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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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Ben Best, Jason Roberts, Pat Halpin Cetmap Atlantic update
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
Added datasets Updated Atlantic datasets 2006 - 2009
Dynamic Predictors (cont’d) Polarity
Mixing Platforms env ship Credit: NODES report illustrations aircraft
Segmentation (cont’d) Non-spanning Spanning (10 km, 1 hr) 5 km 10 km 25 km 50 km 100 km 150 km 250 km
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
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
Observations – Test vs Train • Sperm whale
Predict Density: Sperm whale - Jul Encounter Rate X Group Size
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.
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
Thank you Comments or questions?
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