EuropaBON WP3.1

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Data Stream - modify

Tab created: 2022-01-12

Project

1) Data origin - from where is data coming from

Origin integration node

2) Data process - how is the data processed (if)? And how does it flow?

Data type Options: 1) Raw: data as collected in the field ; 2) Aggregated: raw data is aggregated (not processed!); e.g. occurrence data collected in linear sampling transects of 1 km is aggregated (summed) at 10 km resolution; e.g. data from seasonal sampling is aggregaged at annual level ; 3) Essential Biodiversity Variables (EBVs) ; 4) Ecosystem Services vales (ESS); 5) Indicators: other processed indicators such as for example species trends

Ebv ess type If the data flowing or generated by a given integration node is an EBV or an ESS, please, select the category from the list

Format Format of the data flowing between biodiversity monitoring initiatives and integration nodes or between integration nodes

Method If there is any type of data integration (i.e. if data type is other than Raw or Aggregated) , indicate the method used to integrate data. Please, use the field “Description” below to give further details about the method used to process the data, including relevant bibliographic references and URL links if available.

Frequency Indicate the frequency of the data flow (e.g. annual, seasonal)

3) Data set - which dataset/integrated product is processed and how?

Name Name of the dataset (please provide an informative name for the dataset flowing)

Description

Trends are calculated per survey (=area).

Standard data collected on these surveys comprise numbers of each species of fish sampled in each trawl sample, measured to defined length categories (i.e. 1 cm below, so a fish with a recorded length of 14 cm would be between 14.00 cm and 14.99 cm in length). By dividing these species catch numbers-at-length by the area swept by the trawl on each sampling occasion, the catch data are converted to standardised estimates of fish density-at-length, by species, at each sampling location. Summing these trawl-sample species density-at-length estimates across all trawl samples collected within each sampling stratum in each year (e.g. ICES statistical rectangles), and dividing by the number of trawl samples within each stratum per year, gives estimates of the density (of each species and length category) within each sampling stratum in each year. Summing these sample stratum density estimates across all sampling strata sampled in each year, and dividing by the number of strata sampled, provides estimates of the average density (N), of each species (s) and length category (l), in each year, across the whole area covered by the survey. It is these last density estimates (Ns,l km2) that are used in the calculation of each survey LFI time series. However, the LFI is based on biomass rather than abundance, so these abundance densities have to be converted to biomass density data by applying species weight-at-length relationships (of the form w = alb, where w is weight and a and b are species-specific parameters).

Spatial Scope: Assessment Units
A single LFI time series was selected for each survey and individual survey-based assessments were performed. The individual survey-based assessments within each region were then considered to determine an overall assessment outcome for each region.

Baselines
The published study in the Greater North Sea (Greenstreet et al., 2011) established the early 1980s as the reference period for setting the OSPAR Ecological Quality Objective (EcoQO) for the North Sea first quarter (Q1) international bottom trawl survey (IBTS) LFI. This period was selected on the basis that this was the last time that ICES advice for setting fish quotas was to maintain status quo; i.e. to carry on fishing at prevailing levels because stocks were not considered to be unduly depleted (Greenstreet et al., 2011). Selection of this reference period led to the adoption of an LFI of >0.3 as the EcoQO for the North Sea (Heslenfeld and Enserink, 2008). In the Celtic Sea study, Shephard et al. (2011) noted a period of relatively stable LFI values during the late 1980s and early 1990s, which included the six highest LFI values recorded. The stability of the LFI, combined with these high values, led Shephard et al. (2011) to adopt this period as their reference period, and consequently to suggest an LFI of >0.4 as a possible EcoQO for the Celtic Sea.

Assessment Values
Where feasible, studies discussed in the previous (Baselines) section were drawn on to set assessment values for as many of the 13 groundfish survey LFI time series as possible. A number of approaches for doing this were applied.

Greater North Sea
In the Greater North Sea, the principle surveys used to support assessment were the GNSIntOT1 and GNSIntOT3 surveys. GNSIntOT1 is the same survey analysed by Greenstreet et al. (2011), so the same reference period of the early 1980s was used. The GNSIntOT1 and GNSIntOT3 surveys were strongly correlated, so the linear regression obtained was used to determine an assessment value for GNSIntOT3 that was equivalent to the assessment value for GNSIntOT1. Absolute assessment values could not be set for the remaining four Greater North Sea surveys, so a recovery trends-based approach was adopted.

Celtic Seas
In the Celtic Seas, the United Kingdom west coast groundfish survey (WCGFS) analysed by Shephard et al. (2011) ceased in 2004 and so was no longer available for assessment purposes. However, this analysis could be used to inform assessment value setting for the current surveys. The lowest LFI values reported by Shephard et al. (2011) during the reference period was 0.42, so this was adopted as the assessment value rather than their suggested value of 0.40. Generally, low values were observed between 1994 and 2004, and within this period, the lowest five year moving average LFI (0.138) was observed between 2000 and 2004. The assessment value of 0.42 was three times this lowest five year moving average value. So the procedure for setting assessment values for on-going otter trawl surveys in the Celtic Seas region was as follows:

Determine the lowest five year moving average LFI value;
Identify the period to which this five year low moving average LFI relates. If between 1994 and 2004, then this was deemed a valid five year low LFI;
The assessment value was then set at three times the five year low moving average LFI value.
Summary of Assessment Value Approaches
Where the setting of absolute assessment values was not feasible, a trends-based approach was adopted assuming that the demersal fish community in question had been impacted by fishing and that a recovery trend (increasing LFI values) was therefore necessary to rectify the harm done. Table c summarises the assessment value setting approach used and the resulting assessment values.

Deciding on the species to be included in the analysis is the first step in calculating each survey LFI time series. The FishBase website (www.fishbase.org) provided an ‘ecotype’ classification for all species encountered. The LFI has been designed as an indicator of size composition within demersal fish communities, so species assigned to the Pelagic and Bathypelagic ecotypes were automatically excluded, while species assigned to the Demersal and Bathydemersal ecotypes were automatically included. Species belonging to the Benthopelagic ecotype were generally included, but with the exceptions of Clupea harengus, Dysomma brivirostre, Hyperoplys immaculatus, Ammodytidae (family-level ID code), Salmo (genus-level ID code), Salmo salar and Sarpa salpa, which were all excluded on the basis that these species are relatively poorly sampled by the survey gear.

Determining the appropriate length value (LLF) that defined ‘large fish’ for each survey had next to be determined. Traditionally this has been achieved by fitting 5th degree polynomial functions to LFI time series derived using a range of LLF lengths, from 20 to 50 cm, where the LFILLF=l is calculated as:

and selecting the LFI time series with the best fit. In five instances the LFI time series with the best fit polynomial was not selected to support the assessment because resultant LFI values were either too high, or too low, so that either too great a fraction, or too small a fraction of fish were classified as ‘large fish’. Where this happened the actual signal in the LFI trend was markedly diminished, and an alternative LFI time series that addressed this issue was used instead (Table b). The proportion of total biomass at LLF > 50 cm becomes too small to derive a usable indicator; resulting indicators are too strongly influenced by the ‘noise’ associated with variation in the biomass of fish <50 cm in length.

Brief description of the data flowing (e.g. if the dataset represents aggregated data, please explain here how the aggregation is made); if the data set is a “product” – EBV, ESS, indicator – give more information here about how its values are estimated and provide relevant bibliographic references or URL links if available)

Target biodiversity data Select biodiversity data initiatives included in the dataset. No need to choose them in upper levels when creating products.

Taxonomy

Target taxa Target taxa

check this box if you have a list of taxa to upload for this data stream (no need to check it when sending all species available from data origin). If the DataStream generates a product integrating data for specific taxa, please try to upload the list of taxa included in the product.

Target species List of the target species/habitats covered in the monitoring initiative. If you don´t find a thesaurus for the taxa of your interest, you might need to upload a New Thesaurus first – see tab New Thesaurus – and then, use the option “Choose Thesaurus” to select all or a subset of the taxa concerning this particular DataStream (the species integrated in a particular indicator/EBV/ESS)

Spatial scale

Spatial scale Spatial scale coverage of the dataset

Resolution Spatial scale resolution value (numeric)

Resolution units Spatial scale resolution units (km or m)

4) Data destination - where does the data/product flow to?

Two options here:

a) The data flows into an upper-level integration node b) The data is a generated Product

Select the destination integration node

Dest integration node

Is the product generated used by policy - e.g. EBV, EESV, indicator?

Used by policy Is the indicator generated used by policy ?

Note here that some indicators might currently lack connection to policy but we want to capture them here too because of their potential to inform policy in the future.