Strip Adaptive Cluster Sampling with Application to Lemhi

Penstemon and Cave CricketsKurt Helf, CUPN Ecologist

Tom Rodhouse, UCBN Ecologist

National Park ServiceInventory and Monitoring Program

Cumberland Piedmont Network

National Park ServiceInventory and Monitoring Program

Adaptive Cluster Sampling

• A strategy for sampling rare populations

• Responds to conditions on the ground to “adapt” the sample

• Sampling intensity increases around clusters of population members

• Can provide more precise population estimates

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

Adaptive Cluster Sampling

Initial Sample

Adaptive Sample

Clusters

Networks

If Condition Met…

Probability of sampling a given network k is proportional to its size

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

                           

Strip Adaptive Cluster Sampling

Initial sample of primary units

“strips”

Adapt on secondary units

K Networks

Probability of sampling a given network k is proportional to its width

National Park ServiceInventory and Monitoring Program

K

k

kyMN 1

1ˆ

MN

K

k jk

kjK

j kj

jk yyNM 1 1

22 11)ˆvar(

Modified Horvitz-Thompson EstimatorTotal count

per network

“Partial” inclusion

probability

National Park ServiceInventory and Monitoring Program

Case Study: Population Size of Lemhi Penstemon in Big Hole National Battlefield

National Park ServiceInventory and Monitoring Program

• 2007 census 2009 GRTS sample

2009 GRTS: 1580 plants + 783

n = 150

National Park ServiceInventory and Monitoring Program

2010 GRTS sample 2010 strip ACS sample

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

• 2 m X 50 m primary unit

• 1 m2 secondary units

               

• 2nd order neighborhood

Strip ACS

50 m

n = 150 each

• Digital data entry via Pendragon on PDA

• Pin flags, reel tape, and 2m folding rulers

Field Methods

National Park ServiceInventory and Monitoring Program

Analysis & Results

Coded by hand in R – but see Dryver’s package, Philippi’s script

0

500

1000

1500

2000

2500

3000

3500

Stri

p A

CS

SR

S

GR

TS

Com

bine

d

2010 P. lemhiensis Estimates

Tota

ls a

nd 9

5% C

onfid

ence

Inte

rval

s

= 1618

n=150Combined n=300

k = 46

Width < 3 strips (6 m)

Case Study: Cave Cricket Monitoring in Mammoth Cave National Park

National Park ServiceInventory and Monitoring Program

National Park ServiceInventory and Monitoring Program

= Region 1 plots

= Region 2 plots

= transect pairs

= Compass bearing to plot

= Region Landmark

= Region Baseline

Plot & Transect Sampling in Cave Regions

• Determine temporal changes in population structure (e.g., age class) and relative abundance of cave crickets in managed and unmanaged caves across MACA.

• Detect and assess potential effects of active management decisions, e.g., alteration of cave entrances, lighting regimes, visitor load, etc., on cave cricket ecology within managed caves.

Subjective Photoplot & Transect Monitoring

National Park ServiceInventory and Monitoring Program

• Biased toward largest clusters of roosting cave crickets (though not

always).

• Biased low when clusters highly dispersed.

• Time & labor intensive field methods.

Mean Cluster Size (February)

Region 1: 6.47 + 5.63

Region 2: 9.26 + 5.74

Mean Cluster Size (August)

Region 1: 25.1 + 22.6

Region 2: 9.83 + 8.47

National Park ServiceInventory and Monitoring Program

•Laser transect projector platform & pistol•100m Keson tape to locate random transects•Electronic Distance Measuring Unit for mapping•Rite in the rain field data sheets; sticky notes•Jernigan

10 cm

National Park ServiceInventory and Monitoring Program

Analyzed in R using Philippi’s script

National Park ServiceInventory and Monitoring Program

• Combines best methods of previous protocol.

• Just as data rich.

• Unbiased estimators.

• Estimates of entrance populations!

• Time spent in field similar.

• Less expense since fewer personnel required.

Cave Entrance Obs. Count Est. Total Var Est. Total SD Est. Total Lower CI Upper CICarmichael 39 170.60 4531.12 67.31 59.87 281.33CrockPot 35 95.05 1040.68 32.26 41.99 148.12Frozen Niagara 24 115.06 3420.64 58.49 18.85 211.27Great Onyx 251 645.30 111559.29 334.00 95.86 1194.74Little Beauty 35 104.63 1727.12 41.56 36.27 172.99New Discovery 425 1641.55 295927.80 543.99 746.68 2536.42Salts 52 247.35 10611.74 103.01 77.89 416.80Silent Grove  83 259.59 5678.79 75.36 135.63 383.55Sloan's Crossing 51 161.02 5394.27 73.45 40.20 281.84Temple Hil l 165 477.50 18131.05 134.65 256.00 699.00White 92 243.87 4186.48 64.70 137.43 350.31

Adapted from bat counting protocol obtained from Traci Hemberger (KYDFW); origin of technique unknown but apparently discovered independently multiple times.

http://www.featureanalyst.com/feature_analyst/publications/success/bats_final.pdf

National Park ServiceInventory and Monitoring Program

Conclusions• It works!

But….• Efficiency is sensitive to the size and variability of networks• Penstemon – small networks, time not a big constraint, but

can cover more ground with a large GRTS sample of primary units.

• Other important information – patch size• Unfortunately, not clear how to analyze

trend, or to adapt GRTS initial sample• Hadenoecus– random v. fixed transects; park wide inference

poss. with present sample size & legacy sampling units?; pool of available sampling units constrained by methodology.

National Park ServiceInventory and Monitoring Program

ResourcesCome See Us at the Swap meet!

Check out Steven Thompson’s books

Paul Geissler’s sampling design course-presentations and bibliography from Dave Smith

Talk to HTLN about bladderpod experience

Talk to Tom Philippi!