Time-series datasets are crucial to delivering Good Environmental Status

We are on a mission in Europe to achieve Good Environmental Status (GES) for our seas (Who cares about the MSFD?). As with many marine policy or conservation efforts, good data are critical to success. When it comes to GES, however, ecological time-series data, for seabirds, marine mammals, commercial and non-commercial fish, benthic habitats and plankton, are particularly important. We need ecological time-series data for the development and informing of ecosystem indicators, the setting of environmental targets against a background of climate change, and understanding our marine ecosystems in a holistic manner. Why, when they are so important, does funding these important datasets remain so difficult?

The availability of multidecadal ecological time-series datasets varies with ecosystem component – biomass of commercial fish and seabird abundance are much better monitored than large scale change in benthic habitats. When accounting for the other desirable traits of a robust dataset (sufficient taxonomic detail, spatially representative) the number of suitable time-series further decreases. Plankton, for example, respond to change at a variety of scales, from ephemeral blooms of a single species in a local area to regional-scale decadal changes in community composition. Because of their multiscale dynamics, phytoplankton datasets which are 20-30 years in length, spatially extensive and taxonomically detailed are increasingly important for supporting decision making. These sorts of plankton datasets are very rare; the UK, however, is fortunate enough to be home to the most spatio-temporally extensive plankton dataset in the world, the Continuous Plankton Recorder (CPR) survey, as well as several other multidecadal fixed point plankton time-series such as L4 (PML), Stonehaven (Marine Scotland Science), and the Western Irish Sea time-series (AFBI).

DecadalPCI

Phytoplankton biomass 1960s-2000s calculated from the CPR time-series. Data and understanding gained through analysing long-term datasets, particularly those at wide spatial scales, are critically needed to inform policy objectives.

Taxonomic information provides a crucial understanding of the most basic component of biodiversity: which organisms are present in a region or ecosystem? Across all ecosystem components, fundamental knowledge of taxonomy is necessary to assess diversity, understand community dynamics, gain insights into ecosystem and species responses to climate change, detect non-indigenous species, and identify emerging scientific and policy issues. Research based on taxonomic time-series datasets forms the foundation to understand spatiotemporal changes in global distributions of species and alterations to community composition. The taxonomic construction of ecosystem indicators is therefore important to the achievement of GES. The most sensitive biodiversity indicators are based on species or functional group data. From a plankton perspective, this type of detailed, species level plankton community composition information can only be obtained through analysis by trained taxonomists. Unlike modern analysis techniques (such as automated visual identification, flow cytometry, satellite remote sensing, or fluorometry) which can only discriminate coarse plankton groups, taxonomists can distinguish a wide variety of species relatively efficiently, generating information needed to investigate diversity in complex marine systems. Several recent reviews and inquiries into the state of taxonomy in the UK and worldwide have expressed concern that taxonomy is a discipline in critical decline, with numbers of taxonomists steadily decreasing across all scientific disciplines. This means that ecological time-series with a taxonomic component, as opposed to those measuring bulk ecosystem characteristics, are increasingly in danger.

Because European seas are experiencing both climate-driven and anthropogenically-induced changes, defining targets for GES is not as easy as simply selecting a historical state to which to aspire. In some cases, due to climate change and/or multi-century human exploitation, ecosystems may never recover to the state they were in even a century ago. In the case of target-setting for policy indicators, multidecadal time-series provide necessary context between contemporary and historical ecosystem states. By considering the temporal context revealed by a long time-series, environmental targets can be selected which are both ecologically meaningful (i.e. they represent GES) and realistic (i.e. the targets reflect a vision of GES which acknowledges climate variability and past ecosystem use).

Koslow and CoutureDespite their recognised importance to scientific research and providing evidence for marine policy, sustained funding of many ecological time-series presents a challenge (Edwards et al., 2010; McQuatters-Gollop, 2012; Koslow and Couture, 2013). This is not a new problem; there is a recognised scarcity of long-term ecological datasets, particularly in non-coastal regions, driven by a lack of funding (Edwards et al., 2010; Koslow and Couture, 2013). The principal reasons for the termination of established monitoring programmes are also historically consistent and near-ubiquitous – funding is limited and a time lag exists between data collection and scientific yield (Duarte et al., 1992). Many ecological time-series that support decision making are only partially publicly funded; the CPR survey is a prime example. Supplementary funding, pieced together from disparate income sources, is required to fill this gap; this piecemeal approach is both risky and resource intensive. Not that public funding protects a time-series – the recent cuts at CSIRO may jeopardise some of their long-running monitoring programmes, with serious consequences to marine conservation efforts.

Long-term time series are essential for progressing ecological understanding and underpinning evidence-based environmental policy and we must keep repeating this message – again, and again, and again.

 

Abigail, Plankton and Policy

 

Read more:

McQuatters-Gollop, A., Edwards, M., Helaouët, P., Johns, D.G., Owens, N.J.P., Raitsos, D.E., Schroeder, D., Skinner, J. and Stern, R.F., 2015. The Continuous Plankton Recorder survey: how can long-term phytoplankton datasets deliver Good Environmental Status?. Estuarine, Coastal and Shelf Science, 162: 88-97.

References:

Duarte, C.M., Cebrian, J., Marb”a, N., 1992. Uncertainty of detecting sea change. Nature 356, 190.

Edwards, M., Beaugrand, G., Hays, G.C., Koslow, J.A., Richardson, A.J., 2010. Multidecadal oceanic ecological datasets and their application in marine policy and management. Trends Ecol. Evol. 25, 602e610.

Koslow, J.A., Couture, J., 2013. Ocean sciences: follow the fish. Nat. Online 502, 163e164.

McQuatters-Gollop, A., 2012. Challenges for implementing the Marine Strategy Framework Directive in a climate of macroecological change. Philos. Trans. R. Soc. 370, 5636e5655.

 

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About Abigail McQuatters-Gollop

Marine biologist, guitarist, cat lover, red wine drinker. I like plankton.
This entry was posted in Marine Conservation, MSFD, Plankton, Policy and tagged , , , , , . Bookmark the permalink.

One Response to Time-series datasets are crucial to delivering Good Environmental Status

  1. Pingback: A funded plankton PhD opportunity at Plymouth! | Plankton and Policy

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