Latest Version, 2018 Assessment

Changes in plankton biomass and abundance

This assessment identified changes in biomass and abundance of plankton, which may have consequences on the functioning, dynamics and structure of the whole marine ecosystem. 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 biomass and abundance

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, condition of plankton community is not significantly adversely influenced by anthropogenic drivers”.

Key pressures and impacts

The link between plankton and human activities is complex. Changes due to human activities are superimposed on responses to climate change. Changes in plankton signal the need for further investigative research to establish their cause 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 the 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 for each ecohydrodynamic area within the Greater North Sea and Celtic Seas OSPAR Regions (Figure 1).

Figure 1. Ecohydrodynamic areas in the Greater North Sea and Celtic Seas, coloured by ecohydrodynamic area type and region number. Ecohydrodynamic areas are constructed based on key water column features, which are important to plankton community structure and dynamics.

Monitoring and assessment methods

This indicator enables identification of changes (classified as small, important or extreme) in the production and losses of plankton over time, to show the state of the marine system and better understand changes in other parts of the food web. Data from the Continuous Plankton Recorder and several fixed-point stations in UK waters were used (Figures 1 and 2). Total phytoplankton biomass and abundance of zooplankton are used to represent phytoplankton and zooplankton production and losses.

Figure 2. UK sample locations. Red dots indicate survey tracks of the Continuous Plankton Recorder. Diamonds indicate fixed point sample sites: Plymouth Marine Laboratory (PML – green) and Marine Scotland Science (MSS – yellow).

Assessment thresholds

No assessment thresholds have been applied.

Regional cooperation

The UK is the OSPAR joint indicator lead and UK results are being used in the OSPAR Intermediate Assessment (OSPAR Commission, 2017).

Further information

The amount of marine plankton is, to a large extent, determined by nutrient concentration, loss processes, and climate and hydrodynamic drivers (for example Beaugrand and others, 2009). Total phytoplankton biomass (measured as Chlorophyll-a) and total zooplankton abundance therefore can be considered as proxies of their respective production. Being at the base of the food web and representing, directly or indirectly, a food resource for numerous species at higher trophic levels, such as commercial fish, the fluctuation of these two parameters can have significant impacts on the whole trophic food web structure and functioning as well as on other ecosystem processes such as nutrient recycling. The intrinsic characteristics of plankton organisms, such as small size, lack of commercial exploitation, short life cycles and distribution over the whole globe, render them particularly important for monitoring programmes aiming to assess the state of marine ecosystems. In addition, they have a high potential to reflect environmental changes, both natural and human induced, at variable temporal and spatial scales, for both the short-term and local to long-term and regional.

Indicators based on bulk community properties, such as total phytoplankton biomass and total zooplankton (or copepod) abundance, can be used to assess their community response to anthropogenic pressures and environmental/climate variability (for example Buttay and others, 2015). The challenge is to separate natural variability from the variability induced by anthropogenic pressures. There are, however, robust statistical techniques that can be applied to identify significant components of variation and changes of these components at multiple scales that may indicate major changes in the marine system involving consequences on other ecosystem components and processes.

Bulk community indicators work with both fixed-station time series (the most frequent monitoring programs in European countries) and to large spatio-temporal data sets such as the Continuous Plankton Recorder data or satellite data from offshore monitoring. These two types of data are complementary and may inform on different temporal/spatial scales and pressures. For instance, coastal data can be used to identify plankton indicators that could be linked to anthropogenic pressures due to human activity, while large spatio-temporal scale data in the open ocean can be used to define plankton indicators that could be linked to large hydro-meteorological changes. However, in general it is challenging to separate and identify which pressures are responsible for change, and this requires knowledge of pressure data in order to run further analysis.

Assessment method

Introduction

The methodology employed for this indicator varies slightly depending on the type of data available. First, phytoplankton and zooplankton are considered separately. Second, two main types of data related to different acquisition systems are considered: time series of plankton collected at fixed stations and plankton data from the Continuous Plankton Recorder.

Pre-analysis steps

According to the type of data considered, pre-analysis steps are required.

Zooplankton data

For zooplankton, only copepods (total copepod abundance) are considered in the calculation. Reduction from total zooplankton abundance to total copepod abundance is justified given that copepods are the best described zooplankton group, consistently identified and enumerated in samples and are generally the most abundant and ubiquitous zooplankton taxa in both space and time. In practice, the use of groups, such as copepods, is often favoured over single indicator species. Indeed, some species such as meroplankton can have a very patchy distribution and a highly variable annual fluctuation in abundance between years. These fluctuations are often due to natural physical dynamics rather than anthropogenic stressors (de Jonge, 2007). An indicator based on only one species is also unlikely to represent the whole trophic level to which it belongs, and which is required for the present indicator assessment. To use a group as large as copepods allows the comparison of most of the zooplankton time series which can bring valuable understanding to the plankton dynamic. Thus, as a first step, all copepod species abundance per time unit (sample) are summed.

Phytoplankton data

This same approach as for zooplankton applies for total phytoplankton biomass. Instead of looking at particular species or group, the bulk phytoplankton community is considered through the total phytoplankton biomass. Phytoplankton biomass can be measured as biovolume or carbon content or can be assessed using chlorophyll-a which is present in most phytoplankton organisms. A semi quantitative measurement of phytoplankton biomass is also possible by using the Phytoplankton Colour Index, derived from the Continuous Plankton Recorder. This method estimates the green colour of the plankton community sampled onto a silk net. Both chlorophyll-a and Phytoplankton Colour Index are used in this assessment as they represent the two types of data regularly monitored in many areas and thereby covers most of the OSPAR Maritime Area. Chlorophyll-a concentration is already used as an OSPAR common indicator for the assessment of eutrophication.

Continuous Plankton Recorder data

For the Continuous Plankton Recorder time-series, and large spatio-temporal pelagic data sets in general, a first step of geographical division is necessary. Pelagic habitats boundaries are not fixed and characterized by high spatio-temporal dynamics. Because plankton community composition, distribution, and dynamics are closely linked to their environment, the analysis was performed at scale of the ecohydrodynamic areas (van Leeuwan 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 Leeuwan and others, 2015). Density stratification occurs when the buoyancy of surface waters (influenced by freshwater input or solar heating) is stronger than turbulence and vertical mixing, which limits vertical exchange across the pycnocline (van Leeuwan 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.

Raster images necessary for dividing the Continuous Plankton Recorder data from the Greater North Sea and Celtic Seas into ecohydrodynamic areas have been produced based on the work of van Leeuwan and others (2015) and allow to divide the larger area (Greater North Sea and Celtic Seas) into eco-zones. Years missing more than 4 months within the time-series should be omitted when running the analysis to avoid bias.

Calculation of monthly means

After the data had been fitted to the right geographical scale, and before running the time-series analysis, the data were averaged per month over the whole time-series. This needs to be done for phytoplankton and zooplankton datasets.

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 2). Two near-shore stations (Stonehaven and Loch Ewe) from Marine Scotland Science were used in the analysis, which fall in the indeterminate and fjordic system ecohydrodynamic areas, respectively, that describe the indeterminate regions of the Greater North Sea. The Plymouth Marine Laboratory station (L4) is 13km offshore from Plymouth located in the seasonally stratified ecosydrodynamic area and is sampled for zooplankton and phytoplankton and a suite of other variables on a weekly basis. Unlike the fixed-point stations, data from the Continuous Plankton Recorder survey 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 on 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 at the ecohydrodynamic area level for analysis due to differences in sampling, plankton and chlorophyll-a analysis, and quantification methods. Instead, the datasets were analysed separately. Each dataset has internal quality assurance and quality control procedures to ensure consistency and accuracy of the data.

Methodology and concepts

The present indicator assessment is based on time-series analysis in order to calculate anomalies in phytoplankton biomass and total copepod abundance over time. Anomalies represent deviations from the assumed natural variability of the time-series. The longer a time-series is, the more suited it is for assessing its natural variability. The greater the anomaly (in terms of absolute value, since an anomaly can be positive or negative), the more it indicates that there is an abnormal change in the dynamics of the phytoplankton biomass or zooplankton abundance. Values for anomalies are obtained on the basis that an anomaly equal to zero means that there is no anomaly, and that zero represents the time-series mean (which has been de-seasonalised).

Through this analysis, two type of anomalies are produced: annual anomalies and monthly anomalies. The first are considered as the final results and are used in the present report. However, in order to understand the presented changes (annual anomalies) and to potentially give future recommendations for management, the annual anomalies are considered alongside monthly anomalies.

In order to present the anomalies in a categorized way it has been decided to classify the anomalies based on percentiles. The 5th, 25th, 50th, 75th and 95th percentiles have been used to categorize the anomalies within a whole time-series. Three categories are used:

  • “small change” which correspond to the anomalies within the 25th and 75th percentiles
  • “important change” which correspond to the anomalies within the 5th and 25th percentiles and within the 75th and 95th percentiles
  • “extreme change” which correspond to the anomalies within the 0th and 5th percentiles and within the 95th and 100th percentiles

When the anomaly is within the “small change” range, the change in plankton is less likely to have significant repercussions for other trophic levels. When the anomaly is qualified as “important change” it means something that may affect higher trophic levels may have occurred in that year. When the anomaly is qualified as “extreme change” it means something significant has occurred that is most likely to have repercussions for the other trophic levels. This initial categorisation has been discussed within a restricted group of experts for this round of assessment and should be further discussed in the future, with potential changes and improvements.

When interpreting the results, annual anomalies should first be considered, but interpretation should be informed by monthly anomalies to provide more detail. Since annual anomalies are averaged by year, they may reflect a mix of positive and negative anomalies which are qualified as “small change” rather than presenting strong anomaly signal identified for a certain period. This should be carefully considered when interpreting the results.

This indicator should also be considered in combination with the other pelagic habitat indicators for community composition and lifeforms, as well as with indicators for food webs. The present indicator assessment is calculated for the entire community and will therefore provide additional information for assessing the link with pressures (Domingues and others, 2008). Moreover, each pelagic habitat indicator considers the plankton community assembly at different organisation levels: the lifeform indicator at the functional level of the community, this indicator on total biomass/abundance at the level of aggregated community properties, and the plankton diversity indicator at the species and community structure levels. By combining the information from these three state indicators, a more holistic assessment of plankton dynamics can be obtained than from each indicator individually.

Data analysis

The time-series analysis R (R Core Team, 2017) script is the same for any type of data, both fixed-station and Continuous Plankton Recorder data, and phytoplankton and zooplankton data, as long as the pre-analysis steps have been followed (the step of creating monthly averages is provided as a separate R script). When the data are in the form of monthly means (and the data have to be under the same format originally), the time-series analysis can be run.

Results

Findings from the 2012 UK Initial Assessment

Although there was clear evidence of regional-scale change in the composition and abundance of plankton communities, which had been 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

Consistent with the findings in 2012, this assessment indicates that there was an increase in phytoplankton biomass. Zooplankton (copepod abundance) was not considered in 2012.

Zooplankton status assessment

Some important changes can be seen in Northeast Atlantic zooplankton. In the Greater North Sea, the pattern of yearly changes in zooplankton abundance is similar in each ecohydrodynamic area. Extreme negative changes in zooplankton abundance occurred between 1975 and 1985 and in most areas, indicating a decrease in zooplankton abundance, in accordance with a previously-identified regime shift (Figure 3). From 2004 to 2012 the abundance of zooplankton showed some important changes for several years in all ecohydrodynamic areas except in permanently stratified waters. The permanently stratified waters seem to present fewer important changes in zooplankton abundance than the other ecohydrodynamic areas in general (Figure 3)

In the Celtic Seas, the seasonally stratified waters and the regions of freshwater influence showed similar results, with some periods of notably important changes in zooplankton abundance in the 1990s and after 2004. The Loch Ewe results show some important variations in zooplankton abundances; however, it must be noted that this time-series is of a much shorter duration which will impact the identification of anomalies (Figure 3). All results are preliminary and need to be further interpreted using specific knowledge of the different areas and consider the monthly anomalies also derived in this assessment.

Figure 3. Yearly anomalies for total copepod abundance (deviation from the mean annual abundance across the time-series) for (a) Plymouth Marine Laboratory L4, (b) Continuous Plankton Recorder North Sea permanently stratified waters, and (c) Marine Scotland Science Loch Ewe in the Celtic Sea. The 5th, 25th, 50th, 75th and 95th percentiles have been used to categorise the anomalies within each time-series: “small change” which correspond to the anomalies between the 25th and 75th percentiles; “important change”: 5th-25th and 75th-95th percentiles; and “extreme change”: 1st–5th and 95th-100th percentiles.

Phytoplankton status assessment

At the scale investigated in this assessment, it was observed that after 1994 most North Sea ecohydrodynamic areas showed an increase in phytoplankton biomass. The Celtic Sea has yet to be examined by separate ecohydrodynamic areas, but as a whole, phytoplankton biomass showed variability across years with an increase after the 1980s (Figure 4). For the fixed stations, the data must be interpreted with more care due to the shorter length of the time-series (Figure 4).

Figure 4. Yearly anomalies for phytoplankton biomass (deviation from the mean annual biomass across the time-series) for (a) Continuous Plankton Recorder data for the Celtic Seas subregion and (b) Plymouth Marine Laboratory L4.

Trend assessment

This assessment reveals change in Northeast Atlantic plankton abundance and biomass, although this does not, necessarily, imply deterioration in environmental conditions as the results must be examined with respect to environmental and anthropogenic pressures.

Further information

Zooplankton

Zooplankton results for additional geographic areas and data sets are provided in Figures 5 through 12.

Figure 5. North Sea permanently mixed waters yearly anomalies for total copepod abundance (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

Figure 6. North Sea region of fresh water influence yearly anomalies for total copepod abundance (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

Figure 7. North Sea seasonally stratified waters yearly anomalies for total copepod abundance (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

Figure 8. North Sea indetermined waters yearly anomalies for total copepod abundance (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

Figure 9. Celtic Seas intermittently stratified waters yearly anomalies for total copepod abundance (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

Figure 10. Celtic Seas seasonally stratified waters yearly anomalies for total copepod abundance (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

Figure 11. North Sea intermediately stratified waters yearly anomalies for total copepod abundance (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

Figure 12. Stonehaven station yearly anomalies for total copepod abundance (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

 

Phytoplankton

Phytoplankton results for additional geographic areas and data sets are provided in Figures 13 through 21.

Figure 13. L4 station (near Plymouth) yearly anomalies for total phytoplankton biomass (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

Figure 14. North Sea seasonally stratified waters yearly anomalies for total phytoplankton biomass (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

Figure 15. North Sea seasonally region of freshwater influence yearly anomalies for total phytoplankton biomass (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

Figure 16. North Sea seasonally permanently waters yearly anomalies for total phytoplankton biomass (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

 

Figure 17. North Sea permanently mixed waters yearly anomalies for total phytoplankton biomass (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

Figure 18. North Sea intermittently mixed waters yearly anomalies for total phytoplankton biomass (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

Figure 19. North Sea seasonally indetermined waters yearly anomalies for total phytoplankton biomass (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

Figure 20. Stonehaven station yearly anomalies for total phytoplankton biomass (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.


Figure 21. Loch Ewe station yearly anomalies for total phytoplankton biomass (deviation from the mean annual abundance across the time-series). Colours as in Figures 3 and 4.

Conclusions

It is not yet possible to say if the UK target has or has not been met. This assessment identified, with a medium to high level of confidence, changes in phytoplankton biomass (chlorophyll-a) and zooplankton abundance which may have consequences on the functioning, dynamics and structure of the whole marine ecosystem.

An extensive peer-reviewed research base, however, suggests that prevailing oceanographic and climatic conditions are the overall drivers of change in the indicator in the Northeast Atlantic (low confidence – further work needed for quantitative evidence). Identification and quantitative analysis of drivers of change is needed for phytoplankton biomass and zooplankton abundance in each area. This will greatly increase confidence in the assessment of whether the UK target has or has not been met. These analyses will also inform spatial placement of management measures and support the interpretation of other Marine Strategy Framework Directive (European Commission, 2008) indicators.

Knowledge gaps

Knowledge and data gaps include the need to improve:

  • linking the UK and OSPAR databases to ensure consistency between the UK national assessments and the OSPAR regional assessments
  • assessing phytoplankton biomass in each ecohydrodynamic area in the Celtic Seas subregion
  • using 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)
  • identifying reference periods in the time-series
  • integrating different datasets accounting for spatial and temporal differences in sampling

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

Further information

The following work needs to be taken forward to address the knowledge gaps and increase the confidence of the assessment provided by the indicator:

  • calculation of this indicator for phytoplankton by ecohydrodynamic area for the Celtic Sea
  • use of quantitative evidence to link change in plankton indicator to prevailing conditions or anthropogenic pressures
  • investigate the impact of different time-series durations, and thereby reference periods, on this indicator
  • investigate the impacts of different monitoring methods for chlorophyll-a, which have been identified by work focusing on eutrophication, on this indicator
  • aggregate coastal data into new ecodydrodynamic area type representative of east inshore, west inshore, fjord, and Shetland water types
  • integrate between datasets, including a confidence determination based on spatial and temporal and dataset sampling

Concerning the need for additional data sets, there is a clear recognition that plankton indicators are required for assessment under the Marine Strategy Framework Directive, and future research and monitoring studies have to focus on the acquisition of additional data (Caroppo and others, 2013), especially in coastal offshore (shelf) and oceanic areas.

 

For phytoplankton, only chlorophyll-a and Phytoplankton Colour Index are used for this indicator assessment. Other proxies of chlorophyll-a such as satellite chlorophyll estimations or in vivo fluorescence data could also be used, which would help towards considering a wider spatial and temporal coverage. Semi-automated methods such as in vivo multi-spectral fluorometry and flow cytometry (Houliez and others, 2012; Thyssen and others, 2015; Bonato and others, 2015), complemented with automated image acquisition and analysis which allow higher spatial and temporal resolution should also be considered in the frame of monitoring programs. 

 

For zooplankton, total copepod abundance can be calculated through semi-automated methods coupled with image analysis techniques such as the ZooScan (Romagnan and others, 2015).

 

Therefore, semi-automated methods have the potential for complementing the data sources used here. In addition, such methods can provide additional aspects of the whole plankton community (size class, analysis of smaller size organisms, etc.) that can be very useful for the calculation of the other plankton indicators. Indicators based on carbon content could be also considered since biomass estimations can be derived from image analysis assessment for both phytoplankton and zooplankton.

 

In order to link the results with pressures, pressure data must be acquired (past and present). Further development will consist of incorporating more data-sets and the interpretation of the new results obtained, in order to provide advice for management decisions.

 

Concerning the methodology, the present time-analysis treats the totality of the time-series, which means that the total length of the time-series is considered as the “reference period”. This can introduce potential important bias since it is recognized that important regime shifts can occur through time. However, in the absence of the knowledge of such regime shifts, considering the total time-length of the data within a time-series provides less “bias” in the estimation of anomalies. These specific points should be further discussed with scientific experts and the methodology could be changed accordingly. As knowledge improves through the assessment of this indicator, it may be necessary to further refine the methodology.

 

For future assessments, changes observed could be related to the different pelagic habitat types (ecohydrodynamic areas), as these changes may be influenced by different pressures and environmental factors.

References

Beaugrand B, Luczak C, Edwards M (2009) ‘Rapid biogeographical plankton shifts in the North Atlantic Ocean’ Global Change Biology: volume 15, pages 1790–1803 doi:10.1111/j.1365-2486.2009.01848.x (viewed on 26 October 2018)

Bonato S, Christaki U, Lefebvre A, Lizon F, Thyssen M, Artigas LF (2015) ‘High spatial variability of phytoplankton assessed by flow cytometry, in a dynamic productive coastal area, in spring:the eastern English Channel’ Estuarine Coastal and Shelf Science: volume 154, pages 214-223 (viewed on 26 October 2018)

Buttay L, Miranda A, Casas G, González-Quirós R, Nogueira E (2015) ‘Long-term and seasonal zooplankton dynamics in the northwest Iberian shelf and its relationship with meteo-climatic and hydrographic variability’ Journal of Plankton Research: volume 38, pages 106-121 doi:10.1093/plankt/fbv100 (viewed on 26 October 2018)

Caroppo C, Buttino I, Camatti E, Caruso G, De Angelis R, Facca C, Giovanardi F, Lazzara L, Mangoni O, Magaletti E (2013) ‘State of the art and perspectives on the use of planktonic communities as indicators of environmental status in relation to the EU Marine Strategy Framework Directive’ 44° Congresso della Società Italiana di Biologia Marina Roma, 14-16 maggio 2013 (viewed on 13 November 2018)

de Jonge VN (2007) ‘Toward the application of ecological concepts in EU coastal water management’. Marine Pollution Bulletin: volume 55, pages 407-414 (viewed on 26 October 2018)

Domingues RB, Barbosa A, and Galvão H (2008) ‘Constraints on the use of phytoplankton as a biological quality element within the Water Framework Directive in Portuguese waters’ Marine Pollution Bulletin: volume 56, pages 1389–1395 doi:10.1016/j.marpolbul.2008.05.006 (viewed on 26 October 2018)

European Commission (2008) ‘Directive 2008/56/EC of the European Parliament and of the Council of 17 June 2008 establishing a framework for community action in the field of marine environmental policy (Marine Strategy Framework Directive)’ Official Journal of the European Union L 164, 25.6.2008, pages 19-40 (viewed 21 September 2018)

HM Government (2012) ‘Marine Strategy Part One: UK Initial Assessment and Good Environmental Status’ (viewed on 5 July 2018)

Houliez E, Lizon F, Thyssen M, Artigas LF and Schmitt FG (2012) ‘Spectral fluorometric characterization of Haptophyte dynamics using the FluoroProbe: an application in the eastern English Channel for monitoring Phaeocystis globosa’ Journal of Plankton Research: volume 34, pages 136-151 (viewed on 26 October 2018)

OSPAR Commission (2017) ‘Intermediate Assessment 2017’ (viewed on 21 September 2018)

R Core Team (2017) ‘R: A Language and Environment for Statistical Computing’ R Foundation for Statistical Computing. Vienna, Austria (viewed on 24 October 2018)

Romagnan JB, Aldamman L, Gasparini S, Nival P, Aubert A, Jamet JL, Stemmann L (2016) ‘High frequency mesozooplankton monitoring: Can imaging systems and automated sample analysis help us to describe and interpret changes in zooplankton community composition and size structure – an example from a coastal site’ Journal of Marine Systems: volume 162, pages 18-28 (viewed on 07 May 2019)

Thyssen M, Alvain S, Lefèbvre A, Dessailly D, Rijkeboer M, Guiselin N, Creach V and Artigas L-F (2015) ‘High-resolution analysis of a North Sea phytoplankton community structure based on in situ flow cytometry observations and potential implication for remote sensing’ Biogeosciences: volume 12, pages 4051-4066 (viewed on 26 October 2018)

van Leeuwen S, Tett P, Mills D, Van der Molen J (2015) ‘Stratified and non-stratified areas in the North Sea: Long-term variability and biological and policy implications’ Journal of Geophysical Research: Oceans: volume 120, pages 1–17 doi:10.1002/2014JC010485 (viewed on 26 October 2018)

Acknowledgements

Assessment metadata
Assessment TypeUK MSFD Indicator Assessment
 

D1/D4 Biodiversity and Food webs, Pelagic habitats

Plankton abundance and biomass

 
 
Point of contact emailmarinestrategy@defra.gov.uk
Metadata dateFriday, May 1, 2020
TitlePlankton abundance and biomass (PH2)
Resource abstract

This indicator is used to assess changes in biomass and abundance of plankton against the UK MS target.

Linkage
Conditions applying to access and use

© Crown copyright, licenced under the Open Government Licence (OGL).

Assessment Lineage

The assessment has been carried out using phytoplankton and zooplankton data from the Continuous Plankton Recorder as well as UK fixed point stations.

Phytoplankton and zooplankton data from the Continuous Plankton Recorder (managed by Sir Alister Hardy Foundation) and different UK fixed point stations managed by the UK Environment Agency, Centre of Environment Fisheries and Aquaculture Science, Marine Biological Association, Marine Scotland Science, Plymouth Marine Laboratory, Scottish Association for Marine Science, Scottish Environment Protection Agency.

More details can be found in the assessment method section and detailed description of the different data sets used under the ‘dataset doi’ field of this table.

Dataset metadata

Please, see specific metadata under the Archive for Marine Species and Habitats Data (DASSH) by following the links shown below under ‘dataset doi.

Links to datasets identifiers

please, see field below

Dataset DOI

The following data sets were used in the assessment

https://dx.doi.org/10.17031/1629 (CPR data set)

https://dx.doi.org/10.17031/1632 (PML data set)

https://dx.doi.org/10.17031/1633 (SMHI data set)

https://dx.doi.org/10.17031/1634 (CEFAS data set)

https://dx.doi.org/10.17031/1635 (EA data set)        

https://dx.doi.org/10.17031/1636 (MBA data set)    

https://dx.doi.org/10.17031/1637 (MSS data set)

https://doi.org/10.17031/b84a-7951  (SEPA data set)

https://doi.org/10.17031/nz24-br35 (SAMS data set)

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.

Recommended reference for this indicator assessment

Abigail McQuatters-Gollop1*, Angus Atkinson2, Jacob Bedford1, Mike Best3, Eileen Bresnan4, Kathryn Cook4,5, Michelle Devlin6, David G. Johns8, Margarita Machairopoulou4, April McKinney9, Adam Mellor9, Clare Ostle8, Paul Tett7, and Anais Aubert10 2018. Change in Plankton Abundance and Biomass*. UK Marine Online Assessment Tool, available at: https://moat.cefas.co.uk/biodiversity-food-webs-and-marine-protected-areas/pelagic-habitats/plankton-biomass/

* Adapted from OSPAR Intermediate Assessment 2017 on Phytoplankton Biomass and Zooplankton Abundance

1Center for Marine and Conservation Policy Research

2Plymouth Marine Laboratory

3Environment Agency

4Marine Scotland

5National Oceanography Centre

6Centre for Environment, Fisheries and Aquaculture Science

7Scottish Association for Marine Science

8The Marine Biological Association

9Agri-Food & Biosciences Institute

10National Museum of Natural History, France