Modifications des communautés phytoplanctoniques et zooplanctoniques

Tett et al. (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 (PI). 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 abundance in a multi-dimensional state-space provides a means of monitoring changes in the structure 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 this case two lifeform abundances, which together are used as a pair.

In the example illustrated in Figure afor diatoms and dinoflagellates, the axes of the two dimensional space are the abundance values of the 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.

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 interannual variability); and (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, result 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 can be drawn about this 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 b).

The size and shape of the envelope are sensitive to sampling frequency and the total number 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 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. It is therefore desirable to exclude a proportion (p) of points, to eliminate these extremes, and so the 90-percentile was used. Envelopes are therefore drawn around the cloud of points to include a proportion (p=0.9) of the points: with the 5% of points that were most distant from the cloud's centre and the 5% of points that were closest to the centre, excluded.

For a Plankton Index to be calculated, it is necessary to establish starting conditions as the basis for making comparisons. In the example shown in Figure b, data collected from the indeterminate area within the Greater North Sea (Figure 2) between 2004 and 2008, were used to create an envelope. The envelope, thus drawn (Figure c left) defines a domain in state-space that contains a set of trajectories of the diatom-dinoflagellate component of the marine pelagic ecosystem and thus represents the prevailing regime during the starting period.

The next step is to map a new set of data into the starting condition state-space and compare these new data, at least a dozen points (Figure c right). The value of the PI is the proportion of new points that fall inside the envelope (or to be precise, between the inner and outer envelopes). In the example shown in Figure c, 24% or 17 of the 71 new points lie outside, and the PI is 0.76. A value of 1.0 would indicate no change, and a value of zero 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 getting 17 by chance alone when only 7.1 are expected, can be calculated using a binomial series expansion, or by a chi-square calculation (with 1 df 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 index is produced by comparing the starting condition envelope with data collected from each subsequent year.

Two lifeforms will not be 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 et al., 2013). The rule is that each additional lifeform must be different to those already used and the axis for each new lifeform must be drawn at right angles to all existing axes. The state-space map must therefore be drawn in as many dimensions as there are state variables (lifeforms) but this becomes complicated when considering the number of lifeforms that may need to be used to fully represent the community structure of the plankton.

• 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.
• Time series of the index will be used to track persistent changes in the condition of the plankton over time and to relate any such trend to trends in human and climate pressures.

The period 2004–2008 was selected to represent the starting conditions because each dataset used here (see section ‘The plankton data’) has sufficient data for that period to construct a reliable starting condition envelope. Additional European datasets will be added in the future as resources become available. This envelope represents starting conditions and not reference conditions. The starting condition envelope was compared with data from the subsequent six-year period (2009–2014). The 2009–2014 period was chosen because it is the most recent period with data and is the same temporal length as the European Union Marine Strategy Framework Directive (MSFD) assessment period.

• Starting conditions do not represent reference conditions.

Because plankton community composition, distribution, and dynamics are closely linked to their environment, the analysis was performed at the scale of the ‘ecohydrodynamic’ zones (EHD) (van Leeuwen et al., 2015). EHDs were determined through analysis of a 50-year hindcast using the General Estuarine Transport Model (GETM) 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 and Iberian Coast). EHDs are constructed using density stratification, the most important large-scale physical feature in shallow shelf seas (van Leeuwen et al., 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 Leeuwen et al., 2015). The predominant EHD zone types, based on water column structure, are: permanently mixed throughout the year; permanently stratified throughout the year; regions of freshwater influence (ROFIs); seasonally stratified (for about half the year, including summer); intermittently stratified; and indeterminate regions (inconsistently alternate between the above).

At the time of calculating this assessment (2016), the EHD model was more reliable and detailed for the Greater North Sea than for the Celtic Seas. The model had not been developed for the Bay of Biscay and Iberian Coast so that sub-region was considered as a single region of indeterminate status.

• EHDs are a geographical representation of the conditions which best suit plankton distribution, dynamics and community composition
• EHDs are constructed based on water column density stratification

The assessment has been carried out using phytoplankton and zooplankton data from three sources. The data submitted by the Swedish Meteorological and Hydrological Institute (SMHI) and Plymouth Marine Laboratory (PML, United Kingdom) are fixed-point time series. Four nearshore stations from SMHI were used in the analysis, which fall into the EHD zone that describes the indeterminate regions of the Greater North Sea. Both zooplankton and phytoplankton are sampled once or twice per month all year around. The PML station (L4) is 13 km offshore from Plymouth 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 (CPR) survey is collected at a much broader spatial scale through ships-of-opportunity. CPR data are collected offshore and in the open ocean and are best analysed at a monthly time scale. It should be noted that all other data for this assessment come from CPR data collection on major shipping lanes. The CPR survey is coordinated by the Sir Alister Hardy Foundation for Ocean Science (SAHFOS) in the United Kingdom. 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 separately. Each dataset has internal QA/QC procedures to ensure consistency and accuracy of the data.

As the SMHI stations are located in the indeterminate EHD of the Greater North Sea their data were averaged for analysis. All CPR samples were averaged for each EHD type in each OSPAR sub-region. In the future, more datasets from other OSPAR Contracting Parties will be incorporated into the analysis.

Each lifeform pair comprises two ecologically-relevant lifeforms, which contain common functional traits (Table a). The rationale for selecting the lifeform pairs and additional criteria containing supplementary information on lifeforms is listed in Table a.

The database master species list was built by assigning functional traits to each species for a dataset and then adding additional new datasets to expand the master species list (Figure e). The species list for each new dataset was assigned a unique Aphia ID via the World Register of Marine Species (WoRMS). The new dataset’s species list was then compared with the plankton database’s master species list via Aphia IDs and any new species were identified. This process ensures that each species is only entered in the database once. The new species 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 new species were added to the master species list. Queries were constructed to build lifeforms from the functional trait information (Table b).

A simple method of confidence assessment was used to determine the confidence in each lifeform (Table c). Using expert opinion, each lifeform was evaluated on two characteristics: the ability to identify and speciate 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 vs autotrophic and mixotrophic dinoflagellates’ because the mixotrophic and autotrophic mode of nutrition of many dinoflagellates species is currently uncertain. Thus the accuracy of assigning the lifeform category is low. Likewise, the lifeform pair ‘carnivorous zooplankton vs 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 whether both nuisance and toxin-producing lifeform pairs are required. Only pairs with two high-confidence lifeforms were used in the OSPAR reporting.

• Species are assigned functional traits and combinations of traits are used to construct lifeforms
• Lifeform pairs consist of ecologically-relevant lifeforms, with each pair having an ecological rationale for selection
• A simple confidence assessment method was used to evaluate the confidence in each lifeform. Only pairs containing two high-confidence lifeforms have been used for reporting

This assessment indicates spatial and temporal variability in the plankton community for all lifeforms. This is in accordance with expert knowledge on plankton dynamics from the scientific literature (McQuatters-Gollop et al., 2015). The plankton lifeform indicator has been published in the peer-reviewed literature (Tett et al., 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 et al., 2015). The data and methods used here are therefore considered robust and of high confidence.

The following work needs to be taken forward to address the knowledge gaps and improve the assessment provided by this indicator:

• Determination of the appropriate time period to represent starting conditions;
• Identification and testing of possible assessment value options;
• Refinement of spatial scale of ecohydrodynamic zones in the Bay of Biscay and Iberian Coast;
• Investigation into appropriate Harmful Algal Blooms (HABs) lifeform pair;
• Incorporation of additional plankton datasets as data and resources become available (including other OSPAR regions, especially to address EU Member States’ MSFD reporting requirements);
• Incorporate historical data, and based on these potentially revisit the methodology around setting envelopes;
• Examination of drivers of change in plankton lifeforms in each area, which will allow apportionment of change to climate and anthropogenic pressures;
• Determination of the traits for many plankton species – for some species even information about basic biological characteristics, such as diet, is not yet known;
• Understanding the role of pico and nanoplankton (small plankton composed by cells with sizes between respectively 0.2 μm and 2 μm and 2 μm and 20 µm) in the ecosystem. These small size categories are difficult to measure routinely and are thus mainly ignored. However, they make up a significant proportion of the plankton biomass relevant to marine food webs. Their ecosystem role needs further investigation so that they can be included in an appropriate new or existing lifeform; and

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