The Gap Analysis
 
The objective of the Gap Analysis is to identify which of the data needs of the project can be met by existing data and which will have to be collected through new surveys. Purchasing data or acquiring it through new surveys can both incur significant costs, so the Gap Analysis can be a critical step in establishing the magnitude of the budget required by a mapping programme.
 
The first stage is to consider in detail the question ‘What types of data are needed?’, as the answers are by no means always simple or clear-cut. If you are preparing a map according to an existing habitat classification scheme you must fully understand how that scheme works in order to identify the data types needed to apply the scheme. Different schemes describe and characterise habitats in different ways and may use different types of data or information. Some of the criteria used to discriminate between habitat classes may not be directly measurable, such as the level of exposure on a shore (e.g. ‘moderately exposed’ or ‘sheltered’) or the biological zonation (e.g. sublittoral, infralittoral, circalittoral). Other criteria, such as sediment type, may be expressed in rather general terms (e.g. ‘coarse sediment’) and so not require some of the expensive detailed analyses that may be routine outside the context of habitat mapping. In several hierarchical classification systems, such as EUNIS or the UK’s BioMar system (Connor et al., 2004), the first few levels of the classification relate entirely to physical characteristics, so detailed species inventories may not be needed if the full hierarchy is not to be used. Further information on 'What classification schemes are available?' and the specific data needs of the EUNIS system are considered further in ‘What data does EUNIS use?’. If you are not required to use this ‘top-down approach’ of imposing an existing classification scheme, you will need to consider the range and quality of data that you will need to follow the empirical ‘bottom-up’ approach to differentiating and classifying habitats.
 
The second stage of the gap analysis is to search for existing data that meet your requirements. Points to consider here include availability, quality, and coverage. Just because data exist does not mean to say they are always readily accessible, so online data catalogues are a useful way to start investigating data availability; who owns it, how it can be accessed and at what cost. Matadata catalogues can also provide some indication of whether the data quality is likely to meet the needs of your mapping programme, as the records usually state explicitly if data has been collected to a national or international data standard. If not, you may be able to acquire a sample of the data so you can perform your own quality checks.
 
Where multiple data sets are used, you should also consider issues of data compatibility, as different data sets may have been collected using different instruments or different standards and protocols. It is common for taxonomic nomenclature to vary somewhat between different biological data sets, so checks need to be made for pseudonyms and to establish if species lists can be merged directly or will need some form of translation or truncation to a higher taxonomic level before they can be harmonised. You may be forced to reject available data on the grounds that it does not meet the quality standard required or is not compatible with other data sets that you are able to use. Such rejections will highlight gaps in the available data that must be filled by new surveys or data modelling.
 
Given that the data is of suitable quality for use in the study, you must also determine how well it covers the area you wish to map. Data coverage should assess both the spatial extent and density of the available data and this can frequently be visualised by plotting the available data layers and/or survey lines and sampling points in a GIS. Clearly, where the geographic bounds of the existing surveys do not cover the whole of the area to be mapped, there is a data gap (an absence) that must be filled. However, within the existing surveys, there may be insufficient density of data to suit the needs of the map; the sampling points or survey lines may be too far apart to allow the map to be drawn at the required resolution, in which case additional sample points or survey lines may be needed.
 
The gap analysis should pay specific attention to the inherent limitations of using interpreted data layers such as a map of seabed sediments (see the section on ‘data limitations’). These interpretations may be useful proxies for habitat mapping studies, but it is important to understand the nature of the underlying data and the purpose of the initial interpretation as these rarely match the precise needs of habitat mapping. Existing maps may be the best information available, so they should not be dismissed out of hand but used with appropriate caution. The gap analysis should examine the quality and provenance of existing data interpretations and assess their suitability for use in the mapping programme.
 
The final stage of the gap analysis is to produce a report capturing the essential information for the benefit of all involved in the planning and execution of the project. The report should systematically cover each element of the mapping programme (broad, intermediate and fine scale) and will benefit greatly from the inclusion of a GIS project or workspace showing the available metadata (and data or interpreted layers, if available). The format of the report is likely to be project specific and should be quite detailed. A rapid overview of the Gap Analysis and an ‘aide memoire’ can be provided in tabular form, as illustrated here in a worked example of the Gap Analysis Summary Table (also provided in the Resources section as Gap analysis pro forma). Some details of the mapping area are followed by a table listing a variety of data types commonly used in mapping studies. The table is completed to show which of these will be required and whether existing data sets are available (in full or in part) to a suitable quality standard and density of coverage. Where partial data or no data are available, an assessment is made as to whether the data gaps are best filled by new surveys or by data modelling.
 
Gap Analysis Summary for: Area X, English Channel
Details of mapping area: 10 x 15 km. Depth range 20 to 70 m. Fully saline. Unconsolidated sediments, possible rock outcrops. Existing seabed sediment and solid geology maps available. Some digital bathymetry available from single beam surveys.
Data Types
Required
Available
Is the data
of suitable…
Are new data needed?
Quality?
Coverage?
From survey
From model
Elevation
 
 
 
 
 
 
Topography (heights)
No
 
 
 
 
 
Bathymetry (depths)
Yes
Part
OK
No
Part
 
Slope
Yes
No
 
 
 
Yes
Geological
 
 
 
 
 
 
Lithology
Yes
All
OK
Yes
 
 
Sediment thickness
No
 
 
 
 
 
Sediment/substrate types
Yes
Part
OK
No
Part
 
Bedforms
Yes
 Part
 
 
Part
Part
Granulometry (PSA)
Yes
No
 
 
Yes
 
Geotechnical properties
No
 
 
 
 
 
Biological
 
 
 
 
 
 
Infauna
Yes
Yes
Rejected
 
Yes
 
Epifauna &/or Epiflora
Yes
Part
OK
Poor
Yes
 
Structural fauna (reefs)
Yes
No
 
 
Yes
 
Physical/Oceanographic
 
 
 
 
 
 
Temperature
No
 
 
 
 
 
Light penetration
No
 
 
 
 
 
Wave exposure
No
 
 
 
 
 
Salinity
Yes
Model
OK
OK
 
 
Wave base
Yes
Model
OK
OK
 
 
Tides/currents
Yes
Model
OK
OK
 
 
Seabed shear stress
Yes
Model
OK
No
Yes
Yes
Turbidity
Yes
Model
OK
OK
 
 
Example of a table summarising the output from a desk study and gap analysis of the availability and suitability of data relevant to mapping a hypothetical area in the English Channel
 
The Glenan Archipelago Case Study presents details of a shallow-water mapping project on the coast of Brittany, in France. Section 2 of this document Glenan Archipelago Case Study provides and example of the process of collating and assessing existing data layers to identify data gaps that need to be filled by new surveys.
 
Links to other chapters:
 
 
 
 

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