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Strip Adaptive Cluster Sampling with Application to Lemhi Penstemon and Cave Crickets

National Park Service Inventory and Monitoring Program. Cumberland Piedmont Network. Strip Adaptive Cluster Sampling with Application to Lemhi Penstemon and Cave Crickets Kurt Helf , CUPN Ecologist Tom Rodhouse, UCBN Ecologist. National Park Service Inventory and Monitoring Program.

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Strip Adaptive Cluster Sampling with Application to Lemhi Penstemon and Cave Crickets

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  1. National Park Service Inventory and Monitoring Program Cumberland Piedmont Network Strip Adaptive Cluster Sampling with Application to Lemhi Penstemon and Cave Crickets Kurt Helf, CUPN Ecologist Tom Rodhouse, UCBN Ecologist

  2. National Park Service Inventory 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

  3. Adaptive Cluster Sampling Initial Sample If Condition Met… Adaptive Sample Clusters Networks Probability of sampling a given network k is proportional to its size

  4. 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

  5. National Park Service Inventory and Monitoring Program Modified Horvitz-Thompson Estimator Total count per network “Partial” inclusion probability

  6. National Park Service Inventory and Monitoring Program Case Study: Population Size of Lemhi Penstemon in Big Hole National Battlefield

  7. National Park Service Inventory and Monitoring Program • 2007 census • 2009 GRTS sample n = 150 2009 GRTS: 1580 plants + 783

  8. National Park Service Inventory and Monitoring Program Strip ACS 2010 GRTS sample 2010 strip ACS sample n = 150 each • 2 m X 50 m primary unit • 1 m2 secondary units 50 m • 2nd order neighborhood

  9. Field Methods • Pin flags, reel tape, and 2m folding rulers • Digital data entry via Pendragon on PDA

  10. National Park Service Inventory and Monitoring Program Analysis & Results Coded by hand in R – but see Dryver’s package, Philippi’s script k = 46 Width < 3 strips (6 m)

  11. National Park Service Inventory and Monitoring Program Case Study: Cave Cricket Monitoring in Mammoth Cave National Park

  12. National Park Service Inventory and Monitoring Program • 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

  13. National Park Service Inventory and Monitoring Program 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 • Biased toward largest clusters of roosting cave crickets (though not always). • Biased low when clusters highly dispersed. • Time & labor intensive field methods.

  14. National Park Service Inventory and Monitoring Program 10 cm • 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

  15. National Park Service Inventory and Monitoring Program

  16. National Park Service Inventory and Monitoring Program Adapted from bat counting protocol obtained from Traci Hemberger (KYDFW); origin of technique unknown but apparently discovered independently multiple times. Analyzed in R using Philippi’s script • 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. http://www.featureanalyst.com/feature_analyst/publications/success/bats_final.pdf

  17. National Park Service Inventory 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.

  18. National Park Service Inventory and Monitoring Program Resources Come 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!

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