North-east Atlantic plankton communities are experiencing changes which could have implications for ecosystem functioning. It is not yet possible to say with confidence whether these results suggest that the UK target has or has not been met.

Background

UK Target on plankton distribution

This indicator is used to assess progress against the following target, which is set in the UK Marine Strategy Part One (HM Government, 2012): “At the scale of the Marine Strategy Framework Directive sub-regions, the plankton community distribution is not significantly adversely influenced by anthropogenic drivers, as assessed by indicators of changes in plankton functional types (lifeforms) indices.”

Key pressures and impacts

The link between plankton and human activities is complex. Changes due to human activities are in addition to responses to climate change. Changes in plankton signal the need for further investigative research to establish causes and to identify links between environmental drivers and human pressures such as fishing, nutrients, pollution, renewable development, dredging and waste disposal and microplastics.

Measures taken to address the impacts

No additional measures have been identified, because the links to human pressures or impacts are still unclear. The UK, however, is monitoring climate change impacts through Marine Climate Change Impacts Partnership, the UK Ocean Acidification program and the Climate Change Act (HM Government, 2008). The UK is addressing eutrophication through measures associated with the Marine Strategy Framework Directive (European Commission, 2008) Eutrophication Descriptor.

Monitoring, assessment and regional cooperation

Areas that have been assessed

This indicator assessment was undertaken in the OSPAR Regions, Greater North Sea and Celtic Seas.

Monitoring and assessment methods

Pelagic habitats, which are defined based on key water column features, are important to plankton community structure and dynamics (Figure 1). Indicators based on lifeforms can be used to reveal plankton community responses to pressures. Lifeforms are made up of organisms with the same functional traits. Changes in relative abundance of pairs (called ‘lifeform pairs’) can indicate changes to ecosystem functions, including links between the biological communities of the water and sea floor, ecosystem energy flows and pathways, and food web interactions. At the North East Atlantic regional scale, plankton community change is strongly linked to prevailing climate conditions.

Figure 1. Map of ecohydrodynamic areas in the Greater North Sea (OSPAR region II) and Celtic Seas (OSPAR region III), coloured by ecohydrodynamic type and OSPAR region number. Ecohydrodynamic areas are constructed based on key water column features, which are important to plankton community structure and dynamics. The predominant ecohydrodynamic area types, based on water column structure, are 1) Permanently mixed throughout the year, 2) Permanently stratified throughout the year, 3) Regions of freshwater influence, 4) Seasonally thermally stratified (for about half the year, including summer), 5) Intermittently stratified and 6) Indeterminate regions (inconsistently alternate between the above levels of stratification).

Assessment thresholds

No assessment thresholds have been applied.

Regional cooperation

The UK is the OSPAR indicator lead, and UK results were part of the OSPAR Intermediate Assessment (OSPAR Commission, 2017).

Further information

Indicators based on lifeforms can be used in some hydrographic conditions to assess community response to sewage pollution (Charvet and others, 1998; Tett and others, 2008), anoxia (Rakocinski, 2012), fishing (Bremner and others, 2004), eutrophication (HELCOM, 2012) and climate change (Beaugrand, 2005). Changes in ecologically-meaningful pairs of plankton lifeforms, examined together, can provide information on ecosystem structure and energy flow. Combinations of lifeforms comprising lifeform pairs will depend on the habitat and the objective of the indicator, for example as a measure for pelagic habitats, food webs, seafloor integrity or eutrophication. An important advantage of these plankton indicators is that the proposed concepts are relatively easily transferable to other European regional seas (Gowen and others, 2011; Rombouts and others, 2013).

In practice, the use of functional groups such as lifeforms is often favoured over indicator species, since few species are widespread enough to allow a consistent approach. Also, species abundances  are frequently subject to large inter-annual variation, often due to natural physical dynamics rather than anthropogenic stressors (de Jonge, 2007). Diversity indices are hard to assess due to cryptic speciation (species that look the same under a microscope) within the plankton community, alongside the limitations of identifying plankton using routine light microscopy techniques. Functional group abundance overcomes these difficulties. Indicators based on functional groups have proven relevant for the description of community’s structure and biodiversity, and are more easily intercompared than taxonomic-based indicators (Estrada and others, 2004; Mouillot and others, 2006; Gallego and others, 2012; Garmendia and others, 2012).

When examined in ecologically-relevant pairs, lifeforms can provide an indication of changes in (Table 1; see Gowen and others, 2011):

  • the transfer of energy from primary to secondary producers (changes in phytoplankton and zooplankton)
  • the pathway of energy flow in the food web (changes in gelatinous zooplankton and fish larvae)
  • benthic/pelagic coupling (changes in holoplankton (fully planktonic) and meroplankton (only part of the lifecycle is planktonic, the remainder is benthic))

Table 1. Plankton lifeform pairs and corresponding ecological rationale for their selection.

Lifeform pair

Explanation

Diatoms and dinoflagellates

Dominance by dinoflagellates may be an indicator of eutrophication and result in less desirable food webs.

Pelagic diatoms and tychopelagic (benthic diatoms

Indicator of benthic (sea floor) disturbance and frequency of resuspension events.

Large phytoplankton and small phytoplankton

Size-based indicator of the efficiency of energy flow to higher trophic levels.

Phytoplankton and non-carnivorous zooplankton

Indicator of energy flow and balance between primary producers and primary consumers.

Small copepods and large copepods

Size-based indicator of food web structure and energy flows.

Holoplankton and meroplankton

Indicator of strength of benthic-pelagic coupling and reproductive output of benthic versus pelagic faunas.

Crustaceans and gelatinous zooplankton

Indicator of energy flow and possible trophic pathways.

Gelatinous zooplankton and fish larvae/eggs

Indicator of energy flow and possible trophic pathways.

Both abundance and biomass data can be used to inform lifeform pairs, depending on the lifeform in question and data availability from monitoring programmes. As the knowledge base increases, new pairs can be developed as indicators for other pressures than those currently measured.

The “Plankton Index” of lifeform pairs has been developed to track changes in the state of the plankton in marine waters over time. The main features of the method are:

  • the grouping of species of planktonic organisms into functional types or lifeforms
  • the display of changes in the abundance of each of these lifeforms using a state-space approach
  • calculating a Plankton Index (PI) to quantify possible changes in the state of the plankton relative to baseline or starting conditions
  • relating trends in the PI to trends in human pressure and climate change indices.

Gowen and others (2011) and Tett and others (2008) provide further technical information on the method.

Assessment method

State-space theory

Tett and others (2008) proposed to track changes in the state of the phytoplankton community by means of plots in a state-space and calculating a Plankton Community Index (referred to here as a Plankton Index). The conceptual framework is that ecosystems can be viewed as systems with an instantaneous state defined by values of a set of system state variables which are attributes of the system that change with time in response to each other and external conditions. Building on this approach, and plotting plankton lifeform abundances in a multi-dimensional state-space, provides a means of monitoring changes in the organisation of plankton communities. A state can be defined as a single point in state-space, with co-ordinates provided by the values of the set of state variables, in our case two lifeform abundances, which together are used as a pair.

In the example illustrated in Figure 2, the axes of the two-dimensional space are the abundances of two lifeforms. Each point represents the state of the ecosystem in terms of the two lifeforms at the time the water sample was collected. Subsequent samples yield additional pairs of abundance values that can be mapped onto the lifeform state space.

Figure 2. Mapping the abundance of two lifeforms in state-space. Point A is the ecosystem state at the instant a water sample was taken and is characterised by the abundances of two lifeforms. Another sample, taken in the same location, yields abundances that map to a different point in the diatom-dinoflagellate state-space (point B).

The path between the two states is called a trajectory, and the condition of the phytoplankton is defined by the trajectory drawn in the state-space by a set of points. Such trajectories reflect: (i) cyclic and medium-term variability (the higher order consistencies in the plankton that result from seasonal cycles, species succession and inter-annual variability); (ii) long term variability that might result from environmental pressure. The seasonal nature of plankton production and the succession of species in seasonally stratifying seas results in this trajectory tending in a certain direction (as plankton growth increases in the spring and declines during autumn) such that the trajectory tends towards its starting point. Given roughly constant external pressures the data collected from a particular location over a period of years form a cloud of points in state-space that can be referred to as a regime. Long term variability may show a persistent trend of movement away from a starting point in state-space.

To define a regime, an envelope is drawn about a chosen group of points, using a convex hull method. Because of theoretical arguments that the envelope should be doughnut-shaped with a central hole, bounding curves can be fitted outside and inside the cloud of points (Figure 3).

 

Figure 3. An example of a regime defined by the envelope drawn by the convex hull method. The data are from the ecohydrodynamic area that represents the indeterminate area within the Greater North Sea (EHD II Indet, see Figure 1). The colour of each point corresponds to the season it was sampled within; winter months=blue, spring months =aqua, summer months =yellow, autumn months =purple. (i) shows the data distribution in state space, (ii) adds the outer envelope (thick black line) encompassing 95% of the data, (iii) adds the inner envelope excluding the inner 5% of data.

The size and shape of the envelope are sensitive to sampling frequency and the total numbers of samples. Envelopes are made larger by including extreme outer or inner points, and the larger the envelope, the less sensitive it will be to change in the distribution of points in state-space and therefore less able to detect a change in condition. Conversely, if too many points are excluded the envelope will be small and even minor changes will result in a statistically significant difference from the conditions used to define the envelope. It is therefore desirable to exclude a proportion (p) of points to eliminate these extremes in this case 10%. Envelopes are therefore drawn around the cloud of points to include a proportion (p = 0.9) of the points: with 5% of points that were most distant from the cloud's centre, and 5% of points that were closest to the centre excluded (Figure 3).

In order for a Plankton Index to be calculated, it is necessary to establish starting conditions as the basis for making comparisons (create an envelope as described). For example (Figure 4) choosing data collected between 2004 and 2008 defines a domain in state space that contains a set of trajectories of the diatom-dinoflagellate component on the marine pelagic ecosystem and thus represents the prevailing regime during this chosen starting period.

The next step is to map a new set of data onto the starting condition state-space and compare these new data (at least a dozen points). The value of the Plankton Index is the proportion of new points that fall between the inner and outer envelopes). In the example shown in Figure 4, 24% or 17 of the 71 new points lie outside, and the Plankton Index is 0.76. A value of 1.0 would indicate no change, and a value of 0.0 would show complete change, with all new points plotting outside the starting condition envelope. The envelope was made by excluding 10% of points, so some new points are expected to fall outside: 7.1 (10% of 71), in the case of the example. The exact probability of the observed 17 point falling outside the envelope by chance alone when only 7.1 are expected, is calculated using a binomial series expansion, and a chi-square calculation (with 1 degree of freedom and a 1-tail test). The conclusion is that the value of 0.76, is significantly less than the expected value of 0.9, and so the condition of the phytoplankton in this region as determined by diatoms and dinoflagellates was statistically significantly (p < 0.01) different between the two periods. The time-series of the plankton index is produced by comparing the starting condition envelope with data collected from each subsequent year in turn.

Figure 4. An example for the comparison between two periods within the indeterminate area of the Greater North Sea. Left hand plot shows data from the starting conditions period of 2004 to 2008, used to define the envelope shown by the thick black line. Right hand plot shows data from the comparison period of 2009 to 2014, alongside the same envelope. The Plankton Index (PI) value of 0.76 indicates a significant change between the two timer periods with a binomial p-value of 0.0005 (where significance = p-value < 0.01).

Two lifeforms are not sufficient to describe all of the important characteristics of the plankton. In principle, there is no constraint to adding more lifeforms to the state-space plots (see Tett and others, 2008). The rule is that each additional lifeform has to be different from those already used and the axis for each new lifeform has to be drawn at right-angles to all existing axes. The state-space map is therefore drawn in as many dimensions as there are state variables (lifeforms). However, this becomes complicated when considering the number of lifeforms that we might want to use to fully represent the community structure of the plankton. Therefore:

  1. Two-dimensional state-space plots of specific pairs of plankton lifeforms can be used to investigate pelagic habitats and can be combined to provide a holistic plankton indicator to track changes in the condition of the planktonic component of the pelagic ecosystem.
  2. Time-series of the index will be used to track persistent changes in the condition of the plankton over time and relate any such trend to trends in anthropogenic and climate pressures.

Starting conditions and analysis

The period of 2004 to 2008 was selected for the starting conditions as the majority of datasets used here have adequate data during that period to construct a reliable starting condition envelope. This is not the case, however, with a few of the datasets (Environment Agency, Centre for Environment Fisheries and Aquaculture Science, Scottish Environment Protection Agency: west coast station) and results from these time-series must therefore be interpreted with more care. This envelope represents “starting conditions” and not “reference conditions”. The starting condition envelope was compared with data from the subsequent six-year period (2009 to 2014). The 2009 to 2014 period was chosen as it the most recent period with available data and it is the same temporal length as the Marine Strategy Framework Directive assessment period. The key point is that starting conditions do not represent reference conditions.

Spatial scale

Because plankton community composition, distribution, and dynamics are closely linked to their environment, the analysis was performed at scale of ecohydrodynamic areas (van Leeuwen and others, 2015). Ecohydrodynamic areas were determined through analysis of a 50-year hindcast using the General Estuarine Transport Model physical model of North Sea hydrodynamics (the model is at lower resolution in the Celtic Seas and is not developed for the Bay of Biscay). Ecohydrodynamic areas are constructed using density stratification, the most important large-scale physical feature in shallow shelf seas (van Leeuwen and others, 2015). Density stratification occurs when the buoyancy of surface waters (influenced by fresh-water input or solar heating) is stronger than turbulence and vertical mixing, which limits vertical exchange across the pycnocline (van Leeuwen and others, 2015). The predominant ecohydrodynamic area types, based on water-column structure, are:

  • permanently mixed throughout the year
  • permanently stratified throughout the year
  • regions of freshwater influence
  • seasonally thermally stratified (for approximately half the year, including summer)
  • intermittently stratified
  • indeterminate regions (inconsistently alternate between the above).

At the time of calculating this assessment (2016), the ecohydrodynamic area model was more reliable and detailed for the Greater North Sea than for the Celtic Seas.

The plankton data

The assessment has been carried out using phytoplankton and zooplankton data from the Continuous Plankton Recorder as well as UK fixed point stations (Figure 5). Data from 7 Scottish near-shore stations were used in the analysis. Three, managed by Marine Scotland Science sample the Greater North Sea. They are: Stonehaven (intermittently stratified coastal water), Scapa Flow (Orkney; inshore mixed water), and Scalloway (Shetland; inshore mixed water influenced by the seasonally stratified offshore ecohydrodynamic area regime). Two fixed-point stations sample the Celtic Seas. The station in Loch Ewe, managed by Marine Scotland Science, samples the indeterminate ecohydrodynamic area regime in the Minch. That in the Firth of Lorne, managed by the Scottish Association for Marine Science, samples the permanently stratified ecohydrodynamic area regime that typifies fjords. All sites sample phytoplankton, while Stonehaven and Loch Ewe also sample zooplankton and zooplankton samples from Lorne are currently stored for analysis, pending funding. The stations at the Clyde and Firth of Forth are both monitored by Scottish Environment Protection Agency. No monitoring data exists within the baseline period for these sites. Coastal phytoplankton data from the Environment Agency are collected monthly as part of the Water Framework Directive (European Commission, 2000) monitoring programme. These data span the English coastline and are therefore located in the east and west coast inshore ecohydrodynamic areas. The Plymouth Marine Laboratory station (L4) is 13km offshore from Plymouth located in the seasonally stratified ecohydrodynamic area and is sampled for zooplankton and phytoplankton and a suite of other variables on a weekly basis. Cefas has three SmartBouy stations, but data were insufficient during the starting conditions period. Unlike the fixed-point stations, data from the Continuous Plankton Recorder is collected at a much broader spatial scale through the use of ships of opportunity. Continuous Plankton Recorder data are collected offshore and in the open ocean and are best analysed at a monthly time scale. All other data for this Indicator Assessment comes from the Continuous Plankton Recorder data collection on major shipping lanes. All Continuous Plankton Recorder samples were averaged for each ecohydrodynamic area type in each OSPAR subregion. The Continuous Plankton Recorder survey is coordinated by the Sir Alister Hardy Foundation for Ocean Science in the UK. Data from the different providers were not combined for analysis due to differences in sampling, plankton analysis and enumeration methods. Instead, the datasets were analysed individually. Each dataset has internal quality assurance and quality control procedures to ensure consistency and accuracy of the data.

Figure 5. UK plankton sample locations. Data source accronyms: CPR= continuous plankton recorder, EA= UK Environment Agency, PML= Plymouth Marine Laboratory, MSS=Marine Scotland Science, SAMS= Scottish Association for Marine Science, CEFAS=Centre of Environment Fisheries and Aquaculture Science, SEPA=Scottish Environment Protection Agency.

Lifeform construction

Each lifeform pair consists of two ecologically relevant lifeforms, which contain common functional traits (Table 2). The rationale for selecting the lifeform pairs and additional criteria containing supplementary information on lifeforms are given in Table 2.

Table 2. Lifeform pairs consists of two ecologically-relevant lifeforms. The ‘Additional criteria’ column contains supplementary information regarding particular lifeforms. 

Lifeforms

Additional criteria

Confidence

Explanation

Diatoms vs dinoflagellates

 

High

Dominance by dinoflagellates may be an indicator of eutrophication or change in water column stability and may result in less desirable food webs.

Diatoms vs autotrophic and mixotrophic dinoflagellates

 

Low

Shift in primary producers may indicate eutrophication.

Pelagic diatoms vs tychopelagic diatoms

 

High

Indicator of benthic disturbance and frequency of resuspension events.

Large microphytoplankton vs small microphytoplankton

>20 um cells, not colonies.

High

Size-based indicator of the efficiency of energy flow to higher trophic levels.

<20 um cells, not colonies.

Microphytoplankton vs non-carnivorous zooplankton

Biomass (example Chl, PCI)

High

Indicator of energy flow and balance between primary producers and primary consumers.

Abundance

Carnivorous zooplankton vs non-carnivorous zooplankton

 

Low

Indicator of energy flow and balance between primary consumers and secondary consumers.

Small copepods vs large copepods

Adults <1.9mm (not nauplii or eggs)

High

Size based indicator of food web structure and energy flows.

Adults >2mm

Holoplankton vs meroplankton

 

High

Indicator of strength of benthic-pelagic coupling and reproductive output of benthic versus pelagic faunas.

Crustaceans vs gelatinous zooplankton

 

High

Indicator of energy flow and possible trophic pathways.

Gelatinous zooplankton vs fish Larvae/eggs

Ctenophoress and cnidaria

High

Indicator of energy flow and possible trophic pathways.

Include fish eggs

In development: Nuisance and/or toxin-producing diatoms vs diatoms

Or

Nuisance and/or toxin-producing dinos vs dinos

 

Low

Shift in algal community towards nuisance and/or toxic species which have the potential to impact other higher trophic level indicators.

Ciliates vs microflagellates

Including tintinnids

Low

Shift from primarily autotrophic to a more heterotrophic system.

All species < 20 μm

 

The database master species list was built by assigning functional traits to each species in a dataset, and then adding additional new datasets to expand the master species list (Figure 6). Each species in each new dataset was assigned a unique Aphia ID via the World Register of Marine Species (Figure 6). The new dataset’s species list was then compared with the plankton database’s master species list via Aphia IDs and any newly recorded species or genera were identified. This process ensured that each species is only entered in the database once. These newly recorded taxa were then manually assigned functional traits by searching the literature. Fields were left blank where functional traits for species were unknown. Once traits were assigned, the taxa were added to the master species list. Databse queries were constructed to build lifeforms from the functional trait information (Table 3).

Figure 6. Schematic representation of the process undertaken to assign traits to species, then species to lifeforms. Each species must first be assigned a unique Aphia ID (from WORMS: the World Register of Marine Species) to determine if it is already present in the master species list.

Table 3. Each lifeform is constructed of organisms with particular traits. A database query is then used to assign lifeforms to individual species. Y=yes, ‘Lg’=large, ‘Sm’ = small.

Lifeform

Traits

Criteria

Diatoms

'Diatom' only

PhytoplanktonType=Diatom

Dinoflagellates

'Dinoflagellate' only

PhytoplanktonType=Dinoflagellate

Gelatinous zooplankton

'Gelatinous' only

PlanktonType=Zooplankton AND Gelatinous=Y

Fish larvae/eggs

'Fish' only

ZooType=Fish

Carnivorous zooplankton

'Carnivore' only

PlanktonType=Zooplankton AND Diet=Carnivore

Non-carnivorous zooplankton

'Zooplankton' AND either 'Herbivore', 'Omnivore', OR 'Ambiguous'

PlanktonType=Zooplankton AND (Diet=Herbivore OR Omnivore OR Ambiguous)

Crustaceans

'Crustacean' only

Crustacean=Y

Large phytoplankton

'Phytoplankton' AND 'Lg'

PlanktonType=Phytoplankton AND PhytoplanktonSize=Lg

Small phytoplankton

'Phytoplankton' AND 'Sm'

PlanktonType=Phytoplankton AND PhytoplanktonSize=Sm

Phytoplankton

'Phytoplankton' only

PlanktonType=Phytoplankton

Autotrophic and mixotrophic dinoflagellates

'Dinoflagellate' AND either 'Auto' OR 'Auto/Mixo'

PhytoplanktonType=Dinoflagellate AND (FeedingMech=Auto OR Auto/Mixo)

Pelagic diatoms

'Diatom' AND 'Pelagic'

PhytoplanktonType=Diatom AND DiatomDepth=Pelagic

Tychopelagic diatoms

'Diatom' AND 'Tychopelagic'

PhytoplanktonType=Diatom AND DiatomDepth=Tychopelagic

Nuisance and toxin-producing diatoms

'Diatom' AND either 'Toxic' OR 'Nuisance'

PhytoplanktonType=Diatom AND (HAB = Toxic)

Nuisance and toxin-producing dinoflagellates

'Dinoflagellate' AND either 'Toxic' OR 'Nuisance'

PhytoplanktonType=Dinoflagellate AND (HAB = Toxic)

Holoplankton

'Holoplankton' only

Habitat=Holoplankton

Meroplankton

'Meroplankton' only

Habitat=Meroplankton

Large copepods

'Copepod' AND 'Lg'

Copepod=Y AND ZooSize=Lg

Small copepods

'Copepod' AND 'Sm'

Copepod=Y AND ZooSize=Sm

Ciliates

‘Ciliate'

PhytoplanktonType=Ciliate

Microflagellates

'Phytoplankton' AND 'Sm'

PhytoplanktonType=Dinoflagellate AND PhytoplanktonSize=Sm

A simple method of confidence assessment was used to determine the confidence in each lifeform (Table 4). Using expert opinion, each lifeform was evaluated on three characteristics: the ability of the sampling method to accurately sample and preserve the life form, the accuracy of identifying and enumerating organisms in that lifeform using light microscopy, and the understanding of the accuracy of determining traits assigned to the lifeform. For example, low confidence is assigned to the lifeform pair ‘diatoms versus auto- and mixo-trophic dinoflagellates’ as the mixotrophic and autotrophic mode of nutrition of many dinoflagellates species is currently uncertain. Thus, the accuracy of assigning the life form category is low. Likewise, the lifeform pair ‘carnivorous zooplankton versus non-carnivorous zooplankton’ has a low confidence designation since the feeding habits of many abundant and common zooplankton species remain unknown. Further investigation must also be conducted to decide if both nuisance and toxin-producing lifeform pairs are required. Only pairs with two high-confidence lifeforms were used in the reporting.

Table 4. A simple method of confidence valuation was used to determine the confidence in each lifeform. Only pairs with high confidence used in the OSPAR reporting.

 

Easy to Identify/speciate

Difficult to Identify/speciate

Traits defined

High

Low

Traits undefined

Low

Low

Results

Findings from the 2012 UK Initial Assessment

Although there was clear evidence of regional-scale changes in the composition and abundance of plankton communities, which had been previously linked to rising sea temperatures, plankton as a whole were considered healthy and subject to few direct anthropogenic pressures (HM Government, 2012). However, it is still unclear to what extent natural variability, climate change, ocean acidification, and cascading effects from fishing may be contributing to this change.

Latest findings

Status assessment

Consistent with the findings from 2012, this assessment indicates that there is variability in the plankton community, which is also in accordance with the published scientific literature on plankton dynamics. The data and methods used here are considered robust and of high confidence.

The ‘holoplankton versus meroplankton’ lifeform pair experienced significant changes in most areas, suggesting changes in linkages between the benthic and pelagic components of the ecosystem. Changes have also occurred in the ‘small copepod vs large copepod’ lifeform pair in many areas, suggesting possible alterations to food web structure and energy flows. The ‘pelagic versus tychopelagic’ (benthic) diatom lifeform pair only underwent significant change in a few areas, suggesting no important changes in resuspension events in much of the North-East Atlantic.

The results to date suggest that prevailing conditions are the main drivers of plankton dynamics, indicating that the UK target has been met. However, research is needed to investigate the potential additive effects of the key anthropogenic and climatic pressures, including a better understanding of the magnitude and direction of change.

Trend assessment

North East Atlantic plankton communities showed changes to lifeforms between the starting conditions (2004-2008) and the latest 6-year period (2009-2014) (Figure 7). This does not, necessarily, imply deterioration in environmental conditions as the results must be examined with respect to environmental and anthropogenic pressures.

Figure 7. Starred cells indicate significant change (*p<0.05; **p<0.01) from starting conditions. A Plankton Index of ‘1’ denotes no change from starting conditions while an Index of ‘0’ represents complete change. Grey shading represents where there were not enough/well-represented data to determine a Plankton Index. Data source abbreviations: CPR=continuous plankton recorder, CEFAS=Centre for environment fisheries and aquaculture science, PML=Plymouth Marine Laboratory, MSS=Marine Scotland Science, SAMS=Scottish Association for Marine Science, SEPA=Scottish Environment Protection Agency, EA=Environment Agency. Changes in the Plankton Index do not necessarily indicate a deterioration of environmental conditions, they do, however, indicate change from starting conditions.

Further information

There were significant changes in many lifeform pairs in many ecohydrodynamic areas between the starting conditions and the 2009 to 2014 period (Figure 7). Even though some of the observed changes are statistically significant, this does not suggest that the changes are symptomatic of a deteriorating environment as the results must be examined with respect to environmental and anthropogenic pressures. It is widely acknowledged that prevailing conditions are the key driver of plankton communities in the North-East Atlantic, particularly at the relatively large ecohydrodynamic area scale (as in van Leeuwen and others, 2015; 2016).

Further scientific research is therefore needed to examine the validity of the period chosen to represent starting conditions, the magnitude and direction of change of the Plankton Index with respect to each lifeform pair, and the ecological consequence of such change for each lifeform pair in each ecohydrodynamic area. It is also necessary to investigate links between change in lifeform pairs and anthropogenic and climatic pressures. If changes due to prevailing conditions (such as natural variability and climate change) can be separated from those caused by anthropogenic pressures in each region, this will help to inform management decision making by allowing the application of regionally-targeted management measures only where needed. The position of plankton as the bottom of the marine food web means that an understanding of their responses to prevailing conditions and anthropogenic pressures will also aid interpretation of other indicators such as those for fish communities and food webs.

Conclusions

It is not yet possible to say if the UK target has or has not been met. While the indicator assessment shows that there are changes in UK plankton communities (medium-high confidence), further research is needed to draw conclusions on the magnitude and direction of change, and the key pressures or environmental factors driving change in lifeform pairs. Further interpretation of the results and refinement of the methodology are required. An extensive peer-reviewed research base, however, suggests that prevailing oceanographic and climatic conditions are the overall driver of change in the indicator in the North-East Atlantic (low confidence – further work needed for quantitative evidence). Identification and quantitative analysis of drivers of change is needed for each lifeform pair in each area in order to increase confidence in the assessment and to best inform spatial placement of management measures and support interpretation of other indicators.

Further information

This assessment indicates spatial and temporal variability in the plankton community for all lifeforms, in accordance with expert knowledge on plankton dynamics from the scientific literature (McQuatters-Gollop and others, 2015). The plankton lifeform indicator has been published in the peer reviewed literature (Tett and others, 2008) and has been developed at the sub regional scale using data from the pan-European Continuous Plankton Recorder survey which has documented quality assurance procedures and has supported over 1000 scientific publications (McQuatters-Gollop and others, 2015). The data and methods used here are therefore considered robust and of high confidence.

We cannot yet draw conclusions from our results as additional work is needed to investigate drivers of change. Interpretation of the results and further refinement to the methodology will occur at a later time, but is resource dependent.

Knowledge gaps

Knowledge and data gaps include the need to improve:

  • Data inconsistencies between national and OSPAR level assessments and databases.
  • Lack of quantitative evidence to link change in the indicator to prevailing conditions or anthropogenic pressures.
  • Determining if the starting conditions for each ecohydrodynamic area were in Good Environmental Status so we can decide if trends away from the starting conditions are away from Good Environmental Status (calibration).
  • Integration between datasets accounting for spatial, temporal and dataset sampling and confidence.
  • Development of a harmful algal bloom lifeform pair.

Addressing these gaps will increase confidence in the assessments provided by the indicator:

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Acknowledgements

Assessment metadata
Assessment Type
 
 
 

53,55,57

Point of contact emailmarinestrategy@defra.gov.uk
Metadata dateMonday, January 1, 0001
Title
Resource abstract
Linkage
Conditions applying to access and use
Assessment Lineage
Indicator assessment results
Dataset metadata
Dataset DOIContact marinestrategy@defra.gov.uk

The Metadata are “data about the content, quality, condition, and other characteristics of data” (FGDC Content Standard for Digital Geospatial Metadata Workbook, Ver 2.0, May 1, 2000).

Metadata definitions

Assessment Lineage - description of data sets and method used to obtain the results of the assessment

Dataset – The datasets included in the assessment should be accessible, and reflect the exact copies or versions of the data used in the assessment. This means that if extracts from existing data were modified, filtered, or otherwise altered, then the modified data should be separately accessible, and described by metadata (acknowledging the originators of the raw data).

Dataset metadata – information on the data sources and characteristics of data sets used in the assessment (MEDIN and INSPIRE compliance).

Digital Object Identifier (DOI) – a persistent identifier to provide a link to a dataset (or other resource) on digital networks. Please note that persistent identifiers can be created/minted, even if a dataset is not directly available online.

Indicator assessment metadata – data and information about the content, quality, condition, and other characteristics of an indicator assessment.

MEDIN discovery metadata - a list of standardized information that accompanies a marine dataset and allows other people to find out what the dataset contains, where it was collected and how they can get hold of it.