Conceptual Ecological Modelling of Shallow Sublittoral Mud Habitats to Inform Indicator Selection

This source preferred by Roger Herbert

Authors: Coates, D.A., Alexander, D., Stafford, R. and Herbert, R.J.H.

http://eprints.bournemouth.ac.uk/22573/

http://www.jncc.gov.uk/

Pages: 1-54

Publisher: JNCC

Place of Publication: Peterborough

The purpose of this study is to produce a series of Conceptual Ecological Models (CEMs) that represent the shallow sublittoral mud habitat in the UK. CEMs are diagrammatic representations of the influences and processes that occur within an ecosystem. The models can be used to identify critical aspects of an ecosystem that may be developed for further study, or serve as the basis for the selection of indicators for environmental monitoring purposes. The models produced by this project are ‘control diagrams’, representing the unimpacted state of the environment, free from anthropogenic pressures.

It is intended that the models produced by this project will be used to guide indicator selection for the monitoring of this habitat in UK waters. CEMs will eventually be produced for a range of habitat types defined under the UK Marine Biodiversity Monitoring R&D Programme (UKMBMP), which, along with stressor models designed to show the interactions within impacted habitats, would form the basis of a robust method for indicator selection. This project builds on the work to develop CEMs for shallow sublittoral coarse sediment habitats (Alexander. 2014).

The project scope included the Marine Strategy Framework Directive (MSFD) predominant habitat type ‘shallow sublittoral mud’. This definition includes those habitats that fall into the EUNIS Level 4 classifications A5.33 Infralittoral Sandy Mud, A5.34 Infralittoral Fine Mud, A5.35 Circalittoral Sandy Mud and A5.36 Circalittoral Fine Mud, along with their constituent Level 5 biotopes which are relevant to UK waters. A species list of characterising fauna to be included within the scope of the models was identified using an iterative process to refine the full list of species found within the relevant Level 5 biotopes.

A literature review was conducted using a pragmatic and iterative approach to gather evidence regarding species traits and information that would be used to inform the models and the interactions that occur within the shallow sublittoral mud habitat. All information gathered during the literature review was entered into a data logging pro forma spreadsheet which accompanies this report. Wherever possible, attempts were made to collect information from UK-specific peer-reviewed studies, although other sources were used where necessary. All data gathered was subject to a detailed confidence assessment. Expert judgement by the project team was utilised to provide information for aspects of the models for which references could not be sourced within the project timeframe.

A model hierarchy was developed based on groups of fauna with similar species traits which aligned with previous sensitivity studies of ecological groups. One general control model was produced that indicated the high level drivers, inputs, biological assemblages, ecosystem processes and outputs that occur in shallow sublittoral mud habitats. In addition to this, five detailed sub-models were produced, which each focussed on a particular functional group of fauna within the habitat: tube building fauna, burrowing fauna, suspension and deposit feeding infauna, mobile epifauna, scavengers and predators, and echinoderms and sessile epifauna. Each sub-model is accompanied by an associated confidence model that presents confidence in the links between each model component. The models are split into seven levels and take spatial and temporal scale into account through their design, as well as magnitude and direction of influence. The seven levels include regional to global drivers, water column processes, local inputs/processes at the seabed, habitat and biological assemblage, output processes, local ecosystem functions, and regional to global ecosystem functions.

The models indicate that whereas the high level drivers which affect each functional group are largely similar, the output processes performed by the biota and the resulting ecosystem functions vary both in number and importance between groups. Confidence within the Conceptual Ecological Modelling of Shallow Sublittoral Mud Habitats models as a whole is generally high, reflecting the level of information gathered during the literature review.

Important drivers that influence the ecosystem include factors such as wave exposure, depth, water currents, climate and propagule supply. These factors, in combination with seabed and water column processes, such as primary production, suspended sediments, water chemistry, temperature and recruitment define and influence the food sources consumed by the biological assemblages of the habitat, and the biological assemblages themselves. In addition, the habitat sediment type plays an important factor in shaping the biology of the habitat.

Output processes performed by the biological assemblage are variable between functional faunal groups depending on the specific fauna present and the role they perform within the ecosystem. Important processes include secondary production, biodeposition, bioturbation, bioengineering and the supply of propagules; these in turn influence ecosystem functions at the local scale such as nutrient and biogeochemical cycling, supply of food resources, sediment stability, habitat provision and in some cases microbial activity. The export of biodiversity and organic matter, biodiversity enhancement and biotope stability are the resulting ecosystem functions that occur at the regional to global scale.

Features within the models that are most useful for monitoring habitat status and change due to natural variation have been identified; as have those which may be useful for monitoring to identify anthropogenic causes of change within the ecosystem. Physical and chemical features of the ecosystem have mostly been identified as potential indicators to monitor natural variation, whilst biological factors have predominantly been identified as most likely to indicate change due to anthropogenic pressures.

The data on this page was last updated at 04:42 on November 25, 2017.