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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

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Paige Barlow WILD 8390 Spring 2010

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Paige barlow wild 8390 spring 2010 l.jpg

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


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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


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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


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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


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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


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Optimal sampling design

  • Minimize rank score

  • Minimize sampling effort


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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) ]


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Step 1

Data = Seattle 2005 point counts

Method = Single-season, single-species occupancy models in MARK

Results = Occupancy and detection probabilities for 17 species


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Seattle 2005 avian point counts


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Seattle 2005 avian point counts


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Seattle 2005 avian point counts


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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


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SIMULATE SITE COMPOSITION AND RICHNESS IN R- PROBABILITY OF OCCUPANCY


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SIMULATE SITE COMPOSITION AND RICHNESS IN R- PROBABILITY OF OCCUPANCY

Draw from binomial distribution to determine if species present at site


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Sample simulated sites- Detection probability

Draw from binomial distribution to determine if species detected


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Sampling designs to evaluate


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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


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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


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WinBUGS

R-hat = 1


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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


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Rank Score


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Results – Sampling designs

Summary statistics for 100 simulations of each sampling design


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Results – Sampling designs


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Conclusion

  • Optimal sampling design = 2 samples

  • Regardless of detection

    probability distribution


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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)


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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


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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


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  • Thanks to. . .

    • Dr. Jeff Hepinstall-Cymerman

    • Dr. Mike Conroy

    • Krishna Pacifici

    • Dr. Jim Peterson

    • Myung-Bok Lee

Questions?


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