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Cetmap Atlantic update

Ben Best, Jason Roberts, Pat Halpin. Cetmap Atlantic update. Nov 2011 Draft Report (550 p). Since then…. More datasets Standardize dataset ingestion Enhanced environmental predictors Mix platforms Segment transects Spatial models for Encounters & Group Size

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Cetmap Atlantic update

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  1. Ben Best, Jason Roberts, Pat Halpin Cetmap Atlantic update

  2. Nov 2011 Draft Report (550 p)

  3. Since then… • More datasets • Standardize dataset ingestion • Enhanced environmental predictors • Mix platforms • Segment transects • Spatial models for Encounters & Group Size • Subset for model validation and performance • R scripts to Python tools

  4. Added datasets Updated Atlantic datasets 2006 - 2009

  5. Static Predictors

  6. Dynamic Predictors

  7. Dynamic Predictors (cont’d) Polarity

  8. Mixing Platforms env ship Credit: NODES report illustrations aircraft

  9. Mixing Platforms

  10. Segmentation

  11. Segmentation

  12. Segmentation (cont’d) Non-spanning Spanning (10 km, 1 hr) 5 km 10 km 25 km 50 km 100 km 150 km 250 km

  13. Data Dashboard Det formulas – AIC, GoF, ChiSq N obs by platform, region Fit data specification N obsvsseg length(limited by spp, region, platform) Detprobvs distance - ESW

  14. Model Input Data

  15. Model Formulas – 180 variations 10000s digit - region 1000 - GM 1000s digit - season 1000 - All year 2000 - GM winter: DayOfYear >= 18 AND DayOfYear <= 73 3000 - GM spring-fall: DayOfYear >= 106 AND DayOfYear <= 275 100s digit - env/space/time combination Encounter rate: 100 - env + space + time 200 - env + te(space, time, bs='ts', k=10) 300 - env + space 400 - env + time 500 – env 10s digit - smoothing controls 10 - REML 20 - GCV.Cp, gamma=1.6 30 - GCV.Cp, gamma=1.0, k=5 per predictor 40 - GCV.Cp, gammma=1.6, bs=ts, k=3 50 - GCV.Cp, gammma=1.6, bs=ts, k=4 60 - GCV.Cp, gammma=1.6, bs=ts, k=5 70 - GCV.Cp, gammma=1.6, bs=cs, k=3 80 - GCV.Cp, gammma=1.6, bs=cs, k=4 90 - GCV.Cp, gammma=1.6, bs=cs, k=5 1s digit - customized tweak 0 - base model with no tweaks

  16. Prediction Grid

  17. Observations – Test vs Train • Sperm whale

  18. Explore Data

  19. Fit Model

  20. Predict Density: Sperm whale - Jul Encounter Rate X Group Size

  21. Sperm whale density - Apr

  22. Sperm whale density - May

  23. Sperm whale density - Jun

  24. Sperm whale density - Jul

  25. Sperm whale density - Aug

  26. Sperm whale density - Sep

  27. Tools for Workflows RobertsJJ, BD Best, DC Dunn, EA Treml, PN Halpin (2010) Marine Geospatial Ecology Tools: An integrated framework for ecological geoprocessing with ArcGIS, Python, R, MATLAB, and C++. Environmental Modelling & Software.

  28. Next Steps • Run predictions on rest of species for GoMex • Ingest 2 recent sets of data (NARWSS, UNCW) • Run predictions on Atlantic species • Variance estimation • Rare species modeling approaches (BRT, RandomForests, ZIP Bayes, Maxent…) • Generate static report • Elicit expert review • Validation and sensitivity (segmentations / platform / prediction avg) • Populate CetMap / SEAMAP / MarineCadastre

  29. Thank you Comments or questions?

  30. Maxent for Density? • Allocate density through Maxentsuitability surface (VanDerWal et al 2009; Oliver et al 2012; Jiménez-Valverde 2011) a la RES • or Maxent(occ) * f(GS) = Density • Variability through bootstrapjava -jar maxent.jar -X 25 replicates=100 • 100 runs, 75% randomly chosen presence records • mean, standard deviation and lower confidence limits per pixel

  31. Variable Importance

  32. Interrogate Pixel

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