Statistical analyses to support guidelines for marine avian sampling

Transcription

1 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) Special thanks to our NOAA Hollings Scholar, Diana Rypkema (Cornell University) NOAA/NOS National Centers for Coastal Ocean Science (NCCOS) USGS Patuxent Wildlife Research Center Report to BOEM October 26, 2012

2 Offshore Wind 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

3 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

4 Patuxent Wildlife Research Center U.S. Bureau of Ocean and Energy Management (BOEM) All Lease Blocks 5km x 5km lease blocks Along the Outer Continental Shelf of the Atlantic Ocean

5 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

6 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

7 How many surveys?

8 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

9 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

10 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

11 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

12 Model the data Northern Gannet Spring Count Data Test eight statistical distributions: Poisson Negative binomial Geometric Logarithmic Discretized lognormal Zeta exponential Yule Zeta (power law)

13 Model the data Northern Gannet Spring Count Data Test eight statistical distributions: Poisson Negative binomial Geometric Logarithmic Discretized lognormal Zeta exponential Yule Zeta (power law)

14 Examples of the distributions 1e-01 Positive Poisson (simulated) e e-03 1e-05 1e-01 1e-03 Discretized lognormal (simulated) 1e-01 Positive neg binomial (simulated) e e-03 1e-05 1e-01 1e-03 Zeta (simulated) 1e-01 Positive geometric (simulated) 1e e-01 1e-05 Logarithmic (simulated) 1e-03 1e-05 1e-01 Yule (simulated) 1e-05 1e

15 Results Model Fitting Number species with >500 observations Spring Summer Fall Winter Total

16 Results Model Fitting Number species with >500 observations Discretized lognormal Yule Negative binomial Logarithmic Zeta decay Spring Summer Fall Winter Total

17 Results Model Fitting Spring Summer Fall Winter Total Number species with >500 observations Discretized lognormal 7 (4*) 4 (3*) 8 (3*) 8 (2*) 27 (12*) 1* 3* 1* 1 1 (5*) Yule Negative binomial Logarithmic Zeta decay 3* *Not significantly better for α = (3*)

18 Discretized Lognormal Distribution Criteria: Positive Non zero values Highly skewed Multiplicative effects

19 Model fit Power Analysis Power curves Hot spot (3 x mean) Cold spot (0.33 x mean) Count (log scale) e-03 1e-02 Sim ulated power 1e-01 Discretized lognormal Yule Zeta decay Zeta 1e-04 Probability (log scale) 1e+00 Model selection Power Maps & Significance tests Number of sampling events 25

20 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)

21 Broad summary of results Useful technique Need to do additional focal work on key species of 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

22 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

23 Acknowledgements 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 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 Brian.Kinlan@NOAA.gov x157