Are there known minimal marine ecosystems that are stable?

Are there known minimal marine ecosystems that are stable?

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A more entertaining phrasing of the question would be "what would a marine ecologist put into a perfect aquarium?"

I was reading about Prochlorococcus, among other photoautotrophs, and found that they seem to get their nitrogen from ammonia, which must be produced by some other organism. This set me off wondering what different species I would need in a hypothetical aquarium (how big that would have to be is another question, I guess), such that a sustainable ecological equilibrium occurs (one different from everybody dead, I guess), only requiring sunlight and air from the outside.

I realize that I'll have to put other components like iron etc in the water, but let's say I only put some fixed amount in during setup. What exactly that would have to be is probably another can of worms, so let's keep that out of this question as much as possible.

Has there been research towards such model ecosystems, and are there known minimum configurations?

It's hard to give an authorative "no" to this answer. But maybe there's research that looked for such a system and failed, or maybe marine ecologists use model cultures in their labs that show instabilities (meaning the culture dies, you have to replace the water, or keep adding fertilizer, or keep removing some waste) which allow some generalizations.

This is the one I've heard of - Its a sealed acrylic globe that contains algae and brine shrimp. There are quite a few references in the bibliography here to previous systems.

Of course, it needs sunlight, but all the elements are recycled. Its really closed and has reasonable endurance.

The basic commercial version of his closed world -- sold under the label of "Ecosphere" -- is a glass globe about the size of a large grapefruit. My world #58262 was one of these. Completely sealed inside the transparent ball were four tiny brine shrimp, a feathery mass of meadowgreen algae draped on a twig of coral, and microbes in the invisible millions. A bit of sand sat on the bottom. No air, water, or any other material entered or exited the globe. The thing ate only sunlight.

The oldest living Hanson-world so far is ten years old; that's as long as they have been manufactured. That's surprising since the average life-span of the shrimp swimming inside was thought to be about five years. Getting them to reproduce in their closed world has been problematic, although researchers know of no reason why they could not go on replicating forever. Individual shrimp and algae cells die, of course. What "lives forever" is the collective life, the aggregate life of a community.

All of this is a real subject of research for those interested in creating entirely useful ecosystems.

It turns out that ecospheres scale up well. A huge commercial Ecosphere can weigh in at 200 liters. That's about the volume of a large garbage can -- so big you can't reach your arms around it. Inside a stunning 30-inch-diameter glass globe, shrimp paddle between fronds of algae. But instead of the usual three or four spore-eating shrimp, the giant Ecosphere holds 3,000. It's a tiny moon with its own inhabitants. Here, the law of large numbers takes hold; more is different. More individual lives make the ecosystem more resilient. The larger an Ecosphere is, the longer it takes to stabilize, and the harder it is to kill it. But once in gear, the collective give and take of a vivisystem takes root and persists.

Unfortunately larger systems that can support people have not been made yet.

Algae and man lasted a whole day. For about 24 hours, man breathed into algae and algae breathed into man. Then the staleness of the air drove Shepelev out. The oxygen content initially produced by the algae plummeted rapidly by the close of the first day. In the final hour when Shepelev cracked open the sealed door to clamber out, his colleagues were bowled over by the revolting stench in his cabin. Carbon dioxide and oxygen had traded harmoniously, but other gases, such as methane, hydrogen sulfide, and ammonia, given off by algae and Shepelev himself, had gradually fouled the air. Like the mythological happy frog in slowly boiling water, Shepelev had not noticed the stink.

Here is an academic reference you can use to start looking around if you like…

Alternative stable state

In ecology, the theory of alternative stable states (sometimes termed alternate stable states or alternative stable equilibria) predicts that ecosystems can exist under multiple "states" (sets of unique biotic and abiotic conditions). These alternative states are non-transitory and therefore considered stable over ecologically-relevant timescales. Ecosystems may transition from one stable state to another, in what is known as a state shift (sometimes termed a phase shift or regime shift), when perturbed. Due to ecological feedbacks, ecosystems display resistance to state shifts and therefore tend to remain in one state unless perturbations are large enough. Multiple states may persist under equal environmental conditions, a phenomenon known as hysteresis. Alternative stable state theory suggests that discrete states are separated by ecological thresholds, in contrast to ecosystems which change smoothly and continuously along an environmental gradient.


When the species abundances of an ecological system are treated with a set of differential equations, it is possible to test for stability by linearizing the system at the equilibrium point. [7] Robert May developed this stability analysis in the 1970s which uses the Jacobian matrix.

Although the characteristics of any ecological system are susceptible to changes, during a defined period of time, some remain constant, oscillate, reach a fixed point or present other type of behavior that can be described as stable. [8] This multitude of trends can be labeled by different types of ecological stability.

Dynamical stability Edit

Dynamical stability refers to stability across time.

Stationary, stable, transient, and cyclic points Edit

A stable point is such that a small perturbation of the system will be diminished and the system will come back to the original point. On the other hand, if a small perturbation is magnified, the stationary point is considered unstable.

Local and global stability Edit

Local stability indicates that a system is stable over small short-lived disturbances, while global stability indicates a system highly resistant to change in species composition and/or food web dynamics.

Constancy Edit

Observational studies of ecosystems use constancy to describe living systems that can remain unchanged.

Resistance and inertia (persistence) Edit

Resistance and inertia deal with a system's inherent response to some perturbation.

A perturbation is any externally imposed change in conditions, usually happening in a short time period. Resistance is a measure of how little the variable of interest changes in response to external pressures. Inertia (or persistence) implies that the living system is able to resist external fluctuations. In the context of changing ecosystems in post-glacial North America, E.C. Pielou remarked at the outset of her overview,

"It obviously takes considerable time for mature vegetation to become established on newly exposed ice scoured rocks or glacial till. it also takes considerable time for whole ecosystems to change, with their numerous interdependent plant species, the habitats these create, and the animals that live in the habitats. Therefore, climatically caused fluctuations in ecological communities are a damped, smoothed-out version of the climatic fluctuations that cause them." [9]

Resilience, elasticity and amplitude Edit

Resilience is the tendency of a system to retain its functional and organizational structure and the ability to recover after a perturbation or disturbance. [10] Resilience also expresses the need for persistence although from a management approach it is expressed to have a broad range of choices and events are to be looked at as uniformly distributed. [11] Elasticity and amplitude are measures of resilience. Elasticity is the speed with which a system returns to its original / previous state. Amplitude is a measure of how far a system can be moved from the previous state and still return. Ecology borrows the idea of neighborhood stability and a domain of attraction from dynamical systems theory.

Lyapunov stability Edit

Numerical stability Edit

Focusing on the biotic components of an ecosystem, a population or a community possesses numerical stability if the number of individuals is constant or resilient. [14]

Sign stability Edit

It is possible to determine if a system is stable just by looking at the signs in the interaction matrix.

The relation between diversity and stability has been widely studied. [4] [15] Diversity can operate to enhance the stability of ecosystem functions at various ecological scales. [16] For example, genetic diversity can enhance resistance to environmental perturbations. [17] At the community level, the structure of food webs can affect stability. The effect of diversity on stability in food-web models can be either positive or negative, depending on the trophic coherence of the network. [18] At the level of landscapes, environmental heterogeneity across locations has been shown to increase the stability of ecosystem functions [19]

The term 'oekology' was coined by Ernst Haeckel in 1866. Ecology as a science was developed further during the late 19th and the early 20th century, and increasing attention was directed toward the connection between diversity and stability. [20] Frederic Clements and Henry Gleason contributed knowledge of community structure among other things, these two scientists introduced the opposing ideas that a community can either reach a stable climax or that it is largely coincidental and variable. Charles Elton argued in 1958 that complex, diverse communities tended to be more stable. Robert MacArthur proposed a mathematical description of stability in the number of individuals in a food web in 1955. [21] After much progress made with experimental studies in the 60's, Robert May advanced the field of theoretical ecology and refuted the idea that diversity begets stability. [22] Many definitions of ecological stability have emerged in the last decades while the concept continues to gain attention.

2. Tropical Rainforest Ecosystems

Tropical rainforest ecosystems are found in tropical regions, and they boast a greater diversity of flora and fauna compared to any other type of ecosystem. The term “rainforest” means that these are one of the wettest ecosystems in the world. Namely, these ecosystems generally receive very high rainfall every year, which varies across different rainforests.

The heavy rainfall results in dense, leafy vegetation. The trees grow incredibly tall as they compete for sunlight. Animals live in the tree canopies. Contrary to popular belief that soils in rainforest ecosystems are fertile, rainforest soils are actually nutrient-poor. Any explanation for this?

Nutrients are normally not stored in the soils for very long. The heavy rains experienced in these ecosystems washes organic material from the soil, rendering them nutrient-poor. It is also worth noting that rainforests have high humidity – about 88 percent and 77 percent in the wet and dry season, respectively.

Ecosystem Examples

Ecosystem examples are limitless. An ecosystem does not have to cover a large region. They exist in small ponds, inside human homes, and even in the human gut. Alternatively, ecosystems can cover huge areas of the planet.

A small, shaded pond in a temperate region represents an aquatic ecosystem. Water-logged soil and excess shade affect plant life biodiversity, where only species suited to this environment will proliferate. The availability of producers affects which organisms thrive in and around the pond. Primary consumers (herbivores) must provide enough energy for secondary consumers, and so on. Should pesticides be added to the pond, or should the pond freeze over or become choked with thick layers of weed, the ecosystem of this pond must adjust.

On a much larger scale, but an artificial one, the Eden biome – a smaller representation of the global ecosystem – contains multiple ecosystems for research purposes, where separate domes have varying climates and light levels, and support different producers, consumers and decomposers. In an artificial biome many variables are tightly controlled. One does not usually place a herd of elephants in an artificial biome.

Marine Food Chains and Biodiversity

Students use marine organism cards and trophic level classifications to identify and describe food chains in several marine ecosystems.

Biology, Ecology, Earth Science, Oceanography, Geography, Physical Geography



1. Define the role of marine microbes.
Explain to students that, in a single drop of salt water, thousands of microbes (tiny organisms), including bacteria and phytoplankton (tiny floating plants), are interacting to form the base of the food web for the entire ocean. The oxygen and biomass they produce also sustains terrestrial life. Tell students that phytoplankton (algae) take in sunlight, nutrients, carbon dioxide, and water to produce oxygen and food for other organisms. Ask: What is this process called? (photosynthesis) Explain that other microbes, like many bacteria, play a role at the other end of the food chain by breaking down dead plant and animal material and changing it into a form that can be re-used as nutrients by phytoplankton and other organisms. Ask: What is this process called? (decomposition)

2. Watch the National Geographic video “Tiny New Sea Species Discovered.”

Show students the National Geographic video (2 minutes, 30 seconds) “Tiny New Sea Species Discovered.” Ask:

  • What is the goal of the Census of Marine Life? (for scientists to try to uncover as much as possible about diversity, distribution, and abundance of life in the ocean within ten years)
  • What have scientists learned about the importance of microbes in the ocean? (Microbes play a key role in the way nutrients move through the ocean.)
  • What do all microbes in the global ocean collectively weigh? (the equivalent of 240 billion African elephants, or about 90 percent of all the ocean’s biomass)

Summarize that microbes, including phytoplankton and bacteria, are the beginning and end, respectively, of ocean food chains and are therefore essential components of marine ecosystems.

3. Introduce trophic level vocabulary.
Ask: What is a food chain? Ask students to list the organisms in a terrestrial or aquatic food chain that they are familiar with. Explain to students that the trophic level of an organism is the position it occupies on the food chain. An organism’s trophic level is measured by the number of steps it is away from a primary producer/autotroph (photosynthesizer). Write the trophic levels and definitions listed below on the board, leaving off the examples provided. Have students try to identify the trophic level for each of the organisms on their list. Invite volunteers to share their answers with the class. Discuss the correct answers. Next ask students to brainstorm ocean examples of each trophic level and write their correct responses on the board. Eventually, add all of the examples listed below.

  • primary producer/autotrophs—organisms, like plants, that produce food. Examples: phytoplankton, algae
  • primary consumer/heterotroph—an animal that eats primary producers. Examples: mussels, oysters, krill, copepods, shrimp
  • secondary consumer/heterotroph—an animal that eats primary consumers. Examples: blue claw crab, lobster, seastar, humpback whale, silverside
  • tertiary consumer/heterotroph—an animal that eats secondary consumers. Examples: shark, dolphin
  • apex predator/heterotroph—an animal at the top of the food chain with no predators. Examples: shark, dolphin
  • decomposer/detritivores—organisms that break down dead plant and animal material and wastes and release it again as energy and nutrients in the ecosystem. Examples: bacteria, fungi, worms, crabs

4. Have students watch the National Geographic video “Krill.”
Explain to students they are going to watch a video that highlights a marine food chain. Tell students that while they are watching the film, they are going to write examples of organisms from each trophic level. When the film is over, they will identify each organism’s trophic level using the information from the board. Show students the National Geographic video (2 minutes) “Krill.” After the video is over, allow students a couple of minutes to properly identify the trophic levels of each of the organisms shown in the film. Ask:

  • What is the ultimate source of energy in this ecosystem? (the sun photosynthesis)
  • What is the primary producer in the video? (phytoplankton and other algae)
  • What is the primary consumer in the video? Is it an herbivore or carnivore? (krill herbivore)
  • What secondary and tertiary consumers are shown in the video? Are they herbivores or carnivores? (anchovies, sardines, birds, salmon, tuna, humpback and blue whales carnivores)

5. Have students create food chains.
Remind students that food chains connect organisms through energy transfer among producers, consumers, and decomposers. These energy levels are called trophic levels. A significant amount of energy is lost between trophic levels. Divide students into five groups. Assign each group one of the following marine ecosystems:

Have groups identify the geographic locations of their marine ecosystems on their World Physical Tabletop Maps, included in the Physical World MapMaker Kit. Then give each group its assigned Marine Ecosystem Cards Handout, and each student a Feeding Frenzy worksheet. Have students cut out the ecosystem cards, discuss the activity as a group, and then individually complete the Feeding Frenzy worksheet.

6. Have a whole-class discussion about the marine ecosystems and food chains.

Invite small groups to share their completed Feeding Frenzy worksheets with the whole class. Review each of the five food chains, as well as the ecosystems in which each food chain is likely to be found. Ask:

  • Looking across the different food chains, which of the organisms can make their own food through photosynthesis?
  • Compare the food chains to terrestrial food chains you may know. How are the marine food chains the same? How are they different?
  • How might humans be a part of the food chains?

Informal Assessment

Use the provided Feeding Frenzy Answer Key to assess students' comprehension.

Extending the Learning

Have students use their food chain cards to create food webs. Discuss the role each organism plays in the food web.


The diversity and distribution of marine species depends on physical and biological factors, including temperature and physiological adaptations, biological interactions, habitat area and food availability and quality. As oceans continue to warm and currents to change, we are observing new biogeography patterns and ecological interactions between species (Johnson et al., 2011 Last et al., 2011 Ling & Johnson, 2009 Pecl et al., 2017 Poloczanska et al., 2013 Wernberg et al., 2016 ) as has occurred in the geological past (Chaudhary, Saeedi, & Costello, 2016 Costello & Chaudhary, 2017 Harnik et al., 2012 ). Monitoring biodiversity and abundance of key groups and the extent of living habitats along with physical and biogeochemical EOVs, will assist scientists, managers and policy makers forecast and prepare for an expanding redistribution of species and its ecological, social and economic consequences (García Molinos et al., 2016 ). Our structured, quantitative approach to identify an initial set of biological and ecosystem EOVs provides a framework for monitoring these biological changes regionally and globally. The EOV framework (1) considers societal relevance to inform several international conventions and agreements (Figure S2), (2) builds on a century-long history of exploration and observing from an engaged scientific community and (3) builds on previously proposed technical and scientific frameworks (Constable et al., 2016 Duffy et al., 2013 IOOC BIO-TT, 2016 Lara-Lopez, Moltmann, & Proctor, 2016 Muller-Karger et al., 2014 Pereira et al., 2013 UNESCO-IOC, 2013 , 2014 WMO, 2016 Table S1). The EOVs identified here simplify communication and we hope galvanize support for implementing a valuable and achievable global observing system.

Target investments should be made in strengthening the implementation of EOVs that meet both the criteria of high societal relevance and high technical feasibility at a global level, but that are also fit for purpose (Lindstrom et al., 2012 ). For the identified biological EOVs, societal impact will vary significantly across geographic areas, and will be influenced by specific local and regional needs. For example, a time-series of the more scalable variables (e.g., zooplankton abundance, phytoplankton abundance and diversity), will have significant relevance to understand long-term effects of the climate system at the global level, while a time-series of coral or of mangrove cover, two latitudinally restricted ecosystems which provide important ecosystem services, especially to more vulnerable societies, will have a disproportionate (and more immediate) social and economic impact in comparison to similar sized areas in the open ocean. Some of the EOVs with higher scalability (e.g., zooplankton abundance, phytoplankton diversity and abundance) are currently either measured primarily at higher latitudes by established research centres (Batten & Burkill, 2010 Edwards, Beaugrand, Hays, Koslow, & Richardson, 2010 Edwards et al., 2012 Koslow, Miller, & McGowan, 2015 McQuatters-Gollop et al., 2015 ) or can be partly assessed from satellites (e.g., net primary productivity or NPP Siegel et al., 2016 ). As we move forward with implementation, there are existing approaches that can be used to improve both the global coverage and impact of these EOVs (e.g., assessments from remote sensing platforms Muller-Karger et al., 2013 ), but these will need in situ verification at more local scales. It is essential that as a global system, observations should be accessible to all countries and capacity development and technology transfer will be indispensable elements of the implementation approach, both providing the global coverage and uptake. The cost of implementing a global system is another challenging factor to consider when developing each EOV. Within GOOS, the maturity of an EOV is gauged by considering the ‘readiness’ of the variable in terms of requirements, observations, and data and information (Lindstrom et al., 2012 ). For example, the live coral EOV is considered to be relatively mature as there are well-established programs that monitor the status of coral reefs on a regular basis (see Jackson, Donovan, Cramer, & Lam, 2014 for the Caribbean Smith et al., 2016 for reefs in Hawaii and the central Pacific De'ath, Fabricius, Sweatman, & Puotinen, 2012 GBRMPA, 2014 and Hughes et al., 2017 for the Australian Great Barrier Reef). Determining the cost of a global coral reef monitoring program, however, is complex because it depends on a variety of aspects that vary significantly depending on the reef location (remote vs. local), labour costs, scientific capacity, operational capacity, scales, measured variables and protocol used, all of which are significantly sensitive to the living costs of each particular region (Table S8). While scientific knowledge and advances will underpin the GOOS, economics will determine its success.

4.1 Applicability of the EOVs to globally assess marine life in a changing ocean

Given the complexity of marine ecosystems, some of the key issues to consider for the applicability of these EOVs are (1) what can these EOVs inform and in what time frame, and (2) will the EOVs be useful to detect nonlinear responses.

Measuring phytoplankton biodiversity, community composition and biomass on a sustained basis along with timely detecting global harmful algal blooms is the focus of several ongoing efforts (e.g., the Marine Biodiversity Observation Network or MBON and the IOC-UNESCO's Harmful Algal Bloom program) to make informed decisions (Duffy et al., 2013 Muller-Karger et al., 2014 ). Monitoring the phytoplankton EOV is a practical way to assess ocean ecosystem health and detect changes at multiple levels because many ocean ecosystem services, such as fishery catch potential (Cheung, Watson, & Pauly, 2013 Glantz, 2005 Platt, Fuentes-Yaco, & Frank, 2003 ), detection of harmful algal blooms (Paerl & Huisman, 2009 ), changes in food quality (Winder, Carstensen, Galloway, Jakobsen, & Cloern, 2017 ), and carbon sequestration and flux export to the deep sea (Siegel et al., 2016 ) depend on these microorganisms. Monitoring the phytoplankton community can also help understand top-down pressures (Casini et al., 2009 Frank, Petrie, Choi, & Leggett, 2005 Mozetič, Francé, Kogovšek, Talaber, & Malej, 2012 Prowe, Pahlow, Dutkiewicz, Follows, & Oschlies, 2012 ) and shifts that are occurring at higher trophic levels (Chavez, Ryan, Lluch-Cota, & Ñiquen, 2003 Frederiksen, Edwards, Richardson, Halliday, & Wanless, 2006 Hunt, 2007 Schwarz, Goebel, Costa, & Kilpatrick, 2013 ). Remote sensing is a sustainable means to monitor changes in the abundance of various functional groups of phytoplankton and can provide regional as well as three-dimensional insights into biological impacts from chemical and physical ocean changes when paired with in situ time-series (Boyd et al., 2016 Church, Lomas, & Muller-Karger, 2013 Kavanaugh et al., 2014 , 2016 Rivero-Calle, Gnanadesikan, Del Castillo, Balch, & Guikema, 2015 ). Remote sensing data on ocean colour can estimate changes in chlorophyll and productivity over time scales from seasons to decades (e.g., Behrenfeld et al., 2006 Foster, Griffin, & Dunstan, 2014 ) however, similarly to physical EOVs, multidecadal time-series (30–40 years or even more in the North Pacific) may be needed to finally distinguish between climate variability and climate change (Henson et al., 2010 , 2016 Mantua & Hare, 2002 Minobe, 1997 NASEM, 2016 ). Regardless of the variable and duration, observing is necessary if and when attribution becomes possible, and for a host of other requirements. Fortunately, in many cases EOVs are building on existing long time-series, and existing collections like the CPR are being reanalysed with modern technologies, including genetics and electron microscopy to extend their value to further biological groups. This means that the climate change signal can be detected in some areas or for some taxa while the global observing system is being built. Some scenarios suggest that the strongest signal of a warming ocean will be in large turnover of local community composition (Dutkiewicz, Scott, & Follows, 2013 ) and taxonomic time-series have the advantage of extending back a century or more (e.g., Last et al., 2011 ). The question of which kind of changes in community composition the phytoplankton EOV will have the power to detect will depend on the different organismal responses (Boyd et al., 2008 ). These may include adaptation (Irwin, Finkel, Müller-Karger, & Ghinaglia, 2015 Langer et al., 2006 ), geographic shifts of existing biomes (e.g., Rueda-Roa et al., 2017 Sarmiento et al., 2004 ) or the establishment of completely new communities (Boyd & Doney, 2003 ). Phytoplankton monitoring may detect climate-mediated shifts in biomes, especially in isolated areas or where boundaries are very strong (Boyd et al., 2008 ), however to detect adaptation, time-series of physiological manipulation experiments on natural populations will be required (Boyd et al., 2016 ).

Zooplankton monitoring results may be used in international initiatives, including reporting against SDG 14, to develop global indicators for the assessment of impacts of human activities, e.g., ocean acidification due to rising CO2, plastic pollution, and marine ecosystem health. Zooplankton are distributed throughout the global ocean, and their diversity, even presence or absence of taxa, is sensitive to environmental stresses including warming which may result in regime shifts (Barange et al., 2010 Batten & Burkill, 2010 Beaugrand, Brander, Souissi, & Reid, 2003 Beaugrand et al., 2015 Beaugrand, Ibañez, Lindley, & Reid, 2002 Di Lorenzo et al., 2013 Edwards et al., 2010 , 2012 Wooster & Zhang, 2004 ). Focusing on monitoring selected zooplankton species has been proposed as a means to maximize spatial coverage and detect changes in a timely manner (Wooster & Zhang, 2004 ). Changes in the zooplankton community will also influence higher trophic levels, including fish and large vertebrates (Beaugrand et al., 2003 Bi, Peterson, Lamb, & Casillas, 2011 Richardson, Bakun, Hays, & Gibbons, 2009 ).

Commercial fisheries data have been the most accessible and used to address temporal and spatial changes in fish communities and provide an assessment of the status of fish in the ocean under the impacts of fishing and climate change (FAO, 2016 ). Catch data, collected and made available by the UN Food and Agriculture Organization (FAO) since 1950, have been used in global studies as proxies for fish abundance and evaluation of fish stocks worldwide to compensate for the lack of direct fish abundance indices and stock assessments globally and for all stocks (Froese, Zeller, Kleisner, & Pauly, 2012 Halpern et al., 2012 Kleisner, Zeller, Froese, & Pauly, 2013 ). However, using catch data as indicator of fish abundance or fish spatial distribution can bias the evaluation of fishing impacts on fish resources (Pauly, Hilborn, & Branch, 2013 Shin et al., 2012 ) or of climate change on fish habitats (Reygondeau et al., 2012 ). On the other hand, fisheries-independent, large-scale studies are constrained by the availability of scientific survey data that provide direct estimates of fish abundance and presence, though see Koslow, Goericke, Lara-Lopez, and Watson ( 2015 ) for an excellent example of a long time-series, with distributional changes linked to the changing ocean. Sharing of scientific data and metadata analyses have allowed evaluation of ecosystem and fish populations status under global change (e.g., Booth, Poloczanska, Donelson, Molinos, & Burrows, 2017 Bundy, Bohaboy, et al., 2012 Coll et al., 2016 Hutchings, Minto, Ricard, Baum, & Jensen, 2010 Kleisner et al., 2015 Poloczanska et al., 2013 ), shifts in fish spatial distribution (Last et al., 2011 Pinsky, Worm, Fogarty, Sarmiento, & Levin, 2013 ) and loss of marine biodiversity (McCauley et al., 2015 ). The response of fish population indicators to environmental changes may take generally less than 4 years (Bundy, Coll, Shannon, & Shin, 2012 ), but for more integrated fish community indicators, more than 10 years of data may be needed (Nicholson & Jennings, 2004 ). Survey data time-series, along with physical and plankton EOVs can help in refining our knowledge of fish spatial habitats (Druon, Fromentin, Aulanier, & Heikkonen, 2011 Jones, Dye, Pinnegar, Warren, & Cheung, 2012 ), pressures on fish communities (Fu et al., 2015 Link et al., 2009 ) and causes of interannual to decadal variability (Edwards et al., 2010 Frank, Petrie, Leggett, & Boyce, 2016 ).

Marine megafauna such as seabirds, sea turtles and marine mammals are ideal candidates for understanding and communicating the impacts of climate change on marine ecosystems (Durant et al., 2009 Hawkes, Broderick, Godfrey, & Godley, 2009 Lascelles et al., 2014 Moore, 2008 Moore & Huntington, 2008 Sydeman, Poloczanska, Reed, & Thompson, 2015 Sydeman, Thompson, & Kitaysky, 2012 ). Many populations appear to have consistent migration pathways (Horton et al., 2017 ), and may be having difficulty in adapting to shifts in environmental conditions (Ainley et al., 2005 Barbraud et al., 2011 Hazen et al., 2013 Jenouvrier et al., 2009 MacLeod, 2009 Soldatini, Albores-Barajas, Massa, & Gimenez, 2016 Sprogis, Christiansen, Wandres, & Bejder, 2018 Sydeman et al., 2012 ) and to bottom-up effects caused by changes in the distribution and abundance of prey species (Evans & Bjørge, 2013 Neeman, Robinson, Paladino, Spotila, & O'Connor, 2015 Sydeman et al., 2012 ). Resulting population declines worldwide may have dangerous top-down effects on the structure, function and stability of marine food webs (Estes et al., 2011 McCauley et al., 2015 ). At the same time, the recovery of many whale species has been one of marine conservation success stories although poorly recognized outside the science community (Bejder, Johnston, Smith, Friedlaender, & Bejder, 2016 ). Population trends result mostly from field or satellite observations of entire colonies and from animal telemetry (LaRue et al., 2014 ).

In addition to monitoring marine taxa, monitoring the health status and trends of foundation coastal ecosystems is also of societal relevance. Coral reefs have a long history of monitoring through the engagement of scientists, reef managers and other stakeholders (e.g., the GCRMN Jackson et al., 2014 Obura et al., 2017 Wilkinson, 1998 , 2008 ). Increasing awareness of the importance of coral reefs for biodiversity and ecosystem health indicators in policy circles (Convention for Biological Diversity, 2014a UNEA, 2016 World Heritage Convention, 2017 ) and the intensifying impacts of climate change (Heron et al., 2017 van Hooidonk et al., 2016 Wilkinson et al., 2016 ) emphasize the need for increased global coordination, coverage and consistency. This will require formalizing societal requirements, strengthening and resourcing methods and reporting networks, and developing appropriate reports on coral cover as an indicator of reef health (Flower et al., 2017 ). Implementing capacity development and technology transfer to improve data series will be crucial, as most coral reefs (and certainly those supporting low-income communities) are in developing countries.

Marine vegetation ecosystems, including macroalgal assemblages, seagrass beds and mangrove forests harbour diversified assemblages of many species and contribute important functions and services to coastal ecosystems. These include high primary production, provision of nursery areas for commercially important species, protection from coastal erosion and carbon storage (Aburto-Oropeza et al., 2008 Donato et al., 2011 Ezcurra, Ezcurra, Garcillán, Costa, & Aburto-Oropeza, 2016 Hutchison et al., 2015 Krumhansl et al., 2016 Marbà, Díaz-Almela, & Duarte, 2014 Nagelkerken et al., 2008 Schiel & Foster, 2015 ). These ecosystems are vulnerable to global threats such as ocean warming, and to regional stressors resulting from intensifying human activities along the coast (Boström et al., 2014 Marba & Duarte, 2010 Marbà et al., 2014 Moore & Jarvis, 2008 Reusch, Ehlers, Hämmerli, & Worm, 2005 Waycott et al., 2009 ). Decades of observations and experiments on macroalgal communities, together with international collaborations, have provided a solid basis to understand their response to environmental change (Dayton, 1985 ) and show how highly context-dependent this response is (Krumhansl et al., 2016 ). In these communities, nearly instantaneous changes have been observed in response to heatwaves (Wernberg et al., 2016 ), whereas forest decline in relation to gradual warming has been observed on a decadal scale (Krumhansl et al., 2016 ). Such regime shifts from macroalgal forests to less productive and diversified alternative states dominated by turf-forming algae or barren habitat are increasingly documented worldwide (Filbee-Dexter & Scheibling, 2014 Ling, Johnson, Frusher, & Ridgway, 2009 Strain, Thomson, Micheli, Mancuso, & Airoldi, 2014 Wernberg et al., 2016 ). Similarly, seagrass cover is a sensitive indicator of global change because seagrass productivity and diversity are closely related to its areal coverage, density and biomass. These provide reliable proxies for other associated species and ecosystem processes of interest to conservation, management and fisheries. Field measurements of seagrass cover, density and biomass, are relatively straightforward (Short et al., 2006 ), but since some seagrass beds are also visible from various remote sensing platforms, methods are under active development to increase accuracy of seagrass measurement via satellites and drones (Hossain, Bujang, Zakaria, & Hashim, 2015 ). Lastly, mangroves are considered an important contributor to the blue economy (Aburto-Oropeza et al., 2009 ), but although historical estimates and an atlas of mangrove cover include local status and important species information (e.g., Friess & Webb, 2014 Spalding, 2010 ), they are snapshots using aggregated data from regional or national studies. More often, these studies lack the high spatial and temporal granularity or an agreed measurement method, limiting our understanding of biodiversity, functionality, carbon stocks and conservation associated with mangroves. Remote sensing technology is helping to estimate mangrove cover at a worldwide scale however, these can be limited without onground validation (McOwen et al., 2016 ) to clarify species composition and status, and even the value of good satellite coverage is limited if no one is paying attention (Duke et al., 2017 ).

Essential Ocean Variables are interdependent across trophic levels and ecosystems. These ecosystems are complex with nonlinear dynamics that can experience regime shifts (Fogarty, Gamble, & Perretti, 2016 Rocha, Yletyinen, Biggs, Blenckner, & Peterson, 2014 ), but there is already evidence that some of the proposed EOVs can capture these dynamics. For example, macroalgal canopy cover declines as a nonlinear function of grazing pressure and in response to multiple anthropogenic perturbations. Transitions from macroalgal forests to barren habitat or algal turfs are increasingly documented worldwide (Filbee-Dexter & Scheibling, 2014 Strain et al., 2014 ). Studies integrating observations, models and experiments have shown that macroalgal canopy cover can detect these catastrophic transitions and that early warning indicators can effectively anticipate the approaching tipping point (Benedetti-Cecchi, Tamburello, Maggi, & Bulleri, 2015 Ling et al., 2009 Rindi, Dal Bello, Dai, Gore, & Benedetti-Cecchi, 2017 ), therefore, using macroalgal canopy cover as an early warning system in marine coastal environments is a realistic prospect. The ability to detect nonlinear responses will also depend on sampling resolution in time and space, and improved methods of statistical analysis that can use data in an unaggregated form (Foster et al., 2014 ). Other factors to consider as well are the number of associated (and fit for purpose) physical and biogeochemical variables being sampled concomitantly, the strength of the signal and the complexity of the ecosystem (Metcalf, van Putten, Frusher, Tull, & Marshall, 2014 ), among others. These will vary considerably across EOVs.

We have discussed the scientific applications of the EOVs and how they are increasingly relevant for policy and to guide future management. Some of these EOVs, specifically plankton and those related to coastal habitats, are already being proposed as ECVs under the GCOS framework (WMO, 2016 ). In physical oceanography, essential variables (e.g., temperature) have been collected globally in a standardized manner providing valuable input to the IPCC. At present, there are no biological standards used globally even for well-known important ecosystems as coral reefs. One of the major roles of the global observing system will be to join forces with the observing networks (e.g., the GCRMN) to develop standard methods and to help raise their profile to support national and global reporting.

4.2 Challenges of global implementation of the EOVs

By focusing initial efforts on a small number of essential variables that are well specified, the GOOS EOVs provide a means to promote and facilitate networking, data standardization and consistent reporting, thereby raising their societal impact and relevance. As new technologies and new platforms are developed and more networks that build on existing national and regional observing programs are incorporated in the global observing system, EOVs will be revisited and their technical specifications will evolve. The emerging microbe EOV will be an example of this evolution. Implementing a global observing system of biological variables will face many logistical, technical and conceptual challenges. Some of these will be to: (1) achieve standardization of the measurements, or at least intercomparability of the data, (2) develop scientific and technical innovations that are balanced with long-term stability, (3) have the commitment from the international community to support the cost of the observing system and a clear strategy to develop capacity and transfer technology to where it is most needed, and (4) help to integrate experiments, observation and modelling into the observing system. The integration of models across the environmental, social and economic dimensions and strengthening the data capacity by improving data collection, storage and analysis technologies has been proposed to overcome some of these challenges (Addison et al., 2017 ). This will require standardizing methodologies for indicators and increasing data analysis and computing capacity (software, hardware and connectivity) in the developing world while encouraging international data publication standards and open data (Miloslavich et al., 2016 ). Integrating manipulative experiments with monitoring will provide additional insight on species-specific physiological adaptation mechanisms and suggest new hypothesis which, coupled with modelling, will result in better predictions of future shifts (Boyd et al., 2016 ). The examples provided above illustrate the flexibility of EOVs to test theories and hypotheses relevant to ocean conservation through the integration of observations, models and experiments. While most of the proposed EOVs are part of ongoing monitoring programs to detect broad-scale trends, they are also suitable to experimental manipulation and more process-based studies to identify the underlying factors causing those trends. The distributed experimental approach, where local-scale experiments are embedded in large-scale sampling activities, is a strategy to integrate observational and experimental data (Hewitt, Thrush, Dayton, & Bonsdorff, 2007 Menge et al., 2002 ). In addition, emerging techniques such as Empirical Dynamic Modelling, offer new opportunities to integrate models with observations (Clark et al., 2015 Sugihara et al., 2012 Ye et al., 2015 ). These techniques improve our ability to detect causality in complex ecosystems and can be implemented with short, spatially replicated time-series, which are available and can be maintained for all the proposed EOVs.

International collaboration will be essential in integrating and coordinating these different scaled approaches (Duffy et al., 2013 Lu et al., 2015 ). A significant first step in this direction is the signed agreement between GOOS, the Ocean Biogeographic Information System (OBIS) and the MBON of GEOBON ( This collaboration is intended to build a unified, globally consistent and sustained observing system, committed to open access and data sharing, that will enhance current existing observation scopes and capacities make use of the best available resources implement best practices and international standards and enhance global capacity. While this is a major step forward, it is still not enough. Establishing and/or strengthening collaborations with the proliferating number of ocean stewardship initiatives as well as ensuring the collaboration and commitment from governments and increasing public and policy awareness on the benefits of ocean observations will be the next required steps.

4.3 Building an integrated, multidisciplinary GOOS

We have discussed the relevance of the EOVs to assess and detect spatial and temporal changes in marine biodiversity and ecosystems matched with societal needs. The next step will be their implementation. For implementation to succeed, this global observing system needs to: (1) be multidisciplinary and based on best practices, (2) build on existing observing platforms, and (3) strengthen and expand the current capacities. Measuring biological EOVs in conjunction with the other GOOS physical and biogeochemical EOVs will help characterize the interplay and dependence between the biological, chemical, physical and geological properties of the environment. This multidisciplinary approach is key to comprehensively understand the variety of effects of global change at different spatial and temporal scales across taxonomic groups and ecosystems (O'Brien et al., 2017 ). Many platforms that have traditionally focused on physical observations can be expanded to include biogeochemical and biological observations. Likewise, many biological observations are accompanied by physical measurements that can also have use to the physical oceanographic community (e.g., animal telemetry Hussey et al., 2015 ). An opportunity to strengthen the interaction between biological and physical and biogeochemical platforms could clearly be through the CPR program. The CPR time-series is unique not only for being one of the longest biological time-series in the ocean, but also because it was built using the same piece of sampler gear that is still considered technically excellent. At present, CPR deployment is being extended to further platforms, including some traditionally used only for physical sampling and discussions are underway to study the feasibility of installing biogeochemical sensors (e.g., for oxygen) to take measurements along with the CPR tows (Palacz et al., 2017 ).

Building and expanding on existing multiple observing programs and establishing alliances with global sampling platforms and/or long-term programs such as GO-SHIP (, OceanSITES (, GEOTRACES ( and to emerging observing programs (e.g., the Deep Ocean Observing Strategy— among others, will be of utmost importance.


The discussion and writing of this synthesis was supported by the SCAR AnT-ERA Research Programme. We dedicate this paper to Guido di Prisco, an outstanding, highly ambitious and open-minded Antarctic biologist who contributed substantially to many research initiatives, most recently to this synthesis, before his death in September 2019. We appreciate the comments of the reviewers Paul Dayton and Rolf Gradinger, which led to considerable improvements. Open access funding enabled and organized by Projekt DEAL.

Appendix S1. List of authors with their expertise, on which the synthesis was built.

Appendix S2. Search terms for key messages used in the literature survey in Web of Science including numbers of publications 1970–2020 and 2010–2020 results from 4 September 2020.

Appendix S3. Scheme showing how the confidence of the scientific sub-messages was assessed following the IPCC AR5 and IPBES methodology (Mach et al., 2017 IPBES, 2019 ).

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

About Marine Ecosystems

Ecosystems can vary in size, but all have parts that interact with and are dependent upon each other. Upsetting one component of an ecosystem may affect other parts. If you've ever heard of the phrase ecosystem approach, it is a type of natural resource management involving making decisions regarding the whole ecosystem, rather than various parts. This philosophy realizes that everything in an ecosystem is interconnected. This is why environmentalists and marine biologists must consider entire ecosystems even though they may focus on one creature or plant in it. Everything is tied together.

Estimating MVP

Estimates of MVP have their greatest value in the field of conservation biology, which combines genetic and ecological theories to address global declines in biodiversity. One of the goals of conservation biology is to prevent extinction, which requires managing the small populations that are at greatest risk. To manage such endangered species over decades and centuries, researchers must identify the MVP necessary for the species’ long-term survival. Although ecologists have attempted to define a general MVP estimate that can be applied to numerous species for the purpose of simplifying ecological management, research shows that the MVP estimate for one species differs from that of another because of differences in reproductive rates, habitat requirements, and other factors.

The probability for long-term persistence of a species depends on whether the species can avoid the erosion of genetic variability that can occur in small populations. When genetic variation is reduced, the ability of a species to adapt to environmental change may become restricted. In small populations the genetic diversity of the gene pool may be reduced further by limited mating opportunities, such as when only low numbers of adults or adult members of one sex or the other are present. In these cases, genetic variability can be substantially reduced through inbreeding (mating between close relatives) and genetic drift (random changes in gene frequencies). Inbreeding and genetic drift both can result in an increased chance for the transmission of harmful traits to subsequent generations, which ultimately affects population and species viability (see population ecology).

One of the earliest attempts to define a minimum lower threshold that would prevent the loss of genetic variability in a species was made in 1980 by Australian geneticist Ian Franklin and American biologist Michael Soulé. They created the “ 50/500” rule, which suggested that a minimum population size of 50 was necessary to combat inbreeding and a minimum of 500 individuals was needed to reduce genetic drift. Management agencies tended to use the 50/500 rule under the assumption that it was applicable to species generally. Many experts, however, questioned its validity.

With advances in technology and mathematical theory throughout the 1970s and ’80s, a computer simulation model known as population viability analysis (PVA) was developed to estimate the MVP of a species. The method was later found to be useful for providing more-sophisticated estimates of extinction risk and long-term persistence. PVA can be customized by the researcher to incorporate various data related to a natural history of the species, including its reproduction and dispersal behaviour (movement of individuals among populations). Researchers can also incorporate into their PVA studies factors related to the current genetic context of a species (such as evidence of inbreeding depression, which is the overall decrease in ecological fitness as a result of inbreeding).

In general, the results of PVA modeling indicate that species with high reproductive capacities, such as arthropods and rodents, can accommodate lower MVPs than species with lower reproductive capacities, such as redwood trees and large mammals and some birds. High MVPs typically are found for species that are sedentary (e.g., trees), that do not breed until individuals are several years old, that have mating behaviours in which only a few individuals account for most of the mating, or that show high levels of inbreeding (such as elephants, California condors, and cheetahs).

The PVA model also incorporates environmental and demographic stochasticity. Environmentally stochastic events are random events, such as severe weather, floods, fires, and other ecological disturbances. Demographically stochastic events are random fluctuations in population variables, such as sex ratios and number of births or deaths. Depicting such events with PVA has the effect of increasing the model’s MVP estimate, because both types of phenomena have the potential for reducing population size, either by increasing the death rate or increasing the annual variability with respect to the birth rate.

Estimating MVP with PVA allows scientists to determine which biological parameters (e.g., hunting pressure, disease, habitat loss, inbreeding) will have the greatest impact on the extinction probability of a given species. This information can provide environmental managers with a set of quantitative targets for the minimum critical area required to support a viable population.

One major limitation of PVA is that it requires large amounts of data to make realistic predictions. Therefore, some researchers argue that using a single, universal MVP (such as the 50/500 rule) would streamline conservation efforts. Others, however, maintain that MVPs must be carried out in a case-by-case fashion, because the circumstances that characterize extinction risk differ among species.


We would like to thank the two editors-in chief of this journal, Phil Boon and John Baxter for bringing us together to conceive this article, as well as for their careful revisions and suggestions that improved earlier draft versions. We are also grateful to Hanna Schuster and Joachim Pander for their input and valuable comments. We dedicate this paper to the memory of Professor Brian Moss, a fine freshwater ecologist and a pioneer of restoring and biomanipulating freshwater ecosystems. He was also SJH's kind and excellent Head of Department for some years.