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STATISTICAL ASPECTS OF

COLLECTIONS OF BEES TO

STUDY PESTICIDES

STATISTICAL ASPECTS OF

COLLECTIONS OF BEES TO

STUDY PESTICIDES

N. SCOTT URQUHARTSENIOR RESEARCH SCIENTIST

DEPARTMENT OF STATISTICS

COLORADO STATE UNIVERSITY

EMAP Affiliate

SPACE-TIME AQUATIC RESOURCEMODELING and ANALYSIS PROGRAM

(STARMAP)

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STARMAP FUNDINGSTARMAP FUNDINGSpace-Time Aquatic Resources Modeling and Analysis ProgramSpace-Time Aquatic Resources Modeling and Analysis Program

STARMAP FUNDINGSTARMAP FUNDINGSpace-Time Aquatic Resources Modeling and Analysis ProgramSpace-Time Aquatic Resources Modeling and Analysis Program

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 these presentation.

This research is funded by

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

# CR - 829095

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PATH for TODAYPATH for TODAY

CONTEXT: Environmental Monitoring and Assessment Program (EMAP) + Academic

TOPICS TO CONSIDER:What to Measure = Indicators

Other speakers will address this

Important things to consider in designing a survey

PLAN! , PLAN! , PLAN!

A National or Regional Survey is a Substantial Undertaking

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IMPORTANT THINGS TO CONSIDER IN IMPORTANT THINGS TO CONSIDER IN DESIGNING A SURVEYDESIGNING A SURVEY

IMPORTANT THINGS TO CONSIDER IN IMPORTANT THINGS TO CONSIDER IN DESIGNING A SURVEYDESIGNING A SURVEY

1. Probability Surveys vs Judgment Collections

2. Population Definition

3. Evaluation Units – hives (colonies) or bees

4. Sampling Frames

5. Selecting the Sample Sites

6. Training

7. Collecting the Bees

8. Handling the Collected Bees

9. Quality Assurance

10. Data Management

11. Data Analysis

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1. PROBABILITY SURVEYS versus

JUDGMENT COLLECTIONS

1. PROBABILITY SURVEYS versus

JUDGMENT COLLECTIONS

Specialists Usually Know a Tremendous

Amount About Limited Specific SituationsThis is the way science accumulates knowledge.

But frequently specialists know a lot less about

the overall situation than they think they

do!

An illustration followsSelection of stream segments for spawning studies by

Oregon Department Fisheries and Wildlife

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SELECTION OF STREAM SEGMENTS FOR SELECTION OF STREAM SEGMENTS FOR

SPAWNING STUDIESSPAWNING STUDIES (OREGON DEPARTMENT FISHERIES AND WILDLIFE)(OREGON DEPARTMENT FISHERIES AND WILDLIFE)

SELECTION OF STREAM SEGMENTS FOR SELECTION OF STREAM SEGMENTS FOR

SPAWNING STUDIESSPAWNING STUDIES (OREGON DEPARTMENT FISHERIES AND WILDLIFE)(OREGON DEPARTMENT FISHERIES AND WILDLIFE)

OBJECTIVE: Estimate Number Of Coho Salmon Spawning in Streams of Oregon’s Coast Range

Stream Segments Were Stratified As Being“Low,” “Moderate,” Or “High”, relative to

quality of spawning habitatLow was not sampled; high was sampled at

three times the rate of moderateQuality of spawning habitat was evaluated for

each selected segment

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OBSERVED QUALITYANTICIPATEDQUALITY

LOW MEDIUM HIGH

LOWNOT

SAMPLEDNOT

SAMPLEDNOT

SAMPLED

MEDIUM 73% 17% 10%

HIGH 53% 23% 24%

SELECTION OF STREAM SEGMENTS FOR SELECTION OF STREAM SEGMENTS FOR SPAWNING STUDIES SPAWNING STUDIES

(OREGON DEPARTMENT FISHERIES AND WILDLIFE)(OREGON DEPARTMENT FISHERIES AND WILDLIFE)(continued)(continued)

SELECTION OF STREAM SEGMENTS FOR SELECTION OF STREAM SEGMENTS FOR SPAWNING STUDIES SPAWNING STUDIES

(OREGON DEPARTMENT FISHERIES AND WILDLIFE)(OREGON DEPARTMENT FISHERIES AND WILDLIFE)(continued)(continued)

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SELECTION OF STREAM SEGMENTS FOR SELECTION OF STREAM SEGMENTS FOR SPAWNING STUDIESSPAWNING STUDIES

(OREGON DEPARTMENT FISHERIES AND WILDLIFE)(OREGON DEPARTMENT FISHERIES AND WILDLIFE) continued continued

SELECTION OF STREAM SEGMENTS FOR SELECTION OF STREAM SEGMENTS FOR SPAWNING STUDIESSPAWNING STUDIES

(OREGON DEPARTMENT FISHERIES AND WILDLIFE)(OREGON DEPARTMENT FISHERIES AND WILDLIFE) continued continued

EXAMPLE of “Sampling Where

Investigators Think Most of the Large

Responses Are.”Bad idea if “knowledge” isn’t quite right

Even 10% error rate can make this a very inefficient

sampling approach

ODF&W Classification Was Off LOTS

Further Than 10%.Many other such examples exist.

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2. POPULATION DEFINITION DEFINITION2. POPULATION DEFINITION DEFINITION

A Population is the Set of Objects of

Interest in a Survey Commercial hives

Of cooperating beekeepers

All hives

All hives within 500m of a secondary road

SpeciesAll

Two species of primary interest

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POPULATION DEFINITION DEFINITIONcontinuedcontinued

POPULATION DEFINITION DEFINITIONcontinuedcontinued

So What?!Major distinction

Target population = what you want to talk about

Sampled population = what you can talk about

You probably don’t want to talk about this

sort of population:All commercial hives owned by cooperating

beekeepers within 100 miles of an EPA Regional

Office, and within 500m of a paved secondary road

in June, 2005.

Where you go to collect bees does make a

difference!

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CONCLUSIONS CONCLUSIONS ABOUT JUDGMENT SELECTED SITESABOUT JUDGMENT SELECTED SITES

CONCLUSIONS CONCLUSIONS ABOUT JUDGMENT SELECTED SITESABOUT JUDGMENT SELECTED SITES

Ecologists’ “Typical Sites” Probably Are

Much More Homogeneous Than the Larger

Context of Interest

Nonprobability Samples Can Be Rather

Biased for No Apparent Reason

Typicalness for One Set of Responses Says

NothingNothing About Typicalness for Any Other

Response, i.e. Any Response Not Used in

Determining Typicalness

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3. EVALUATION UNITS – HIVES (COLONIES) OR BEES?3. EVALUATION UNITS – HIVES (COLONIES) OR BEES?

So what?

If Hives (or colonies) Are Your

Evaluation Units, You MustSelect hives in the sampling process

Have a response which can be attached to

a selected hive

Give final answers in terms hivesEx: Proportion of hives (colonies) with yy > xx

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4. SAMPLING FRAMES4. SAMPLING FRAMES

A Sample Frame Provides a Means to

Identify or Locate the Individual Units

in the PopulationMay be a list

The basis for most of the older sampling theory

Often is imperfect! Sometimes, badly so!

Many living things must be selected by

their location

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PLAUSIBLE SPATIAL SAMPLING FRAMES(Courtesy of Tony Olsen, EMAP, US EPA)

PLAUSIBLE SPATIAL SAMPLING FRAMES(Courtesy of Tony Olsen, EMAP, US EPA)

Use 6th Field HUCs as Spatial Units.  Select sample of HUCs incorporation landcover/use into

probability of selection.  Then find beekeepers within HUC.  Sample locations where hives are set up.

Same as Above, Except Ignore Beekeepers.  Go out an trap any bees at selected points within HUC -

possibly use landcover again within HUC as selection probability.

Use NRI Sample Points as Frame and Subsample Them. Use NASS Spatial Frame Sample Points and Subsample

Them. Use NLCD (8million pixels). 

Select GRTS sample of pixels based on landcover class.  Either trap bees or use that the identify if bee hives are present (in some way).  Have to do oversample if expect most pixels to not have hives.....

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PLAUSIBLE SPATIAL SAMPLING FRAMES(Courtesy of Tony Olsen, EMAP, US EPA)

PLAUSIBLE SPATIAL SAMPLING FRAMES(Courtesy of Tony Olsen, EMAP, US EPA)

JARGON!!! - means what?HUC = Hydrologic Unit Code

NRI = National Resources Inventory – oriented toward soil erosion (Iowa State U)

NASS = National Agricultural Statistical Survey

NLCD = National Land Cover Data

GRTS = Generalized Randomized Tessellation StratifiedVERY promising approach – provides easy and

defensible way to accommodate access denials, etc

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PLAUSIBLE SPATIAL SAMPLING FRAMES(Courtesy of Tony Olsen, EMAP, US EPA)

Where to Find Info

PLAUSIBLE SPATIAL SAMPLING FRAMES(Courtesy of Tony Olsen, EMAP, US EPA)

Where to Find Info

JARGON!!! - where to find out more about

the content the jargon representsHUC: http://water.usgs.gov/GIS/huc.html

NRI: http://www.nrcs.usda.gov/technical/NRI/

NASS: http://www.usda.gov/nass/

NLCD: http://www.epa.gov/mrlc/nlcd.html

GRTS: http:oregonstate.edu/dept/statistics

epa_program/docs/

spatial_balance_imperfect_frame.pdf

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A PLAUSIBLE SPATIAL SAMPLING FRAMEHydrologic Units

A PLAUSIBLE SPATIAL SAMPLING FRAMEHydrologic Units

Level 1 – “Two digit” 21 major geographic areas, or regions

Level 2 – “Four Digit” divides the 21 regions into 222

subregions

Level 3 – “Six Digit” 352 hydrologic accounting units

Level 4 – “Eight Digit” There are 2150 Cataloging Units in the

Nation

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5. SELECTING THE SAMPLE SITES5. SELECTING THE SAMPLE SITES

There are Lots of Ways to Select

Collection Sites – Depending OnObjectives

Sampling Frame

Units chosen (hives or bees)

Possible stratification factors

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SELECTING THE SAMPLE SITEScontinued

SELECTING THE SAMPLE SITEScontinued

One Which Has Come Out of the EMAP Experience:Generalized Randomized Tessellation

Stratified (GRTS) SamplingIt allows

Spatially distributed sites Variable sampling rates – depending factors of interest A well-defined means for adding sites to accommodate

problems like access denial

Implemented in several computational contexts Using GIS, or Statistical software

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6. TRAINING6. TRAINING

Data Cannot Be Combined Across Areas,

etc Unless It is Comparable Across Those

Same Features

IMPLICATION: Good Training is Critical to

Assure Consistent ProceduresVarious plausible contingencies must be

identified in advance, and

Plans made for how they should be dealt with

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7. COLLECTING THE BEES7. COLLECTING THE BEES

Make Sure Field Crews Follow the

Collection ProtocolsBe sure collection times don’t collide with

fair labor laws

Does a federal employee need to be a

member of each field crew?For safety purposes, crews may need to have at

least two members

Collect the Bees As Planned

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8. HANDLING THE COLLECTED BEES8. HANDLING THE COLLECTED BEES

Ship the Collected Material to the

Appropriate Labs, According to

Specified ProtocolsNeed ice?

Consider crew logistics, likehousing, transportation, permits, location of

shipping point, availability of shipping point by

day of the week

Plan for custody of the collected material

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9. QUALITY ASSURANCE9. QUALITY ASSURANCE

EPA has Stringent Quality Assurance

(QA) ProcessesApproval of a QA plan may take several

monthsPlan for that

Implication: Indicator(s) needs to be

chosen early in the process

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10. DATA MANAGEMENT10. DATA MANAGEMENT

This Will Be a Much Larger Effort Than

You May ExpectThis has a QA component, too

20 – 30% of resources! Not 5%!

The collected information becomes part of

the public record. You need to plan to make it available to various

interested parties!

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11. DATA ANALYSIS11. DATA ANALYSIS

Plan Intended Summaries from the

Beginning

Record and keep track of all of the

design information,Like the rate at which various kinds of sites

were selected

Consider making estimated cumulative

distribution functions (cdf) a major part of

the survey summary

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STUDY CONTEXTSTUDY CONTEXT FOR FOR

ILLUSTRATION ILLUSTRATION OF CDFsOF CDFs

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ESTIMATED CUMULATIVE DISTRIBUTION FUNCTION ESTIMATED CUMULATIVE DISTRIBUTION FUNCTION (cdf) OF SECCHI DEPTH, EMAP AND “DIP-IN”(cdf) OF SECCHI DEPTH, EMAP AND “DIP-IN”

ESTIMATED CUMULATIVE DISTRIBUTION FUNCTION ESTIMATED CUMULATIVE DISTRIBUTION FUNCTION (cdf) OF SECCHI DEPTH, EMAP AND “DIP-IN”(cdf) OF SECCHI DEPTH, EMAP AND “DIP-IN”

Use cdfs – tails often are of interest

Confidence bounds

Misinformation from convenience data

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END OF PREPARED TALKEND OF PREPARED TALKEND OF PREPARED TALKEND OF PREPARED TALK

QUESTIONS ARE WELCOME

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HYDROLOGIC UNITSHYDROLOGIC UNITSHYDROLOGIC UNITSHYDROLOGIC UNITS

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