nps temporal conference # 1. nps temporal conference # 2 designing panel surveys specifically...

NPS Temporal Conference # 1


DESIGNING PANEL SURVEYSSPECIFICALLY RELEVANT TO NATIONAL PARKS

IN THE NORTHWEST

N. Scott UrquhartN. Scott UrquhartSenior Research ScientistSenior Research ScientistDepartment of StatisticsDepartment of Statistics

Colorado State UniversityColorado State UniversityFort Collins, CO 80527-1877Fort Collins, CO 80527-1877


INFERENCE PERSPECTIVES

Design BasedDesign Based Inferences rest on the probability structure

incorporated in the sampling plan Completely defensible; very minimal assumptions Limiting relative to using auxiliary information

Model AssistedModel Assisted Uses models to compliment underlying sampling

structure Has opportunities for use of auxiliary information

Model Based (eg: spatial statistics)Model Based (eg: spatial statistics) Ignores sampling plan Defensibility lies in defense of model


APPROACH OF THIS PRESENTATION

Use tools from the arena ofUse tools from the arena of Model assisted and Model based analyses

To study the performance of To study the performance of Design based & Model-assisted analyses

WHY?WHY? Without models,

performance evaluations need simulation

Before substantial data have been gatheredBefore substantial data have been gathered No basis for values to enter into simulation studies


STATUS & TRENDS OVER TIME STATUS & TRENDS OVER TIME IN ECOLOGICAL RESOURCES IN ECOLOGICAL RESOURCES

OF A REGIONOF A REGION

MAJOR POINTSMAJOR POINTS

STATUS & TRENDS OVER TIME STATUS & TRENDS OVER TIME IN ECOLOGICAL RESOURCES IN ECOLOGICAL RESOURCES

OF A REGIONOF A REGION

MAJOR POINTSMAJOR POINTS

Regional trend Regional trend site trend site trend Detection of trend requires substantial elapsed timeDetection of trend requires substantial elapsed time

Regional OR intensive site

Almost all indicators have substantial patterns inAlmost all indicators have substantial patterns intheir variabilitytheir variability

Design to capitalize on this; don’t fight it.

Minimize effect of site variability with planned Minimize effect of site variability with planned revisits – specific plans will be illustratedrevisits – specific plans will be illustrated

Design tradeoffs: TREND Design tradeoffs: TREND vs vs STATUSSTATUS


REGIONAL TREND REGIONAL TREND SITE TREND SITE TREND

The predominant theme of ecology: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


REGIONAL TREND REGIONAL TREND SITE TREND SITE TREND( - CONTINUED)( - CONTINUED)

The predominant theme of ecologyThe predominant theme of ecologyversusversus

A Substantial (any) Agency Focus: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


REGIONAL TREND REGIONAL TREND SITE TREND SITE TREND( - CONTINUED - III)( - CONTINUED - III)

The predominant theme of ecologyThe predominant theme of ecologyversusversus

A substantial agency (EPA) focus: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 ServiceOr National Park Service All riparian areas in Olympic National Park All riparian areas in National Parks in the

coastal Northwest


TREND ACROSS TIME - What is it?

Any response which changes across time in a Any response which changes across time in a generally generally

Increasing or Decreasing

Manner shows trendManner shows trend Monotonic change is not essential.

If trend of this sort is present, If trend of this sort is present, it will be detectable as linear trend.it will be detectable as linear trend.

This does NOTNOT mean trend must be linear (examples follow)

Any specified form is detectable Time = years, here


TREND ACROSS TIME - What is it?(continued)

TREND = YES

50

70

90

1989 1991 1993 1995

Year

TREND = NO; PATTERN = YES

50

70

90

1989 1991 1993 1995 1997

Year

TREND = YES, PATTERN = YES

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1955 1965 1975 1985 1995 2005

YEAR

CA

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(p

pm

)


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1955 1965 1975 1985 1995 2005

YEAR

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1955 1965 1975 1985 1995

YEAR

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TREND DETECTION REQUIRES TREND DETECTION REQUIRES SUBSTANTIAL ELAPSED TIMESUBSTANTIAL ELAPSED TIME

IT IS NEARLY IMPOSSIBLE TO DETECT IT IS NEARLY IMPOSSIBLE TO DETECT TREND IN LESS THAN FIVE YEARS. WHY?TREND IN LESS THAN FIVE YEARS. WHY?

vart ti

( )( )

2

2

( )t ti 2


BIOLOGICAL INDICATORS HAVE SOMEWHAT BIOLOGICAL INDICATORS HAVE SOMEWHAT MORE VARIABILITY THAN PHYSICAL MORE VARIABILITY THAN PHYSICAL INDICATORS – BUT THIS VARIES, TOOINDICATORS – BUT THIS VARIES, TOO

Subsequent slides show the relative amount of Subsequent slides show the relative amount of variability variability

Ordered by the amount of residual variability: least to most (aquatic responses)

Acid Neutralizing CapacityAcid Neutralizing Capacity Ln(Conductance)Ln(Conductance) Ln(Chloride)Ln(Chloride) pH(Closed system)pH(Closed system) Secchi DepthSecchi 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 TemperatureMaximum Temperature

And others, both aquatic and terrestrial


POPULATION VARIANCE: POPULATION VARIANCE:

YEAR VARIANCE:YEAR VARIANCE:

RESIDUAL VARIANCE:RESIDUAL VARIANCE:

2( )SITE

2( )YEAR

IMPORTANT COMPONENTS OF VARIANCEIMPORTANT COMPONENTS OF VARIANCE

2( )RESIDUAL


POPULATION VARIANCE: 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

2( )SITE

IMPORTANT COMPONENTS OF VARIANCEIMPORTANT COMPONENTS OF VARIANCE( - CONTINUED)( - CONTINUED)


YEAR VARIANCE: YEAR VARIANCE:

Concordant variation among values of an indicator (response) across years for ALLALL 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)

2( )YEAR

IMPORTANT COMPONENTS OF VARIANCEIMPORTANT COMPONENTS OF VARIANCE( - CONTINUED II)( - CONTINUED II)


Residual component of varianceResidual 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 VARIANCEIMPORTANT COMPONENTS OF VARIANCE( - CONTINUED - III)( - CONTINUED - III)

2( )RESIDUAL


SOURCE OF DATA FOR ESTIMATES OF COMPONENTS OF VARIANCE

EMAP Surface Waters: EMAP Surface Waters: Northeast Lakes Pilot 1991 - 1994 Northeast Lakes Pilot 1991 - 1994

About 450 observationsAbout 450 observations Over four years Including about 350 distinct lakes Design allowed estimation of several residual

components


COMPOSITION OF TOTAL VARIANCE - NE LAKES

0.00 0.20 0.40 0.60 0.80 1.00

Maximum Temperature

Ln( # rotifer taxa)

Ln( # zooplankton taxa)

Ln(Chlorophyll A)

Ln(Total Phosphorus)

Ln(Total Nitrogen)

Secchi Depth

pH(Closed system)

Ln(Chloride)

Ln(Conductance)

Acid Neutralizing Capacity

PROPORTION OF VARIANCE

RESIDUAL COMPONENT OF VARIANCE

LAKE COMPONENT OF VARIANCE

YEAR


SOURCE OF COMPONENTS OF VARIANCE FROM NW HABITAT

Oregon Department of Fisheries and Wildlife – stream habitat surveyOregon Department of Fisheries and Wildlife – stream habitat surveyGRADIENT: Stream gradient measured on siteWIDTH: Wetted stream widthACW: Active Channel ACH: Active Channel HeightUNITS100: Number of distinct habitat units per 100 meters of stream length NOPOOLS: Number of pools in the surveyed reachPOOLS100: Number of pools per 100 metersPCTPOOL: % of reach length in poolsPCTFINES: % stream substrate that is sand or finer particle sizePCTGRAVEL: % of stream stubstrate that is gravel sized particlesRIFSNDOR: % of riffle stream length that is sand or finer particle sizeRIFGRAV: % of riffle stream length that is gravel sized particlesSHADE: % stream channel shadedLOG(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)


COMPOSITION OF TOTAL VARIANCE NW HABITAT

0.00 0.10

Gradient

Active Channel Width

UNITS100

Pools per 100m

% Fines

% Riffle fine

% Shaded

LOG(VOLUME L WD)

pH(Closed system)

Ln(Conductance)



LAKE COMPONENT OF VARIANCE

YEAR


SOURCE OF COMPONENTS OF VARIANCE FROM GRAND CANYON

Grand Canyon Monitoring and Research CenterGrand 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’ dataUsing first two years’ data Mike Kearsley – UNA

Design = spatially balancedDesign = spatially balanced With about 1/3 revisited


COMPOSITION OF TOTAL VARIANCEGRAND CANYON -- NEAR RIVER VEGETATION

0.00 0.20 0.40 0.60 0.80 1.00

Veg - 25K cfs

Veg - 35K cfs

Veg - 45K cfs

Veg - 60K cfs

Richness - 15K cfs

Richness - 25K cfs

Richness - 35K cfs

Richness - 45K cfs

Richness - 60K cfs



SITE COMPONENT OF VARIANCELAKE COMPONENT

YEAR


ALL VARIABILITY IS OF INTERESTALL VARIABILITY IS OF INTEREST

The site component of variance is one of the major The site component of variance is one of the major descriptors of the regional populationdescriptors of the regional population

The year component of variance often is small to The year component of variance often is small to small to estimate. It is a major enemy for detecting small to estimate. It is a major enemy for detecting trend over time.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.


ALL VARIABILITY IS OF INTERESTALL VARIABILITY IS OF INTEREST( - CONTINUED)( - CONTINUED)

Residual variance characterizes the inherent Residual variance characterizes the inherent variation in the response or indicator.variation in the response or indicator.

But some of its subcomponents may contain useful But some of its subcomponents may contain useful management informationmanagement information

CREW EFFECTS ===> training VISIT EFFECTS ===> need to reexamine definition of

index (time) window or evaluation protocol MEASUREMENT ERROR ===> work on

laboratory/measurement problems


DESIGN TRADE-OFFS: TREND DESIGN TRADE-OFFS: TREND vs vs STATUSSTATUS

How do we detect trend in spite of all of this How do we detect trend in spite of all of this variation?variation?

Recall two old statistical “friends.”Recall two old statistical “friends.” Variance of a mean, and Blocking


DESIGN TRADE-OFFS: TREND DESIGN TRADE-OFFS: TREND vs vs STATUSSTATUS( - CONTINUED)( - CONTINUED)

VARIANCE OF A MEAN: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 ---

var meanm

( ) 2


DESIGN TRADE-OFFS: TREND DESIGN TRADE-OFFS: TREND vs vs STATUSSTATUS( - CONTINUED - II)( - CONTINUED - II)

ConsequentlyConsequently

BecomesBecomes

Note that the regional averaging of slopes has the Note that the regional averaging of slopes has the same effect as continuing to monitor at one site for a same effect as continuing to monitor at one site for a much longer time period.much longer time period.

var meanm

( ) 2

var regional mean slopem t ti

( )( )

1 2

2


DESIGN TRADE-OFFS: TREND DESIGN TRADE-OFFS: TREND vs vs STATUSSTATUS( - CONTINUED - III)( - CONTINUED - III)

Now, Now, 22, in total, is large., in total, is large.

If we take one regional sample of sites at one time, If we take one regional sample of sites at one time, and another at a subsequent time, the site and another at a subsequent time, the site component of variance is included in component of variance is included in 22..

Enter the concept of blocking, familiar from Enter the concept of blocking, familiar from experimental design.experimental design.

Regard a site like a block Periodically revisit a site The site component of variance vanishes from the

variance of a slope.


NOW PUT IT ALL TOGETHER

Question: “ What kind of temporal design should Question: “ What kind of temporal design should you use for Northwest National Parks?you use for Northwest National Parks?

We’ll investigate two (families) of recommended We’ll investigate two (families) of recommended designs.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.We’ll use the panel notation Trent set out.


RECOMMENDATION OF FULLER and BREIDT

Based on the Natural Resources Inventory (NRI)Based on the Natural Resources Inventory (NRI) Iowa State & US Department of Agriculture

Oriented toward soil erosion & Changes in land use

Their recommendationTheir recommendation Pure panel = [1-0] = “Always Revisit” Independent = [1-n] = “Never Revisit”

Evaluation contextEvaluation context No trampling effect – remotely sensed data No year effects

Administrative reality of potential variation in Administrative reality of potential variation in funding from year to yearfunding from year to year

MATH RECOME… 100% 50% 0% 50%


TEMPORAL LAYOUT OF [(1-0), (1-n)]YEARYEAR 11 22 33 44 55 66 77 88 99 1010 1111 1212 1313 1414 1515 1616 1717 1818 1919 2020

[1-0][1-0] XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX

[1-n][1-n] XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX

XX


FIRST TEMPORAL DESIGN FAMILY

30 site visits per year30 site visits per year

[1-0][1-0] 3030 2020 1010 00

[1-n][1-n] 00 1010 2020 3030

ALWAYSALWAYS

REVISITREVISIT

NEVERNEVER

REVISITREVISIT


POWER TO DETECT TREND

FIRST TEMPORAL DESIGN FAMILY NO YEAR EFFECT

0

0.2

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YEARS

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30:020:1010:200:30

Always Revisit

Never Revisit



FIRST TEMPORAL DESIGN FAMILY, MODEST (= SOME) YEAR EFFECT

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YEARS

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FIRST TEMPORAL DESIGN FAMILYBIG (= LOTS) YEAR EFFECT

0

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YEARS

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30:020:1010:200:30


FOREST INVENTORY ANALYSIS (FIA) HAS A SYSTEMATIC SPATIAL DESIGN

WITH [1-9]

Doesn’t match up well with [1-0] and [1-n]Doesn’t match up well with [1-0] and [1-n] We need to investigate alternativesWe need to investigate alternatives

YEARYEAR 11 22 33 44 55 66 77 88 99 1010 1111 1212 1313 1414 1515 1616 1717 1818 1919 2020 2121

FIAFIA XX XX XX


SERIALLY ALTERNATING TEMPORAL DESIGN [(1-3)4 ] SOMETIMES USED BY

EMAP

YEARYEAR 11 22 33 44 55 66 77 88 99 1010 1111 1212 1313 1414 1515 1616 1717 1818 1919 2020 2121

FIAFIA XX XX XX

[(1-3)[(1-3)4 4 ]] XX XX XX XX XX XX

XX XX XX XX XX

XX XX XX XX XX

XX XX XX XX XX


SERIALLY ALTERNATING TEMPORAL DESIGN [(1-3)4 ] SOMETIMES USED BY

EMAP

YEARYEAR 11 22 33 44 55 66 77 88 99 1010 1111 ……

FIAFIA XX XX

[(1-3)[(1-3)4 4 ]] XX XX XX ……

XX XX XX ……

XX XX XX ……

XX XX ……

Unconnected in an experimental design senseUnconnected in an experimental design sense Very weak design for estimating year effects, if present


SPLIT PANEL [(1-4)5 , --- ]

YEARYEAR 11 22 33 44 55 66 77 88 99 1010 1111 1212 1313 1414 1515 1616 1717 1818 1919 2020 2121

FIAFIA XX XX XX

[(1-4)[(1-4)5 5 ]] XX XX XX XX XX

XX XX XX XX

XX XX XX XX

XX XX XX XX

XX XX XX XX

AGAIN, Unconnected in an experimental design AGAIN, Unconnected in an experimental design sensesense Matches better with FIA Still a very weak design for estimating year effects, if

present


SPLIT PANEL [(1-4)5 ,(2-3)5 ]

This Temporal Design IS connectedThis Temporal Design IS connectedHas three panels which match up with FIAHas three panels which match up with FIA

YEARYEAR 11 22 33 44 55 66 77 88 99 1010 1111 1212 1313 1414 1515 1616 1717 1818 1919 2020 2121

FIAFIA XX XX XX

[(1-4)[(1-4)5 5 ]] XX XX XX XX XX

XX XX XX XX

XX XX XX XX

XX XX XX XX

XX XX XX XX

[(2-3)[(2-3)5 5 ]] XX XX XX XX XX XX XX XX XX

XX XX XX XX XX XX XX XX





SECOND TEMPORAL DESIGN FAMILY

30 site visits per year30 site visits per year

[1-4][1-4] 3030 2020 1010 00

[2-3][2-3] 00 55 1010 1515



SECOND TEMPORAL DESIGN FAMILY NO YEAR EFFECT

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SECOND TEMPORAL DESIGN FAMILYSOME YEAR EFFECT

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SECOND TEMPORAL DESIGN FAMILYLOTS OF YEAR EFFECT

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COMPARISON OF POWER TO DETECT TRENDDESIGN 1 & 2 = ROWS

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YEAR EFFECT

NONE SOME LOTS



VARYING YEAR EFFECT AND TEMPORAL DESIGN

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TEMPORAL DESIGN 1NONE

SOME

LOTS


STANDARD ERROR OF STATUS

TEMPORAL DESIGN 1, NO YEAR EFFECT

0

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YEARS

SE

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30:0 20:10 10:20 0:30

TOTAL OF 30 SITES

110 SITES VISITED BY

YEAR 5 410 SITES VISITED BY

YEAR 20



TEMPORAL DESIGN 1, SOME YEAR EFFECT

0

0.1

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YEARS

SE

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30:0 20:10 10:20 0:30



TEMPORAL DESIGN 1, LOTS OF YEAR EFFECT

0

0.1

0.2

0.3

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0 5 10 15 20

YEARS

SE

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30:0 20:10 10:20 0:30



TEMPORAL DESIGN 2, NO YEAR EFFECT

0

0.1

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YEARS

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30:020:5

10:10 0:15

TOTAL OF 150 SITES

TOTAL OF 75 SITES



TEMPORAL DESIGN 2, SOME YEAR EFFECT

0

0.1

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0 5 10 15 20

YEARS

SE

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30:020:5

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TEMPORAL DESIGN 2, LOTS OF YEAR EFFECT

0

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YEARS

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30:020:5

10:10 0:15


SO WHAT?

Regardless of evaluation circumstances,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 Temporal design 2 is better than temporal design 1 in relevant casesrelevant cases Its power is only slightly influenced by split between panels


METADATA

Really important for your successorsReally important for your successors Like your grandchildrens’ generation

I’ll comment about this later in the conference if you I’ll comment about this later in the conference if you want me towant me to


This research is funded by

U.S.EPA – Science To AchieveResults (STAR) ProgramCooperativeAgreement

# 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


TEMPORAL DESIGN 1ALWAYS REVISIT

TIME PERIOD ( ex: YEARS)PANEL 1 2 3 4 5 6 7 8 9 10 11 12 13 ...

1 X X X X X X X X X X X X X


TEMPORAL DESIGN 2:

NEVER REVISIT

TIME PERIOD ( ex: YEARS)PANEL 1 2 3 4 5 6 7 8 9 10 11 12 13 ... 1 X 2 X 3 X 4 X 5 X 6 X 7 X 8 X 9 X


TEMPORAL DESIGN 3:AUGMENTED SERIALLY ALTERNATING

TIME PERIOD ( ex: YEARS)PANEL 1 2 3 4 5 6 7 8 9 10 11 12 13 ... 0 X X X X X X X X X X X X X 1 X X X X 2 X X X 3 X X X 4 X X X


TEMPORAL DESIGN 4: SPLIT PANEL

SERIALLY ALTERNATINGPLUS SERIALLY ALTERNATING WITH CONSECUTIVE YEAR REVISITS

TIME PERIOD ( ex: YEARS)PANEL 1 2 3 4 5 6 7 8 9 10 11 12 13 ... 1 X X X X 1A X X X X X X X 2 X X X 2A X X X X X X 3 X X X 3A X X X X X X 4 X X X 4A X X X X X X


DESIGN EFFECT

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DESIGNS 1, 3, & 4

DESIGN 2

A


LAKE EFFECT; DESIGNS 2 & 4VAR LAKE = 1, 2, 5

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DESIGN 4ALL VARIANCES

DESIGN 2LAKE VAR = 5

1

2

B


YEAR EFFECT - DESIGNS 2 & 4

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DESIGN 4

YEAR EFFECT0, 0.05, 0.10

TOP CURVES FOR 0.00

C


STANDARD ERROR OF ESTIMATED STATUS -ALL DESIGNS

0

0.2

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TIME ( =Years )

ST

AN

DA

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ER

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DESIGN 1

DESIGN 2

DESIGNS 3 & 4

D


SIZE OF TREND EFFECT: DESIGN 4

0

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TIME ( = YEARS )

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D = 0.03 = 0.02

= 0.015

= 0.01

E


SAMPLE SIZE EFFECT - DESIGN 4

0

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n = 240

n = 120

n = 60

F


SECCHI DEPTH

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1% PER YEAR3% PER YEAR


ln ( CHLOROPHYLL a )

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1% PER YEAR

3% PER YEAR


ln(TOTAL PHOSPHORUS)

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PO

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1% PER YEAR

3% PER YEAR


ln( NUMBER ZOOPLANKTON TAXA)

0

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TIME ( = YEARS )

PO

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R f

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EN

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1% PER YEAR

3% PER YEAR

nps temporal conference # 1. nps temporal conference # 2 designing panel surveys specifically...

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