (2003). Myers, W. L., Patil, G. P., Taillie, C., and Wardrop, D. (2003). Synoptic environmental indicators as image analogs for landscape analysis. Synoptic environmental indicators as image analogs for landscape analysis. Community Ecology, 4(2), 205-217. Community Ecology, 4(2), 205-217.

121. Myers, W. L., Patil, G. P., and Taillie, C. (2003). Myers, W. L., Patil, G. P., and Taillie, C. (2003). Doubly segmented proxy images for multiscale landscape ecology and ecosystem health. Doubly segmented proxy images for multiscale landscape ecology and ecosystem health. Community Ecology, 4(2), 163-183. Community Ecology, 4(2), 163-183.

122. Patil, G. P., and Taillie, C. (2003). Patil, G. P., and Taillie, C. (2003). Geographic and network surveillance via scan statistics for critical area detection. Geographic and network surveillance via scan statistics for critical area detection. Statistical Science, 18(4), 457-465. Statistical Science, 18(4), 457-465.

123. Patil, G. P., Balbus, J., Biging, G., JaJa, J., Myers, W. L., and Taillie, C. (2004). Patil, G. P., Balbus, J., Biging, G., JaJa, J., Myers, W. L., and Taillie, C. (2004). Multiscale advanced raster map analysis system: Definition, design and development. Multiscale advanced raster map analysis system: Definition, design and development. Environmental and Ecological Statistics, 11, 113-138. Environmental and Ecological Statistics, 11, 113-138.

124. Patil, G. P., and Taillie, C. (2004). Patil, G. P., and Taillie, C. (2004). Upper level set scan statistic for detecting arbitrarily shaped hotspots. Upper level set scan statistic for detecting arbitrarily shaped hotspots. Environmental and Ecological Statistics, 11, 183-197. Environmental and Ecological Statistics, 11, 183-197.

125. Patil, G. P. (2003). Patil, G. P. (2003). Overview: Landscape Health Assessment (2003). Overview: Landscape Health Assessment (2003). In Managing for Healthy Ecosystems, In Managing for Healthy Ecosystems, D. Rapport, W. Lasley, D. Rolston, O. Nielsen, C. Qualset, and A. Damania, eds. D. Rapport, W. Lasley, D. Rolston, O. Nielsen, C. Qualset, and A. Damania, eds. CRC Press/Lewis Publ. pp. 559-565. CRC Press/Lewis Publ. pp. 559-565.

126. Patil, G. P. (2003). Patil, G. P. (2003). Statistical ecology and environmental statistics. Statistical ecology and environmental statistics. In Encyclopedia of Life Support Systems, In Encyclopedia of Life Support Systems, EOLSS Publishers Co., Ltd. EOLSS Publishers Co., Ltd. Jeff Wood, ed. Jeff Wood, ed.

127. Patil, G. P.(2004). Patil, G. P.(2004). Editorial: Special institutional thematic issue: Center for Statistical Ecology and Environmental Statistics. Editorial: Special institutional thematic issue: Center for Statistical Ecology and Environmental Statistics. Environmental and Ecological Statistics, 11, 109-112. Environmental and Ecological Statistics, 11, 109-112.

128. Patil, G. P., and Taillie, C. (2004). Patil, G. P., and Taillie, C. (2004). Multiple indicators, partially ordered sets, and linear extensions: Multi-criterion ranking and prioritization. Multiple indicators, partially ordered sets, and linear extensions: Multi-criterion ranking and prioritization. Environmental and Ecological Statistics, 11, 183-197. Environmental and Ecological Statistics, 11, 183-197.

129. Member, Member, NSF Peer Review Panel for ‘Homeland Security and Geographic Information Systems’ NSF Peer Review Panel for ‘Homeland Security and Geographic Information Systems’ February, 2004 February, 2004

130. Member, Member, NSF Peer Review Panel for ‘New Mathematical and Statistical Tools for Understanding Complex Systems in the Environment (MSPA-CSE)’ NSF Peer Review Panel for ‘New Mathematical and Statistical Tools for Understanding Complex Systems in the Environment (MSPA-CSE)’ June, 2004 June, 2004

131. Member, EPA Science Advisory Board, Regional Vulnerability Assessment Panel, for ‘EPA’s Regional Vulnerability Assessment Methods for Multi-Scale Decision-Making’ Member, EPA Science Advisory Board, Regional Vulnerability Assessment Panel, for ‘EPA’s Regional Vulnerability Assessment Methods for Multi-Scale Decision-Making’ October, 2004 October, 2004 Invited Invited

132. Invited Speaker, Invited Speaker, ‘Detection and Delineation of Critical Areas for Assessment and Management at Landscape Scales using Cellular Synoptic Data,’ ‘Detection and Delineation of Critical Areas for Assessment and Management at Landscape Scales using Cellular Synoptic Data,’ International Society for Ecosystem Health Conference on Linkages Between Biodiversity, Ecosystem Health, and Human Health, International Society for Ecosystem Health Conference on Linkages Between Biodiversity, Ecosystem Health, and Human Health, Washington, DC, June 2002. Washington, DC, June 2002.

133. Invited Speaker, Invited Speaker, ‘Detection and Delineation of Critical Areas for Assessment and Management at Landscape Scales using Cellular Synoptic Data,’ ‘Detection and Delineation of Critical Areas for Assessment and Management at Landscape Scales using Cellular Synoptic Data,’ The International Environmetrics Society Meetings, The International Environmetrics Society Meetings, Genoa, Italy, June 2002. Genoa, Italy, June 2002.

134. Invited Speaker, Invited Speaker, ‘Prioritization and Ranking of Watersheds Based on Watershed Indicators ‘Prioritization and Ranking of Watersheds Based on Watershed Indicators Without Having to Integrate Indicators for Multi-Criterion Decision Support,’ Without Having to Integrate Indicators for Multi-Criterion Decision Support,’ The International Environmetrics Society Meetings, The International Environmetrics Society Meetings, Genoa, Italy, June 2002. Genoa, Italy, June 2002.

135. Invited Speaker, Invited Speaker, ‘Geographic and Network Surveillance for Arbitrarily Shaped Hotspots: Next Generation of Potential Outbreak Detection and Prioritization System.’ ‘Geographic and Network Surveillance for Arbitrarily Shaped Hotspots: Next Generation of Potential Outbreak Detection and Prioritization System.’ National Syndromic Surveillance Conference, National Syndromic Surveillance Conference, New York City, September 2002. New York City, September 2002.

136. Presented paper, Presented paper, ‘Multiscale Advanced Raster Map Analysis System Definition, Design, and Development.’ ‘Multiscale Advanced Raster Map Analysis System Definition, Design, and Development.’ RESE Conference, RESE Conference, Stockholm, Sweden, November 2002. Stockholm, Sweden, November 2002.

137. Invited Keynote Lecture, Invited Keynote Lecture, “Environmental Health and a New Generation of Statistical Methods”, “Environmental Health and a New Generation of Statistical Methods”, MISTRA Conference on Remote Sensing for the Environment, MISTRA Conference on Remote Sensing for the Environment, Government of Sweden, Government of Sweden, Stockholm, November 2002. Stockholm, November 2002.

138. Invited Joint Inaugural Lecture with C. R. Rao, Invited Joint Inaugural Lecture with C. R. Rao, International Conference on Environment and International Conference on Environment and Health related Quality of Life: Statistical Perspectives, Health related Quality of Life: Statistical Perspectives, France 2002. France 2002.

139. Invited paper, Invited paper, ‘Upper Level Set Scan Statistic for Detection Arbitrarily Shaped Hotspots,’ Joint Statistical Meetings, ‘Upper Level Set Scan Statistic for Detection Arbitrarily Shaped Hotspots,’ Joint Statistical Meetings, San Francisco, San Francisco, August 2003.* August 2003.* *Also a session chairman. *Also a session chairman.

140. Invited Poster Presentation, Keystone Homeland Security Summit, Invited Poster Presentation, Keystone Homeland Security Summit, University Park, University Park, April 2003. April 2003.

141. Invited presentation, Invited presentation, ‘Biosurveillance Geoinformatics of Hotspot Detection and Prioritization for Biosecurity,’ ‘Biosurveillance Geoinformatics of Hotspot Detection and Prioritization for Biosecurity,’ Washington Statistical Society, Washington Statistical Society, Washington, DC, Washington, DC, February 2004. February 2004.

142. ECOSYSTEM HEALTH AND ITS MEASUREMENT AT LANDSCAPE SCALE Invited Speakers and Authors: Abstracts 1. W. L. Myers, G. P. Patil, and C. Taillie Title: Comparative Patterns of Habitat Richness and Rarity for Pennsylvania Vertebrates Keywords: Biodiversity, Habitat, Spatial Pattern, Richness, Rarity, Vertebrates, Ecosystem Health. 2. D. H. Wardrop, J. A. Bishop, M. Easterling, W. L. Myers, G. P. Patil, and C. Taillie Title: Use of Landscape and Land Use Parameters for Classification and Characterization of Watersheds in the Mid-Atlantic across Five Physiographic Provinces. Keywords: Atlantic Slope Consortium, Mid-Atlantic Region, Characterization of Watersheds, Classification of Watersheds, Environmental and Ecological Indicators, Landscape Parameters, Land Use Parameters. 3. G. P. Patil, W. L. Myers, and C. Taillie Title: Detection and Delineation of Critical Areas for Assessment and Management at Landscape Scales using Cellular Synoptic Data. Keywords: Hotspots and Hotspot Areas, Critical Areas and Corridors, Biocomplexity, Regionalized Ecosystem Health, Ecosystem Distress Syndrome, Adjacency Relations, Cellular Tesselations, Connected Components, Zonation Tree, Spatial Scan Statistics, Echelons. Genoa/Washington, DC

143. SYLLABUS for PARMA SHORT COURSE Multiscale Advanced Raster Map Analysis Instruction Methods and Tools for the following: 1. Multicriteria Rankings and Fuzzy Ranks. Posets, Hasse Diagrams, and Linear Extensions. Elevated Cluster Prioritization, Environmental Factor Prioritization, Multicriteria Comparisons and Decisions. 2. Echelons and Families of Echelons. Surface Topology and Upper Level Sets. Geographic Surveillance, Elevated Cluster Detection, Change Detection and Regional Change Patterns Analysis, Multiscale Assessment, Regional Echelon Partitions, Hotspots, Critical Areas, Corridors, Outbreaks. 3. Multiscale Landscape Pattern Metrics. Scaling Domains, Multiscale Fragmentation Profiles, Eigen values as Fractals. 4. Modeling and Simulation Devices. Markov Random Fields, Multivariate Disjunctive Indicator Geostatistics, and Hierarchical Markov Transition Matrix Models. Uncertainity Analysis, Confidence Statements, Statistical Significance, Inferential Geoinformatics. 5. Userfriendly Software System for Multiscale Map and Surface Analysis, Geospatial Data Management, Data Mining, Data Analysis, Visualization and Communication. 6. Ecological Sampling, Environmental Monitoring, Observational Economy Encounter Sampling, Transect Sampling, Composite Sampling, Ranked Set Sampling, Adaptive Sampling 7. Pattern-based Image Analysis For Landscape Ecology and Ecosystem Health. MARMAP Website: http://www.stat.psu.edu/~gpp/newpage11.htmhttp://www.stat.psu.edu/~gpp/newpage11.htm

144. Organizer(s): Ganapati P. Patil, Pennsylvania State University Ganapati P. PatilGanapati P. Patil Chair(s): Ganapati P. Patil, Pennsylvania State University Ganapati P. PatilGanapati P. Patil 8:35 AM 8:35 AM Geographical and Network Hotspot Detection, Prioritization, and Early Warning — Charles Taillie, Pennsylvania State University; Ganapati P. Patil, Pennsylvania State University Charles TaillieGanapati P. Patil Charles TaillieGanapati P. Patil 8:55 AM 8:55 AM Coupling Biological Impairment of Freshwater Streams and Human Dimensions Denice Wardrop, Pennsylvania State University; Wayne Myers, Pennsylvania State University; Patil Ganapati, Pennsylvania State University; Charles Taillie, Pennsylvania State University Denice Wardrop Wayne MyersPatil GanapatiCharles Taillie Denice Wardrop Wayne MyersPatil GanapatiCharles Taillie 9:15 AM 9:15 AM Mid-Atlantic Watersheds Classification and Prioritization for Protection and Restoration Wayne Myers, Pennsylvania State University; Mary McKenney-Easterling, Pennsylvania State University; Kristen Hychka, Pennsylvania State University; Bronson Griscom, Canaan Valley Institute; Joseph Bishop, Pennsylvania State University; Gian Rocco, Pennsylvania State University; Robert Brooks, Pennsylvania State University; George Constantz, Canaan Valley Institute; Ganapati P. Patil, Pennsylvania State University; Charles Taillie, Pennsylvania State University Wayne MyersMary McKenney-EasterlingKristen Hychka Bronson GriscomJoseph BishopGian RoccoRobert BrooksGeorge ConstantzGanapati P. PatilCharles Taillie Wayne MyersMary McKenney-EasterlingKristen Hychka Bronson GriscomJoseph BishopGian RoccoRobert BrooksGeorge ConstantzGanapati P. PatilCharles Taillie 9:35 AM 9:35 AM Biostochastics for Remote Sensing of the Environment with Applications Bo Ranneby, Swedish University of Agricultural Sciences; J. Yu, Swedish University of Agricultural Sciences; M. Ekstrom, Swedish University of Agricultural Sciences Bo Ranneby J. YuM. Ekstrom Bo Ranneby J. YuM. Ekstrom 9:55 AM 9:55 AM Disc: Elizabeth M. Middleton, NASA Elizabeth M. MiddletonElizabeth M. Middleton 10:15 AM 10:15 AM Floor Discussion JOINT STATISTICAL MEETINGS 2004 JSM Toronto, Canada Invited Session on Statistical Geoinformatic Surveillance and Security

145. JOINT STATISTICAL MEETINGS 2003 JSM San Francisco, CA Invited Session on Hotspot Detection, Delineation, and Prioritization for Geographic Surveillance and Early Warning System Organizer G. P. Patil, The Pennsylvania State University Director, Center for Statistical Ecology and Environmental Statistics Chair G. P. Patil, The Pennsylvania State University Invited Speakers and Presentations G. P. Patil and C. Taillie Hotspot Detection, Delineation, and Prioritization for Geographic Surveillance and Early Warning System Martin Kulldorff and Linda Pickle Elliptical Spatial and Cylindrical Spatiotemporal Scan Statistics in Geographic Surveillance Renato Assunção and Luiz Duczmal Stochastic Annealing Spatial and Spatiotemporal Scan Statistics in Geographic Surveillance Invited Discussant Thomas A. Louis Department of Biostatistics Johns Hopkins Bloomberg School of Public Health

146. Organizer: G. P. Patil Chair: G. P. Patil 2:00 – 2:05Chair 2:05 – 2:30Charles Taillie and Ganapati P. Patil ‘Multiscale Raster Map Analysis: Stochastic Models, Statistical Methods, and Computational Tools’ 2:30 – 2:55Stephen Stehman and Ray Czaplewski ‘Statistical Issues in Assessing the Accuracy of Multi- Category Raster Maps’ 2:55 – 3:20Koji Kurihara, Wayne Myers, and Ganapati P. Patil ‘Cellular Automation of Surface Understanding with Echelon Analysis and Its Applications to Geospatial Change Detection’ 3:20 – 3:45W. L. Myers, Koji Kurihara, Ganapati P. Patil and Ryan Vraney ‘Finding Upper Level Sets in Cellular Surface Data Using Echelons and SaTScan’ 3:45 – 3:50Floor Discussion JOINT STATISTICAL MEETINGS 2002 JSM New York, NY Invited Session on Multiscale Raster Map Analysis System for Digital Government in the 21st Century

147. Cross-Disciplinary Classroom Materials Stat 548: Spring 2004 February 10, 2004: Atlantic Slope Consortium: Watershed Classification PowerPoint Presentation by Denice Wardrop February 12, 2004: Atlantic Slope Consortium: Framework PowerPoint Presentation by Denice Wardrop February 19, 2004: Cluster Analysis: Finding Groups in Data PowerPoint Presentation by Charles Taillie February 2004: Watershed Characterization and Prioritization PowerPoint Presentation by Kristen Hychka