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Archive for trophic transfer

Species loss alters ecosystem function in plankton communities

Posted by mmaheigan 
· Monday, February 8th, 2021 

Climate change impacts on the ocean such as warming, altered nutrient supply, and acidification will lead to significant rearrangement of phytoplankton communities, with the potential for some phytoplankton species to become extinct, especially at the regional level. This leads to the question: What are phytoplankton species’ redundancy levels from ecological and biogeochemical standpoints—i.e. will other species be able to fill the functional ecological and/or biogeochemical roles of the extinct species? Authors of a paper published recently in Global Change Biology explored these ideas using a global three-dimensional computer model with diverse planktonic communities, in which single phytoplankton types were partially or fully eliminated. Complex trophic interactions such as decreased abundance of a predator’s predator led to unexpected “ripples” through the community structure and in particular, reductions in carbon transfer to higher trophic levels. The impacts of changes in resource utilization extended to regions beyond where the phytoplankton type went extinct. Redundancy appeared lowest for types on the edges of trait space (e.g., smallest) or those with unique competitive strategies. These are responses that laboratory or field studies may not adequately capture. These results suggest that species losses could compound many of the already anticipated outcomes of changing climate in terms of productivity, trophic transfer, and restructuring of planktonic communities. The authors also suggest that a combination of modeling, field, and laboratory studies will be the best path forward for studying functional redundancy in phytoplankton.

Figure caption: Examples of the modelled ecological and biogeochemical responses to the extinction of different phytoplankton species.Figure caption: Examples of the modelled ecological and biogeochemical responses to the extinction of different phytoplankton species.

 

Authors:
Stephanie Dutkiewicz (Massachusetts Institute of Technology)
Philip W. Boyd (Institute for Marine and Antarctic Studies, University of Tasmania)
Ulf Riebesell (GEOMAR Helmholtz Centre for Ocean Research Kiel)

Climate-driven pelagification of marine food webs: Implications for marine fish populations

Posted by mmaheigan 
· Friday, January 22nd, 2021 

Global warming changes the conditions for all ocean life, with wide-ranging consequences. It is particularly difficult to predict the impact of climate change on fish because fish production is conditioned on both temperature and food resource (zooplankton and benthic organisms) changes. Climate change projections from Earth system models show a negative amplification of changes in global ocean net primary production (NPP), with an approximate doubling of production decreases from net primary producers to mesozooplankton. This “trophic amplification” continues up the marine food web to fishes. A new study published in Frontiers in Marine Science illustrates this amplification clearly when fishes are defined by their maximum body size, which describes their position in the food web (Figure 1a). However, decreases in globally integrated biomass and production were not limited to differences in size alone. Importantly, reduced abundances also varied by fish functional type (Figure 1b).

Figure 1: a) Percent change in net primary production (NPP), mesozooplankton (MesoZ) production, all medium (M) fishes, and all large (L) fishes from Historic (1951-2000) to the RCP 8.5 Projection (2051-2100). b) Percent change in production of forage fish, large pelagic fish, demersal fish, and benthic invertebrates in Projection (2051-2100) from Historic (1951-2000). c) Absolute change in the ratio of zooplankton production to seafloor detrital flux as the difference of the Projection (2051-2100) from the Historic (1951-2000). d) Percent change in zooplankton production (dashed grey), percent change in seafloor detrital flux (solid grey), and absolute change in the ratio of their means during the Historic and Projection time periods relative to 1951.

Despite the “pelagification” of marine food webs caused by unequal decreases in secondary production (Figure 1d) and subsequent increases in pelagic zooplankton production relative to seafloor detritus production (Figure 1c,d), large pelagic fish (e.g., tunas and billfishes) suffered the greatest declines and the highest degree of projection uncertainty. The result was a shift from benthic-based ecosystems historically dominated by large demersal fish (e.g., cods and flounders) towards pelagic-based ones dominated by smaller forage fish (e.g., sardines and herring). Any positive impacts of the pelagification of food resources on large pelagic fish were overwhelmed by the negative impacts of the overall reduction in global productivity, compounded by warming-induced increases in metabolic demands. Both the degree of change in the productivity of large pelagic fish and the magnitude of trophic amplification were sensitive to the temperature dependence of metabolic rates. Thus, better constraints are needed on empirical estimates of the effect of temperature on physiological rates to project the impacts of climate change on fish biomass and marine ecosystem structure.

Ocean fish harvests currently supply ~15% of global protein demand. Reduced primary production will decrease the total amount of fish available to harvest for human food, while the pelagification of ecosystems could require large and expensive structural modifications to fisheries, including gear, location, regional and international management plans, consumer demands, and market values.

 

Authors:
Colleen M. Petrik (Texas A&M University)
Charles A. Stock (Geophysical Fluid Dynamics Laboratory)
Ken H. Andersen (Technical University of Denmark)
P. Daniël van Denderen (International Council for the Exploration of the Seas)
James R. Watson (Oregon State University)

Where the primary production goes determines whether you catch tuna or cod

Posted by mmaheigan 
· Friday, September 6th, 2019 

Fishes are incredibly diverse, fill various roles in their ecosystems, and are an important resource—economically, socially, and nutritionally. The relationship between primary productivity and fish catches is not straightforward; fisheries oceanographers and managers have long struggled to predict abundances and fully understand the controls of cross-ecosystem differences in fish abundances and assemblages. A recent study in Progress in Oceanography modeled the relationships between fish abundances and assemblages and ecosystem factors such as physical properties and plankton productivity.

The mechanistic model simulated feeding, growth, reproduction, and mortality of small pelagic forage fish, large pelagic fish, and demersal (bottom-dwelling) fish in the global ocean using plankton food web estimates and ocean conditions from a high-resolution earth system model of the 1990s. Modeled fish assemblages were more related to the separation of secondary production into pelagic zooplankton or benthic fauna secondary production than to primary productivity. Specifically, the ratio of pelagic to benthic production drove spatial differences in dominance by large pelagic fish or by demersal fish. Similarly, demersal fish abundance was highly sensitive to the efficiency of energy transfer from exported surface production to benthic fauna.

The model results offer a systematic understanding of how marine fish communities are structured by spatially varying environmental conditions. With global climate change, the expected decrease in exported primary production would lead to fewer demersal fish around the world. This model provides a framework for testing the effect of changing conditions on fish communities at a global scale, which can also help inform managers of potential impacts on economic, social, and nutritional resources worldwide.

Figure 1: (A) Sample food web with three fish types, two habitats, two prey categories, and feeding interactions (arrows). Dashed arrow denotes feeding only occurs in shelf regions with depth <200 m. (B) Fraction of large pelagic vs. demersal fishes (LP/(LP+D)) as a function of the ratio of zooplankton production lost to higher predation (Zoop) to detritus flux to the seafloor (Bent) averaged over large marine ecosystems. Solid line: predicted linear model response, dashed lines: standard error. (Lower panels) Circles=mean biomasses (g m-2) and lines=fluxes of biomass (g m-2 d-1) through the pelagic (top 100m) and benthic components of the food webs at two test locations, (C) Peruvian Upwelling (PUP) ecosystem and (D) Eastern Bering Sea (EBS) shelf ecosystem. Circles and lines scale with the modeled biomasses and fluxes. Circle color key: Gray=net primary productivity (NPP); yellow=medium and large zooplankton; red=forage fish; blue=large pelagic fish; brown=benthos; green=demersal fish.

 

Authors:
Colleen M. Petrik (Princeton University, Texas A&M University)
Charles A. Stock (NOAA Geophysical Fluid Dynamics Laboratory)
Ken H. Andersen (Technical University of Denmark)
P. Daniël van Denderen (Technical University of Denmark)
James R. Watson (Oregon State University)

 

Zooplankton play a key and diverse role in the ocean carbon cycle

Posted by mmaheigan 
· Thursday, December 7th, 2017 

How does the enormous diversity of zooplankton species, life cycles, size, feeding ecology, and physiology affect their role in ocean food webs and cycling of carbon?

In the 2017 issue of Annual Review of Marine Science, Steinberg and Landry review the fundamental and multifaceted roles that zooplankton play in the cycling and export of carbon in the ocean. Carbon flows through marine pelagic ecosystems are complex due to the diversity of zooplankton consumers and the many trophic levels they occupy in the food web–from single-celled herbivores to large carnivorous jellyfish. Zooplankton also contribute to carbon export processes through a variety of mechanisms (mucous feeding webs, fecal pellets, molts, carcasses, and vertical migrations).


Figure 1.  Pathways of cycling and export of carbon by zooplankton in the ocean.

Climate change and other stressors are already affecting zooplankton abundance, distribution, and life cycles, and are predicted to result in widespread changes in zooplankton carbon cycling in the future. These changes will affect both the larger marine food web that depends upon zooplankton for food (fish) or recycled products for growth (primary producers) and the amount of carbon exported into the deep sea–where far from contact with the atmosphere it no longer contributes to global warming.

 

Authors:

Deborah K. Steinberg, Virginia Institute of Marine Science, The College of William and Mary
Michael R. Landry, Scripps Institution of Oceanography

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

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

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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

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