Dynamic Oceans and Dynamic Ecosystems

Transcription

1 Dynamic Oceans and Dynamic Ecosystems SST Southwest Fisheries Science Center, Environmental Research Division UCSC Cooperative Institute for Marine Ecosystems and Climate NASA SWFSC ERD Elliott L. Hazen U.S. Department of Commerce National Oceanic and Atmospheric Administration NOAA Fisheries Page 1

2 Cumulative Risks in the California Current Multiple Risks Ship strikes Bycatch / entanglement Noise Climate change Use satellite data to model species and risk in near real time Maxwell, Hazen, et al Nature Comm.; 2013 CCIEA HABITAT RISK IMPACT U.S. Department of Commerce National Oceanic and Atmospheric Administration NOAA Fisheries Page 2

3 Dynamic Ocean Management Ryan et al Block et al Management that changes in space and time, at scales relevant for animal movement and human use. Hobday et al. 2014, Lewison et al. 2015, Maxwell et al Maxwell et al U.S. Department of Commerce National Oceanic and Atmospheric Administration NOAA Fisheries Page 3

4 Dynamic Ocean Management TurtleWatch WhaleWatch Mandatory Voluntary EcoCast Scales et al J Appl Ecol U.S. Department of Commerce National Oceanic and Atmospheric Administration NOAA Fisheries Page 4

5 TurtleWatch Voluntary, yet effective SST U.S. Department of Commerce National Oceanic and Atmospheric Administration NOAA Fisheries Page 5

6 Southern Bluefin Tuna in Australia NowCast 06/12/ week forecast 6/24/ month forecast 08/12/2016 Hobday and Hartmann 2006, Hobday et al. 2011, Hobday et al. in review U.S. Department of Commerce National Oceanic and Atmospheric Administration NOAA Fisheries Page 6

7 Species Distribution Modeling Distribution / behavioral data e.g. sightings data, tag data, foraging events Probability of occurrence predicted from environmental covariates June 23rd, 2010 Habitat preference Statistical models Sampled predictive data June 23rd, 2010 Log (eke) June 23rd, 2010 SST SSHa Habitat / non-habitat e.g. Generalized Additive Mixed Models, Boosted Regression Trees U.S. Department of Commerce National Oceanic and Atmospheric Administration NOAA Fisheries Page 7

8 California Drift Gillnet Fishery 200m # vessels U.S. Department of Commerce National Oceanic and Atmospheric Administration NOAA Fisheries Page 8

9 EcoCast Fishing zones predicted based on ocean features, catch potential, and weighted by bycatch risk Good fishing zones served via web and mobile devices Models to include: hard cap species, risk weightings, seasonal forecasting

10 California Drift Gillnet Fishery Data Types: Satellite tracking data + NOAA Fishery observer data marine mammal survey data Bycatch: Leatherback sea turtles Bycatch: Blue sharks Daily JPL GHRSST # of individuals caught Chl 8-day SeaWIFS, MODIS, VIRRS composite EKE Daily AVISO at 25km SSHa and SD Daily AVISO at 25km Y winds 8-day QSCAT and ASCAT at 25km Target catch: Swordfish ETOPO1 at # of individuals caught Bycatch: California sea lions 20 Bathymetry and SD # of individuals caught SST and Standard Deviation # of individuals caught Data Products U.S. Department of Commerce National Oceanic and Atmospheric Administration NOAA Fisheries Page 10

11 California Drift Gillnet Fishery Blue Shark Tracking Swordfish Observer California Sea Lion Leatherback Turtle Blue Shark Observer U.S. Department of Commerce National Oceanic and Atmospheric Administration NOAA Fisheries Page 11

12 California Drift Gillnet Fishery 2012 bycatch predictions Blue Shark Observer Blue Shark Tracking Leatherback Turtle California Sea Lion Sealion weighting = Sealion weighting = U.S. Department of Commerce National Oceanic and Atmospheric Administration NOAA Fisheries Page 12

13 California Drift Gillnet Fishery 2012 EcoCast predictions Swordfish Observer Blue Shark Observer Blue Shark Tracking Leatherback Turtle California Sea Lion Sealion weighting = U.S. Department of Commerce National Oceanic and Atmospheric Administration NOAA Fisheries Page 13

14 Dynamic Ocean Management Next Steps 1. Data-assimilative ROMS instead of Satellite fields 2. Derived frontal products (Scales et al. 2014) and Finite Size / Time Lyapunov Exponents from Aviso for mesoscale activity 3. Explore forecasting models (e.g. NMME) for use in pro-active planning, risk analyses, and management strategy evaluations.

15 Questions? Sealion weighting = Sealion weighting = -0.05