Abstract
Assessing changes in the state of marine biodiversity is critical for underpinning sustainable marine management. As the cumulative effects of multiple anthropogenic pressures on marine ecosystems are increasingly understood, and the importance of marine ecosystems in providing goods and services to society is increasingly recognised, marine management frameworks are transitioning towards an ecosystem-based approach, explicitly addressing biodiversity change. Under the European Marine Strategy Framework Directive (MSFD), biodiversity indicators are used to assess the state of marine ecosystems against defined targets. Plankton communities are a key component of these biodiversity assessments and indicators for both phytoplankton and zooplankton are used to assess for change in the state of ‘pelagic habitats’. Key challenges exist however, in the attribution of change in plankton indicators to the underlying drivers. The effects of climate change must be understood and accounted for when interpreting indicator changes against targets. Long-temporal scale information is needed to understand the effects of climate on plankton communities. So far, when assessing pelagic habitats under the MSFD in the North East Atlantic, the full temporal extent of plankton data available hasn’t been used. Specifically, the role of long temporal scale data in the setting of reference conditions from which pelagic habitats are assessed for change is unclear. The dynamics of the plankton indicators used in MSFD pelagic habitat assessment have also not been fully explored over long temporal scales. In this thesis, long-temporal scale data is used to identify ‘shifting baselines’ in plankton communities within the North Sea, and resolve the use of historical data in setting reference conditions. Furthermore, long temporal scale data is applied to further develop the indicators used in pelagic habitat assessments in the North East Atlantic, including indicators based on functional lifeform groups, by understanding their response to changing climatic and oceanographic conditions. Zooplankton communities show clear directional change in response to climate over multidecadal time scales, with phytoplankton communities being highly stochastic in time. This directional zooplankton community change was driven by select taxa. To this extent, the dynamics of policy indicators, including functional ‘lifeform’ groups are driven by the dynamics of select individual taxa, highlighting that fine taxonomic resolution data is needed to interpret changes in indicators during policy assessments. Recommendations for policy are then outlined as to how this information from long-temporal scale plankton data can be formally incorporated into the ecosystem assessment process. Notably, a surveillance role for long temporal scale plankton data in the formal assessment of biodiversity under the MSFD is developed. Under the MSFD, climate variation and anthropogenic climate change is an ecosystem driver outside the scope of management, and instead referred to as ‘prevailing conditions’. It is important to track changes in prevailing conditions however, in order to interpret the outcomes of biodiversity assessments and design appropriate and adaptive targets and management measures. As illustrated by analyses in this thesis, changes in plankton can track and inform on changing prevailing conditions, and so when applied as surveillance indicators, can provide useful supplementary and contextual information. Therefore, although the full temporal extent of plankton information has been so far underused in ecosystem assessments under the Marine Strategy Framework Directive, it can have multiple roles in the sustainable management of the marine environment under climate change.
Keywords
Marine policy, Biodiversity indicators, Plankton
Document Type
Thesis
Publication Date
2019
Recommended Citation
Bedford, J. (2019) Applying long temporal scale plankton data to marine strategy development under climate change. Thesis. University of Plymouth. Retrieved from https://pearl.plymouth.ac.uk/bms-theses/303