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Statistical guidelines for sampling marine avian populations
Statistical guidelines for sampling marine avian populations
Elise F. ZipkinBrian Kinlan
Allison SussmanMark Wimer
Allan F. O’Connell
Elise F. ZipkinBrian Kinlan
Allison SussmanMark Wimer
Allan F. O’Connell
USGS Patuxent Wildlife Research Center NOAA National Ocean Service
4th International Wildlife Management Conference – July 2012
Seabirds in the Atlantic
Where are the birds?
Not a lot known about the distribution and abundances in the Atlantic
• Difficult to survey• Rough conditions• Patchily distributed• Highly mobile
Off shore wind power garnering lots of interest
• Many states have implemented a 20% renewable energy by 2020 mandate
• Public perception of oil spills is poor
Where are the birds? Wind development
U.S. Bureau of Ocean and Energy Management (BOEM)
• 5km x 5km lease blocks
• Along the Outer Continental Shelf of the Atlantic Ocean
All Lease Blocks
Patuxent Wildlife Research Center
Objectives
Develop a framework for assessing:
1) which lease blocks are “hot spots” and “cold spots”
2) the required surveying effort to guide BOEM and industry in determining wind turbine placement
What is a hot/cold spot?
Hot spot = A lease block with an average species specific abundance that is three times the mean of the region
Cold spot = A lease block with an average species specific abundance that is one third the mean of the region
The Atlantic Seabird Compendium• >250,000 seabird observations from U.S. Atlantic waters
• Collected from 1978 through 2011
• Data collected using a mix of methods including non‐scientific approaches
The Atlantic Seabird Compendium• >250,000 seabird observations from U.S. Atlantic waters
• Collected from 1978 through 2011
• Data collected using a mix of methods including non‐scientific approaches
We used:• 32 scientific data sets – 28 ship‐based, 4 aerial
• Transects were standardized to 4.63km
• 44,176 survey transects representing 463 species
Two part approach
1) Determine the best statistical distribution to model the count data for each species in each season
2) Use the best fitting distribution to produce power analyses
The rest of the talk
1) Describe the broad two part approach
2) Integrate an example using Northern Gannets
Two part approach
1) Determine the best statistical distribution to model the count data for each species in each season
2) Use the best fitting distribution to produce power analyses
Part 1: Model the data
Test eight statistical distributions:
Poisson Negative binomial Geometric Logarithmic Discretized lognormal Zeta decay Yule Zeta (power law)
Northern Gannet spring count data
Examples of the distributions
1 2 5 10 20
1e-0
51e
-03
1e-0
1
Positive Poisson (simulated)
1 2 5 10 20 50 100 200
1e-0
51e
-03
1e-0
1
Positive neg binomial (simulated)
1 2 5 10 20 50 100
1e-0
51e
-03
1e-0
1
Positive geometric (simulated)
1 2 5 10 20 50 100 200
1e-0
51e
-03
1e-0
1
Logarithmic (simulated)
1 5 10 50 100 500 1000
1e-0
51e
-03
1e-0
1
Discretized lognormal (simulated)
1 100 10000
1e-0
51e
-03
1e-0
1
Zeta (simulated)
1 100 10000
1e-0
51e
-03
1e-0
1
Yule (simulated)
Part 1: Results
Spring Summer Fall Winter Total
Number species with >500 observations 12 10 15 11 48
Part 1: Results
Spring Summer Fall Winter Total
Number species with >500 observations 12 10 15 11 48
Discretized lognormal
Yule
Negative binomialLogarithmicZeta decay
Part 1: Results
Spring Summer Fall Winter Total
Number species with >500 observations 12 10 15 11 48
Discretized lognormal 7 (4*) 4 (3*) 8 (3*) 8 (2*) 27 (12*)
Yule 1* 3* 1* 1 1 (5*)
Negative binomialLogarithmicZeta decay
3* 0 (3*)
Part 1: Results
Northern Gannet
Discretized lognormal top distribution for fall and spring
Discretized lognormal and Yule fit equally well in winter and summer
1 5 10 50 500
1e-0
41e
-03
1e-0
21e
-01
1e+0
0
Count (log scale)
Prob
abili
ty (l
og s
cale
)
Discretized lognormalYuleZeta decayZeta
Two part approach
1) Determine the best statistical distribution to model the count data for each species in each season
2) Use the best fitting distribution to produce power analyses
Part 2: Power analysis
Part 2: Power
analysis for Northern gannets in the spring
*Focusing only on lease blocks where individuals were observed
Part 2: Northern gannet results
Reference mean = 6.9 individuals per lease block conditional on presence
5 10 15 20 25
0.0
0.2
0.4
0.6
0.8
1.0
Number of sampling events
Sim
ulat
ed p
ower
Hot spot (3 x mean)Cold spot (0.33 x mean)
Part 2: Northern gannet results
Reference mean = 6.9 individuals per lease block conditional on presence
5 10 15 20 25
0.0
0.2
0.4
0.6
0.8
1.0
Number of sampling events
Sim
ulat
ed p
ower
Hot spot (3 x mean)Cold spot (0.33 x mean)
Part 2: Northern gannet results
1 5 10 50 100 500 1000
1e-0
51e
-03
1e-0
1
Counts
Freq
uenc
y
Discretized lognormal
Reference mean = 6.9 individuals per lease block conditional on presence
Part 2: Northern gannet results
Summary of results
• Seabirds tend to be highly aggregated and require skewed statistical distributions to accurately describe populations
• For many species, we need a large number of surveys to detect areas with atypical abundances
Implications for wind power
• Intensive sampling in multiple seasons will be required to determine potential impacts on seabirds
• A possible approach could be to combine data on functionally similar species or species of high conservation value
Acknowledgments
• The many researchers and their crews who collected the data used in our analyses
• Emily Silverman, Diana Rypkema
• The Bureau of Ocean, Energy, Management (BOEM) for funding model development and analysis