Factors affecting the large scale distribution of deep sea corals and sponges in the Alaskan ecosystems of the North Pacific Ocean
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1 Factors affecting the large scale distribution of deep sea corals and sponges in the Alaskan ecosystems of the North Pacific Ocean Chris Rooper, Rachel Wilborn, and Pamela Goddard Alaska Fisheries Science Center, Seattle, WA, USA
2 Outline Large-scale modeling studies Basin-wide (literature) Regional-scale modeling studies Alaska Transect-scale studies Alaska Caveats Summary Future directions
3 Basin-scale modeling that include Alaska Bryan and Metaxas 2007 (Gorgonians) ENFA Temperature, Slope, Current and Chlorophyll a Bryan TL, Metaxas A (2007) Predicting suitable habitat for deep-water gorgonian corals on the Atlantic and Pacific Continental Margins of North America. Mar Ecol Prog Ser 330:
4 Basin-scale modeling that include Alaska Tittensor et al. 2009, 2010 (Stony corals) ENFA, MaxEnt Aragonite saturation, DIC, temperature, primary productivity, Oxygen Ocean floor Seamounts Tittensor D.P., Baco-Taylor A.R., Brewin P., Clark M.R., Consalvey M., Hall-Spencer J., Rowden A.A., Schlacher T., Stocks K., Rogers A.D. (2009) Predicting global habitat suitability for stony corals on seamounts. J Biogeogr,36, Tittensor DP, Baco AR, Hall-Spencer JM, Orr JC, Rogers CAD (2010) Seamounts as refugia from ocean acidification for cold-water stony corals. Mar Ecol 31:
5 Basin-scale modeling that include Alaska Yesson et al (Octocorals) MaxEnt Calcite saturation*, Slope, Productivity, Temperature, Salinity Yesson C, Taylor ML, Tittensor DP, Davies AJ and others (2012) Global habitat suitability of cold-water octocorals. J Biogeogr 39:
6 Regional-scale modeling Alaska Gulf of Alaska
7 Alaska Gulf of Alaska Objectives for 3 Alaska Large Marine Ecosystems Predict presence or absence of sponge and coral Predict abundance of sponge and coral
8 Dependent Data: Bottom trawl survey catch (EBS, AI & GOA) Validation (either by year AI-2012 or random sample) Flavors: Upright sponges (vase, branching, monolithic, etc.) Hard coral (Primnoidae, Stylasteridae, Plexauridae, Paragorgidae, Acanthogorgiidae, Cladopathidae, Isididae) Primnoidae Stylasteridae Coral Diversity Presence/absence Log(CPUE + ½*minimum value)
9 Independent Data: Seafloor characteristics based on Zimmermann et al. bathymetry compilation Only western Aleutians is shown R software Mapping (raster, rgdal, gstat, sp, mapping, etc.) Analyses (mgcv, PresenceAbsence, proc, tweedie)
10 Independent Data: Current speed - Al Hermann (ROMS) and Egbert & Erofeeva (2002) Source: ROMS model Interpolation method: IDW n = 1,917 Source: OSU Tidal Inversion model Interpolation method: Kriging n = 3,051
11 Independent Data: Aspect (combines bathymetry and mean current direction) 0 180
12 Independent Data: Aspect (combines bathymetry and mean current direction) 0 180
13 Independent Data: Bottom Temperature (trawl surveys) & Ocean color (MODIS) MODIS Data Source: Bottom trawl survey Interpolation method: Kriging n = 2,814 Source: Ocean Productivity Website (OSU) Interpolation method: IDW n = 19,929
14 Method Part I. Data extraction Maximum tidal current Mean current speed Aspect Slope,.. Data Eastings Northings Hauljoin Cruise Coral All_spong Upright_spbathyt rugt slopet tmaxt colorkt speedt btempt sedt aspectt
15 Method Part II. Generalized Additive Modeling Details: MGCV package in R Presence-absence = Binomial distribution Log(CPUE) = Gaussian or Tweedie Diversity = Poisson k = 30 for bivariate term, 4 for univariate terms Backwards elimination of insignificant variables based on GCV
16 Results Variable Relationships Aleutian Islands Eastern Bering Sea
17 Results III. Predictions Longitude Latitude Maximum tidal current Ocean color Slope Best fitting GAM model y = s(longitude, latitude)+s(maximum tidal current)+s(ocean color)+s(slope)
18 Results III. Predictions Longitude Latitude Maximum tidal current Probability Ocean color Slope Best fitting GAM model y = s(longitude, latitude)+s(maximum tidal current)+s(ocean color)+s(slope)
19 Predicted Probability of Presence Trawl data
20 Predicted Catch-per-Unit-of-Effort (CPUE) Trawl data
21 Predicted Presence or Absence Trawl data Camera data DRAFT FOR REVIEW BY SSC
22 Predicted Density Camera data DRAFT FOR REVIEW BY SSC
23 Predicted Probability of Presence Trawl data
24 Predicted Catch-per-Unit-of-Effort (CPUE) Trawl data
25 Which variables are important across ecosystems?
26 How about within ecosystems?
27 Small Scale Modeling
28 Underwater Camera Surveys ( ) Aleutian Islands (n = 216) Gulf of Alaska (n = 43) Bering Sea (n = 250) Stereo camera systems Groundtruthing study sites Standard 15 minute transects (~1000 m 2 ) Random sampling design (except GOA) n ~ 510 sites
29 Density, Densities of All Structure-forming Invertebrates
30 Why? Region Transects with rocky habitat Transects with coral Gulf of Alaska 35% 30% Aleutian Islands 63% 60% Bowers Bank 42% 47% Eastern Bering Sea 19% 13% 67.7% 64.5% 35.5% 32.3%
31 Density, Distribution, Coral, Sponge, and Pennatulacean Substrate Associations All corals and sponges (except pennatulaceans) were more frequently observed on rocky substrates ***Almost 42% of habitat surveyed was identified as rocky substrate.
32 Distribution, & Vertical Structure * * * * Demosponges, hexactinellids, and primnoids all significantly taller (p<0.01) on rocky habitats Sea whips significantly taller (p<0.01) on unconsolidated substrate
33 Aleutian Islands If there are rocks, there will be some coral or sponge Percent rocky substrate and related invertebrate density Strong association of coral (p<0.0001, R 2 =0.54)and sponge (0.0001, R 2 =0.58) to rocky substrate
34 Models with substrate Coral ~ Substrate + Tidal Current + Depth Explains 60% of variance in density 50% of presence Sponge ~ Substrate + Tidal Current + Depth Explains 66% of variance in density, 55% of presence
35 Caveats and other issues Sample design Often not incorporated Model validation Rarely attempted Use of proxy variables Fishing history Usually not included
36 Open or closed to mobile fishing gear
37 Summary Large scale processes (basin-wide) Aragonite/Calcite saturation depth Temperature Productivity Currents Slope Medium scale processes (~ km Depth/Slope Temperature Particulate organic carbon Current speeds Small scale processes (~1 m 10 km Substrate Current speeds
38 Large-scale mapping Ensemble models Future Directions Reducing the number of proxy variables Predicting effects of climate change on distribution Impacts from human activities (fishing, deep-sea mining, oil and gas development Moving coral and sponges into stock assessment framework
39 Prediction Averaging
40 Model Error
41 Unified coral model
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