# 1 STATISTICAL ASPECTS OF COLLECTIONS OF BEES TO STUDY PESTICIDES N. SCOTT URQUHART SENIOR RESEARCH SCIENTIST DEPARTMENT OF STATISTICS COLORADO STATE UNIVERSITY EMAP Affiliate SPACE-TIME AQUATIC RESOURCE MODELING and ANALYSIS PROGRAM (STARMAP)

# 2 STARMAP FUNDING Space-Time Aquatic Resources Modeling and Analysis Program The work reported here today was developed under the STAR Research Assistance Agreement CR 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 Achieve Results (STAR) Program Cooperative Agreement # CR

# 3 PATH 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

# 4 IMPORTANT THINGS TO CONSIDER IN DESIGNING 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

# 5 1. PROBABILITY SURVEYS versus JUDGMENT COLLECTIONS  Specialists Usually Know a Tremendous Amount About Limited Specific Situations  This is the way science accumulates knowledge.  But frequently specialists know a lot less about the overall situation than they think they do!  An illustration follows Selection of stream segments for spawning studies by Oregon Department Fisheries and Wildlife

# 6 SELECTION OF STREAM SEGMENTS FOR SPAWNING STUDIES (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 habitat  Low was not sampled; high was sampled at three times the rate of moderate  Quality of spawning habitat was evaluated for each selected segment

# 7 SELECTION OF STREAM SEGMENTS FOR SPAWNING STUDIES (OREGON DEPARTMENT FISHERIES AND WILDLIFE) (continued)

# 8 SELECTION OF STREAM SEGMENTS FOR SPAWNING STUDIES (OREGON DEPARTMENT FISHERIES AND WILDLIFE) 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.

# 9 DEFINITION 2. POPULATION 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  Species All Two species of primary interest

# 10 DEFINITION continued POPULATION DEFINITION continued  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,  Where you go to collect bees does make a difference!

# 11 CONCLUSIONS ABOUT 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 Nothing  Typicalness for One Set of Responses Says Nothing About Typicalness for Any Other Response, i.e. Any Response Not Used in Determining Typicalness

# EVALUATION UNITS – HIVES (COLONIES) OR BEES?  So what?  If Hives (or colonies) Are Your Evaluation Units, You Must  Select hives in the sampling process  Have a response which can be attached to a selected hive  Give final answers in terms hives Ex: Proportion of hives (colonies) with yy > xx

# SAMPLING FRAMES  A Sample Frame Provides a Means to Identify or Locate the Individual Units in the Population  May 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

# 14 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.....

# 15 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 Stratified VERY promising approach – provides easy and defensible way to accommodate access denials, etc

# 16 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 represents  HUC:  NRI:  NASS:  NLCD:  GRTS: epa_program/docs/ spatial_balance_imperfect_frame.pdf

# 17 A PLAUSIBLE SPATIAL SAMPLING FRAME Hydrologic 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

# SELECTING THE SAMPLE SITES  There are Lots of Ways to Select Collection Sites – Depending On  Objectives  Sampling Frame  Units chosen (hives or bees)  Possible stratification factors

# 19 SELECTING THE SAMPLE SITES continued  One Which Has Come Out of the EMAP Experience:  Generalized Randomized Tessellation Stratified (GRTS) Sampling It 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

# TRAINING  Data Cannot Be Combined Across Areas, etc Unless It is Comparable Across Those Same Features  IMPLICATION: Good Training is Critical to Assure Consistent Procedures  Various plausible contingencies must be identified in advance, and  Plans made for how they should be dealt with

# COLLECTING THE BEES  Make Sure Field Crews Follow the Collection Protocols  Be 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

# HANDLING THE COLLECTED BEES  Ship the Collected Material to the Appropriate Labs, According to Specified Protocols  Need ice?  Consider crew logistics, like housing, transportation, permits, location of shipping point, availability of shipping point by day of the week  Plan for custody of the collected material

# QUALITY ASSURANCE  EPA has Stringent Quality Assurance (QA) Processes  Approval of a QA plan may take several months Plan for that  Implication: Indicator(s) needs to be chosen early in the process

# DATA MANAGEMENT  This Will Be a Much Larger Effort Than You May Expect  This 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!

# 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

# 26 STUDY CONTEXT FOR ILLUSTRATION OF CDFs

# 27 ESTIMATED CUMULATIVE DISTRIBUTION FUNCTION (cdf) OF SECCHI DEPTH, EMAP AND “DIP-IN”  Use cdfs – tails often are of interest  Confidence bounds  Misinformation from convenience data

# 28 END OF PREPARED TALK  QUESTIONS ARE WELCOME

# 29 BACK

# 30 HYDROLOGIC UNITS BACK