1 / 28

Paige Barlow WILD 8390 Spring 2010

Evaluating the effects of avian point count study designs on ranking sites by species richness through multi-species, single-season occupancy models. Paige Barlow WILD 8390 Spring 2010. Research problem. Manage 14 sites

EllenMixel
Download Presentation

Paige Barlow WILD 8390 Spring 2010

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Evaluating the effects of avian point count study designson ranking sites by species richness through multi-species, single-season occupancy models Paige BarlowWILD 8390Spring 2010

  2. Research problem • Manage 14 sites • For avian biodiversity: sites with more species receive more protection • Sampling design  encounter history  species richness occupancy model  rank sites according to species richness • Optimal sampling design • Rank sites accurately • Minimize sampling effort

  3. Objectives • Fundamental objective = conserve avian biodiversity on the 14 sites • Means objective • Sample 14 sites • Estimate species richness • Rank 14 sites according to species richness

  4. Context • State variables • Species richness = fundamental • Occupancy = means • Framework for simulation • Avian point counts from 2005 • 14 sites in remnant forest in Seattle, WA • Matrix of development • High rate of urbanization

  5. General methods • 17 species detected in 14 sites from 2005 Seattle data  Single-season, single-species occupancy models in MARK  Occupancy and detection probability estimates for each species • Simulation model in program R  14 sites with different species compositions and richness  multiple sampling designs to generate detection histories • Species richness occupancy model in WinBUGS  estimate species richness from detection histories • Sampling design evaluation  rank sites according to true richness and estimated richness 

  6. Optimal sampling design • Minimize rank score • Minimize sampling effort

  7. Detection probability distribution over time Detection probability for each species at each site Detection History Number of species at each site Species richness at each site Environment Number of times to sample each site Species composition at each site Prioritize sites for conservation min[ Σ(abs(EstRank-TrueRank) ]

  8. Step 1 Data = Seattle 2005 point counts Method = Single-season, single-species occupancy models in MARK Results = Occupancy and detection probabilities for 17 species

  9. Seattle 2005 avian point counts

  10. Seattle 2005 avian point counts

  11. Seattle 2005 avian point counts

  12. Step 2 Input = Occupancy and detection probabilities for 17 species estimated from Seattle 2005 point counts Methods = Simulation model in R Output = Detection histories for 17 species across 14 sites

  13. SIMULATE SITE COMPOSITION AND RICHNESS IN R- PROBABILITY OF OCCUPANCY

  14. SIMULATE SITE COMPOSITION AND RICHNESS IN R- PROBABILITY OF OCCUPANCY Draw from binomial distribution to determine if species present at site

  15. Sample simulated sites - Detection probability Draw from binomial distribution to determine if species detected

  16. Sampling designs to evaluate

  17. Step 3 Input = Detection histories for 17 species across 14 sites generated in R Methods = Species richness occupancy model in WinBUGS Output = Species richness estimates for 14 sites

  18. Estimate species richness • Detection histories generated in R simulation • R2WinBUGS package • Estimate species richness in WinBUGS • 50,000 iterations • 2 chains • Discarding first 25,000 iterations • Thinning at every 50 iterations • p(.)psi(.) • Diagnostics • Trace • Kernal densities • Gelman-Rubin statistic • 100 simulations for each sampling design

  19. WinBUGS R-hat = 1

  20. Step 4 Input = Species richness estimates for 14 sites from WinBUGS Methods = Rank sites according to true species richness and estimated species richness, calculate rank score Output = Rank score reflects performance of sampling design

  21. Rank Score

  22. Results – Sampling designs Summary statistics for 100 simulations of each sampling design

  23. Results – Sampling designs

  24. Conclusion • Optimal sampling design = 2 samples • Regardless of detection probability distribution

  25. Discussion • Some problems estimating species richness with 6 sample design • Nearly all 000000 = species absent • Sampling design with 2 samples still not particularly good at ranking sites (mean rank score ≈ 65)

  26. Discussion • More sophisticated occupancy models • Survey-specific covariates  p • Site-specific covariates  psi • Calculate rank score differently • Focus on top 3 ranked sites • Rather than absolute value of difference, consider frequency of number of ranks off • Increase number of iterations and simulations • Flexible simulation model design  evaluate other sampling designs

  27. Discussion • Adjust fundamental objective • Only a few species separate sites with most and fewest species • Being off by 1 species at a few sites  very different ranking • Fundamental objective combining species richness and occupancy of critical species

  28. Thanks to. . . • Dr. Jeff Hepinstall-Cymerman • Dr. Mike Conroy • Krishna Pacifici • Dr. Jim Peterson • Myung-Bok Lee Questions?

More Related