In marine ecology, it is widely accepted that monitoring data spanning long time-scales are essential for understanding ecosystem change, especially for the detection of community shifts linked to climate variability. Data spanning multiple decades are also essential for biodiversity policy, as they allow us to detect if current ecosystem state represents a change due to anthropogenic pressures and climate change. The Continuous Plankton Recorder dataset, for example, provides consistent plankton community data in the North Sea since 1958, and these data are used in a variety of policy assessments.
One method identified for further increasing temporal scale in ecological studies is the ‘rescue’ and reuse of historical data sources that otherwise may be deemed redundant. For example, the digitisation of historical fisheries log books can help in understanding past fish stocks beyond the time-scope of routine contemporary fisheries monitoring. This approach aids in avoiding ‘shifting baselines syndrome’- the phenomenon where the magnitude of change is underestimated when using just contemporary data, leading to lower ambition in target setting and management measures. In our new paper, we used a ‘rescued’ digitised dataset of plankton samples taken by ICES between 1902 and 1912, to understand whether the beginning of the CPR survey time-period represents a community that was already on a trajectory of change.
Integrating disparate plankton datasets, especially historical datasets, is, however, extremely challenging. As an example, the scientific names of plankton taxa can change over time, as a result of developing taxonomic knowledge. This means that the same organism may be included in different surveys but under a different name. Luckily, the World Register of Marine Species (WoRMS) documents these changes, and we used this database to update all taxon names to their most contemporary equivalent. Another example is that different surveys use different methods to collect their data, and information on how data is collected (‘metadata’) is often absent or limited from rescued historical datasets. Whereas the CPR uses a continuous sampling method along a transect, the ICES dataset comes from station-based plankton net samples. This means that differences in the species lists between two datasets may be a result of sampling biases. We therefore compiled a list of taxa that were common in both datasets and that are likely to be sampled well by the different methodologies.
By then exploring the relative occurrence frequencies of this representative taxa list, we could look for indicative community changes from a baseline set at the beginning of the 20th century. We found a significant difference in community composition between this baseline and the start of the CPR time-period in the 1960s, and this change was bigger in zooplankton communities than in phytoplankton communities. Furthermore, this change was driven by select taxa, with many of the taxa showing relative stability through time. These changes coincided with an increasing SST in the North Sea, implicating climate as a potential driver of this change. Similar corroborative evidence for the role of temperature driving plankton community change was found when comparing taxa over the whole extended time period (1902-1912 + 1960-2015). For example, the copepod Centropages typicus and the multi-species group ‘Bivalve larvae’ were found to show significant variation in response to SST over the wider dataset, extending the time-scale of changes previously detected using just the CPR data.
These results illustrate that a ‘stable’ period in time may be arbitrary to define for North Sea plankton communities, as even the start of CPR survey may represent a changing plankton community. This means that historical time periods may not be a useful method for defining state-based targets for marine biodiversity policy. This is especially true for the use of ‘rescued’ historical datasets, where sampling and analysis biases are unlikely to be able to be fully resolved. Instead, historical plankton datasets are most useful in providing contextual information on large scale ecosystem drivers, such as climate change, to marine biodiversity assessments.
Jake, Plankton and Policy
Bedford, J., Johns, D.G. and McQuatters-Gollop, A., (2018). A century of change in North Sea plankton communities explored through integrating historical datasets. ICES Journal of Marine Science. https://doi.org/10.1093/icesjms/fsy148