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Fish and epibenthic assemblages in the Chukchi Sea: observations and predictions

Fish and epibenthic assemblages in the Chukchi Sea: observations and predictions. BA Bluhm , BL Norcross, K Iken , F Huettmann , BA Holladay (all University of Alaska Fairbanks), BI Sirenko (Zoological Institute RAS). Demersal fish and epifauna.

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Fish and epibenthic assemblages in the Chukchi Sea: observations and predictions

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  1. Fish and epibenthic assemblages in the Chukchi Sea: observations and predictions BA Bluhm, BL Norcross, K Iken, F Huettmann, BA Holladay (all University of Alaska Fairbanks), BI Sirenko (Zoological Institute RAS)

  2. Demersal fish and epifauna • Plumb-staff beam trawl, 7 mm mesh (4 mm in cod end) • 2-5 min hauls on bottom • Sort, count, weight, identify • 2004-2009, 165 fish st, 42 epifauna st

  3. Why care? • Climate signal integrators • Prey for subsistence species • Species of potential subsistence and commercial fisheries (snow crab) • Contribution to carbon cycling • Commitment to Circum-polar Biodiversity Monitoring Program(Arctic Council)

  4. Faunal similarity Fish Central North Coastal Northeast Transform: Square root Resemblance: S17 Bray Curtis similarity Fish-Epifauna 2D Stress: 0.22 d h i j Central east c Herald Valley f e Southwest g b Coastal a Similarity 30 East Siberian Sea

  5. Observed fish assemblages Herald Canyon Wrangel Isl. East Siberian Sea Icy Cape Alaska Chukotka Clustering based on fish biomass, square-root transformed, Bray-Curtis similarity

  6. Characteristic species Herald Canyon Wrangel Isl. East Siberian Sea Icy Cape Alaska Chukotka Taxa contributing ≥10% to within cluster similarity

  7. Observed fish-epifauna assemblages Clustering based on fish and epifauna biomass, square-root transformed, Bray-Curtis similarity Point Hope Kotzebue Sound Bering Strait

  8. Characteristic species Taxa contributing ≥10% to within cluster similarity (fish species contributed ≤7%) Mean fish biomass per cluster 2-10% of total haul biomass Point Hope Kotzebue Sound Bering Strait

  9. Environmental variables considered

  10. Predicted assemblages Bathymetry (m) 0 - 25 26 - 50 51 - 75 76 - 150 151 - 1000 north 1001 - 4000 northeast Herald Canyon Wrangel Isl. central East Siberian Sea Icy Cape coastal Alaska Chukotka Bering Sea

  11. Environmental niches for fish assemblages North: near mean summer sea ice extent, low bottom temperature Central : high chlorophyll a, near ice edge, muddy sediment, >40 m north northeast central coastal Northeast: coarse sediment, <40 m, high(er) bottom temperature, rel. low chlorophyll Coastal: near coast, high surface and bottom temperatures, far from ice edge Figure 3: Examples of response curves from the TreeNet routine. One example  of the highest or second highest ranking predictors is shown for each of the four dominant fish clusters. Examples were chosen to document the importance of predictors related to hydrography, sediment characteristics and food availability. High relative values in the y-axis of the response curve indicate presence of a given cluster while low values indicate absence. Larger distances between the two values imply stronger niche preferences than smaller distances.  

  12. Thanks! • Funding through RUSALCA (NOAA-CIFAR), PEW Environmental Group (US Arctic Program), CMI (Fish), • Species identifications aided by Drs L Cole, K Coyle, D Fautin, A Gebruk, M Hoberg, P Kuklinski, C Mah, CW Mecklenburg, E Rodriguez, A Rogaecheva, I Smirnov, O Tendal • Vessel support, Ship crews and trawl teams of Prof. Khromov, Oscar Dyson /AFSC/NOAA, Oshoru Maru / Hokkaido Univ.

  13. Temporal comparison of epifauna and food web in the southern Chukchi Sea (2004, 2009, 2012): First results BA Bluhm, KB Iken, C Serratos (all University of Alaska Fairbanks), B Sirenko (Zoological Institute RAS)

  14. Why care? Epifauna • Climate signal integrators (long-lived) • Prey for subsistence species • Species of potential subsistence and commercial fisheries (snow crab) • Contribution to carbon cycling • Commitment to Circum-polar Biodiversity Monitoring Program (Arctic Council) Food web • Carbon flow • Food web length – carbon transfer efficiency • Pelagic-benthic coupling • Plumb-staff beam trawl, 7 mm mesh (4 mm in cod end) • 2-5 min hauls on bottom • Sort, count, weight, identify

  15. Time series stations BSW AW ACW Chukchi Sea D C Freshwater inflow, gravel E B A F G Hard substrate Bering Sea

  16. Biomass and composition C G D A B Russian coast Anadyr Water Bering Shelf Water E F Alaskan coast Caveat: No replicate trawl hauls

  17. Biomass trend

  18. Snow crab: abundant but small in Chukchi Mean 69 SD 29 N=344 Mean 40 SD 11 N=2669

  19. Community structure stable Russian coast Anadyr Water Point Hope Coastal Current

  20. Food web – trophic levels 2009 2004 18 • AW consumers depleted in δ15N compared to ACW in both years • Use of fresher (=isotopically light) material through shorter food chains in AW 16 14 POM 12 Surface deposit - bivalves δ15N Strongylocentrotus droebach. Neptunea sp. 10 Leptasterias sp. Nephtys sp. Pagurus rathbuni 8 Hyas coarctatus 2012 Chionoecetes opilio 6 Argis lar Gymnocanthus tricuspis Myoxocephalus scorpius 4 Lumpenus fabricii ACW AW ACW AW Boreogadus saida

  21. Food web – carbon source 2009 2004 - 14 • Consumer δ13C depleted in ACW • – possible freshwater signal - 15 - 16 POM • Depleted δ13C POM in AW in 2009 • – strong freshwater signal in 2009 Surface deposit - bivalves - 17 Strongylocentrotus droebach. Neptunea sp. - 18 Leptasterias sp. Nephtys sp. - 19 δ13C Pagurus rathbuni Hyas coarctatus - 20 2012 Chionoecetes opilio - 21 Argis lar Gymnocanthus tricuspis - 22 Myoxocephalus scorpius Lumpenus fabricii - 23 Boreogadus saida - 24 - 25 ACW AW ACW AW 2004 results: Iken K et al (2010) Deep-Sea Research II 57: 71-85

  22. Learned so far? Epifauna Biomass variable between years Individual species can drive tends (stock fluctuations in snow crab? Community structure stable in area, different by substrate and water mass Combination of metrics tell more than one metric Food web Food web reflects water masses (tight pelagic-benthic coupling in AW) Food web structure stable between 2004 and 2009 Food source signal variable at point measurement

  23. Thanks! • Funding through NOAA-CIFAR NA08OAR4320870, CIFAR IPY • Ship crews and trawl team of Prof. Khromov, B. Holladay • Crab funding (CMI, BOEM), and lab team • Stable isotope lab team • Species identifications aided by Drs L Cole, K Coyle, D Fautin, A Gebruk, M Hoberg, P Kuklinski, C Mah, CW Mecklenburg, E Rodriguez, A Rogaecheva, I Smirnov, O Tendal

  24. RUSALCA Synthesis - Bio Bio: Have • Species distributions • Community distributions • Biomass / abundance distributions • Food web • Some fluxes • Some rates (benthic respiration, copepod egg production) • Variability / change over time (to varying degrees) Need from phys-chem-geo • Spatial and temporal patterns of environmental conditions on different scales (next slide) for water column and (near) bottom (latitude / longitude, depth, value) • Joint interpretation! • Mapping support across projects for special issue?

  25. Life cycles provide integration scales Bacterialarvae zooplanktonfishes benthosmammals µmmmcmdmm Daymonthyeardecadecentury

  26. Possible papers • One overarching highlights paper (or extended editorial to special issue) • Regional highlight, system description as multi-year composite: Herald Canyon area • Temporal variability highlight: focus on DBO 3 • All multidisciplinary, multi-author, multinational • Unique and complementary to other synthesis efforts • Need lead team (Russia/USA) or interdisciplinary post-doc based in both countries (Liza-Maria concept)

  27. RUSALCA Synthesis - Bio Future Need next decade? • Continue time series (minimum DBO transect 3?) • Increased integration with phys-chem-geo • More rates: Current rates, e.g. grazing, growth, age • Future rates through experiments? • Thermal windows and physiological plasticity? • Link to sea ice? • Predictive capability? • Carbon flux model? • Interdisciplinary post-docs with Russia-US advisory team, based in two countries?

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