Statistical analyses to support guidelines for marine avian sampling - - PowerPoint PPT Presentation

Statistical analyses to support guidelines for marine avian sampling

Brian Kinlan (NOAA) Elise F. Zipkin (USGS) Allan F. O’Connell (USGS) Allison Sussman (USGS) Mark Wimer (USGS) Chris Caldow (NOAA) NOAA/NOS National Centers for Coastal Ocean Science (NCCOS) USGS Patuxent Wildlife Research Center Report to BOEM—October 26, 2012

Special thanks to our NOAA Hollings Scholar, Diana Rypkema (Cornell University)

Off shore wind power garnering lots of interest

• Many states have

implemented a 20% renewable energy by 2020 mandate

• Need for siting and

environmental assessment

Offshore Wind

Where are birds more likely to aggregate?

Not a lot known about the distribution and abundances in the Atlantic

• Difficult to survey
• Rough conditions
• Patchily distributed
• Highly mobile/variable

U.S. Bureau

• f 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 survey effort to guide BOEM and industry in determining wind turbine placement

What is a hot/coldspot?

Hot spot = A lease block with an average species specific abundance that is some multiple >1 (e.g., 3x) the mean of the region Cold spot = A lease block with an average species specific abundance that is some multiple <1 (e.g., 1/3x) the mean of the region

How many surveys?

• >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

The Atlantic Seabird Compendium

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) Conduct power analysis and significance testing on the basis of this distribution

Two part approach

1) Determine the best statistical distribution to model the count data for each species in each season 2) Conduct power analysis and significance testing on the basis of this distribution

Model the data

Test eight statistical distributions:

Poisson Negative binomial Geometric Logarithmic Discretized lognormal Zeta‐exponential Yule Zeta (power law)

Northern Gannet Spring Count Data

Model the data

Test eight statistical distributions:

Poisson Negative binomial Geometric Logarithmic Discretized lognormal Zeta exponential Yule Zeta (power law)

Northern Gannet Spring Count Data

Examples of the distributions

1 2 5 10 20 1e-05 1e-03 1e-01

Positive Poisson (simulated)

1 2 5 10 20 50 100 200 1e-05 1e-03 1e-01

Positive neg binomial (simulated)

1 2 5 10 20 50 100 1e-05 1e-03 1e-01

Positive geometric (simulated)

1 2 5 10 20 50 100 200 1e-05 1e-03 1e-01

Logarithmic (simulated)

1 5 10 50 100 500 1000 1e-05 1e-03 1e-01

Discretized lognormal (simulated)

1 100 10000 1e-05 1e-03 1e-01

Zeta (simulated)

1 100 10000 1e-05 1e-03 1e-01

Yule (simulated)

Results‐Model Fitting

Spring Summer Fall Winter Total

Number species with >500 observations

12 10 15 11 48

Spring Summer Fall Winter Total

Number species with >500 observations

12 10 15 11 48

Discretized lognormal Yule Negative binomial Logarithmic Zeta decay

Results‐Model Fitting

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 binomial Logarithmic Zeta decay

3* 0 (3*)

*Not significantly better for α = 0.05

Results‐Model Fitting

• Criteria:
• Positive
• Non‐zero values
• Highly skewed
• Multiplicative effects

Discretized Lognormal Distribution

Model fit  Power Analysis

1 5 10 50 500 1e-04 1e-03 1e-02 1e-01 1e+00

Count (log scale) Probability (log scale)

Discretized lognormal Yule Zeta decay Zeta

Model selection

5 10 15 20 25 0.0 0.2 0.4 0.6 0.8 1.0

Number of sampling events Simulated power

Hot spot (3 x mean) Cold spot (0.33 x mean)

Power curves Power Maps & Significance tests

Products

• Interim report (Jan 2012)
• Mid‐Term Technical Report (July 2012)
• Presented at 4th International Wildlife Management

Conference in South Africa (July 2012)

• First peer‐reviewed journal article published in J. Statistical

Methodology (Zipkin et al. 2012).

• Second journal article in prep for submission in Nov‐Dec

2012

• Final report (Oct‐Nov 2012)
• Digital data‐PDF, ArcGIS (Nov 2012)

Broad summary of results

• Useful technique
• Need to do additional

focal work on key species

• f interest
• Most areas of the Atlantic

need additional sampling to have adequate power to detect hotspots/coldspots

• Maps could be used to

select well‐studied areas where less additional sampling required

• Rare species a challenge

Discussion

• Overview of draft final report
• General walk‐through
• Look at and discuss results for species of interest
• Discuss issues
• Spatial scale
• Temporal scale/environmental variability
• Spatial and temporal trends
• Rare species/data poor situations
• Comparison to other approaches
• Detectability and other observer/platform issues
• Next steps/practical applications

Acknowledgements

Brian.Kinlan@NOAA.gov 301‐713‐3028 x157

Mid-Atlantic Predictive Modeling Project

Rob Rankin (NOAA Biogeo) Allan O’Connell (USGS-Patuxent) Andrew Gilbert (BRI) Beth Gardner (NC State) Mark Wimer (USGS Patuxent) Allison Sussman (USGS Patuxent) Charlie Menza (NOAA Biogeo) Chris Caldow (NOAA Biogeo) Tom McGrath (NOAA Biogeo) & others Data : Atlantic Seabird Survey Compendium Funding: BOEM, USGS

Sampling Design/Power Analysis Project

Diana Rypkema (NOAA Hollings Scholar) Emily Silverman (USFWS) Jeffery Leirness (USFWS) Technical Reviewers – Jim Baldwin (USDA Forest Service), Jocelyn Brown-Saracino (DOE), David Bigger (BOEM), BOEM Renewable Energy/Avian Biology Team Data : Atlantic Seabird Survey Compendium Funding: BOEM, USGS