1. U.S Department of the Interior U.S. Geological Survey Designing an Integrated Monitoring Program for Coniferous Forests: beyond the forest and the trees in Olympic National Park K. Jenkins, A. Woodward, E. Schreiner, Cat Hoffman, Roger Hoffman

2. Underpinnings “Many of the ecological monitoring efforts currently being conducted in the parks are of short duration and focused on very specific resource issues and threats.”

3. Underpinnings “Many of the ecological monitoring efforts currently being conducted in the parks are of short duration and focused on very specific resource issues and threats. Unlike those efforts, prototype monitoring programs will strive to develop a better understanding of national park ecosystem dynamics and ecological integration. ” --Natural Resource Inventory and Monitoring in National Parks. 1995. National Park Service

4. Underpinnings: Integration (???) “To put or bring parts together into a whole” --N. Webster

5. Underpinnings: Integration “To put or bring parts together into a whole” --N. Webster Multidisciplinary studies Cross-scale studies

6. Underpinnings: Integration “To put or bring parts together into a whole” --N. Webster Multidisciplinary studies Cross-scale studies Co-location of Study Sites Consistent Sampling Design and Protocols

7. Monitoring Design What How Where How Much When

8. Monitoring Design STRESSORS: Identify key agents of change FOCAL SPECIES: Identify key species of interest SYSTEM HEALTH: Identify key properties and processes Stages Scoping Modeling Integration Protocol Development

9. Monitoring Design STRESSORS: Identify key agents of change FOCAL SPECIES: Identify key species of interest SYSTEM HEALTH: Identify key properties and processes Predict Stress/Response Relationships Describe Linkages among Components and Processes Stages Scoping Modeling Integration Protocol Development

10. Monitoring Design STRESSORS: Identify key agents of change FOCAL SPECIES: Identify key species of interest SYSTEM HEALTH: Identify key properties and processes Predict Stress/Response Relationships Describe Linkages among Components and Processes Conceptual Framework Sampling Framework Stages Scoping Modeling Integration Protocol Development

11. Monitoring Design STRESSORS: Identify key agents of change FOCAL SPECIES: Identify key species of interest SYSTEM HEALTH: Identify key properties and processes Predict Stress/Response Relationships Describe Linkages among Components and Processes Conceptual Framework Sampling Framework Stages Scoping Modeling Integration Protocol Development Sampling Methods Evaluation

12. Context

16. Conceptual Design

17. Spatial Effort Measurement Effort Replication Effort The Challenge: Allocation of Effort

18. Spatial Effort Measurement Effort Replication Effort The Challenge: Allocation of Effort ‘Extensive’ Design

19. Spatial Effort Measurement Effort Replication Effort The Challenge: Allocation of Effort ‘Intensive’ Design ‘Extensive’ Design

20. Extensive MonitoringLandscape Composition Forest Disturbance Forest Structure and Composition Animal Richness/Distribution

21. Characteristics: Extensive Design Parkwide Inference Focus on Landscapes and Communities Focus on Large-scale patterns and Distribution Low-intensity measurements

22. Example: Extensive Design

23. Sampling Plan

24. Extensive MonitoringLandscape Composition Forest Disturbance Forest Structure and Composition Animal Richness/Distribution Intermediate Scale Browse Intensity Large-mammal abundance Tree recruitment in riparian Berry Production

25. Characteristics: Intermediate Design Objectives define scale and intensity Focus on Communities and Populations Moderate Intensity quantitative assessment indices of abundance sample measurements

26. Example: Intermediate Design

27. Extensive MonitoringLandscape Composition Forest Disturbance Forest Structure and Composition Animal Richness/Distribution Intermediate Scale Climate Forest Processes Animal Comm./Popn. Intensive Monitoring

28. Characteristics: Intensive Design Effort defines inference high intensity/local scale costly instrumentation high sampling effort High potential for co-location Multi-disciplinary studies NPS ‘reference’ sites

29. Example: Intensive Design

30. Summary of Concepts_____________ Long-term, integrated monitoring program Access challenges Trade-offs among sampling intensity, inference and replication

31. Questions for Sampling Design What is the question (how do we detect change?)

32. Questions for Sampling Design What is the question (how do we detect change?) How much sampling is enough?

33. Questions for Sampling Design What is the question (how do we detect change?) How much sampling is enough? Where shall we monitor (spatial sampling frame)?

34. Questions for Sampling Design What is the question (how do we detect change)? How much sampling is enough? Where shall we monitor (spatial sampling frame)? How often is enough (temporal sampling frame)?

35. Example: Extensive Design

38. Example: Intermediate Design

39. Example: Intensive Design

40. Distribution of Samples in Time Year Cover D1 D3 D2

41. What is the Question? Period means Regression (linear, exponential, etc.) Rank-based (non-parametric) Permutation methods Identifying extremes (e.g., Probability of Conformity)

42. How much sampling is enough? Rule of thumb (6 is enough) Power analysis

43. Pros of Power Analysis Forces one to plan analysis before the data are collected

44. Pros of Power Analysis Forces one to plan analysis before the data are collected Puts you in the ballpark of and adequate “n”

45. Pros of Power Analysis Forces one to plan analysis before the data are collected Puts you in the ballpark of and adequate “n” Useful for evaluating effectiveness of on- going monitoring

46. Cons of Power Analysis “Canned” programs are available but they require some “fudge” factors

47. Cons of Power Analysis “Canned” programs are available but they require some “fudge” factors Don’t distinguish sources of variance

48. Cons of Power Analysis “Canned” programs are available but they require some “fudge” factors Don’t distinguish sources of variance May disqualify attributes that are highly variable

49. Motivation for Contract Pilot data sets - birds, bats, small mammals, vegetation (3 years) We wanted to investigate different ways of detecting trend We wanted power analyses to determine adequate “n” We focused on common species

50. Integrated Monitoring Revisited The Devil IS in the Details What Scoping

51. Integrated Monitoring Revisited The Devil IS in the Details What Where When Conceptual Design Scoping

52. Integrated Monitoring Revisited The Devil IS in the Details What How Where How Much When Conceptual Design Scoping Protocol Development