Archive for modeling – Page 7

Tiny marine animals strongly influence the carbon cycle

Posted by mmaheigan 
· Thursday, August 31st, 2017 

What controls the amount of organic carbon entering the deep ocean? In the sunlit layer of the ocean, phytoplankton transform inorganic carbon to organic carbon via a process called photosynthesis. As these particulate forms of organic carbon stick together, they become dense enough to sink out of the sunlit layer, transferring large quantities of organic carbon to the deep ocean and out of contact with the atmosphere.

However, all is not still in the dark ocean. Microbial organisms such as bacteria, and zooplankton consume the sinking, carbon-rich particles and convert the organic carbon back to its original inorganic form. Depending on how deep this occurs, the carbon can be physically mixed back up into the surface layers for exchange with the atmosphere or repeat consumption by phytoplankton. In a recent study published in Biogeosciences, researchers used field data and an ecosystem model in three very different oceanic regions to show that zooplankton are extremely important in determining how much carbon reaches the deep ocean.

Figure 1. Particle export and transfer efficiency to the deep ocean in the Southern Ocean (SO, blue circles), North Atlantic Porcupine Abyssal Plain site (PAP, red squares) and the Equatorial Tropical North Pacific (ETNP, orange triangles) oxygen minimum zone. a) particle export efficiency of fast sinking particles (Fast PEeff) against primary production on a Log10 scale. b) transfer efficiency of particles to the deep ocean expressed as Martin’s b (high b = low efficiency). Error bars in b) are standard error of the mean for observed particles, error too small in model to be seen on this plot.

In the Southern Ocean (SO), zooplankton graze on phytoplankton and produce rapidly sinking fecal pellets, resulting in an inverse relationship between particle export and primary production (Fig. 1a). In the North Atlantic (NA), the efficiency with which particles are transferred to the deep ocean is comparable to that of the Southern Ocean, suggesting similar processes apply; but in both regions, there is a large discrepancy between the field data and the ecosystem model (Fig. 1b), which poorly represents particle processing by zooplankton. Conversely, much better data-model matches are observed in the equatorial Pacific, where lower oxygen concentrations mean fewer zooplankton; this reduces the potential for zooplankton-particle interactions that reduce particle size and density, resulting in a lower transfer efficiency.

This result suggests that mismatches between the data and model in the SO and NA may be due to the lack of zooplankton-particle parameterizations in the model, highlighting the potential importance of zooplankton in regulating carbon export and storage in the deep ocean. Zooplankton parameterizations in ecosystem models must be enhanced by including zooplankton fragmentation of particles as well as consumption. Large field programs such as EXPORTS could help constrain these parameterisation by collecting data on zooplankton-particle interaction rates. This will improve our model estimates of carbon export and our ability to predict future changes in the biological carbon pump. This is especially important in the face of climate-driven changes in zooplankton populations (e.g. oxygen minimum zone (OMZ) expansion) and associated implications for ocean carbon storage and atmospheric carbon dioxide levels.

 

Authors:
Emma L. Cavan (University of Tasmania)
Stephanie A. Henson (National Oceanography Centre, Southampton)
Anna Belcher (University of Southampton)
Richard Sanders (National Oceanography Centre, Southampton)

Do rivers supply nutrients to the open ocean?

Posted by mmaheigan 
· Wednesday, May 24th, 2017 

Rivers carry large amounts of nutrients (e.g., nitrogen and phosphorus) to the sea, but we do not know how much of that riverine nutrient supply escapes biological and chemical processing in shallow coastal waters to reach the open ocean. Most global ocean biogeochemical models, which are typically unable to resolve coastal processes, assume that either all or none of the riverine nutrients entering coastal waters actually contribute to open ocean processes.

While we know a good deal about the dynamics of individual rivers entering the coastal ocean, studies to date have been limited to a few major river systems, mainly in in developed countries. Globally, there are over 6,000 rivers entering the coastal ocean. In a recent study, Sharples et al (2017) devised a simple approach to obtain a global-scale estimate of riverine nutrient inputs based on the knowledge that low-salinity waters entering the coastal ocean tend to form buoyant plumes that turn under the influence of Earth’s daily rotation to flow along the coastline. Using published data on such flows and incorporating the effect of Earth’s rotation, they obtained estimates of typical cross-shore plume width and compared them to the local width of the continental shelf. This was used to calculate the residence time of riverine nutrients on the shelf, which is the key to estimating how much of a given nutrient is consumed in shelf waters vs. how much is exported to the open ocean.

Global distribution of the amount of riverine dissolved inorganic nitrogen that escapes the continental shelf to reach the open ocean.

The results indicate that, on a global scale, 75% (80%) of the nitrogen (phosphorus) supplied by rivers reaches the open ocean, whereas 25% (20%) of the nitrogen (phosphorus) is consumed on the shelf (e.g., fueling coastal productivity). Limited knowledge of nutrient cycling and consumption in shelf waters represents the primary source of uncertainty in this study. However, well-defined global patterns related to human land use (e.g., agricultural fertilizer use in developed nations) emerged from this analysis, underscoring the need to understand how land-use changes and other human activities will alter nutrient delivery to the coastal ocean in the future.

 

Authors:
Jonathan Sharples (School of Environmental Sciences, University of Liverpool, UK)
Jack Middelburg (Department of Earth Sciences, Utrecht University, Netherlands)
Katja Fennel (Department of Oceanography, Dalhousie University, Canada)
Tim Jickells (School of Environmental Sciences, University of East Anglia, UK)

Reconciling fisheries catch and ocean productivity in a changing climate

Posted by mmaheigan 
· Thursday, March 16th, 2017 

Phytoplankton provide the energy that fuels marine food webs, yet differences in fisheries catch across global ecosystems far exceed accompanying differences in phytoplankton production. Nearly 50 years ago, John Ryther hypothesized that this contrast must arise from synergistic interactions between phytoplankton production and food webs. New perspectives on global fish catch, fishing effort, and a prototype high-resolution global earth system model allowed us to revisit Ryther’s supposition and explore its implications under climate change. After accounting for a small number of lightly fished ecosystems, we find that stark differences in regional catch can be explained with an energetically constrained model that a) resolves large inter-regional differences in the benthic and pelagic energy pathways connecting phytoplankton and fish; b) reduces trophic transfer efficiencies in warm, tropical ecosystems; and, less critically, c) associates elevated trophic transfer efficiencies with benthic systems. The same food web processes that accentuate spatial differences in phytoplankton production in the contemporary ocean also accentuated temporal trends under climate change, with projected fish catch changes in some areas exceeding 50% (Figure 1). Our results, recently published in PNAS, demonstrate the importance of marine resource management strategies that are robust to potentially significant changes in fisheries productivity baselines. These results also provide impetus for efforts to improve constraints on regional ocean productivity projections that often disagree in present earth system models.

Figure 1: Projected percent changes in net phytoplankton production (left) and fisheries catch (right) between 2050-2100 and 1950-2000 under a high greenhouse gas emission scenario (RCP8.5) in GFDL’s ESM2M-COBALT Earth System Model. Contours are shown for +/- 50%.

 

Authors: Charles A. Stocka, Jasmin G. Johna, Ryan R. Rykaczewskib,c, Rebecca G. Aschd, William W.L. Cheunge, John P. Dunnea, Kevin D. Friedlandf, Vicky W.Y. Lame, Jorge L. Sarmientod, and Reg A. Watsong

aGeophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration 
bSchool of the Earth, Ocean, and Environment, University of South Carolina 
cDepartment of Biological Sciences, University of South Carolina
dAtmospheric and Oceanic Sciences Program, Princeton University
eNippon Foundation-Nereus Program, Institute of Oceans and Fisheries, The University of British Columbia
fNational Marine Fisheries Service, Narragansett, RI
gInstitute for Marine and Antarctic Studies, University of Tasmania, Australia

Oceanic fronts enhance carbon transport to the ocean’s interior through both subduction and amplified sinking

Posted by mmaheigan 
· Wednesday, March 1st, 2017 

Mesoscale fronts are regions with potentially enhanced nutrient fluxes, phytoplankton production and biomass, and aggregation of mesozooplankton and higher trophic levels. However, the role of these features in transporting organic carbon to depth and hence sequestering CO2 from the atmosphere has not previously been determined. Working with the California Current Ecosystem Long Term Ecological Research (CCE LTER) program, we determined that the flux of sinking particles at a stable front off the coast of California was twice as high as similar fluxes on either side of the front, or in typical non-frontal waters of the CCE in a recent study by Stukel et al. (2017) published in Proceedings of the National Academy of Sciences.

This increased export flux was tied to enhanced silica-ballasting by Fe-stressed diatoms and to an abundance of mesozooplankton grazers. Furthermore, downward transport of particulate organic carbon by subduction at the front led to additional carbon export that was similar in magnitude to sinking flux, suggesting that these fronts (which are a common feature in productive eastern boundary upwelling systems) are an important conduit for carbon sequestration. These enhanced carbon export mechanisms at episodic and mesoscale features need to be included in future biogeochemical forecast models to understand how a changing climate will affect marine CO2 uptake.

Authors

Michael R. Stukel (Florida State University)
Lihini I. Aluwihare, Katherine A. Barbeau, Ralf Goericke, Arthur J. Miller, Mark D. Ohman, Angel Ruacho, Brandon M. Stephens, Michael R. Landry (University of California, San Diego)
Hajoon Song (Massachusetts Institute of Technology)
Alexander M. Chekalyuk (Lamont-Doherty Earth Observatory)

A framework for ENSO predictability of marine ecosystem drivers along the US West Coast

Posted by mmaheigan 
· Thursday, February 16th, 2017 

The US West Coast eastern boundary upwelling system supports one of the most productive marine ecosystems in the world and is a primary source of ecosystem services for the US (e.g., fishing, shipping, and recreation). Long-term historical observations of physical and biological variables in this region have been collected since the 1950s (e.g., the CalCOFI program and now including the coastal ocean observing systems), leading to an excellent foundation for understanding the ecosystem impacts of dominant climate fluctuations such as the El Niño-Southern Oscillation (ENSO). In the northeast Pacific, ENSO impacts a wide range of physical and biotic processes, including temperature, stratification, winds, upwelling, and primary and secondary production. The El Niño phase of ENSO, in particular, can result in extensive geographic habitat range displacements and altered catches of fishes and invertebrates, and impact vertical and lateral export fluxes of carbon and other elements (Jacox et al., this issue; Anderson et al., this issue; Ohman et al., this issue). However, despite empirical observations and increased understanding of the coupling between climate and marine ecosystems along the US West Coast, there has been no systematic attempt to use this knowledge to forecast marine ecosystem responses to individual ENSO events. ENSO forecasting has become routine in the climate community. However, little has been done to forecast the impacts of ENSO on ecosystems and their services. This becomes especially important considering the occurrence of recent strong El Niño events (such as 2015-16) and climate model projections that suggest that ENSO extremes may become more frequent (Cai et al. 2015).

The joint US CLIVAR/OCB/NOAA/PICES/ICES workshop on Forecasting ENSO impacts on marine ecosystems of the US West Coast (Di Lorenzo et al. 2017) held in La Jolla, California, in August 2016 outlined a three-step strategy to better understand and quantify the ENSO-related predictability of marine ecosystem drivers along the US West Coast (Figure 1). The first step is to use a high-resolution ocean reanalysis to determine the association between local ecosystem drivers and regional forcing patterns (RFPs). The identification of ecosystem drivers will depend on the ecosystem indicators or target species selected for prediction (Ohman et al., this issue). The second step is to objectively identify the tropical sea surface temperature (SST) patterns that optimally force the RFPs along the US West Coast region using available long-term large-scale reanalysis products. While the goal of the first two steps is to understand the dynamical basis for predictability (Figure 1, blue path), the final third step (Figure 1, orange path) aims at quantifying the predictability of the RFPs and estimating their prediction skill at seasonal timescales. This third step can be implemented using the output of multi-model ensemble forecasts such as the North America Multi-Model Ensemble (NMME) or by building efficient statistical prediction models such as Linear Inverse Models (LIMs; Newman et al. 2003).

Figure 1. Framework for understanding and predicting ENSO impacts on ecosystem drivers. Blue path shows the steps that will lead to Understanding of the ecosystem drivers and their dependence on tropical Pacific anomalies. Orange path shows the steps that will lead to quantifying the Predictability of marine ecosystem drivers along the US West Coast that are predictable from large-scale tropical teleconnection dynamics.

 

Important to the concept of ENSO predictability is the realization that the expressions of ENSO are very diverse and cannot be identified with a few indices (Capotondi et al. 2015; Capotondi et al., this issue). In fact, different expressions of sea surface temperature anomalies (SSTa) in the tropics give rise to oceanic and atmospheric teleconnections that generate different coastal impacts in the northeast Pacific. For this reason, we will refer to ENSO as the collection of tropical Pacific SSTa that lead to deterministic and predictable responses in the regional ocean and atmosphere along the US West Coast.

In the sections below, we articulate in more detail the elements of the framework for quantifying the predictability of ENSO-related impacts on coastal ecosystems along the US West Coast (Figure 1). Our focus will be on the California Current System (CCS), reflecting the regional expertise of the workshop participants. Specifically, we discuss (1) the ecosystem drivers and what is identified as such; (2) RFP definitions; and (3) the teleconnections from the tropical Pacific and their predictability.

Ecosystem drivers in the California Current System

The impacts of oceanic processes on the CCS marine ecosystem have been investigated since the 1950s when the long-term California Cooperative Oceanic Fisheries Investigations (CalCOFI) began routine seasonal sampling of coastal ocean waters. The CalCOFI program continues today and has been augmented with several other sampling programs (e.g., the coastal ocean observing network), leading to an unprecedented understanding of how climate and physical ocean processes, such as upwelling, drive ecosystem variability and change (e.g., see more recent reviews from King et al.2011; Ohman et al. 2013; Di Lorenzo et al. 2013).

The dominant physical oceanographic drivers of ecosystem variability occur on seasonal, interannual, and decadal timescales and are associated with changes in (1) SST; (2) upwelling velocity; (3) alongshore transport; (4) cross-shore transport; and (5) thermocline/nutricline depth (see Ohman et al., this issue). This set of ecosystem drivers emerged from discussions among experts at the workshop. Ecosystem responses to these drivers include multiple trophic levels, including phytoplankton, zooplankton, small pelagic fish, and top predators, and several examples have been identified for the CCS (see summary table in Ohman et al., this issue).

While much research has focused on diagnosing the mechanisms by which these physical drivers impact marine ecosystems, less is known about the dynamics controlling the predictability of these drivers. As highlighted in Ohman et al. (this issue), most of the regional oceanographic drivers (e.g., changes in local SST, upwelling, transport, thermocline depth) are connected to changes in large-scale forcings (e.g., winds, surface heat fluxes, large-scale SST and sea surface height patterns, freshwater fluxes, and remotely forced coastally trapped waves entering the CCS from the south). In fact, several studies have documented how large-scale changes in wind patterns associated with the Aleutian Low and the North Pacific Oscillation drive oceanic modes of variability such as the Pacific Decadal Oscillation and the North Pacific Gyre Oscillation (Mantua et al. 1997; Di Lorenzo et al. 2008; Chhak et al. 2009; Ohman et al., this issue; Jacox et al., this issue; Anderson et al., this issue; Capotondi et al., this issue) that influence the CCS. However, these large-scale modes only explain a fraction of the ecosystem’s atmospheric forcing functions at the regional-scale. Thus, it is important to identify other key forcings to gain a more complete mechanistic understanding of CCS ecosystem drivers (e.g., Jacox et al. 2014; 2015).

Atmospheric and oceanic regional forcing patterns

The dominant large-scale quantities that control the CCS ecosystem drivers are winds, heat fluxes, and remotely forced coastally trapped waves (Hickey 1979). Regional expressions or patterns of these large-scale forcings have been linked to changes in local stratification and thermocline depth (Veneziani et al. 2009a; 2009b; Combes et al. 2013), cross-shore transport associated with mesoscale eddies (Kurian et al. 2011; Todd et al. 2012; Song et al. 2012; Davis and Di Lorenzo 2015b), and along-shore transport (Davis and Di Lorenzo 2015a; Bograd et al. 2015). For this reason, we define the regional expressions of the atmospheric and remote wave forcing that are optimal in driving SST, ocean transport, upwelling, and thermocline depth as the RFPs. To clarify this concept, consider the estimation of coastal upwelling velocities. While a change in the position and strength in the Aleutian Low has been related to coastal upwelling in the northern CCS, a more targeted measure of the actual upwelling vertical velocity and nutrient fluxes that are relevant to primary productivity can only be quantified by taking into account a combination of oceanic processes that depend on multiple RFPs such as thermocline depth (e.g., remote waves), thermal stratification (e.g., heat fluxes), mesoscale eddies, and upwelling velocities (e.g., local patterns of wind stress curl and alongshore winds; see Gruber et al 2011; Jacox et al. 2015; Renault et al. 2016). In other words, if we consider the vertical coastal upwelling velocity (w) along the northern CCS, a more adequate physical description and quantification would be given from a linear combination of the different regional forcing functions w = Σn ∝n * RFPn rather than w = ∝*Aleutian Low.

The largest interannual variability in the Pacific that impacts the RFPs is ENSO, which also constitutes the largest source of seasonal (3-6 months) predictability. During El Niño and La Niña, atmospheric and oceanic teleconnections from the tropics modify large-scale and local surface wind patterns and ocean currents of the CCS and force coastally trapped waves.

ENSO teleconnections and potential seasonal predictability of the regional forcing patterns

While ENSO exerts important controls on the RFPs in the CCS, it has become evident that ENSO expressions in the tropics vary significantly from event to event, leading to different responses in the CCS (Capotondi et al., this issue). Also, as previously pointed out, the CCS is not only sensitive to strong ENSO events but more generally responds to a wide range of tropical SSTa variability that is driven by ENSO-type dynamics in the tropical and sub-tropical Pacific. For this reason, we define an “ENSO teleconnection” as any RFP response that is linked to ENSO-type variability in the tropics.

ENSO can influence the upwelling and circulation in the CCS region through both oceanic and atmospheric pathways. It is well known that equatorial Kelvin waves, an integral part of ENSO dynamics, propagate eastward along the Equator and continue both northward (and southward) along the coasts of the Americas as coastally trapped Kelvin waves after reaching the eastern ocean boundary. El Niño events are associated with downwelling Kelvin waves, leading to a deepening of the thermocline, while La Niña events produce a shoaling of the thermocline in the CCS (Simpson 1984; Lynn and Bograd 2002; Huyer et al. 2002; Bograd et al. 2009; Hermann et al. 2009; Miller et al. 2015). The offshore scale of coastal Kelvin waves decreases with latitude, and the waves decay while propagating northward along the coast due to dissipation and radiation of westward propagating Rossby waves. In addition, topography and bathymetry can modify the nature of the waves and perhaps partially impede their propagation at some location. Thus, the efficiency of coastal waves of equatorial origin in modifying the stratification in the CCS is still a matter of debate. To complicate matters, regional wind variability south of the CCS also excites coastally trapped waves, which supplement the tropical source.

In the tropics, SST anomalies associated with ENSO change tropical convection and excite mid-troposphere stationary atmospheric Rossby waves that propagate signals to the extratropics, the so-called atmospheric ENSO teleconnections (Capotondi et al., this issue). Through these atmospheric waves, warm ENSO events favor a deepening and southward shift of the Aleutian Low pressure system that is dominant during winter, as well as changes in the North Pacific Subtropical High that is dominant during spring and summer, resulting in a weakening of the alongshore winds, reduced upwelling, and warmer surface water. These changes are similar to those induced by coastal Kelvin waves of equatorial origin, making it very difficult to distinguish the relative importance of the oceanic and atmospheric pathways in the CCS. In addition, due to internal atmospheric noise, the details of the ENSO teleconnections can vary significantly from event to event and result in important differences along the California Coast (Figure 2).

Figure 2. Schematic of ENSO teleconnection associated with different flavors of tropical SSTa. (a) Atmospheric teleconnections of the canonical eastern Pacific El Niño tend to impact the winter expression of the Aleutian Low, which in turn drives an oceanic SSTa anomaly that projects onto the pattern of the Pacific Decadal Oscillation (PDO). (b) Atmospheric teleconnections of the central Pacific El Niño tend to impact the winter expression of the North Pacific High, which in turn drives an oceanic SSTa anomaly that projects onto the pattern of the North Pacific Gyre Oscillation (NPGO). The ENSO SSTa maps are obtained by regressing indices of central and eastern Pacific ENSO with SSTa. The other maps are obtained by regression of SSTa/SLPa with the PDO (a) and NPGO (b) indices.

 

El Niño events exhibit a large diversity in amplitude, duration, and spatial pattern (Capotondi et al. 2015). The amplitude and location of the maximum SST anomalies, whether in the eastern (EP) or central (CP) Pacific, can have a large impact on ENSO teleconnections (Ashok et al. 2007; Larkin and Harrison 2005). While “canonical” EP events induce changes in the Aleutian Low (Figure 2b), CP events have been associated with a strengthening of the second mode of North Pacific atmospheric variability, the North Pacific Oscillation (NPO; Figure 2a; Di Lorenzo et al. 2010; Furtado et al. 2012). In addition, it is conceivable that EP events have a larger Kelvin wave signature than CP events, resulting in different oceanic influences in the CCS.

In summary, while the ENSO influence on the CCS physical and biological environments is undeniable, several sources of uncertainty remain about the details of that influence. This uncertainty arises in the physical environment on seasonal timescales from many sources, including the diversity of ENSO events, the intrinsic unpredictable components of the atmosphere, and the intrinsic unpredictable eddy variations in the CCS. We also need to distinguish between physically forced ecosystem response versus intrinsic biological variability, which is potentially nonlinear and likely unpredictable. Skill levels need to be quantified for each step of the prediction process (i.e., ENSO, teleconnections, local oceanic response, local ecosystem response) relative to a baseline—for example the persistence of initial condition, which is also being exploited for skillful predictions of the large marine ecosystem at the seasonal timescale (Tommasi et al., this issue). The target populations should be exploitable species that are of interest to federal and state agencies that regulate certain stocks. Models are currently being developed to use ocean forecasts to advance top predator management (Hazen et al., this issue). The implementation of this framework (Figure 1) for practical uses will require a collaborative effort between physical climate scientists with expertise in predicting and understanding ENSO and biologists who have expertise in understanding ecosystem response to physical climate forcing.

Authors

Emanuele Di Lorenzo (Georgia Institute of Technology)
Arthur J. Miller (Scripps Institution of Oceanography)

References

Ashok, K., S.K. Behera, S.A. Rao, H. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnections. J. Geophys. Res., 112, doi:10.1029/2006JC003798

Bograd, S. J., M. Pozo Buil, E. Di Lorenzo, C. G. Castro, I. D. Schroeder, R. Goericke, C. R. Anderson, C. Benitez-Nelson, and F. A. Whitney, 2015: Changes in source waters to the Southern California Bight. Deep-Sea Res. Part II-Top. Stud. Oceanogr., 112, 42-52, doi:10.1016/j.dsr2.2014.04.009.

Bograd, S. J., I. Schroeder, N. Sarkar, X. Qiu, W. J. Sydeman, and F. B. Schwing, 2009: Phenology of coastal upwelling in the California Current. Geophy. Res. Lett., 36, doi: 10.1029/2008GL035933.

Cai, W. J., and Coauthors, 2015: ENSO and greenhouse warming. Nature Climate Change, 5, 849-859, doi:10.1038/nclimate2743.

Capotondi, A., and Coauthors, 2015: Understanding ENSO Diversity. Bull. Amer. Meteor. Soc., 96, 921-938, doi:10.1175/BAMS-D-13-00117.1.

Chhak, K. C., E. Di Lorenzo, N. Schneider, and P. F. Cummins, 2009: Forcing of low-frequency ocean variability in the northeast Pacific. J. Climate, 22, 1255-1276, doi:10.1175/2008jcli2639.1.

Davis, A., and E. Di Lorenzo, 2015a: Interannual forcing mechanisms of California Current transports I: Meridional Currents. Deep-Sea Res. Part II-Top. Stud. Oceanogr., 112, 18-30, doi:10.1016/j.dsr2.2014.02.005.

Davis, A., and E. Di Lorenzo, 2015b: Interannual forcing mechanisms of California Current transports II: Mesoscale eddies. Deep-Sea Res. Part II-Top. Stud. Oceanogr., 112,  31-41, doi:10.1016/j.dsr2.2014.02.004.

Di Lorenzo, E., and Coauthors, 2017: Forecasting ENSO impacts on marine ecosystems of the US West Coast, Joint US CLIVAR/NOAA/PICES/ICES Report, https://usclivar.org/meetings/2016-enso-ecosystems, forthcoming.

Di Lorenzo, E., and Coauthors, 2008: North Pacific Gyre Oscillation links ocean climate and ecosystem change. Geophys. Res. Lett., 35, doi:10.1029/2007gl032838.

Di Lorenzo, E., and Coauthors, 2013: Synthesis of Pacific Ocean climate and ecosystem dynamics. Oceanogr., 26, 68-81, doi: 10.5670/oceanog.2013.76.

Di Lorenzo, E., K. M. Cobb, J. C. Furtado, N. Schneider, B. T. Anderson, A. Bracco, M. A. Alexander, and D. J. Vimont, 2010: Central Pacific El Nino and decadal climate change in the North Pacific Ocean. Nature Geosci., 3, 762-765, doi:10.1038/ngeo984.

Furtado, J. C., E. Di Lorenzo, B. T. Anderson, and N. Schneider, 2012: Linkages between the North Pacific Oscillation and central tropical Pacific SSTs at low frequencies. Climate Dyn., 39, 2833-2846, doi:10.1007/s00382-011-1245-4.

Gruber, N., Z. Lachkar, H. Frenzel, P. Marchesiello, M. Munnich, J. C. McWilliams, T. Nagai, and G. K. Plattner, 2011: Eddy-induced reduction of biological production in eastern boundary upwelling systems. Nature Geosci., 4, 787-792, doi:10.1038/ngeo1273.

Hermann, A. J., E. N. Curchitser, D. B. Haidvogel, and E. L. Dobbins, 2009: A comparison of remote vs. local influence of El Nino on the coastal circulation of the northeast Pacific. Deep Sea Res. Part II: Top. Stud. Oceanogr., 56, 2427-2443, doi: 10.1016/j.dsr2.2009.02.005.

Hickey, B. M., 1979. The California Current system—hypotheses and facts. Prog. Oceanogr., 8, 191-279, doi: 10.1016/0079-6611(79)90002-8.

Huyer, A., R. L. Smith, and J. Fleischbein, 2002: The coastal ocean off Oregon and northern California during the 1997–8 El Nino. Prog. Oceanogr., 54, 311-341, doi: 10.1016/S0079-6611(02)00056-3.

Jacox, M. G., A. M. Moore, C. A. Edwards, and J. Fiechter, 2014: Spatially resolved upwelling in the California Current System and its connections to climate variability. Geophy. Res. Lett., 41, 3189-3196, doi:10.1002/2014gl059589.

Jacox, M. G., J. Fiechter, A. M. Moore, and C. A. Edwards, 2015: ENSO and the California Current coastal upwelling response. J. Geophy. Res.-Oceans, 120, 1691-1702, doi:10.1002/2014jc010650.

Jacox, M. G., S. J. Bograd, E. L. Hazen, and J. Fiechter, 2015: Sensitivity of the California Current nutrient supply to wind, heat, and remote ocean forcing. Geophys. Res. Lett., 42, 5950-5957, doi:10.1002/2015GL065147.

Jacox, M. G., E. L. Hazen, K. D. Zaba, D. L. Rudnick, C. A. Edwards, A. M. Moore, and S. J. Bograd, 2016: Impacts of the 2015-2016 El Niño on the California Current System: Early assessment and comparison to past events. Geophys. Res. Lett., 43, 7072-7080, doi:10.1002/2016GL069716.

King, J. R., V. N. Agostini, C. J. Harvey, G. A. McFarlane, M. G. G. Foreman, J. E. Overland, E. Di Lorenzo, N. A. Bond, and K. Y. Aydin, 2011: Climate forcing and the California Current ecosystem. Ices J. Mar. Sci., 68, 1199-1216, doi:10.1093/icesjms/fsr009.

Kurian, J., F. Colas, X. Capet, J. C. McWilliams, and D. B. Chelton, 2011: Eddy properties in the California Current System. J. Geophy. Res.-Oceans, 116, doi:10.1029/2010jc006895.

Larkin, N. K. and D. E. Harrison, 2005: On the definition of El Niño and associated seasonal average US weather anomalies. Geophy. Res. Lett. 32, doi: 10.1029/2005GL022738.

Lynn, R. J. and S. J. Bograd, 2002: Dynamic evolution of the 1997–1999 El Niño–La Niña cycle in the southern California Current system. Prog. Oceanogr., 54, 59-75, doi: 10.1016/S0079-6611(02)00043-5.

Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Amer. Meteorol. Soc., 78, 1069-1079, doi:10.1175/1520-0477(1997)078<1069:apicow>2.0.co;2.

Marchesiello, P., J. C. McWilliams, and A. Shchepetkin, 2003: Equilibrium structure and dynamics of the California Current System. J. Phys. Oceanogr., 33, 753-783, doi: 10.1175/1520-0485(2003)33<753:ESADOT>2.0.CO;2.

McCreary, J. P., P. K. Kundu, and S. Y. Chao, 1987: On the dynamics of the California Current System. J. Mar. Res., 45, 1-32, doi: 10.1357/002224087788400945.

Miller, A. J., H. Song, and A. C. Subramanian, 2015: The physical oceanographic environment during the CCE-LTER Years: Changes in climate and concepts. Deep Sea Res. Part II: Top. Stud. Oceanogr., 112, 6-17, doi: 10.1016/j.dsr2.2014.01.003.

Newman, M., and Coauthors, 2016: The Pacific Decadal Oscillation, Revisited. J. Climate, 29, 4399-4427, doi:10.1175/jcli-d-15-0508.1.

Ohman, M. D., K. Barbeau, P. J. S. Franks, R. Goericke, M. R. Landry, and A. J. Miller, 2013: Ecological transitions in a coastal upwelling ecosystem. Oceanogr., 26, 210-219, doi: 10.5670/oceanog.2013.65.

Renault, L., C. Deutsch, J. C. McWilliams, H. Frenzel, J.-H. Liang, and F. Colas, 2016: Partial decoupling of primary productivity from upwelling in the California Current system. Nature Geosci, 9, 505-508, doi:10.1038/ngeo2722.

Simpson, J. J., 1984; El Nino‐induced onshore transport in the California Current during 1982‐1983. Geophy. Res. Lett., 11, 233-236, doi: 10.1029/GL011i003p00233.

Song, H., A. J. Miller, B. D. Cornuelle, and E. Di Lorenzo, 2011: Changes in upwelling and its water sources in the California Current System driven by different wind forcing. Dyn. Atmos. Oceans, 52, 170-191, doi:10.1016/j.dynatmoce.2011.03.001.

Todd, R. E., D. L. Rudnick, M. R. Mazloff, B. D. Cornuelle, and R. E. Davis, 2012: Thermohaline structure in the California Current System: Observations and modeling of spice variance. J. Geophy. Res.-Oceans, 117, doi:10.1029/2011jc007589.

Veneziani, M., C. A. Edwards, J. D. Doyle, and D. Foley, 2009: A central California coastal ocean modeling study: 1. Forward model and the influence of realistic versus climatological forcing. J. Geophy. Res.-Oceans, 114, doi:10.1029/2008jc004774.

Veneziani, M., C. A. Edwards, and A. M. Moore, 2009: A central California coastal ocean modeling study: 2. Adjoint sensitivities to local and remote forcing mechanisms. J. Geophy. Res.-Oceans, 114, doi:10.1029/2008jc004775.

 

 

 

ENSO impacts on ecosystem indicators in the California Current System

Posted by mmaheigan 
· Thursday, February 16th, 2017 

El Niño-Southern Oscillation (ENSO) events activate long-distance teleconnections through the atmosphere and ocean that can dramatically impact marine ecosystems along the West Coast of North America, affecting diverse organisms ranging from plankton to exploitable and protected species. Such ENSO-related changes to marine ecosystems can ultimately affect humans in many ways, including via depressed plankton and fish production, dramatic range shifts for many protected and exploited species, inaccessibility of traditionally fished resources, more prevalent harmful algal blooms, altered oxygen and pH of waters used in mariculture, and proliferation of pathogens. The principal objective of the Forecasting ENSO Impacts on Marine Ecosystems of the US West Coast workshop was to develop a scientific framework for building an ENSO-related forecast system of ecosystem indicators along the West Coast of North America, including major biological and biogeochemical responses. Attendees realized that a quantitative, biologically-focused forecast system is a much more challenging objective than forecasting the physical system alone; it requires an understanding of the ocean-atmospheric physical system and of diverse organism-level, population-level, and geochemical responses that, in aggregate, lead to altered ecosystem states.

In the tropical ocean, important advances have been made in developing both intensive observational infrastructure (Global Tropical Moored Buoy Array) and diverse dynamical and statistical models that utilize these data in ENSO forecasting. These forecasts are made widely available (e.g., NOAA’s Climate Prediction Center). The most sophisticated ENSO-forecasting efforts use global, coupled ocean-atmosphere climate models that extend ENSO-forecasting skill into seasonal climate forecasting skill for other regions, including the California Current System (CCS). However, both these measurement systems and forecast models are restricted to the physical dynamics of ENSO, rather than biotic and biogeochemical consequences.

Primary modes of influence of El Niño on marine organisms

In this brief discussion, we focus primarily on the warm (El Niño) phases of ENSO, which can have large and generally negative ecosystem consequences, although changes accompanying the cold phases (La Niña) can also be significant. We primarily address pelagic ocean processes, which merely reflect the expertise of the participants at the workshop. Physical mechanisms by which ENSO impacts the U.S. West Coast are more completely explained in Jacox et al. (this issue).

El Niño affects organisms and biogeochemistry via both local and advective processes (Figure 1). ENSO-related changes in the tropics can affect the CCS through an atmospheric teleconnection (Alexander et al. 2002) to alter local winds and surface heat fluxes, and through upper ocean processes (thermocline and sea level displacements and geostrophic currents) forced remotely by poleward propagating coastally trapped waves (CTWs) of tropical origin (Enfield and Allen 1980; Frischkencht et al. 2015; Figure 1). It is important to recognize that ecosystem effects will occur through three primary mechanisms: (1) via the direct action of altered properties like temperature, dissolved O2, and pH on the physiology and growth of marine organisms; (2) through food web effects as changes in successive trophic levels affect their predators (bottom up) or prey (top down); and (3) through changes in advection related to the combination of locally forced Ekman transport and remotely forced geostrophic currents, typically involving poleward and/or onshore transport of organisms. Advective effects can be pronounced, transporting exotic organisms into new regions and altering the food web if these imported species have significant impacts as predators, prey, competitors, parasites, or pathogens.

Figure 1. Schematic illustration of dominant mechanisms through which ENSO impacts biological and biogeochemical processes in the California Current System. Processes include both local effects (e.g., heat budget, winds) and advective effects. Such processes can influence organisms via: (1) (yellow arrow) direct physiological responses to changes in temperature, O2, pH, etc.; (2) (orange arrows) effects that propagate through the food web, as successive trophic levels affect their predators (bottom up, upward-facing orange arrows) or prey (top down, downward-facing orange arrows); (3) (blue arrows) direct transport effects of advection. Top predators are not included here. CTW indicates coastally trapped waves.

 

I. Poleward and onshore transport

Active, mobile marine fishes, seabirds, reptiles, and mammals may move into new (or away from old) habitats in the CCS as ENSO-related changes occur in the water column and render the physical-chemical characteristics and prey fields more (or less) suitable for them. Planktonic organisms are often critical prey and are, by definition, subject to geographic displacements as a consequence of altered ocean circulation that accompanies El Niño events. Most commonly, lower latitude organisms are transported poleward to higher latitudes in either surface flows or in an intensified California Undercurrent (Lynn and Bograd 2002). However, some El Niño events are accompanied by onshore flows (Simpson 1984), potentially displacing offshore organisms toward shore (Keister et al. 2005).

Two of the most celebrated examples of poleward transport come from distributions of pelagic red crabs (Pleuroncodes planipes) and the subtropical euphausiid (or krill, Nyctiphanes simplex), both of which have their primary breeding populations in waters off Baja California, Mexico (Boyd 1967; Brinton et al. 1999). Pelagic red crabs were displaced approximately 10° of latitude, from near Bahia Magdalena, Baja California, northward to Monterey, California (Glynn 1961; Longhurst 1967) during the El Niño of 1958-1959. This early event was particularly well documented because of the broad latitudinal coverage of the California Cooperative Oceanic Fisheries Investigations (CalCOFI) cruises at the time. Such El Niño-related northward displacements have been documented repeatedly over the past six decades (McClatchie et al. 2016), partly because the red crabs often strand in large windrows on beaches and are conspicuous to the general public. The normal range of the euphausiid Nyctiphanes simplex is centered at 25-30°N (Brinton et al. 1999). N. simplex has been repeatedly detected far to the north of this range during El Niño, extending at least to Cape Mendocino (40.4°N) in 1958 (Brinton 1960), to northern Oregon (46.0°N) in 1983 (Brodeur 1986), and to Newport, Oregon (44.6°N; Keister et al. 2005) and northwest Vancouver Island (50.7°N; Mackas and Galbraith 2002) in 1998. In spring of 2016, N. simplex were extremely abundant in the southern California region (M. Ohman and L. Sala, personal communication) and detected as far north as Trinidad Head (41.0°N) but not in Newport, Oregon (W. Peterson, personal communication). Sometimes such El Niño-related occurrences of subtropical species are accompanied by declines in more boreal species (e.g., Mackas and Galbraith 2002; Peterson et al. 2002), although this is not always the case.

Among the organisms displaced during El Niños, the consequences of transport of predators are poorly understood but likely significant in altering the food web.  Subtropical fishes can be anomalously abundant in higher latitudes during El Niño (Hubbs 1948; Lluch-Belda et al. 2005; Pearcy and Schoener 1987; Pearcy 2002; Brodeur et al. 2006), with significant consequences for the resident food web via selective predation on prey populations.

II. Habitat compression

Many species are confined to a specific habitat that may compress during El Niño. This phenomenon has been observed repeatedly for species and processes related to coastal upwelling in the CCS. During major El Niño events, as the offshore extent of upwelled waters is reduced and becomes confined close to the coast, the zone of elevated phytoplankton (observed as Chl-a) compresses markedly to a narrow zone along the coastal boundary (e.g., Kahru and Mitchell 2000; Chavez et al. 2002). For example, during the strong El Niño spring of 1983, the temperate euphausiid Euphausia pacifica was present in low densities throughout Central and Southern California waters, but 99% of the biomass was unusually concentrated at a single location (station 80.51) very close to Point Conception, where upwelling was still pronounced (E. Brinton, personal communication). The spawning habitat of the Pacific sardine (Sardinops sagax) was narrowly restricted to the coastal boundary during El Niño 1998, but one year later during La Niña 1999, the spawning habitat extended a few hundred kilometers farther offshore (Lo et al. 2005). Market squid, Doryteuthis opalescens, show dramatically lower catches during El Niño years (Reiss et al. 2004), but in 1998, most of the catch was confined to a small region in Central California (Reiss et al. 2004). During the El Niño in spring 2016, vertical particle fluxes measured by sediment traps were reduced far offshore but remained elevated in the narrow zone of coastal upwelling very close to Point Conception (M. Stukel, personal communication).

III. Altered winds and coastal upwelling

Upwelling-favorable winds along the US West Coast may decline during El Niño conditions (Hayward 2000, but see Chavez et al. 2002) and vertical transports can be reduced (Jacox et al. 2015), mainly during the winter and early spring (Black et al. 2011). Independent of any changes in density stratification (considered below), these decreased vertical velocities can lead to diminished nutrient fluxes, reduced rates of primary production, and a shift in the size composition of the plankton community to smaller phytoplankton and zooplankton (Rykaczewski and Checkley 2008). Such changes at the base of the food web can have major consequences for a sequence of consumers at higher trophic levels, as both the concentration and suitability of prey decline.

However, there are potential compensatory effects of reduced rates of upwelling. Diminished upwelling also means less introduction of CO2-rich, low-oxygen waters to coastal areas (Feely et al. 2008; Bednaršek et al. 2014), with potential benefits to organisms that are sensitive to calcium carbonate saturation state or hypoxic conditions. Furthermore, reduced upwelling implies lower Ekman transport and potentially reduced cross-shore fluxes far offshore within coastal jets and filaments (cf., Keister al. 2009).

IV. Increased stratification and deepening of nutricline

El Niño-related warming of surface waters and increased density stratification can result from advection of warmer waters and/or altered local heating. Evidence suggests that the pycnocline (Jacox et al. 2015) and nitracline (Chavez et al. 2002) deepen during stronger El Niños. This effect, independent of variations in wind stress, also leads to diminished vertical fluxes of nitrate and other limiting nutrients and suppressed rates of primary production. Decreased nitrate fluxes appear to explain elevated 15N in California Current zooplankton (Ohman et al. 2012) and decreased krill abundance (Lavaniegos and Ohman 2007; Garcia-Reyes et al. 2014) during El Niño years. For example, the 2015-16 El Niño resulted in a pronounced warming of surface waters and depressed Chl-a concentrations across a broad region of the CCS (McClatchie et al. 2016).

V. Direct physiological responses to altered temperature, dissolved O2, pH

Most organisms in the ocean—apart from some marine vertebrates—are ectothermic, meaning they have no capability to regulate their internal body temperature. Heating or cooling of the ocean therefore directly influences their rates of metabolism, growth, and mortality. Most organisms show not only high sensitivity to temperature variations but nonlinear responses. A typical temperature response curve or “thermal reaction norm” (e.g., of growth rate) is initially steeply positive with increasing temperature, followed by a narrow plateau, then abruptly declines with further increases in temperature (e.g., Eppley 1972). Different species often show different thermal reaction norms. Hence, El Niño-related temperature changes may not only alter the growth rates and abundances of organisms, but also shift the species composition of the community due to differential temperature sensitivities.

Similarly, El Niño-induced variations in dissolved oxygen concentration and pH can have marked consequences for physiological responses of planktonic and sessile benthic organisms and, for active organisms, potentially lead to migrations into or out of a suitable habitat. Interactions between variables (Boyd et al. 2010) will also lead to both winners and losers in response to major ENSO-related perturbations.

Altered parasite, predator populations, and harmful algal blooms

ENSO-related changes can favor the in situ proliferation or introduction of predators, parasites, pathogens, and harmful algal blooms. Such outbreaks can have major consequences for marine ecosystems, although some are relatively poorly studied. For example, a recent outbreak of sea star wasting disease thought to be caused by a densovirus adversely affected sea star populations at numerous locations along the West Coast (Hewson et al. 2014). While not specifically linked to El Niño, this outbreak was likely tied to warmer water temperatures. Because some sea stars are keystone predators capable of dramatically restructuring benthic communities (Paine 1966), such pathogen outbreaks are of considerable concern well beyond the sea stars themselves.

Domoic acid outbreaks, produced by some species of the diatom genus Pseudo-nitzschia, can result in closures of fisheries for razor clams, Dungeness crab, rock crab, mussels, and lobsters, resulting in significant economic losses. While the causal mechanisms leading to domoic outbreaks are under discussion (e.g., Sun et al. 2011; McCabe et al. 2016), warmer-than-normal ocean conditions in northern regions of the CCS have been linked to domoic acid accumulation in razor clams, especially when El Niño conditions coincide with the warm phase of the Pacific Decadal Oscillation (McKibben et al. 2017).

ENSO diversity, non-stationarity, and consequences of secular changes

There is considerable interest in understanding the underlying dynamical drivers that lead to different El Niño events (Singh et al. 2011; Capotondi et al. 2015). Although there appears to be a continuum of El Niño expression along the equatorial Pacific, some simplify this continuum to a dichotomy between Eastern Pacific (EP) and Central Pacific (CP) events (Capotondi et al 2015). Whether EP and CP El Niños have different consequences for mid-latitude ecosystems like the California Current Ecosystem is an area of open research, but some evidence suggests that differences in timing and intensity of biological effects may exist (cf. Fisher et al. 2015). While some studies (e.g., Lee and McPhaden 2010) suggest that the frequency of CP El Niños is increasing, the evidence is not definitive (Newman et al. 2011). In addition to questions about the ecosystem consequences of El Niño diversity, there are unknowns regarding interactions between El Niño, decadal-scale variability (Chavez et al. 2002), and secular changes in climate (Figure 2, Ohman, unpubl.), which suggest a non-stationary relationship between California Current zooplankton and El Niño. An index of the dominance of warm water krill from CalCOFI sampling in Southern California shows that for the first 50 years there was a predictable positive relationship between these warm water krill and El Niño. This relationship held during both EP and CP El Niño events from 1950-2000. However, the relationship appeared to weaken after 2000. The warm water krill index was negatively correlated with the moderate El Niño of 2009-10. While the krill index again responded to the major El Niño of 2015-16 and the preceding year of warm anomalies (Bond et al. 2015; Zaba and Rudnick 2016), the magnitude of the response was not comparable to what had been seen in earlier decades. It is unclear whether such results are merely the consequence of interannual variability in the mode of El Niño propagation (Todd et al. 2011) or a change in the relationship between El Niño forcing and ecosystem responses.

 

Figure 2. Covariability of California Current euphausiids (krill, blue lines) with an index of ENSO off California (de-trended sea level anomaly [DTSLA] at San Diego, green lines). Note the markedly different relationship between euphausiids and DTSLA after 2000. Sustained excursions of DTSLA exceeding one standard deviation (i.e., above upper dotted red line) are expressions of El Niño (or of the warm anomaly of 2014-2015). Red arrows indicate specific events categorized as either eastern Pacific (EP) or central Pacific (CP) El Niño events (Yu et al. 2012), apart from 2015-2016 which could be either CP or EP. The Warm-Cool euphausiid index is based on the difference in average log carbon biomass anomaly of the four dominant warm water euphausiids in the CCS minus the average anomaly of the four dominant cool water euphausiids (species affinities from Brinton and Townsend 2003). Euphausiid carbon biomass from springtime CalCOFI cruises off Southern California, lines 77-93, nighttime samples only. Dotted blue lines indicate years of no samples (Ohman, personal communication).

Conclusions

While the potential modes of El Niño influence on biological and biogeochemical processes in the CCS are numerous, not all processes are of first order consequence to all organisms. Forecasting ENSO effects on a given target species will likely focus on a limited number of governing processes. Table 1 illustrates some of the specific types of organisms susceptible to El Niño perturbations and the suspected dominant mechanism. We look forward to developing a framework for forecasting such responses in a quantitative manner.

Ecosystem indicator Region and season Change during El Niño Time scale of response Regional ocean processes
Primary production Entire CCS

winter, spring, summer

 Declines Variable lag;

Instantaneous or time-lagged

 Reduced upwelling, nutrient fluxes; Deeper nutricline and weaker winds
Pseudo-nitzschia diatoms; Domoic Acid Entire CCS

spring-summer

 Blooms  

1-3 month lag

 Elevated temperature; Altered nutrient stoichiometry
Copepod assemblage NCCS

spring-summer

 Warm water species appear Nearly instantaneous Poleward advection; Reduced upwelling, warmer temperature
 

Subtropical euphausiids

  

SCCS

spring-summer

  

Increase

 Nearly instantaneous; persists beyond Niño event Poleward advection
Cool water euphausiids Entire CCS

spring-summer

 Decrease Time-lagged Reduced upwelling; Anomalous advection
Pelagic red crabs SCCS & CCCS

winter, spring, summer

 Increase Nearly instantaneous Poleward advection
Market squid CCCS & SCCS

winter & spring

 Collapse Instantaneous for distribution; time-lagged for recruitment Warmer temperature/deeper thermocline; Reduces spawning habitat
Pacific sardine Entire CCS

winter-spring

 Changes in distribution;

Compression of spawning habitat

 Instantaneous for spawning and distribution, recruitment time-lagged, biomass is time-integrated Wind stress, cross-shore transport

 
Northern anchovy CCCS & SCCS

winter-spring

 Changes in distribution;

Compression of spawning habitat

 Instantaneous for spawning and distribution, recruitment time-lagged, biomass is time-integrated Reduced upwelling; Anomalous advection

 
Juvenile salmon survival NCCS

spring-summer

 Decrease in Pacific NW Time-integrated Reduce river flow, decreased food supply in ocean
Adult sockeye salmon

(Fraser River)

 NCCS

summer

 Return path deflected northward to Canadian waters Time-integrated Ocean temperature, including Ekman controls
Warm assemblage of mesopelagic fish SCCS

spring (?)

 Increase Lagged 0-3 months Poleward and onshore advection
Common murre

(reproductive success)

 CCCS

winter-spring

 Decrease Time-Lagged, time-integrated Prey (fish) availability; Thermocline depth; Decreased upwelling?
Top predator reproduction and abundance Entire CCS Species-dependent Time-integrated Advection of prey, altered temperature, upwelling, mesoscale structure
Top predator distribution Entire CCS Altered geographic distributions Instantaneous or time-lagged Advection of prey, altered temperature, upwelling, mesoscale structure
Table 1.   Examples of water column biological processes and organisms known to be affected by El Niño in the California Current System. Columns indicate the type of organism; approximate geographic region and season of the effect; direction of change in response to El Niño; temporal pattern of response (immediate, time-lagged, time-integrated); and the hypothesized oceanographic processes driving the organism response. CCS = California Current System; NCCS, CCCS, and SCCS denote northern, central, and southern sectors of the CCS.

 

Authors

Mark D. Ohman (Scripps Institution of Oceanography)
Nate Mantua (NOAA Southwest Fisheries Science Center)
Julie Keister (University of Washington)
Marisol Garcia-Reyes (Farallon Institute)
Sam McClatchie (NOAA Southwest Fisheries Science Center)

References

Alexander, M. A., I. Blade, M. Newman, J. R. Lanzante, N. C. Lau, and J. D. Scott, 2002: The atmospheric bridge: The influence of ENSO teleconnections on air-sea interaction over the global oceans. Journal of Climate, 15, 2205-2231, doi: 10.1175/1520-0442(2002)015<2205:TABTIO>2.0.CO;2

Bednaršek, N., R. A. Feely, J. C. P. Reum, B. Peterson, J. Menkel, S. R. Alin, and B. Hales, 2014: Limacina helicina shell dissolution as an indicator of declining habitat suitability owing to ocean acidification in the California Current Ecosystem. Proc. Roy. Soc. B-Biolog. Sci., 281, doi: 10.1098/rspb.2014.0123.

Black, B. A., I. D. Schroeder, W. J. Sydeman, S. J. Bograd, B. K. Wells, and F. B. Schwing, 2011: Winter and summer upwelling modes and their biological importance in the California Current Ecosystem. Glob. Change Bio., 17, 2536-2545, doi: 10.1111/j.1365-2486.2011.02422.x.

Bond, N. A., M. F. Cronin, H. Freeland, and N. Mantua, 2015: Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophy. Res. Lett., 42, 3414-3420, doi: 10.1002/2015GL063306.

Boyd, C. M., 1967: The benthic and pelagic habitats of the red crab, Pleuroncodes planipes. Pacific Science, 21, 394-403.

Boyd, P. W., R. Strzepek, F. X. Fu, and D. A. Hutchins, 2010: Environmental control of open-ocean phytoplankton groups: Now and in the future. Limnol. Oceanogr., 55, 1353-1376, doi: 10.4319/lo.2010.55.3.1353.

Brinton, E., 1960: Changes in the distribution of euphausiid crustaceans in the region of the California Current. CalCOFI Reports, 7, 137-146, http://www.calcofi.org/publications/calcofireports/v07/Vol_07_Brinton.pdf.

Brinton, E., M. D. Ohman, A. W. Townsend, M. D. Knight, and A. L. Bridgeman, 1999: Euphausiids of the World Ocean. Vol. CD-ROM, MacIntosh version 1.0, UNESCO Publishing.

Brodeur, R. D., 1986: Northward displacement of the euphausiid Nyctiphanes simplex Hansen to Oregon and Washington waters following the El Niño event of 1982-83. J. Crustacean Bio., 6, 686-692, doi: 10.2307/1548382.

Brodeur, R. D., S. Ralston, R. L. Emmett, M. Trudel, T. D. Auth, and A. J. Phillips, 2006: Anomalous pelagic nekton abundance, distribution, and apparent recruitment in the northern California Current in 2004 and 2005. Geophy. Res. Lett., 33, doi:10.1029/2006gl026614.

Capotondi, A., and Coauthors, 2015: Understanding ENSO Diversity. Bull. Amer. Meteor. Soc., 96, 921-938, doi: 10.1175/BAMS-D-13-00117.1.

Chavez, F. P., and Coauthors, 2002: Biological and chemical consequences of the 1997–1998 El Niño in central California waters. Prog. Oceanogr., 54, 205-232, doi: 10.1016/S0079-6611(02)00050-2.

Enfield, D., and J. Allen, 1980: On the structure and dynamics of monthly mean sea-level anomalies along the Pacific coast of North and South America. J. Phys. Oceanogr., 10, 557–578, doi: 10.1175/1520-0485(1980)010<0557:OTSADO>2.0.CO;2.

Eppley, R. W., 1972: Temperature and phytoplankton growth in the sea. Fish. Bull, 70, 1063-1085, http://fishbull.noaa.gov/70-4/eppley.pdf.

Feely, R. A., C. L. Sabine, J. M. Hernandez-Ayon, and D. H. Ianson, B., 2008: Evidence for upwelling of corrosive “acidified” water onto the continental shelf. Science, 320, 1490-1492, doi: 10.1126/science.1155676.

Fisher J. L., W. T. Peterson, and R. R. Rykaczewski, 2015: The impact of El Niño events on the pelagic food chain in the northern California Current. Glob. Change Bio., 21, 4401–4414, doi: 10.1111/gcb.13054.

Frischknecht, M., M. Münnich, and N. Gruber, 2015: Remote versus local influence of ENSO on the California Current System, J. Geophys. Res. Oceans, 120, 1353–1374, doi:10.1002/2014JC010531.

García-Reyes, M., J. L. Largier, and W. J. Sydeman, 2014: Synoptic-scale upwelling indices and predictions of phyto-and zooplankton populations. Prog. Oceanogr., 120, 177-188, doi: 10.1016/j.pocean.2013.08.004.

Glynn, P. W., 1961: The first recorded mass stranding of pelagic red crabs, Pleuroncodes planipes, at Monterey Bay, California, since 1859, with notes on their biology. Cal. Fish Game, 47, 97-101.

Hayward, T. L., 2000: El Niño 1997-98 in the coastal waters of Southern California: a timeline of events. CalCOFI Reports, 41, 98-116, http://www.calcofi.org/publications/calcofireports/v41/Vol_41_Hayward.pdf.

Hewson, I., and Coauthors, 2014: Densovirus associated with sea-star wasting disease and mass mortality. Proc. Nat. Acad. Sci., 111, 17278-17283, doi: 0.1073/pnas.1416625111.

Hubbs, C. L., 1948: Changes in the fish fauna of western North America correlated with changes in ocean temperature, J. Mar. Res., 7, 459– 482, http://www.nativefishlab.net/library/textpdf/20041.pdf.

Jacox, M. G., J. Fiechter, A. M. Moore, and C. A. Edwards, 2015: ENSO and the California Current coastal upwelling response. J. Geophy. Res. Oceans, 120, 1691-1702, doi: 10.1002/2014JC010650.

Jacox, M.G. …..   [this issue of Variations]  PLEASE ADD FULL REFERENCE

Kahru, M., E. Di Lorenzo, M. Manzano-Sarabia, and B. G. Mitchell, 2012: Spatial and temporal statistics of sea surface temperature and chlorophyll fronts in the California Current. J. Plank. Res., 34, 749-760, doi: 10.1093/plankt/fbs010.

Kahru, M., and B. G. Mitchell, 2000: Influence of the 1997-98 El Niño on the surface chlorophyll in the California Current. Geophys.Res.Lett., 27, 2937-2940, doi: 10.1029/2000GL011486

Keister, J. E., T. J. Cowles, W. T. Peterson, and C. A. Morgan, 2009: Do upwelling filaments result in predictable biological distributions in coastal upwelling ecosystems? Prog. Oceanogr., 83, 303-313, doi: 10.1016/j.pocean.2009.07.042.

Keister, J. E., T. B. Johnson, C. A. Morgan, and W. T. Peterson, 2005: Biological indicators of the timing and direction of warm-water advection during the 1997/1998 El Nino off the central Oregon coast, USA. Mar. Ecol. Prog. Ser., 295, 43-48, http://hdl.handle.net/1957/26294.

Lavaniegos, B. E., and M. D. Ohman, 2007: Coherence of long-term variations of zooplankton in two sectors of the California Current System. Prog. Oceanogr., 75, 42-69, doi: 10.1016/j.pocean.2007.07.002.

Lee, T., and M. J. McPhaden, 2010: Increasing intensity of El Nino in the central-equatorial Pacific. Geophy. Res. Lett., 37, doi: 10.1029/2010gl044007.

Lluch-Belda, D., D. B. Lluch-Cota, and S. E. Lluch-Cota, 2005: Changes in marine faunal distributions and ENSO events in the California Current. Fish. Oceanogr., 14, 458– 467, doi: 10.1111/j.1365-2419.2005.00347.x.

Lo, N. C. H., B. J. Macewicz, and D. A. Griffith, 2005: Spawning biomass of Pacific sardine (Sardinops sagax), from 1994–2004 off California. CalCOFI Reports, 46, 93-112, https://swfsc.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-463.pdf.

Longhurst, A. R., 1967: The pelagic phase of Pleuroncodes planipes Stimpson (Crustacea, Galatheidae) in the California Current. Cal. Coop. Ocean. Fish. Invest. Rep., 11, 142-154, https://decapoda.nhm.org/pdfs/29796/29796.pdf.

Lynn, R. J., and S. J. Bograd, 2002: Dynamic evolution of the 1997-1999 El Nino-La Nina cycle in the southern California Current System. Prog. Oceanogr., 54, 59-75, doi: 10.1016/S0079-6611(02)00043-5.

Mackas, D. L., and M. Galbraith, 2002: Zooplankton community composition along the inner portion of Line P during the 1997-1998 El Nino event. Prog. Oceanogr., 54, 423-437, doi: 10.1016/S0079-6611(02)00062-9.

McCabe, R. M., and Coauthors, 2016: An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions. Geophys. Res. Lett., 43, 10366-10376, doi: 10.1002/2016gl070023

McClatchie, S., and Coauthors, 2016: State of the California Current 2015-16: Comparisons with the 1997-98 El Niño. CalCOFI Reports, 57, 1-57, http://calcofi.org/publications/calcofireports/v57/Vol57-SOTCC_pages.5-61.pdf.

McKibben, S. M., W. Peterson, M. Wood, V. L. Trainer, M. Hunter, and A. E. White, 2017: Climatic regulation of the neurotoxin domoic acid. Proc. Nat. Acad. Sci., 114, 239-244, doi: 10.1073/pnas.1606798114.

Newman, M., S.-I. Shin, and M. A. Alexander, 2011: Natural variation in ENSO flavors. Geophy. Res. Lett., 38, doi:10.1029/2011GL047658.

Ohman, M. D., G. H. Rau, and P. M. Hull, 2012: Multi-decadal variations in stable N isotopes of California Current zooplankton. Deep Sea Res. I, 60, 46-55, doi: 10.1016/j.dsr.2011.11.003.

Paine, R. T., 1966: Food web complexity and species diversity. Amer. Natural., 100, 65-75, http://www.jstor.org/stable/2459379.

Pearcy, W. G., 2002: Marine nekton off Oregon and the 1997 – 98 El Niño. Prog. Oceanogr., 54, 399-403, doi: 10.1016/S0079-6611(02)00060-5.

Pearcy, W. G., and A. Schoener, 1987: Changes in the marine biota coincident with the 1982– 1983 El Niño in the northeastern subarctic Pacific Ocean. J. Geophy. Res., 92, 14,417– 14,428, doi: 10.1029/JC092iC13p14417.

Peterson, W. T., J. E. Keister, and L. R. Feinberg, 2002: The effects of the 1997-99 El Niño/La Niña events on hydrography and zooplankton off the central Oregon coast. Prog. Oceanogr., 54, 381-398, doi: 10.1016/S0079-6611(02)00059-9.

Reiss, C. S., M. R. Maxwell, J. R. Hunter, and A. Henry, 2004: Investigating environmental effects on population dynamics of Loligo opalescens in the Southern California Bight. CalCOFI Reports, 45, 87-97, http://web.calcofi.org/publications/calcofireports/v45/Vol_45_Reiss.pdf.

Rykaczewski, R. R., and D. M. Checkley, Jr., 2008: Influence of ocean winds on the pelagic ecosystem in upwelling regions. Proc. Nat. Acad. Sci., 105, 1965-1970, doi: 10.1073/pnas.0711777105.

Simpson, J. J., 1984: El Niño-induced onshore transport in the California Current during 1982-1983. Geophy. Res. Lett., 11, 241-242, doi: 10.1029/GL011i003p00233.

Singh, A., T. Delcroix, and S. Cravatte, 2011: Contrasting the flavors of El Niño-Southern Oscillation using sea surface salinity observations. J. Geophy. Res., 116, doi:10.1029/2010JC006862.

Sun, J., D. A. Hutchins, Y. Y. Feng, E. L. Seubert, D. A. Caron, and F. X. Fu, 2011: Effects of changing pCO2 and phosphate availability on domoic acid production and physiology of the marine harmful bloom diatom Pseudo-nitzschia multiseries. Limnol. Oceanogr., 56, 829-840, doi: 10.4319/lo.2011.56.3.0829.

Todd, R. E., D. L. Rudnick, R. E. Davis, and M. D. Ohman, 2011: Underwater gliders reveal rapid arrival of El Nino effects off California’s coast. Geophy. Res. Lett., 38, doi: 10.1029/2010gl046376.

Yu, J. Y., Y. H. Zou, S. T. Kim, and T. Lee, 2012: The changing impact of El Nino on US winter temperatures. Geophy. Res. Lett., 39, doi: 10.1029/2012gl052483.

Zaba, K. D., and D. L. Rudnick, 2016: The 2014–2015 warming anomaly in the Southern California Current System observed by underwater gliders. Geophy. Res. Lett., 43, 1241-1248, doi: 10.1002/2015GL067550.

Modeling to aid management of marine top predators in a changing climate

Posted by mmaheigan 
· Thursday, February 16th, 2017 

Marine top predators can include species that occupy a high trophic level (e.g., predatory sharks), have few predators (e.g., marine turtles), or can exert top-down control on food webs due to their large energetic demands (e.g., whales). While many species in the open ocean are widely distributed (e.g., Read et al. 2013; Reygondeau et al. 2012), the higher trophic levels are noteworthy as they are also wide-ranging (e.g., tuna (Itoh et al. 2003; Block et al. 2011; Hobday et al. 2015), seabirds (Shaffer et al. 2006), turtles (Shillinger et al. 2008; Briscoe et al. 2016)). These wide-ranging species can serve as ecological linkages within and across ocean basins, through both ontogenetic (larvae to adult) and seasonal migrations (Boustany et al. 2010; Hobday et al. 2015; Briscoe et al. 2016). Many wide-ranging marine animals show site fidelity at particular times during their lives or have relatively small and well-defined areas of critical habitat, which facilitates both exploitation (e.g., Hobday et al. 2015) and protection (e.g., Ban et al. 2014). This fidelity can be related to the temporal and spatial predictability of their physical habitats, as evidenced by predictable seasonal aggregations of high-trophic fishes, birds, turtles, and mammals (Scales et al. 2014), which is aided by sensory capabilities that permit them to locate specific physical and biological features.

These species are also of interest, as they are often charismatic, providing commercial, cultural, or ecological value (e.g., Weng et al. 2015). Top predators in marine ecosystems are supported by the productivity of primary and secondary consumers; thus they integrate a range of processes across these lower levels in the trophic food web. Their relatively long life spans and wide-ranging movements mean that many marine predator populations integrate variability across larger spatial and temporal scales than many lower-trophic-level populations (Shaffer et al. 2006). Top predators also have movement and sensory capabilities that permit active targeting of biophysical features (Scales et al. 2014). These characteristics make assessments of top predator populations particularly valuable for investigations of large-scale ecosystem variability and change.

For example, in the California Current System (CCS), plankton (Fisher et al. 2015; Lluch-Belda et al. 2005) and nekton (Lynn 2003; Phillips et al. 2007; Lluch-Belda et al. 2005) exhibit distributional shifts associated with El Niño-Southern Oscillation (ENSO) events, which are echoed by changes in the distribution of top predators. While the distribution of planktonic organisms is indicative of changes in circulation and habitat suitability, shifts in the distributions of top predators and other nekton are often the result of changes in migration patterns based on the availability of prey. Historical observations of the distributions of top predators indicate that along the West Coast of North America, populations are typically displaced poleward during El Niño events. Also, distributions of species with ranges that are typically offshore (e.g., highly migratory fishes) are contracted towards the West Coast, and the catch-per-unit-effort of tunas and yellowtail are often increased in response to the increased availability to nearshore fishers (Sydeman and Allen 1999; Benson et al. 2002; Henderson et al. 2014). Shifts in species distributions attributed to El Niño are often documented in local newspapers and fishing reports as well as in scientific publications (Lluch-Belda et al. 2005; Cavole et al. 2016). However, many predator populations resident to the CCS (e.g., common murre, Cassin’s auklet, and splitnose rockfishes) exhibit extreme negative productivity anomalies during El Niño (Black et al. 2014), and these events are the most prominent anomalies in time-series spanning multiple decades. Mass strandings of pinnipeds (e.g., sea lions) and die-offs of seabirds have also been associated with El Niño events. These unusual mortality events have been attributed to reduced availability of forage fishes and the exacerbated effects of harmful algal blooms that accompanied past El Niño events (McCabe et al. 2016).

This nearshore compression of viable habitat can also expose these species to a range of relatively concentrated anthropogenic threats, including fishing, oil and gas exploration, transport, and pollution (e.g., Ban et al. 2014). For example, Maxwell et al. (2013) combined electronic tracking from eight top predator species in the CCS with data on 24 anthropogenic stressors to develop a metric of cumulative utilization and impact. The distribution of these stressors and species showed that comprehensive management approaches are required, as no single approach was likely to be successful. Predicting the time-varying distribution and abundance of these and other high-trophic-level species may offer additional management insight, and allow a dynamic approach to management and conservation (e.g., Hobday et al. 2014; Scales et al. 2014; Lewison et al. 2015; Maxwell et al. 2015; Hazen et al. 2016).

Using top predators to monitor ocean changes

There is a suite of tools available for monitoring the response of top predator populations and distributions to variation in environmental conditions. At-sea surveys record the presence and abundance of air-breathing marine predators, such as seabirds, whales, sea turtles, and pinnipeds, which can be reliably sighted at the ocean surface or detected using acoustic methods. Standardized, repeat surveys such as the California Cooperative Oceanic Fisheries Investigations (CalCOFI; Bograd et al. 2003) and NOAA’s Cetacean Ship Surveys (e.g., CalCurCEAS 2014; Rankin et al. 2016) provide longitudinal datasets informative for understanding population trends and regional habitat preferences (Forney et al. 2015; Sydeman et al. 2014). When combined with in situ measurements of physical conditions and prey distributions, survey datasets generate insight into the finer-scale biophysical mechanisms that underlie the dynamics of predator-prey interactions (Benoit-Bird et al. 2013; Embling et al. 2012). Over broader scales, aerial surveys are useful for mapping distributions of air-breathers (Barlow & Forney 2007), and as new technologies become more widely available—such as autonomous underwater vehicles (AUVs), unmanned aerial vehicles (UAVs) (Christiansen et al. 2016; White et al. 2016), and passive acoustics (Morano et al. 2012)—they are increasingly used to survey and study predator populations.

Animal tracking and telemetry allow for remote acquisition of data describing movements and behaviors of marine predators as they move freely through their natural environment. Tracking individuals of known age, sex, body condition, and breeding status has revealed previously cryptic at-sea behaviors (Block et al. 2011; Hazen et al. 2012; Hussey et al. 2015). For example, satellite telemetry has revealed the complexities of ocean-basin scale migrations in several populations (e.g., seabirds (Clay et al. 2016; Shaffer et al. 2006), sea turtles (Briscoe et al. 2016), and pinnipeds (Robinson et al. 2012)). Understanding migratory behaviors improves our knowledge of phenology (timing) and increases chances of detecting climate change responses. Telemetry datasets have also proven particularly powerful in identifying important foraging habitats (e.g., Block et al. 2011; Grecian et al. 2016; Raymond et al. 2015). When linked with measures of body condition or population-level metrics, such as breeding success, tracking datasets provide novel insights into population status and responses to physical variability (e.g., Biuw et al. 2007). Together, these technologies have revolutionized understanding of at-sea habitat use by marine predator populations across the global ocean and hold promise for the use of top predators themselves as monitors of ecosystem change.

Additional statistical tools are necessary to relate predator distribution data to their prey and the environment. Species Distribution Models (SDMs) quantify predator habitat preferences by combining movement or distribution datasets with physical data from in situ measurements, satellite remote sensing, or ocean models (Robinson et al. 2011). A variety of techniques are used for modeling habitat preferences, such as Resource Selection Functions (e.g., generalized linear or additive models), machine learning (e.g., regression or classification trees; Elith & Leathwick 2009), and ensemble predictions from multiple algorithms (Scales et al. 2015). SDMs can enhance the value of tracking data in identifying foraging habitats and provide insight into how predictability in the locations of at-sea habitats links to persistence in the physical environment historically, in real-time, or for future projections (Hobday and Hartmann 2006; Hazen et al. 2013; Becker et al. 2014; Hazen et al. 2016). Individual-based or agent-based models link biological responses to heterogeneity and variability in the physical environment using sets of mechanistic rules that underlie biophysical interactions. To date, individual-based models have been used most extensively for lower trophic-level marine predators, such as small pelagic fish (e.g., Pethybridge et al. 2013), as the mechanisms that link the distributions of these organisms to biophysical conditions are generally better understood than for top predators. However, this approach has distinct advantages for modeling top predator habitat use as it explicitly includes prey-field dynamics, an aspect often missing from SDMs owing to the lack of empirical data describing broad-scale prey distributions. Recent advances using regional ocean models with an individual-based model framework have proven effective in modeling predator habitat selection (e.g., California sea lions (Fiechter et al. 2016)) and hold promise for forecasting top predator distributions in changing oceanic seascapes. In particular, a combination of statistical and mechanistic models can identify non-stationarity in predator-environment relationships and can use energetic and movement rules to incorporate prey into predictive models (Muhling et al. 2016).

Managing for a changing climate

Marine top predators are actively managed in many regions to provide social (e.g., tourism), economic (e.g., harvesting), or ecological benefits (e.g., healthy reefs). Traditional management approaches remain an important tool for managing top predators exploited in marine fisheries and addressing conservation objectives. However, in regions with both short-term and long-term change, static spatial management may not represent the best solution when there are competing goals for ocean use (protection or exploitation), as oceanic habitats are mobile and static protection often requires large areas to cover all of the critical habitat for a particular time period (Hobday et al. 2014; Maxwell et al. 2015). Instead, dynamic spatial management may be a suitable alternative, provided that species movements are predictable and suitable incentives exist (Hobday et al. 2014; Maxwell et al. 2015; Lewison et al. 2015). Several approaches, using data and models described in the previous section, can be used to develop a dynamic management approach in response to variable species distributions, including those based on historical patterns (e.g., past responses to ENSO), real-time, and forecasted prediction of species occurrence. Real-time approaches can use observed data (e.g., satellite data or assimilated ocean model output), while seasonal and decadal approaches require validated models and forecasts of ocean state (Figure 1).

Figure 1. Decisions relevant to fisheries, aquaculture, and conservation sectors at forecasting timescales are noted above the time line. Seasonal forecasting is considered most useful for proactive marine management at this time, with decadal forecasting in its infancy. Modified from Hobday et al. 2016.

 

The longest standing real-time example comes from the Australian Eastern Tuna and Billfish Fishery (ETBF; Hobday and Hartmann 2006). Fishers in this multi-species longline fishery often target different species—yellowfin, bigeye, and southern bluefin tunas; marlin; and swordfish—depending on seasonal availability and prevailing ocean conditions, and are themselves subjected to management decisions that alter their fishing behavior. In this region, dynamic ocean management was first used in 2003 to reduce unwanted bycatch of quota-limited southern bluefin tuna (SBT). The distribution of likely SBT habitat, which can change rapidly with the movement of the East Australian Current, was used to dynamically regulate fisher access to east coast fishing areas. A habitat preference model was used to provide near real-time advice to management about the likely SBT habitat (Hobday et al. 2010). Managers use these habitat preference reports to frequently update spatial restrictions to fishing grounds, which involve dividing the ocean into a series of zones based on expected distribution of SBT. These restrictions limit unwanted interactions by fishers that do not hold SBT quota (SBT cannot be landed without quota and in that situation must be discarded) and allow access to those that do have SBT quota to operate efficiently (Hobday et al. 2010). The underlying habitat model has evolved from a surface temperature-based model to an integrated surface and sub-surface model, and currently includes a seasonal forecasting element to aid managers and fishers planning for future changes in the location of the habitat zones (Hobday et al. 2011). This ongoing improvement and adaptation of the system has seen new oceanographic products tested and included in the operational model. This dynamic approach has reduced the need for large area closures while still meeting the management goal but does require more flexible fishing strategies to be developed, including planning vessel movements, home port selection, and quota purchase.

In parallel with improved biological data, numerical climate forecast systems have greatly improved over the last 30 years and now have the capability to provide useful seasonal forecasts (National Research Council, 2010). Dynamic forecast systems include i) global climate models (GCMs), which consist of atmosphere, ocean, land, and ice components; ii) observations from multiple sources (e.g., satellites, ships buoys); iii) an assimilation system to merge the observations with the model’s “first guess” to initialize forecasts; and iv) post-processing software to display and disseminate the model output. Such systems are currently used to make forecasts at scales on the order of 100 km on seasonal and even decadal timescales (e.g., Kirtman et al. 2014; Meehl et al. 2014; Stock et al. 2015). In addition, output from the GCMs is being used to drive much higher-resolution forecasts from regional ocean models (Siedlecki et al. 2016). Model skill on seasonal timescales is a function of persistence, multi-year climate modes (e.g., ENSO, IOD), and its teleconnections and transport by ocean currents. Model skill on decadal timescales arises due to anthropogenic climate change and slowly evolving ocean circulation features such as the Atlantic Meridional Overturning Circulation (AMOC; Salinger et al. 2016). GCM-based forecast systems are currently being used to predict sea surface temperature, sub-surface temperatures, and other ocean conditions that are subsequently used in marine resource applications described above (Hobday et al. 2011; Eveson et al. 2015, Figure 2). However, skill from statistical methods is currently on par with those from much more complex and computer-intensive numerical models (Newman 2013; Jacox et al. 2017), and forecast skill will always be limited regardless of the quality of both models and observations due to the chaotic elements of the climate system.

Figure 2. Ecosystem predictions require a suite of inputs and modeling steps to ensure both physical and biological components in the ecosystem are adequately represented. Physical models (from 1 degree to 1/10 degree downscaled models) can be used to predict higher trophic level distributions directly or can be used to drive individual based movement models of prey and predator to incorporate trophic dynamics in ecosystem predictions.

 

Where do we go from here?

As described above, ocean forecast systems and biological data are being linked to advance top predator management. Three priorities to strengthen this link and better inform management efforts include: (i) gather and share data, (ii) identify effective measures and improve mechanistic understanding of prey availability, and (iii) understand the spatial and temporal overlap between humans and particular focal species.

First, while the capacity to monitor marine top predators has made considerable strides in recent years (Hussey et al. 2015), juvenile portions of many top predator populations can be under-represented and need particular attention (Hazen et al. 2012). Tagging efforts provide detailed data on animal movement and can provide finer-scale data than traditional shipboard surveys. However, there are only a few examples of broad-scale tagging efforts that allow for measurement of diversity and multi-species habitat use, such as the Tagging of Pacific Predators and the Ocean Tracking Network (Block et al. 2011, Hussey et al. 2015). There is a growing trend for these data to be made widely available in repositories (e.g., Ocean Biogeographic Information System Spatial Ecological Analysis of Megavertebrate Populations (OBIS-SEAMAP), Seabird Tracking Database) that allow for greater synthesis than individual datasets alone (Halpin et al. 2006; Lascelles et al. 2016). This should be encouraged as standard practice, as in the oceanographic community (e.g., Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP) database). Data collection must continue, as climate variability and change influence the relationship between top predators and their environment, and additional data are necessary to both test and refine predictive models.

Second, while the movements of many highly migratory predators are tied closely to prey availability, most models of marine top predator habitat use remotely sensed or in situ oceanographic measurements as proxies for prey distribution, which is rarely available. The difficulty is in measuring prey distribution at the scales appropriate for predators (e.g., Torres et al. 2008). We can measure fine-scale foraging behavior using archival tag data and associated prey measurements (e.g., Goñi et al. 2009; Hazen et al. 2009), but these ship-based approaches cannot provide data at the scales used in management-focused habitat models (see Lawson et al. 2015). We can model prey distributions mechanistically to inform models of top predator movements (Fiechter et al. 2016), but these approaches have not yet been coupled with real-time prediction. Prey data at migration-wide scales would greatly improve both statistical and mechanistic models by offering insight to where residence times are highest, yet these data remain difficult beyond fine-spatial scales (Benoit-Bird et al. 2013; Boyd et al. 2015).

Finally, both animals and humans use the marine environment at multiple spatial and temporal scales. For example in the Pacific, blue whales migrate from high-latitude foraging grounds to tropical breeding grounds seasonally and travel to discrete foraging hotspots based on prey availability (Bailey et al. 2009), and container vessels are making decisions such as ship speed, choice of shipping lanes, and port of call on multiple time scales as well (Hazen et al. 2016). This requires information on long-term habitat pathways and high-use areas (e.g., for static protection), as well as the shorter-term (e.g., seasonal) triggers of migration and identification of ocean features that result in high prey aggregations and increased residence times. Comparably, a fisher may change her long-term investment decisions (e.g., quota purchase, hiring crew) based on projections of long-term stock dynamics, or may decide when to start fishing seasonally based on weather and proximity to port, or when to set a net based on when fish schools are plentiful (Figure 1). Thus, management approaches could also be nested to include real-time predictions, seasonal forecasts, and decadal projections to inform multiple management processes (Hobday and Hartmann 2006; Hobday et al. 2011; Salinger et al. 2016). This suite of dynamic spatial management tools would represent an adaptive strategy robust to shifting habitats and species in response to climate variability and change.

 

Authors

Elliott Hazen (NOAA Southwest Fisheries Science Center)
Mike Alexander (NOAA Earth System Research Laboratory)
Steven Bograd (NOAA Southwest Fisheries Science Center)
Alistair J. Hobday (CSIRO, Australia)
Ryan Rykaczewski (University of South Carolina)
Kylie L. Scales (University of the Sunshine Coast, Australia)

References

Ban, N. C., and Coauthors, 2014: Better integration of sectoral planning and management approaches for the interlinked ecology of the open oceans. Mar. Policy, 49, 127-136, doi:10.1016/j.marpol.2013.11.024.

Barlow, J., and K. A. Forney, 2007: Abundance and population density of cetaceans in the California Current ecosystem. Fish. Bull., 105, 509-526.

Becker, E. A., K. A. Forney, D. G. Foley, R. C. Smith, T. J. Moore, and J. Barlow, 2014: Predicting seasonal density patterns of California cetaceans based on habitat models. End. Sp. Res., 23, 1-22, doi:10.3354/esr00548.

Benoit-Bird, K. J., and Coauthors, 2013: Prey patch patterns predict habitat use by top marine predators with diverse foraging strategies. PLoS One, 8, e53348, doi:10.1371/journal.pone.0053348.

Benson, S. R., D. A. Croll, B. Marinovic, F. P. Chavez, and J. T. Harvey, 2002: Changes in the cetacean assemblage of a coastal upwelling ecosystem during El Niño 1997-98 and the La Niña 1999. Prog. Oceanogr., 54, 279-291, doi:10.1016/S0079-6611(02)00054-X.

Biuw, M., and Coauthors, 2007: Variations in behavior and condition of a Southern Ocean top predator in relation to in situ oceanographic conditions. Proc. Nat. Acad. Sci., 104, 13705-13710, doi:10.1073/pnas.0701121104.

Black, B. A., W. J. Sydeman, D. C. Frank, D. Griffin, D. W. Stahle, M. García-Reyes, R. R. Rykaczewski, S. J. Bograd, and W. T. Peterson, 2014: Six centuries of variability and extremes in a coupled marine-terrestrial ecosystem. Science, 345,1498-1502, doi:10.1126/science.1253209.

Block, B. A., and Coauthors, 2011: Tracking apex marine predator movements in a dynamic ocean. Nature, 475, 86-90, doi:10.1038/nature10082.

Bograd, S. J., D. A. Checkley, and W. S. Wooster, 2003: CalCOFI: A half century of physical, chemical, and biological research in the California Current System. Deep Sea Res. II, 50, 2349-2353, doi:10.1016/S0967-0645(03)00122-X.

Boustany, A., R. Matteson, M. R. Castleton, C. J. Farwell, and B. A. Block, 2010: Movements of Pacific bluefin tuna (Thunnus orientalis) in the Eastern North Pacific revealed with archival tags. Prog. Oceanogr., 86, 94-104, doi:10.1016/j.pocean.2010.04.015.

Boyd, C., R. Castillo, G. L. Hunt, A. E. Punt, G. R. VanBlaricom, H. Weimerskirch, and S. Bertrand, 2015: Predictive modelling of habitat selection by marine predators with respect to the abundance and depth distribution of pelagic prey. J. Animal Ecol., 84, 1575-1588, doi:10.1111/1365-2656.12409.

Briscoe, D. K., and Coauthors, 2016: Multi-year tracking reveals extensive pelagic phase of juvenile loggerhead sea turtles in the North Pacific. Movement Ecol., doi:10.1186/s40462-016-0087-4.

Cavole, L. M., and Coauthors, 2016: Biological impacts of the 2013–2015 warm-water anomaly in the Northeast Pacific: Winners, losers, and the future. Oceanogr., 29, 273–285, doi:10.5670/oceanog.2016.32.

Christiansen, F., L. Rojano-Doñate, P. T. Madsen, and L. Bejder, 2016: Noise levels of multi-rotor unmanned aerial vehicles with implications for potential underwater impacts on marine mammals. Front. Mar. Sci., 3, 277, doi:10.3389/fmars.2016.00277.

Clay, T. A., A. Manica, P. G. Ryan, J. R. Silk, J. P. Croxall, L. Ireland, and R. A. Phillips, 2016: Proximate drivers of spatial segregation in non-breeding albatrosses. Sci. Rep., 6, doi:10.1038/srep29932.

Elith, J., and J. R. Leathwick, 2009: Species distribution models: ecological explanation and prediction across space and time. Ann. Rev. Ecol., Evol., Syst., 40, 677-697, doi:10.1146/annurev.ecolsys.110308.120159.

Embling, C. B., J. Illian, E. Armstrong, J. van der Kooij, J. Sharples, K. C. J. Camphuysen, and B. E. Scott, 2012: Investigating fine-scale spatio-temporal predator-prey patterns in dynamic marine ecosystems: a functional data analysis approach. J. App. Ecol., 49, 481-492, doi:10.1111/j.1365-2664.2012.02114.x.

Eveson, J. P., A. J. Hobday, J. R. Hartog, C. M. Spillman, and K. M. Rough, 2015: Seasonal forecasting of tuna habitat in the Great Australian Bight. Fish. Res., 170, 39–49, doi:10.1016/j.fishres.2015.05.008.

Fiechter, J., L. Huckstadt, K. Rose, and D. Costa, 2016: A fully coupled ecosystem model to predict the foraging ecology of apex predators in the California Current. Mar. Ecol. Prog. Ser., 556, 273-285, doi:10.3354/meps11849.

Fisher, J. L., W. T. Peterson, and R. R. Rykaczewski, 2015: The impact of El Niño events on the pelagic food chain in the northern California Current. Glob. Change Biol., 21, 4401–4414, doi:10.1111/gcb.13054.

Forney, K. A., E. A. Becker, D. G. Foley, J. Barlow, and E. M. Oleson, 2015: Habitat-based models of cetacean density and distribution in the central North Pacific. End. Sp. Res., 27, 1-20, doi:10.3354/esr00632.

Grecian, W.J ., and Coauthors, 2016: Seabird diversity hotspot linked to ocean productivity in the Canary Current Large Marine Ecosystem. Biol. Lett., 12, 20160024, doi:10.1098/rsbl.2016.0024.

Goñi, N., I. Arregui, A. Lezama, H. Arrizabalaga, and G. Moreno, 2009: Small scale vertical behaviour of juvenile albacore in relation to their biotic environment in the Bay of Biscay. Tagging and tracking of marine animals with electronic devices. Reviews: Methods and Technologies in Fish Biology and Fisheries, J. Nielsen, J. R. Sibert, A. J. Hobdayet al. Netherlands, Eds., Springer, 9, 51-76, doi:10.1007/978-1-4020-9640-2.

Halpin, P. N., A. J. Read, B. D. Best, K. D. Hyrenbach, E. Fujioka, M. S. Coyne, L. B. Crowder, S. A. Freeman, and C. Spoerri, 2006: OBIS-SEAMAP: developing a biogeographic research data commons for the ecological studies of marine mammals, seabirds, and sea turtles. Mar. Ecol. Prog. Ser., 316, 239-246, doi:10.3354/meps316239.

Haury, L. R., J. A. McGowan, and P. H. Wiebe, 1978: Patterns and processes in the time-space scales of plankton distributions. Patterns in Plankton Communities, J. H. Steele, Ed., Plenum Press, New York, New York, 277-377.

Hazen, E. L., S. M. Maxwell, H. Bailey, S. J. Bograd, M. Hamann, P. Gaspar, B. J. Godley, and G. L. Shillinger, 2012: Ontogeny in marine tagging and tracking science: technologies and data gaps. Mar. Ecol. Prog. Ser., 457, 221-240, doi:10.3354/meps09857.

Hazen, E. L., and Coauthors, 2013: Predicted habitat shifts of Pacific top predators in a changing climate. Nat. Climate Change, 3, 234-238, doi:10.1038/nclimate1686.

Hazen, E. L., and Coauthors, 2016: WhaleWatch: a dynamic management tool for predicting blue whale density in the California. J. App. Ecol., doi: 10.1111/1365-2664.12820.

Henderson, E. E., K. A. Forney, J. P. Barlow, J. A. Hildebrand, A. B. Douglas, J. Calambokidis, and W. S. Sydeman, 2014: Effects of fluctuations in sea-surface temperature on the occurrence of small cetaceans off Southern California. Fish. Bull., 112, 159–177.

Hobday, A. J., and Coauthors, 2014: Dynamic ocean management: Integrating scientific and technological capacity with law, policy and management. Stanford Environ. Law J., 33, 125-165.

Hobday, A. J., K. Evans, J. P. Eveson, J. H. Farley, J. R. Hartog, M. Basson, and T. A. Patterson, 2015: Distribution and migration – Southern bluefin tuna (Thunnus maccoyii). Biology and Ecology of Bluefin Tuna. T. Kitagawa and S. Kimura. London, Eds., CRC Press,189-210, doi:10.1201/b18714-12.

Hobday, A. J., J. Hartog, C. Spillman, and O. Alves, 2011: Seasonal forecasting of tuna habitat for dynamic spatial management. Can. J. Fish. Aq. Sci., 68, 898–911, doi:10.1139/f2011-031.

Hobday, A. J., and K. Hartmann, 2006: Near real-time spatial management based on habitat predictions for a longline bycatch species. Fish. Mgmt. Ecol., 13, 365-380, doi:10.1111/j.1365-2400.2006.00515.x.

Hobday, A. J., J. R. Hartog, T. Timmis, and J. Fielding, 2010: Dynamic spatial zoning to manage southern bluefin tuna capture in a multi-species longline fishery. Fish. Oceanogr., 19(3), 243–253, doi:0.1111/j.1365-2419.2010.00540.x.

Jacox, M. G., M. A. Alexander, C. A. Stock, and G. Hervieux, 2017: On the skill of seasonal sea surface temperature forecasts in the California Current System and its connection to ENSO variability. Climate Dyn., in review.

Hussey, N. E., and Coauthors, 2015: Aquatic animal telemetry: a panoramic window into the underwater world. Science, 348, 1255642, doi:10.1126/science.1255642.

Kirtman, B.P., and Coauthors 2014: The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction. Bull. Amer. Meteor. Soc., 95, 585–601, doi:10.1175/BAMS-D-12-00050.1.

Lascelles, B. G., and Coauthors, 2016: Applying global criteria to tracking data to define important areas for marine conservation. Div. Dist., 22, 422-431, doi:10.1111/ddi.12411.

Lawson, G. L., L. A. Hückstädt, A. C. Lavery, F. M. Jaffré, P. H. Wiebe, J. R. Fincke, D. E. Crocker, and D. P. Costa, 2015: Development of an animal-borne “sonar tag” for quantifying prey availability: test deployments on northern elephant seals. Anim. Biotelem., 3, 22, doi:10.1186/s40317-015-0054-7.

Lewison, R. L., and Coauthors, 2015: Dynamic ocean management: Identifying the critical ingredients of dynamic approaches to ocean resource management. Biosci., 65, 486-498, doi:10.1093/biosci/biv018.

Lluch-Belda, D., D. Lluch-Cota, and S. Lluch-Cota, 2005: Changes in marine faunal distributions and ENSO events in the California Current. Fish. Oceanogr., 14, 458–467, doi:10.1111/j.1365-2419.2005.00347.x.

Lynn, R. J., 2003: Variability in the spawning habitat of Pacific sardine (Sardinops sagax) off southern and central California. Fish. Oceanogr., 12, 541–553, doi:10.1046/j.1365-2419.2003.00232.x.

Maxwell, S. M., and Coauthors, 2013: Cumulative human impacts on marine predators. Nat. Comm., 4, 2688, doi:10.1038/ncomms3688.

Maxwell, S. M., and Coauthors, 2015: Dynamic ocean management: Defining and conceptualizing real-time management of the ocean. Mar. Pol., 58, 42–50, doi:10.1016/j.marpol.2015.03.014.

Meehl, G., and Coauthors, 2014: Decadal climate prediction: An update from the trenches. Bull. Amer. Meteor. Soc., 95, 243–267, doi:10.1175/BAMS-D-12-00241.1.

McCabe, R. M., and Coauthors, 2016: An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions. Geophys. Res. Lett., 43, 10,366–10,376, doi:10.1002/2016GL070023.

Morano, J. L., Rice, A. N., Tielens, J. T., Estabrook, B. J., Murray, A., Roberts, B. L., and Clark, C. W., 2012: Acoustically detected year‐round presence of right whales in an urbanized migration corridor. Con.s Biol., 26, 698-707, doi:10.1111/j.1523-1739.2012.01866.x.

Muhling, B. A., R. Brill, J. T. Lamkin, M. A. Roffer, S. K. Lee, Y. Liu, and F. Muller-Karger, 2016: Projections of future habitat use by Atlantic bluefin tuna: mechanistic vs. correlative distribution models. ICES J. Mar. Sci., doi:10.1093/icesjms/fsw215.

National Research Council, 2010: Assessment of intraseasonal to interannual climate prediction and predictability. Washington, DC: The National Academies Press. doi:10.17226/12878.

Pethybridge, H., D. Roos, V. Loizeau, L. Pecquerie, and C. Bacher, 2013: Responses of European anchovy vital rates and population growth to environmental fluctuations: an individual-based modeling approach. Ecol. Mod., 250, 370-383, doi:10.1016/j.ecolmodel.2012.11.017.

Queiroz, N., N. E. Humphries, G. Mucientes, N. Hammerschlag, F. P. Lima, K. L. Scales, P. I. Miller, L. L. Sousa, R. Seabra, and D. W. Sims, 2016: Ocean-wide tracking of pelagic sharks reveals extent of overlap with longline fishing hotspots. Proc. Nat. Acad. Sci., 113, 1582-1587, doi:10.1073/pnas.1510090113.

Rankin, S., F. Archer, J. L. Keating, J. N. Oswald, M. Oswald, A. Curtis, and J. Barlow, 2016: Acoustic classification of dolphins in the California Current using whistles, echolocation clicks, and burst pulses. Mar. Mamm. Sci., doi:10.1111/mms.12381.

Raymond, B., M. A. Lea, T. Patterson, V. Andrews-Goff, R. Sharples, J. B. Charrassin, M. Cottin, L. Emmerson, N. Gales, R. Gales, and S. D. Goldsworthy, 2015: Important marine habitat off east Antarctica revealed by two decades of multi-species predator tracking. Ecography, 38, 121-129, doi:10.1111/ecog.01021.

Read, B. A., and Coauthors, 2013: Pan genome of the phytoplankton Emiliania underpins its global distribution. Nature, doi:10.1038/nature12221.

Reygondeau, G., O. Maury, G. Beaugrand, J.-M. Fromentin, A. Fonteneau, and P. Cury, 2012: Biogeography of tuna and billfish communities. J. Biogeogr., 39, 114-129, doi:10.1111/j.1365-2699.2011.02582.x.

Robinson, L., J. Elith, A. J. Hobday, R. G. Pearson, B. E. Kendall, H. P. Possingham and A. J. Richardson, 2011: Pushing the limits in marine-based species distribution modelling: lessons from the land present challenges and opportunities. Glob. Ecol. Bio., 20, 789–802, doi:10.1111/j.1466-8238.2010.00636.x.

Robinson, P. W., and Coauthors, 2012: Foraging behavior and success of a mesopelagic predator in the northeast Pacific Ocean: insights from a data-rich species, the northern elephant seal. PLoS One, 7, e36728, doi: 10.1371/journal.pone.0036728.

Salinger, J., and Coauthors, 2016: Decadal-scale forecasting of climate drivers for marine applications. Adv. Mar. Biol. 74: 1-68. doi:10.1016/bs.amb.2016.04.002.

Scales, K. L., P. I. Miller, S. N. Ingram, E. L. Hazen, S. J. Bograd, and R. A. Phillips, 2015: Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models. Div. Dist., 22, 212-224, doi:10.1111/ddi.12389.

Scales, K. L., P. I. Miller, L. A. Hawkes, S. N. Ingram, D. W. Sims and S. C. Votier, 2014: On the Front Line: frontal zones as priority at-sea conservation areas for mobile marine vertebrates. J. App. Ecol., 51, 1575–1583, doi:10.1111/1365-2664.12330.

Shaffer, S. A., and Coauthors 2006: Migratory shearwaters integrate oceanic resources across the Pacific Ocean in an endless summer. Proc. Nat. Acad. Sci., 103, 12799–12802, doi:10.1073/pnas.0603715103.

Shillinger, G. L., and Coauthors, 2008: Persistent leatherback turtle migrations present opportunities for conservation. PLoS Biol., 6, e171. doi:10.1371/journal.pbio.0060171.

Siedlecki, S. A., I. C. Kaplan, A. J. Hermann, T. T. Nguyen, N. A. Bond, J. A. Newton, G. D. Williams, W. T. Peterson, S. R. Alin, and R. A. Feely, 2016: Experiments with seasonal forecasts of ocean conditions for the northern region of the California Current upwelling system. Sci. Rep., 6, 27203, doi:10.1038/srep27203.

Stock, C. A., K. Pegion, G. A. Vecchi, M. A. Alexander, D. Tommasi, N. A. Bond, P. S. Fratantoni, R. G. Gudgel, T. Kristiansen, T. D. O’Brien, Y. Xue and X. Yang X, 2015: Seasonal sea surface temperature anomaly prediction for coastal ecosystems. Prog. Oceanogr., 137, 219-236, doi:10.1016/j.pocean.2015.06.007.

Sydeman, W. J., and S. G. Allen, 1999: Pinniped population dynamics in central California: correlations with sea-surface temperature and upwelling indices. Mar. Mamm. Sci., 15(2), 446-461, doi:10.1111/j.1748-7692.1999.tb00812.x.

Sydeman, W. J., S. A. Thompson, J. A. Santora, J. A. Koslow, R. Goericke, and M. D. Ohman, 2014: Climate-ecosystem change off southern California: time-dependence seabird predator-prey numerical responses. Deep Sea. Res. II, 158–170. doi:10.1016/j.dsr2.2014.03.008.

Weng, K. C., E. Glazier, S. Nicol and A.J. Hobday, 2015: Fishery management, development and food security in the Western and Central Pacific in the context of climate change. Deep Sea Res. II, 113, 301-311, doi:10.1016/j.dsr2.2014.10.025.

White, C. F., Y. Lin, C. M. Clark, and C. G. Lowe, 2016: Human vs robot: Comparing the viability and utility of autonomous underwater vehicles for the acoustic telemetry tracking of marine organisms. J. Exp. Mar. Biol. Ecol., 485, 112-118, doi:10.1016/j.jembe.2016.08.010.

Willis-Norton, E., E. L. Hazen, S. Fossette, G. Shillinger, R. R. Rykaczewski, D. G. Foley, J. P. Dunne, and S. J. Bograd, 2015: Climate change impacts on leatherback turtle pelagic habitat in the Southeast Pacific. Deep Sea Res. II, 113, 260-267, doi:10.1016/j.dsr2.2013.12.019.

Marine mixotrophs exploit multiple resource pools to balance supply and demand

Posted by mmaheigan 
· Sunday, November 20th, 2016 

“So, in the sea, there are certain objects concerning which one would be at a loss to determine whether they be animal or vegetable.”  Aristotle, The History of Animals

Our understanding of marine ecosystems is strongly influenced by the terrestrial macroscopic world we see around us. For example, the distinction between phytoplankton and zooplankton reflects the very familiar divide between plants and animals. Mixotrophs are organisms that blur this distinction by combining photosynthetic carbon fixation and the uptake of inorganic nutrients with the ingestion of living prey (1). In the macroscopic terrestrial realm, the obvious examples of mixotrophs are the carnivorous plants. These organisms are so well known because they confound the otherwise clear divide between autotrophic plants and heterotrophic animals – in terrestrial environments, mixotrophs are the exception rather than the rule. There appear to be numerous reasons for this dichotomy involving constraints on surface area to volume ratios, the energetic demands of predation, and access to essential nutrients and water. Without dwelling on these aspects of macroscopic terrestrial ecology, it appears that many of the most important constraints are relaxed in aquatic microbial communities. Plankton have no need for the fixed root structures that would prevent motility, and in the three-dimensional fluid environment, they are readily exposed to both inorganic nutrients and prey. In addition, their small size and high surface area to volume ratios increase the potential efficiency of light capture and nutrient uptake. As such, mixotrophy is a common and widely recognised phenomenon in marine ecosystems. It has been identified in a very broad range of planktonic taxa and is found throughout the eukaryotic tree of life. Despite its known prevalence, the potential impacts of mixotrophy on the global cycling of nutrients and carbon are far from clear. In this article, I discuss the ecological niche and biogeochemical role of mixotrophs in marine microbial communities, describing some recent advances and identifying future challenges.

A ubiquitous and important strategy

Mixotrophy appears to be a very broadly distributed trait, appearing in all marine biomes from the shelf seas (2) to the oligotrophic gyres (3), and from the tropics (4) to the polar oceans (5). Within these environments, mixotrophy is often a highly successful strategy. For example, in the subtropical Atlantic, mixotrophic plankton make up >80% of the pigmented biomass, and are also responsible for 40-95% of grazing on bacteria (3, 4). Similar abundances and impacts have also been observed in coastal regions (2, 6).

How does the observed prevalence of mixotrophy affect the biogeochemical and ecological function of marine communities? To understand the potential answers to this question, it is helpful to review the constraints associated with the assumption of a strict dichotomy between autotrophic phytoplankton and heterotrophic zooplankton. Within this paradigm, primary production is restricted to the base of the food web, tightly coupled to the supply of limiting nutrients. Furthermore, the vertical export of
carbon is limited by the supply of exogenous (or “new”) nutrients (7), since any local regeneration of nutrients from organic matter is also associated with the local remineralisation of dissolved inorganic carbon. Energy and biomass are passed up the food web, but the transfer across trophic levels is highly inefficient (8) (Fig. 1) because the energetic demands of strictly heterotrophic consumers can only be met by catabolic respiration.

In the mixotrophic paradigm, several of these constraints are relaxed. Primary production is no longer exclusively dependent on the supply of inorganic nutrients because mixotrophs can support photosynthesis with nutrients derived from their prey. This mechanism takes advantage of the size-structured nature of marine communities (9), with larger organisms avoiding competitive exclusion by eating their smaller and more efficient competitors (10-12). In addition, the energetic demands of mixotrophic consumers can be offset by phototrophy, leading to increased efficiency of carbon transfer through the food web (Fig. 1). These two mechanisms dictate that mixotrophic ecosystems can fix and export more carbon for the same supply of limiting nutrient, relative to an ecosystem strictly divided between autotrophic phytoplankton and heterotrophic zooplankton (12).

The trophic flexibility associated with mixotrophy appears likely to have a profound effect on marine ecosystem function at the global scale. Fig. 2 contrasts the simulated fluxes of carbon and nitrogen through the intermediate nanoplankton (2-20 μm diameter) size class of a global ecosystem model (12). The left-hand maps show the balance of autotrophic and heterotrophic resource acquisition in a model with mutually exclusive phytoplankton and zooplankton. At low latitudes and especially in the
oligotrophic subtropical gyres, the inorganic nitrogen supply is acquired almost exclusively by the smallest and most competitive phytoplankton (not shown). This leaves an inadequate supply for larger and less competitive phytoplankton, and as such, the larger size classes are dominated by zooplankton (as indicated by the purple shading in Fig. 2a, b). In the more productive polar oceans and upwelling zones, grazing pressure prevents the smaller phytoplankton from exhausting the inorganic nitrogen supply, leaving enough for the larger phytoplankton to thrive in these regions (as indicated by the green shading).

The right-hand maps in Fig. 2 show the balance of autotrophic and heterotrophic resource acquisition in the intermediate size-class of an otherwise identical model containing only mixotrophic plankton. As in the model with mutually exclusive phytoplankton and zooplankton, the inorganic nitrogen supply in the oligotrophic gyres is exhausted by the smallest phytoplankton (see the purple shading in Fig. 2c). However, Fig. 2d indicates that this is not enough to stop photosynthetic carbon fixation among the mixotrophic nanoplankton. The nitrogen acquired from prey is enough to support considerable photosynthesis in a size class for which phototrophy would otherwise be impossible. For the same supply of inorganic nutrients, this additional supply of organic carbon serves to enhance the transfer of energy and biomass through the microbial food web, increasing community carbon:nutrient ratios and leading to as much as a three-fold increase in mean organism size and a 35% increase in vertical carbon flux (12).

Trophic diversity and ecosystem function

Marine mixotrophs are broadly distributed across the eukaryotic tree of life (13). The ability to combine photosynthesis with the digestion of prey has been identified in ciliates, cryptophytes, dinoflagellates, foraminifera, radiolarians, and coccolithophores (14). Perhaps the only major group with no identified examples of mixotrophy are the diatoms, which have silica cell walls that may hinder ingestion of prey. While some mixotroph species are conceptually more like plants (that eat), others are more like animals (that photosynthesise). A number of conceptual models have been developed to account for this observed diversity. One scheme (15) identified three primarily autotrophic groups that use prey for carbon, nitrogen or trace compounds, and two primarily heterotrophic groups that use photosynthesis to delay starvation or to increase metabolic efficiency. More recently, an alternative classification (16, 17) identified three key groups on a spectrum between strict phototrophy and strict phagotrophy. According to this classification, primarily autotrophic mixotrophs can synthesise and fully regulate their own chloroplasts, whereas more heterotrophic forms must rely on chloroplasts stolen from their prey. Among this latter group, the more specialised species exploit only a limited number of prey species, but can manage and retain stolen chloroplasts for relatively long periods. In contrast, generalist mixotrophs target a much wider range of prey, but any stolen chloroplasts will degrade within a matter of hours or days (18).

This diversity of trophic strategies is clearly more than most biogeochemical modellers would be prepared to incorporate into their global models. Nonetheless, many of the conceptual groups identified above are associated with the ability of mixotrophs to rectify the often-imbalanced supply of essential resources in marine ecosystems (19). This is clearly relevant to the coupling of elemental cycles in the ocean, and it appears likely that the relative abundance of different trophic strategies can impact the biogeochemical function of marine communities (1, 20). For example, recent work suggests that a differential temperature sensitivity of autotrophic and heterotrophic processes can push mixotrophic species towards a more heterotrophic metabolism with increasing temperatures (21). An important goal is therefore to accurately quantify and account for the global-scale effects of mixotrophy on the transfer of energy and biomass through the marine food web and the export of carbon into the deep ocean. We also need to assess how these effects might be sensitive to changing environmental conditions in the past, present, and future.

These processes are not resolved in most contemporary models of the marine ecosystem, which are often based on the representation of a limited number of discrete plankton functional types (22). In terms of resolving mixotrophy, it is not the case that these models have overlooked the one mixotrophic group. Instead, it may be more accurate to say that the groups already included have been falsely divided between two artificially distinct categories. As such, modelling mixotrophy in marine ecosystems is not just a case of increasing complexity by adding an additional mixotrophic component. Instead, progress can be made by understanding the position, connectivity, and influence of mixotrophic and non-mixotrophic organisms within the food web as an emergent property of their environment, ecology, and known eco-physiological traits. This is not a simple task, but progress might be made by identifying the fundamental traits that underpin the observed diversity of functional groups. To this end, a recurring theme in mixotroph ecology is that plankton exist on a spectrum between strict autotrophy and strict heterotrophy (14, 23, 24). Competition along this spectrum is typically framed in terms of the costs and benefits of different modes of nutrition. Accurate quantification of these costs and benefits should allow for a much clearer understanding of the trade-offs between different mixotrophic strategies (25), and how they are selected in different environments. In the future, a combination of culture experiments, targeted field studies, and mathematical models should help to achieve this goal, such that this important ecological mechanism can be reliably and parsimoniously incorporated into global models of marine ecosystem function.

Author

Ben Ward (University of Bristol)

References

1. Stoecker, D. K., Hansen, P. J., Caron, D. A. & Mitra, Annual Rev. Marine Sci. 9, forthcoming (2017).
2. Unrein, F., Gasol, J. M., Not, F., Forn, I. & Massana, R. The ISME Journal 8, 164–176 (2013).
3. Zubkov, M. V. & Tarran, G. A. Nature 455, 224–227 (2008).
4. Hartmann, M. et al. Proc. Nat. Acad. Sci. 5756–5760 (2012). doi:10.1073/pnas.1118179109
5. Gast, R. J. et al. J. Phycol. 42, 233–242 (2006).
6. Havskum, H. & Riemann, B. Marine Ecol. Prog. Ser. 137, 251–263 (1996).
7. Dugdale, R. C. & Goering, J. J. Limnol. Oceanogr. 12, 196–206 (1967).
8. Lindeman, R. L. Ecol. 23, 399–417 (1942).
9. Sieburth, J. M., Smetacek, V. & Lenz, J. Limnol. Oceanogr. 23, 1256–1263 (1978).
10. hingstad, T. F., Havskum, H., Garde, K. & Riemann, B. Ecol. 77, 2108–2118 (1996).
11. Våge, S., Castellani, M., Giske, J. & Thingstad, T. F. Aquatic Ecol. 47, 329–347 (2013).
12. Ward, B. A. & Follows, M. J. Proc. Nat. Acad. Sci. 113, 2958–2963 (2016).

What controls the distribution of dissolved organic carbon in the surface ocean?

Posted by mmaheigan 
· Friday, November 11th, 2016 

Around 662 billion tons of organic carbon are dissolved in the ocean, making the pool one of Earth’s major, exchangeable carbon reservoirs. Dissolved organic carbon (DOC) has many ecological functions. It can form complexes with metals (1); absorb UV and visible light, acting as a “sunscreen” for marine microorganisms and controlling primary production in the upper water column (2); it has antioxidant activity, reacting with free radicals in the media (3); but most importantly, it serves as substrate for the microbial loop and as a vehicle for carbon sequestration in the ocean. Therefore, DOC plays an important role in climate on geological time scales.

Because the amount of atmospheric CO2 is of the same magnitude as the DOC pool, and is closely linked to it through exchange, variations in one of these reservoirs can affect the other, impacting the carbon cycle with consequences for climate. Significant net DOC remineralization would lead to an increase of atmospheric CO2, enhancing greenhouse warming at the surface of the Earth. Net oxidation of only 1% of the seawater DOC pool within 1 year would be sufficient to generate a CO2 flux of 7 PgC/yr, comparable to that produced annually by fossil fuel combustion (4). It has also been proposed that a large-scale oxidation of DOC may have prevented a dramatic global glaciation (‘snowball earth’) in the Neoproterozoic period (5).

Despite its importance, knowledge about DOC dynamics is relatively limited; in fact, it was considered highly inert until about three decades ago when a new analytical technique for measuring it via high-temperature catalytic oxidation stimulated new interest (6). The technique eventually provided more accurate DOC values, showing that it was more involved in the carbon cycle than previously thought and that its concentrations vary with depth, time, and location. Considering DOC distributions observed in the surface Atlantic Ocean (Fig. 1), we see values in the subtropical gyres of 65-70 µmol Kg-1, the highest concentrations in the tropics (> 70 µmol Kg-1), the lowest in the Southern Ocean (< 50 µmol Kg-1), and moderate concentrations in the northern North Atlantic (55-60 µmol Kg-1); this pattern is consistent in other ocean basins. So what controls this distribution and can we predict it? Even with improved analytical techniques, DOC is not a variable that can be measured easily at sea, and the sampling must be done carefully since it is easy to contaminate. Therefore, DOC data are typically fewer than those of other more readily determined variables such as nutrients and oxygen. If we could predict DOC from variables for which much greater global ocean coverage exists, we could fill the very large spatial and temporal gaps in the DOC fields.

DOC is produced in the upper water column by phytoplankton (primary producers). Actually, half of the inorganic carbon that is fixed by phytoplankton is transformed to DOC. Heterotrophic microbes consume most of that DOC, but ~ 4% of global annual net primary production (~ 2 Pg C y-1) (7) accumulates as DOC, much of which is exported to the mesopelagic via vertical mixing and convergence, thus contributing to the biological carbon pump.

New primary production, the foundation of a system’s net community production (NCP), depends on new nutrients reaching the euphotic zone, which happens primarily via upwelling in divergence zones and winter vertical mixing. NCP is the balance of the carbon generated by primary producers minus that lost through heterotrophic respiration (prokaryotes and animals). It can be estimated either by a loss of reactants (CO2 or nutrients) or a gain in products (suspended POC, DOC, and export production) (8).

In our work, we needed to establish the fraction of NCP that was present in dissolved form (i.e., the net DOC production ratio, or NDPr). For that, we simply estimated NCP from the nitrate (NO3–) that is consumed in the euphotic zone (DNO3–):

ΔNO3– = new NO3– (introduced from deeper layers) – remaining NO3– (at surface) (Eq. 1)

In the same way, we also calculated net accumulated DOC, or ΔDOC:

ΔDOC = DOC in euphotic zone – DOC introduced from deeper layers (Eq. 2)

The ratio between ΔDOC and ΔNO3– gave us the NDPr:

NDPr = ΔDOC/ΔNO3– (Eq. 3)

NDPr was calculated throughout the Atlantic Ocean using observations of DOC and NO3– from >15 international oceanographic cruises over the last decade, including those occupied by the US Repeat Hydrography program (Fig. 1). Values of NDPr mostly varied between 0.1 and 0.4 (Fig. 2), with the exception of the North Atlantic Subtropical Gyre (NASG), where NDPr values reach >0.8 at times. After sensitivity testing, we applied a NDPr value of 0.17 to the entire basin, which yielded the smallest error between calculated and observed DOC concentrations. Applying this NDPr value to ΔNO3– (i.e. NCP) obtained from cruise data, we estimate ΔDOC (Eq. 4), in which 6.6 is the molar conversion from N to C units:

ΔDOC= ΔNO3– * 6.6 * 0.17 = NCP * 0.17 (Eq. 4)

To obtain the calculated DOC concentration (DOCcalculated), we added the DOC concentration of underlying source waters (DOCsource) to ΔDOC (Eq. 5):

DOCcalculated = DOCsource + ΔDOC (Eq. 5)

When comparing calculated vs. observed DOC (Fig. 3), we found significant agreement (R2 = 0.64; p < 0.001; n=268) throughout the Atlantic, except in the western North Atlantic, where observed DOC > estimated DOC, especially in the southern sector. After this validation of our approach using nutrients and DOC observations, we applied the method to the more extensive NO3– distributions available in the World Atlas Ocean (WOA) climatology to develop a DOCcalculated map for the entire Atlantic (Fig. 4a). The calculated values agree well with the observations, with a total error of 8.94%.

How much DOC is annually produced in the surface Atlantic Ocean? Total organic carbon export (considered equivalent to NCP) in the Atlantic has been estimated to be 4.15-4.3 Pg C y-1 (9, 10). Applying the 0.17 NDPr (equation 3) indicates that 0.70-0.75 Pg C y-1 accumulates in the Atlantic surface as DOC; as such, the Atlantic accounts for ~36% of the global net DOC production ~2 Pg C y-1.

In permanently stratified areas like the southern sectors of the NASG, our approach is invalid since there is little nutrient input from underlying depths. Also, the static view of our approach does not take into account advection that will modify the DOC distributions, nor does it account for eventual removal of accumulated and advected DOC by microbes. To account for these influences on distributions, we applied the ΔNO3– measurements to a steady-state ocean circulation model including terrestrial DOC inputs and DOC remineralization (Fig. 4b). In the model, zonal advection is evident through enrichment of DOC in the Caribbean Sea. Also, inputs of terrestrial DOC are observed near the outflow of the Amazon River. However, the model only slightly improved the match between observations and modeled DOC, with a total error of 8.71% vs. the 8.94% obtained before the model application.

The correspondence between observations and modeled values was good, considering that we are comparing observations of DOC from cruises during specific seasons with estimates based on more idealized nutrient climatology. The main mismatch is found in the western NASG, where observations can reach 13 µmol Kg-1 higher than calculated values. Local production and/or allochthonous inputs of either new nutrients or DOC must be considered. Local production of DOC could result from addition of nitrogen from sources beyond vertical mixing such as diazotrophic N2 fixation, atmospheric deposition, and river runoff. Alternatively, DOC can be concentrated by evaporation, as is sea salt. However, none of these explain the high DOC values observed in the NASG. DOC flux estimated from dissolved organic nitrogen (DON) released by N2 fixation (11) is too low to explain the extra DOC. Regarding the atmospheric deposition, aerosol optical depth data suggest higher deposition in the eastern than in the western North Atlantic (11), and no excess of DOC is observed there. According to salinity distributions from the World Ocean Atlas, advection of DOC from the closest major rivers (Amazon and Orinoco) does not extend far enough northward to explain the NASG anomaly. Salinity normalization of DOC does not erase the feature, indicating that evaporation is not the cause. Those elevated values of carbon are found during cruises from 2003 in the same area (12), so it appears to be a persistent feature. The anomaly also coincides with a DON maximum and a light stable isotope (δ15N) composition in the particulate organic carbon based on measurements recorded in 2004 (13). An explanation for these anomalies has not been confirmed.

 

Conclusions

New nutrients are the fundamental driver of net DOC accumulation in the surface Atlantic Ocean. As such, climate-driven changes in ocean dynamics, which will affect the supply of nutrients to the euphotic zone, will affect the DOC inventory. The effects of climate change on the nutrient supply to the upper water column are not well known, but they will depend on the opposing influences of thermal stratification and upwelling intensification. Some authors predict an intensification and spatial homogenization of coastal upwelling systems (14, 15). Such would increase the nutrient input to the euphotic zone and the net DOC production. In contrast, others have reported that ocean warming should intensify thermal stratification, reducing nutrient flux by vertical mixing in regions not affected by coastal upwelling systems (16, 17). Depending on which of these phenomena dominate, the nutrient supply will change, in turn changing the DOC budget and its distribution. Furthermore, the percentage of NCP accumulating as DOC (i.e. NDPr), found here to be ~17%, could change in response to a shift in the balance of autotrophs and heterotrophs. This multitude of influencing factors will undoubtedly impact the future course of the oceanic DOC budget.

 

Authors

Cristina Romera-Castillo (Univ. of Vienna) and Dennis A. Hansell (RSMAS, Univ. Miami)

Acknowledgments

The authors thank the other co-author, Robert T. Letscher, from the more extended version of this published work. Also to Dr. X.A. Álvarez-Salgado for the use of DOC data he collected during cruises supported by the Spanish government. Data collection on US CLIVAR sections and involvement by C.R.-C. and D.A.H. were supported by US National Science Foundation OCE1436748.

References

  1. Midorikawa, T., E. Tanoue, 1998. Mar. Chem. 62, 219-239.
  2. Arrigo, K. R., C. W., Brown, 1996. Mar. Ecol. Prog. Ser. 140, 207-216.
  3. Romera-Castillo, C., R. Jaffé, 2015. Mar. Chem. 177, 668–676.
  4. Hedges, J. I. 2002. In: Hansell, D., Carlson, C. (Eds.), 2002. Biogeochemistry of marine dissolved organic matter. Academic Press, San Diego, pp. 1-33.
  5. Peltier, W. R. et al., 2007. Nature 450, 813-819.
  6. Hansell, D. A., C. A. Carlson, 2015. Eos, 96, doi:10.1029/2015EO033011.
  7. Hansell, D. A., et al., 2009. Oceanography 22, 202-211.
  8. Hansell, D. A., C. A. Carlson, 1998. Global Biogeochem. Cycles 12, 443-453.
  9. Laws, E. A., et al., 2000. Global Biogeochem. Cycles 14, 1231-1246.
  10. Dunne, J. P., et al., 2007. Global Biogeochem. Cycles 21, GB4006.
  11. Benavides, M., et al., 2013. J. Geophys. Res.: Oceans 118, 3406-3415.
  12. Carlson, C. A. et al., 2010. Deep-Sea Res. Pt II 57, 1433-1445.
  13. Landolfi, A. et al., 2016. Deep-Sea Res. Part I 111, 50-60.
  14. Sydeman, W. J. et al., 2014. Science 345, 77-80.
  15. Wang, D. et al., 2015. Nature 518, 390-394.
  16. Cermeño, P. et al., 2008. PNAS 105, 20344-20349.
  17. Bopp L, et al., 2013. Biogeosciences 10, 6225-6245.
  18. Schlitzer, R., 2015. Ocean Data View. Available at https://odv.awi.de
  19. Romera-Castillo, C. et al., 2016. PNAS 113, 10497–10502.

Exploring sources of uncertainty in ocean carbon uptake projections

Posted by Katherine Joyce 
· Tuesday, October 25th, 2016 

Having absorbed ~30% of the carbon dioxide released to the atmosphere by human activities, the oceans play an important role in mitigating warming and other climate-related impacts of rising carbon dioxide levels. Predictions of future climate change thus require more accurate projections of ocean carbon uptake. Using two different model suites, a recent study by Lovenduski et al. (2016) published in Global Biogeochemical Cycles documents the relative contributions of internal climate variability, emissions scenario, and model structure to overall uncertainty in ocean carbon uptake predictions on both regional and global scales. Figure from Lovenduski et al. (2016).

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