1. NPS Temporal Conference # 1

2. NPS Temporal Conference # 2 DESIGNING PANEL SURVEYS SPECIFICALLY RELEVANT TO NATIONAL PARKS IN THE NORTHWEST N. Scott Urquhart Senior Research Scientist Department of Statistics Colorado State University Fort Collins, CO 80527-1877

3. NPS Temporal Conference # 3 INFERENCE PERSPECTIVES  Design Based  Inferences rest on the probability structure incorporated in the sampling plan Completely defensible; very minimal assumptions Limiting relative to using auxiliary information  Model Assisted  Uses models to compliment underlying sampling structure  Has opportunities for use of auxiliary information  Model Based (eg: spatial statistics)  Ignores sampling plan  Defensibility lies in defense of model

4. NPS Temporal Conference # 4 APPROACH OF THIS PRESENTATION  Use tools from the arena of  Model assisted and  Model based analyses  To study the performance of  Design based &  Model-assisted analyses  WHY?  Without models, performance evaluations need simulation  Before substantial data have been gathered  No basis for values to enter into simulation studies

5. NPS Temporal Conference # 5 STATUS & TRENDS OVER TIME IN ECOLOGICAL RESOURCES OF A REGION MAJOR POINTS  Regional trend  site trend  Detection of trend requires substantial elapsed time  Regional OR intensive site  Almost all indicators have substantial patterns in their variability  Design to capitalize on this; don’t fight it.  Minimize effect of site variability with planned revisits – specific plans will be illustrated  Design tradeoffs: TREND vs STATUS

6. NPS Temporal Conference # 6 REGIONAL TREND  SITE TREND  The predominant theme of ecology:  Ecological processes  How does a specific kind of ecosystem function Energy flows Food webs Nutrient cycling  Most studies of such functions must be temporally Temporally intensive –What material goes from where to where? Consequently spatially restrictive  In this situation: Temporal trend = site trend

7. NPS Temporal Conference # 7 REGIONAL TREND  SITE TREND ( - CONTINUED)  The predominant theme of ecology versus  A Substantial (any) Agency Focus:  All of an ecological resource In an area or region Across all of the variability present there  Most government regulations Apply to a whole area or region Only a few apply to specific sites The definition of a “region” certainly depends on what agency makes the regulation

8. NPS Temporal Conference # 8 REGIONAL TREND  SITE TREND ( - CONTINUED - III)  The predominant theme of ecology versus  A substantial agency (EPA) focus:  An entire region, like Lakes in the Adirondack Mountains All lakes in Northeastern US All (wadeable) streams the mid-Appalachian Mountains  Or National Park Service  All riparian areas in Olympic National Park  All riparian areas in National Parks in the coastal Northwest

9. NPS Temporal Conference # 9 TREND ACROSS TIME - What is it?  Any response which changes across time in a generally  Increasing or  Decreasing Manner shows trend Manner shows trend  Monotonic change is not essential.  If trend of this sort is present, it will be detectable as linear trend. NOT  This does NOT mean trend must be linear (examples follow) Any specified form is detectable Time = years, here

10. NPS Temporal Conference # 10 TREND ACROSS TIME - What is it? (continued)

11. NPS Temporal Conference # 11

12. NPS Temporal Conference # 12 TREND DETECTION REQUIRES SUBSTANTIAL ELAPSED TIME  IT IS NEARLY IMPOSSIBLE TO DETECT TREND IN LESS THAN FIVE YEARS. WHY?

13. NPS Temporal Conference # 13 BIOLOGICAL INDICATORS HAVE SOMEWHAT MORE VARIABILITY THAN PHYSICAL INDICATORS – BUT THIS VARIES, TOO  Subsequent slides show the relative amount of variability  Ordered by the amount of residual variability: least to most (aquatic responses) Acid Neutralizing Capacity Acid Neutralizing Capacity Ln(Conductance) Ln(Conductance) Ln(Chloride) Ln(Chloride) pH(Closed system) pH(Closed system) Secchi Depth Secchi Depth Ln(Total Nitrogen) Ln(Total Nitrogen) Ln(Total Phosphorus) Ln(Total Phosphorus) Ln(Chlorophyll A) Ln(Chlorophyll A) Ln( # zooplankton taxa) Ln( # zooplankton taxa) Ln( # rotifer taxa) Ln( # rotifer taxa) Maximum Temperature Maximum Temperature And others, both aquatic and terrestrial

14. NPS Temporal Conference # 14  POPULATION VARIANCE:  YEAR VARIANCE:  RESIDUAL VARIANCE: IMPORTANT COMPONENTS OF VARIANCE

15. NPS Temporal Conference # 15  POPULATION VARIANCE:  Variation among values of an indicator (response) across all sites in a park or group of related parks, that is, across a population or subpopulation of sites IMPORTANT COMPONENTS OF VARIANCE ( - CONTINUED)

16. NPS Temporal Conference # 16  YEAR VARIANCE: ALL  Concordant variation among values of an indicator (response) across years for ALL sites in a regional population or subpopulation  NOT variation in an indicator across years at a single site  Detrended remainder, if trend is present Effectively the deviation away from the trend line (or other curve) IMPORTANT COMPONENTS OF VARIANCE ( - CONTINUED II)

17. NPS Temporal Conference # 17  Residual component of variance  Residual component of variance  Has several contributors  Year*Site interaction This contains most of what ecologists would call year to year variation, i.e. the site specific part  Index variation Measurement error Crew-to-crew variation (minimize with documented protocols and training) Local spatial = protocol variation Short term temporal variation IMPORTANT COMPONENTS OF VARIANCE ( - CONTINUED - III)

18. NPS Temporal Conference # 18 SOURCE OF DATA FOR ESTIMATES OF COMPONENTS OF VARIANCE  EMAP Surface Waters: Northeast Lakes Pilot 1991 - 1994  About 450 observations  Over four years  Including about 350 distinct lakes  Design allowed estimation of several residual components

19. NPS Temporal Conference # 19

20. NPS Temporal Conference # 20 SOURCE OF COMPONENTS OF VARIANCE FROM NW HABITAT Oregon Department of Fisheries and Wildlife – stream habitat survey GRADIENT: Stream gradient measured on site WIDTH: Wetted stream width ACW: Active Channel ACH: Active Channel Height UNITS100: Number of distinct habitat units per 100 meters of stream length NOPOOLS: Number of pools in the surveyed reach POOLS100: Number of pools per 100 meters PCTPOOL: % of reach length in pools PCTFINES: % stream substrate that is sand or finer particle size PCTGRAVEL: % of stream stubstrate that is gravel sized particles RIFSNDOR: % of riffle stream length that is sand or finer particle size RIFGRAV: % of riffle stream length that is gravel sized particles SHADE: % stream channel shaded LOG(PIECESLWD +0.01): Number of pieces of large woody debris per 100 meters. LOG(VOLUMELWD +0.01): Volume of large woody debris (m^3/100 meters) RESIDPD: Volume of residual pools (pools remaining if streamflow stopped)

21. NPS Temporal Conference # 21

22. NPS Temporal Conference # 22 SOURCE OF COMPONENTS OF VARIANCE FROM GRAND CANYON  Grand Canyon Monitoring and Research Center  Effects of Glen Canyon Dam on the near River Habitat in the Grand Canyon  At various heights above the river  Height is measured as the height of the river’s water at various flow rates Eg: 15K cfs, 25K cfs, 35K cfs, 45K cfs & 60K cfs  Using first two years’ data  Mike Kearsley – UNA  Design = spatially balanced  With about 1/3 revisited

23. NPS Temporal Conference # 23

24. NPS Temporal Conference # 24 ALL VARIABILITY IS OF INTEREST  The site component of variance is one of the major descriptors of the regional population  The year component of variance often is small to small to estimate. It is a major enemy for detecting trend over time.  If it has even a moderate size, “sample size” reverts to the number of years.  In this case, the number of visits and/or number of sites has no practical effect.

25. NPS Temporal Conference # 25 ALL VARIABILITY IS OF INTEREST ( - CONTINUED)  Residual variance characterizes the inherent variation in the response or indicator.  But some of its subcomponents may contain useful management information  CREW EFFECTS ===> training  VISIT EFFECTS ===> need to reexamine definition of index (time) window or evaluation protocol  MEASUREMENT ERROR ===> work on laboratory/measurement problems

26. NPS Temporal Conference # 26 DESIGN TRADE-OFFS: TREND vs STATUS  How do we detect trend in spite of all of this variation?  Recall two old statistical “friends.”  Variance of a mean, and  Blocking

27. NPS Temporal Conference # 27 DESIGN TRADE-OFFS: TREND vs STATUS ( - CONTINUED)  VARIANCE OF A MEAN:  Where m members of the associated population have been randomly selected and their response values averaged.  Here the “mean” is a regional average slope, so "  2 " refers to the variance of an estimated slope ---

28. NPS Temporal Conference # 28 DESIGN TRADE-OFFS: TREND vs STATUS ( - CONTINUED - II)  Consequently  Becomes  Note that the regional averaging of slopes has the same effect as continuing to monitor at one site for a much longer time period.

29. NPS Temporal Conference # 29 DESIGN TRADE-OFFS: TREND vs STATUS ( - CONTINUED - III)  Now,  2, in total, is large.  If we take one regional sample of sites at one time, and another at a subsequent time, the site component of variance is included in  2.  Enter the concept of blocking, familiar from experimental design.  Regard a site like a block  Periodically revisit a site  The site component of variance vanishes from the variance of a slope.

30. NPS Temporal Conference # 30 NOW PUT IT ALL TOGETHER  Question: “ What kind of temporal design should you use for Northwest National Parks?  We’ll investigate two (families) of recommended designs.  All illustrations will be based on 30 site visits per year, as Andrea recommended.  General relations are uninfluenced by number of sites visited per year, but specific performance is.  We’ll use the panel notation Trent set out.

31. NPS Temporal Conference # 31 RECOMMENDATION OF FULLER and BREIDT  Based on the Natural Resources Inventory (NRI)  Iowa State & US Department of Agriculture Oriented toward soil erosion & Changes in land use  Their recommendation  Pure panel = [1-0] = “Always Revisit”  Independent = [1-n] = “Never Revisit”  Evaluation context  No trampling effect – remotely sensed data  No year effects  Administrative reality of potential variation in funding from year to year MATH RECOME… 100% 50% 0% 50%

32. NPS Temporal Conference # 32 TEMPORAL LAYOUT OF [(1-0), (1-n)]YEAR1234567891011121314151617181920 [1-0]XXXXXXXXXXXXXXXXXXXX [1-n]X X X X X X X X X X X X X X X X X X X X

33. NPS Temporal Conference # 33 FIRST TEMPORAL DESIGN FAMILY  30 site visits per year [1-0]3020100 [1-n]0102030 ALWAYSREVISITNEVERREVISIT

34. NPS Temporal Conference # 34 POWER TO DETECT TREND FIRST TEMPORAL DESIGN FAMILY NO YEAR EFFECT Always Revisit Never Revisit

35. NPS Temporal Conference # 35 POWER TO DETECT TREND FIRST TEMPORAL DESIGN FAMILY, MODEST (= SOME) YEAR EFFECT

36. NPS Temporal Conference # 36 POWER TO DETECT TREND FIRST TEMPORAL DESIGN FAMILY BIG (= LOTS) YEAR EFFECT

37. NPS Temporal Conference # 37 FOREST INVENTORY ANALYSIS (FIA) HAS A SYSTEMATIC SPATIAL DESIGN WITH [1-9]  Doesn’t match up well with [1-0] and [1-n]  We need to investigate alternatives YEAR123456789101112131415161718192021 FIAXXX

38. NPS Temporal Conference # 38 SERIALLY ALTERNATING TEMPORAL DESIGN [(1-3) 4 ] SOMETIMES USED BY EMAP YEAR123456789101112131415161718192021 FIAXXX [(1-3) 4 ] XXXXXX XXXXX XXXXX XXXXX

39. NPS Temporal Conference # 39 SERIALLY ALTERNATING TEMPORAL DESIGN [(1-3) 4 ] SOMETIMES USED BY EMAP YEAR1234567891011… FIAXX [(1-3) 4 ] XXX… XXX… XXX… XX…  Unconnected in an experimental design sense  Very weak design for estimating year effects, if present

40. NPS Temporal Conference # 40 SPLIT PANEL [(1-4) 5, --- ] YEAR123456789101112131415161718192021 FIAXXX [(1-4) 5 ] XXXXX XXXX XXXX XXXX XXXX  AGAIN, Unconnected in an experimental design sense  Matches better with FIA  Still a very weak design for estimating year effects, if present

41. NPS Temporal Conference # 41 SPLIT PANEL [(1-4) 5,(2-3) 5 ]  This Temporal Design IS connected  Has three panels which match up with FIA YEAR123456789101112131415161718192021 FIAXXX [(1-4) 5 ] XXXXX XXXX XXXX XXXX XXXX [(2-3) 5 ] XXXXXXXXX XXXXXXXX XXXXXXXX XXXXXXXX XXXXXXXX

42. NPS Temporal Conference # 42 SECOND TEMPORAL DESIGN FAMILY  30 site visits per year [1-4]3020100 [2-3]051015

43. NPS Temporal Conference # 43 POWER TO DETECT TREND SECOND TEMPORAL DESIGN FAMILY NO YEAR EFFECT

44. NPS Temporal Conference # 44 POWER TO DETECT TREND SECOND TEMPORAL DESIGN FAMILY SOME YEAR EFFECT

45. NPS Temporal Conference # 45 POWER TO DETECT TREND SECOND TEMPORAL DESIGN FAMILY LOTS OF YEAR EFFECT

46. NPS Temporal Conference # 46 COMPARISON OF POWER TO DETECT TREND DESIGN 1 & 2 = ROWS YEAR EFFECT NONE SOME LOTS

47. NPS Temporal Conference # 47 POWER TO DETECT TREND VARYING YEAR EFFECT AND TEMPORAL DESIGN

48. NPS Temporal Conference # 48 STANDARD ERROR OF STATUS TEMPORAL DESIGN 1, NO YEAR EFFECT TOTAL OF 30 SITES 110 SITES VISITED BY YEAR 5 410 SITES VISITED BY YEAR 20

49. NPS Temporal Conference # 49 STANDARD ERROR OF STATUS TEMPORAL DESIGN 1, SOME YEAR EFFECT

50. NPS Temporal Conference # 50 STANDARD ERROR OF STATUS TEMPORAL DESIGN 1, LOTS OF YEAR EFFECT

51. NPS Temporal Conference # 51 STANDARD ERROR OF STATUS TEMPORAL DESIGN 2, NO YEAR EFFECT TOTAL OF 150 SITES TOTAL OF 75 SITES

52. NPS Temporal Conference # 52 STANDARD ERROR OF STATUS TEMPORAL DESIGN 2, SOME YEAR EFFECT

53. NPS Temporal Conference # 53 STANDARD ERROR OF STATUS TEMPORAL DESIGN 2, LOTS OF YEAR EFFECT

54. NPS Temporal Conference # 54 SO WHAT?  Regardless of evaluation circumstances,  Trend detection improves the more the same sites are revisited  Status estimation improves as the number of distinct sites visited increases  Temporal design 2 is better than temporal design 1 in relevant cases  Its power is only slightly influenced by split between panels

55. NPS Temporal Conference # 55 METADATA  Really important for your successors  Like your grandchildrens’ generation  I’ll comment about this later in the conference if you want me to

56. NPS Temporal Conference # 56 This research is funded by U.S.EPA – Science To Achieve Results (STAR) Program Cooperative Agreement # CR - 829095 The work reported here today was developed under the STAR Research Assistance Agreement CR-829095 awarded by the U.S. Environmental Protection Agency (EPA) to Colorado State University. This presentation has not been formally reviewed by EPA. The views expressed here are solely those of presenter and STARMAP, the Program he represents. EPA does not endorse any products or commercial services mentioned in this presentation. FUNDING ACKNOWLEDGEMENT

57. NPS Temporal Conference # 57 TEMPORAL DESIGN 1 ALWAYS REVISIT

58. NPS Temporal Conference # 58 TEMPORAL DESIGN 2 : NEVER REVISIT

59. NPS Temporal Conference # 59 TEMPORAL DESIGN 3: AUGMENTED SERIALLY ALTERNATING

60. NPS Temporal Conference # 60 TEMPORAL DESIGN 4: SPLIT PANEL SERIALLY ALTERNATING PLUS SERIALLY ALTERNATING WITH CONSECUTIVE YEAR REVISITS

61. NPS Temporal Conference # 61

62. NPS Temporal Conference # 62

63. NPS Temporal Conference # 63

64. NPS Temporal Conference # 64

65. NPS Temporal Conference # 65

66. NPS Temporal Conference # 66

67. NPS Temporal Conference # 67

68. NPS Temporal Conference # 68

69. NPS Temporal Conference # 69

70. NPS Temporal Conference # 70