EuropaBON WP3.1

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Data Stream - modify

Tab created: 2022-01-13

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) and per region.

Standard data collected on these surveys comprises numbers of each species of fish sampled in each trawl sample, measured to defined length categories (i.e. a fish with a recorded length of 14 cm would be between 14.00 cm and 14.99 cm in length). By dividing the species catch numbers-at-length by the area swept by the trawl on each sampling occasion, the catch data are converted to estimates of fish density-at-length, by species, at each sampling location in each year. 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 an estimate 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. Summing these density estimates (Ns,l / km2) across all length classes provides the required estimate of species population abundance density (Ns / km2) in each year for each survey.

Two different sensitivity metrics were used to identify species considered to be sensitive to fishing mortality. The Average Life-history Trait (ALHT) metric is the same as the metric developed by Greenstreet et al. (2012b). The ‘Proportion Failing to Spawn’ (PFS) is a new sensitivity metric developed to address flaws in the earlier metric identified by the ICES Working Group on the Ecosystem Effects of Fishing Activities (ICES, 2016). Development of the PFS metric is fully documented in a supporting paper (Greenstreet et al., 2017a). Analyses for both metrics are presented to demonstrate that assessment outcomes were not unduly affected by choice of sensitivity metric. Where choice of metric affects the assessment outcome, the principal assessment outcome should be based on the PFS metric. Both metrics rely on the availability of several life-history trait parameters for each species. For example, maximum recorded length (Lmax), von Bertalanffy ultimate body length or length infinity (Linf), von Bertalanffy growth term (K), length at maturity (Lmat), and age at maturity (Amat). Compilation of these parameters for all 485 species encountered across the 12 groundfish surveys in the North-East Atlantic, and where missing, the methods used to estimate them, are fully described in Greenstreet et al., 2017b.

For each survey, the number of sensitive species encountered was first established. However, almost by definition, these species are among the rarest in each surveyed community, so for many species the data available were too sparse to support meaningful assessment. Only species encountered on 50% or more of occasions that each survey was carried out were considered to have been sufficiently well sampled as to support formal assessment. In the overwhelming majority of cases, box plots comparing the sensitivity metric scores of species meeting and failing to meet this 50% criterion confirm that species deemed sufficiently well sampled as to support formal assessment were representative, in terms of their sensitivity metric scores (and hence life-history trait composition), of the full suite of sensitive species encountered by each survey (Figure a and Figure b). Thus, if recovery was observed in a significant fraction of the species capable of supporting formal assessment, the same might be expected among the species sampled too infrequently to support formal assessment.

Spatial Scope: Assessment Units
For each of the 12 groundfish surveys, population abundance density time-series were determined for all sensitive species, identified by either sensitivity metric, that were deemed capable of supporting formal assessment. Individual survey-based assessments were then performed. The individual survey-based assessments across the whole of the Celtic Seas and the Greater North Sea regions were then considered to determine overall assessment outcomes.

Baselines
None of the surveys extend sufficiently far back in time as to provide an adequate reference period to establish species abundance levels commensurate with acceptable status. A trends based assessment approach, relying on the use of trends based assessment values, was therefore adopted.

Assessment Values
By virtue of their sensitivity to additional human-related mortality, the population abundance of each sensitive species sampled by each survey is assumed to have declined as a result of past human activities. There is good evidence that fishing mortality has indeed caused declines in the populations of sensitive species.

Thus trends based assessment values related to population recovery constituted the primary basis for assessment; sensitive species should be increasing in abundance. However, should this primary assessment give an unacceptable outcome, or the results prove uncertain, a secondary assessment can be performed. The secondary assessment addresses an alternative question of whether further decline in the population abundance of sensitive species has at least been halted (Greenstreet et al., 2012b).

OSPAR Intermediate Assessment (IA) 2017 Indicator Assessment values are not to be considered as equivalent to proposed European Union (EU) Marine Strategy Framework Directive (MSFD) criteria threshold values, however they can be used for the purposes of their MSFD obligations by those Contracting Parties that wish to do so.

Primary Assessment: Recovery
Abundance trends for most species are not monotonic, so a simple parametric trends based approach (e.g. linear regression) is not appropriate; instead a non-parametric approach has been used. Essentially, the entirety of each survey time series acts as the reference period. The assessment value is set as: abundance in the assessment year must lie in the upper 25% of all abundance values observed throughout the time series.

For the purposes of the Intermediate Assessment (IA) 2017, the assessment yearwas the last year in each survey time series for which data were available. However, to explore recent trends, preceding years back to 2010 were also defined as the assessment year. The approximate period when data would have been available for the initial assessments required in 2012 under the European Union Marine Strategy Framework Directive (MSFD) was 2010. In assigning each consecutive preceding year as the assessment year, the length of the time series available to determine the upper 25% of all target abundance values diminishes. This places a limit on how far back in time the assessment can be pushed, particularly for surveys that only commenced more recently.

The surveys therefore provide abundance density estimates for each sensitive species in each year: a species-specific abundance metric for each sensitive species. These data can be ranked, and the ranking representing the lower boundary of the upper 25th percentile of all data for each species establishes the species-specific abundance metric-level assessment valuefor each species-specific abundance metric. In the stipulated assessment year, each species-specific abundance metric ranking should be greater than the ranking representing the species-specific abundance metric-level assessment value.

The sensitive species abundance indicator-level assessment value is defined based on probability. Random walk simulations suggest that the probability of the last abundance estimate datum in a time series falling into the upper 25th percentile of all data is 0.332. Knowing the number of assessed sensitive species in each survey, and the probability of any one species-specific abundance metric meeting its upper 25th percentile species-specific abundance metric-level assessment value, allows the sensitive species abundance indicator-level assessment value to be defined as the value that represents a significant (p<0.05) departure from the binomial distribution.

Secondary Assessment: Halt Further Decline
The logic underlying the alternative secondary assessment is identical to the logic described in relation to the primary assessment of population recovery objectives. The same simple non-parametric trends based approach can be used, except that in this instance, for each sensitive species in each survey, abundance in the assessment year must lie outside the lower 25% of all abundance values observed throughout the time series for the metric to meet its assessment value.

Following the same logic described for the primary assessment, the annual species-specific abundance metric data are ranked and the ranking representing the lower boundary of the upper 75th percentile of all data established as the new species-specific abundance metric-level assessment value; thus, in the stipulated assessment year, each species-specific abundance metric rank should be greater than the ranking representing the species-specific abundance metric-level assessment value.

The sensitive species abundance indicator, for any given survey, is defined as the number of species in any assessment year whose species-specific abundance metric meets or exceeds its species-specific abundance metric-level assessment value of being within the upper 75th percentileof all their own abundance data over the whole survey time series, up to and including the year defined as the assessment year. The random walk simulation suggest that the probability of the last abundance datum in a time series falling into the upper 25th percentile of all data is 0.332 for the primary assessment. Similarly, for the secondary assessment the probability of the last abundance datum in a time series falling into the lower 25th percentile of all data is also 0.332 and thus the probability of the last abundance datum in a time series falling into the upper 75th percentile of all data is 1 - 0.332 = 0.668. Following an identical logic to the recovery primary assessment, the number of assessed sensitive species and the probability of any one species-specific abundance metric meeting its upper 75th percentile metric-level target is known for surveys in the secondary assessment, allowing to define the sensitive species abundance indicator-level assessment value as the sensitive species abundance indicator value that represents a significant (at p<0.05) departure from the binomial distribution.

Assessment Integration Approaches
Two integration approaches, ‘probabilistic’ and ‘averaging’ were applied to determine integrated assessment outcomes at the regional scale.

Probabilistic Integration
Since the p<0.05 significance level, used to identify significant departures from a binomial distribution, is applied s the basis for setting sensitive species abundance indicator-level assessment values for each individual survey assessment, it follows that the probability of observing any one survey sensitive species abundance indicator actually meeting its sensitive species abundance indicator-level assessment value by chance is p≤0.05.  Therefore the binomial distribution can again be used to determine the number of surveys where the indicator meets the indicator-level assessment value for a given number of individual survey assessments for this to represent a significant deviation from the binomial distribution. The probability of observing the sensitive species abundance indicator-level assessment value being met by chance is low. This results in the reliability of the probabilistic integration approach becoming questionable when the number of individual survey assessment outcomes that require integration becomes smaller.

Six survey assessment outcomes were available for both the Celtic Seas and Greater North Seas regions. This was considered to be just sufficient to allow for the use of the probabilistic integration method. Observing sensitive species abundance indicator-level assessment values met in two or more of the six surveys would represent a significant departure from the binomial distribution in both regions.

Averaging Integration
To apply an averaging integration approach, all survey sensitive species abundance indicator values were first converted to a Common-scale Indicator Score (CIS) by expressing sensitive species abundance indicator values as a fraction of their sensitive species indicator-level assessment values. Where individual indicator assessment values were met or exceeded the CIS was ≥1, where assessment values were not met the CIS was <1. These CIS values could then be averaged across all surveys carried out within each region to derive averaged integrated assessment outcomes. Where the averaged CIS was ≥1, the averaged integrated assessment outcome conferred acceptable status.

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.