Recovery in the population abundance of sensitive fish species
The UK target has been met in the Celtic Sea but not in the Greater North Sea. The decline in abundance of sensitive fish species has been halted in the Celtic Seas and Greater North Sea. However, significant recovery of populations is only apparent in the Celtic Seas.
Background
UK Target on the abundance of sensitive fish species
This indicator is used to assess progress against the following target, set in the UK Marine Strategy Part One (HM Government,2012): “At the scale of the Marine Strategy Framework Directive Sub-Regions, populations of sensitive fish species are not significantly impacted by human activities: the population abundance density and population biomass density of sensitive fish should meet individual indicator targets for recovery in a statistically significant proportion of species monitored.”
Key pressures and impacts
Fish species with life history traits such as large ultimate body size, slow growth rate, large length, and late-age-at-maturity, are particularly sensitive to additional sources of mortality, including fishing mortality. Populations of such species are known to have declined markedly in abundance through the 20th century, a period of considerable expansion in fishing activity across the area assessed. Recovery in population abundance among a significant fraction of these species is therefore needed.
Measures taken to address the impacts
The UK Marine Strategy Part Three (HM Government, 2015) states that all parts of the marine fish community have been impacted by human activities, but there have been recent improvements in the status of some of these fish communities, primarily as a result of a reduction in fishing pressure. With the existing policy in place and the introduction of the reformed Common Fisheries Policy, it is expected that there will be a further reduction in the overall fishing pressure which will reduce fishing impacts on both target and non-target sensitive species.
Monitoring, assessment, and regional cooperation
Areas that have been assessed
The assessment was based on data collected by 12 groundfish surveys carried out across two OSPAR Regions: the Greater North Sea and the Celtic Seas.
Monitoring and assessment methods
Data from the standardised monitoring programmes (groundfish surveys) that occur each year were used to determine population abundance density time-series for all sensitive species. Individual survey-based assessments were then performed to determine overall assessment outcomes across the whole of the Celtic Seas and the Greater North Sea.
Assessment thresholds
Trends-based assessment thresholds relating 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 its results prove uncertain, a secondary assessment can be performed which relates to the halting of further population decline.
Regional cooperation
The UK has been the indicator lead for abundance of sensitive fish species for the recent OSPAR Intermediate Assessment (OSPAR Commission, 2017), the UK results of which are presented here.
Further information
Life-history theory explains the evolution of species’ life-history traits under different mortality scenarios. Stable environments, with low disturbance mortality, support communities characterised by ‘K-type’ life-history traits (including: large body size, slow growth rate, late age and larger size at maturation, and lower fecundity) while communities in regions of higher disturbance are more dominated by species with opposite ‘r-type’ traits (MacArthur and Wilson, 1967; May, 1976; Stearns, 1977; 1992; Roff, 1993; Huston, 1994; Reznick and others, 2002). Life-history theory predicts that heavily exploited fish communities (those with a high disturbance mortality) should have fewer K-type and more r-type species. Elasmobranch species are generally characterised by K-type traits, and many elasmobranch populations declined markedly during the 20th century (Frisk and others, 2001; Greenstreet and Hall, 1996; Walker and Hislop, 1998; Greenstreet and others, 1999b; Greenstreet and Rogers, 2000; van Strien and others, 2009). Teleost species with similar K-type life-histories also declined (Philippart, 1998; Rijnsdorp and others, 1996). By the 1960s, average life-history trait composition among the demersal fish assemblage had become more r-type orientated (Jennings and others, 1999a; Greenstreet and Rogers, 2000; 2006; Greenstreet and others, 2012a), and in closely related pairs of species, the species with the more K-type life-history traits showed the greatest population decline at a time when fishing activity was high (Jennings and others, 1998). A species’ life-history trait composition indicates its capacity to cope with additional mortality, and so determines its sensitivity to human activities that raise mortality rates above natural ambient levels. Species with K-type life-history traits are particularly sensitive to the additional mortality associated with fishing (Jennings and others, 1998; Gislason and others, 2008; Hobday and others, 2011; Le Quesne and Jennings, 2012).
Fishing activity is widespread across the assessment area and has been intense for a century or more (for example: Rijnsdorp and others, 1996; Jennings and others, 1999b; Greenstreet and others, 1999a; 2009; 2011; Piet and Jennings, 2005; Shephard and others, 2011; Modica and others, 2014). As populations of fish species with K-type life-history traits across the region are likely to be in a depleted state, achieving acceptable status for these sensitive species will require population recovery. However, some species may be unable to sustain any level of fishing mortality, which means population recovery may not be possible for all sensitive species if any sustainable fishing industry is to be maintained (Le Quesne and Jennings, 2012).
To support the assessment of the status of fish communities under the Marine Strategy Framework Directive (European Commission, 2008), Greenstreet and others (2012b) developed a sensitivity metric that could be used to identify suites of sensitive fish species among the species sampled by groundfish surveys operating across the geographic area. They proposed a trends-based approach to setting targets related to population recovery for each sensitive species sampled by a survey, followed by the use of the binomial distribution to determine whether population recovery had occurred among a significant fraction of the suite of sensitive fish species sampled in any given survey.
Assessment method
Indicator metric and data collection
Population abundance metrics were determined using data collected by 12 groundfish surveys carried out across two separate regions: the Greater North Sea and the Celtic Seas. Six survey datasets were available for analysis for each region (Table 1).
Table 1. List of groundfish surveys, the region in which they operate, and the period over which they have been undertaken. The survey acronym naming convention consists of (1) first 2–3 capitalised letters indicate the region (CS: Celtic Seas; GNS: Greater North Sea), (2) subsequent capitalised and lowercase letters indicate the country involved (Fra: France; Eng: England; Ire: Republic of Ireland; NIr: Northern Ireland; Sco: Scotland; Ger: Germany; Int: International ICES North Sea bottom trawl survey; Net: Netherlands), (3) two capitalised letters indicate the type of survey (OT: otter trawl; BT: beam trawl), (4) final number indicates the season in which the survey is primarily undertaken (1: January to March; 3: July to September; 4: October to December).
|
Region |
Survey Acronym |
Survey Period |
|
Celtic Seas |
CSEngBT3 |
1993–2015 |
|
CSIreOT4 |
2003–2015 |
|
|
CSNIrOT1 |
1992–2015 |
|
|
CSNIrOT4 |
1992–2015 |
|
|
CSScoOT1 |
1985–2016 |
|
|
CSScoOT4 |
1995–2015 |
|
|
Greater North Sea |
GNSEngBT3 |
1990–2015 |
|
GNSFraOT4 |
1988–2015 |
|
|
GNSGerBT3 |
2002–2015 |
|
|
GNSIntOT1 |
1983–2016 |
|
|
GNSIntOT3 |
1998–2016 |
|
|
GNSNetBT3 |
1999–2015 |
Standard data collected on these surveys comprises numbers of each species of fish sampled in each trawl sample, measured to defined length categories (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 (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 (denoted N), of each species (denoted S) and length category (denoted l), in each year, across the whole area covered by the survey. Summing these density estimates (denoted Ns,l / km2 where ‘/ km2’ indicates per unit area in km2) across all length classes provides the required estimate of species population abundance density (denoted 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 metric (denoted ALHT) is the same as the metric developed by Greenstreet and others, (2012b). The ‘Proportion Failing to Spawn’ metric (denoted PFS) is a new sensitivity metric developed to address flaws in the earlier metric identified by the International Council for the Exploration of the Sea Working Group on the Ecosystem Effects of Fishing Activities (ICES, 2016). Development of the Proportion Failing to Spawn metric is fully documented in a supporting paper (ICES, 2017). Analyses for both metrics are presented to demonstrate that assessment outcomes were not unduly affected by the choice of sensitivity metric. Where the choice of metric affects the assessment outcome, the principal assessment outcome should be based on the Proportion Failing to Spawn metric. Both metrics rely on the availability of several life-history trait parameters for each species. For example, maximum recorded length (denoted Lmax), von Bertalanffy ultimate body length or length infinity (denoted Linf), von Bertalanffy growth term (denoted K), length at maturity (denoted Lmat), and age at maturity (denoted 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 and others 2017a and 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. For each survey region, only species encountered on 50% or more of the surveys conducted 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 life-history trait composition and thus sensitivity to fishing mortality, of the full suite of sensitive species encountered by each survey (Figure 1 and Figure 2). 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.
Figure 1. Box plots comparing the Average Life-history trait sensitivity metric scores of species sampled in each groundfish survey region (see Table 1 for details of survey designation codes) that either met or failed to meet the ≥ 50% criterion for being sampled during surveys in these areas.
The median sensitivity score is shown by the horizontal line, the mid-50th-percentile of scores by the extent of the grey box, and the range of score by the horizontal bar extending out of the box. Where probability scores (P= numerical values) are shown on the plot, this indicates a statistically significant difference in sensitivity scores between those species capable of supporting formal assessment and those species failing to meet the ≥ 50% criterion.
Figure 2. Box plots comparing the Proportion Failing to Spawn sensitivity metric scores of species sampled in each groundfish survey (see Table 1 for details of the survey designation codes) that either met or failed to meet the ≥ 50% criterion. for being sampled during surveys in these areas.
The median sensitivity score is shown by the horizontal line, the mid-50th-percentile of scores by the extent of the grey box, and the range of score by the horizontal bar extending out of the box. Where probability scores (P= numerical values) are shown on the plot, this indicates a statistically significant difference in sensitivity scores between those species capable of supporting formal assessment and those species failing to meet the ≥ 50% criterion.
Spatial scope
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 to provide an adequate reference period to establish if species abundance levels commensurate with acceptable status. A trends-based assessment approach, relying on the use of trends-based thresholds, was therefore adopted.
Assessment thresholds
Trends-based assessment thresholds related to population recovery constituted the primary basis for assessment. However, when this primary assessment gave an unacceptable outcome or the results proved uncertain, a secondary assessment was performed to addresses an alternative question of whether further decline in the population abundance of sensitive species had at least been halted (Greenstreet and others, 2012b).
Primary assessment: Recovery
Abundance trends for most species are not monotonic, so a simple parametric trends-based approach such as 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, and the target 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 OSPAR Intermediate Assessment (OSPAR Commission, 2017), the assessment year was the last year in each survey time-series for which data were available. However, to explore recent trends, consecutive 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 Marine Strategy Framework Directive was 2010. In assigning each year of data as the assessment year, the length of the time-series available to determine the upper 25% of all abundance values target diminishes as the assessment year approaches the beginning of the time series. This places a limit on how far back in time this process can be applied, particularly for surveys that commenced more recently.
The surveys 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, with the ranking representing the lower boundary of the upper 25th-percentile of all data for each species. This value is the species-specific abundance metric-level assessment threshold for 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 threshold. For any given survey, the actual sensitive species abundance indicator is then 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 threshold of being within the upper 25th- percentile of all the abundance data over the whole survey time-series, up to and including the year defined as the assessment year. Random walk simulations 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. 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 threshold, the sensitive species abundance indicator-level assessment threshold can 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 this alternative assessment is identical to that described above in relation to 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. Following the same logic described above, the annual species-specific abundance metric data are ranked with the ranking representing the lower boundary of the upper 75th-percentile of all data established as the new species-specific abundance metric-level assessment threshold. 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 threshold. For any given survey, the sensitive species abundance indicator 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 threshold, with it being within the upper 75th percentile. This includes all the abundance data gathered over the entire survey time-series, up to and including the year defined as the assessment year. The probability of the last abundance datum in a time series falling into the lower 25th percentile of data is the same as that of it falling into the upper 25th percentile: 0.332 based on random walk simulations. There probability of falling into the upper 75th percentile is therefore 0.668 (equal to 1-0.332). The sensitive species abundance indicator-level assessment threshold can be defined as the sensitive species abundance indicator value that represents a significant (at p<0.05) departure from the binomial distribution.
Assessment integration
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 as the basis for setting sensitive species abundance indicator-level assessment thresholds for each survey assessment, the probability of observing any one survey sensitive species abundance indicator meeting its sensitive species abundance indicator-level assessment threshold by chance is p ≤ 0.05. For a given number of individual survey assessments the binomial distribution can again be used to determine the number of surveys where this occurs, for this to represent a significant deviation from the binomial distribution. With the probability of observing the sensitive species abundance indicator-level assessment threshold being met by chance being so low, the reliability of this probabilistic integration approach becomes questionable as the number of individual survey assessment outcomes requiring integration gets smaller. Six survey assessment outcomes were available for both the Celtic Seas and Greater North Seas regions, and this was considered just sufficient to allow the use of the probabilistic integration method. In both regions, seeing sensitive species abundance indicator-level assessment thresholds met in two or more of the six surveys would represent a significant departure from the binomial distribution.
Averaging Integration
To apply an averaging integration approach, all survey sensitive species abundance indicator values were first converted to a common-scale indicator score by expressing sensitive species abundance indicator values as a fraction of their sensitive species indicator-level assessment thresholds. Where individual assessment thresholds were met or exceeded the common-scale indicator score was greater than or equal to 1, where assessment thresholds were not met the common-scale indicator score was less than 1. These common-scale indicator scores could then be averaged across all surveys carried out within each region to derive averaged integrated assessment outcomes. Again, where the averaged common-scale indicator score was greater than or equal to 1, the averaged integrated assessment outcome conferred acceptable status.
Results
Findings from the UK Initial Assessment
This indicator was not considered as part of the UK Initial Assessment (HM Government, 2012).
Latest findings
Status assessment
The abundance of sensitive fish species was assessed against two sets of assessment thresholds. The first assessment examines whether population recovery is underway and the secondary assessment examines whether population decline has been halted. For this assessment, the assessment year was the last year in each survey time series for which data were available. Both assessments use two sensitivity metrics to define suites of sensitive species: average life-history trait and proportion failing to spawn. Both metrics rely on species’ life trait information. The results produced by the two metrics were generally consistent with one-another, demonstrating assessment outcomes were robust with respect to the choice of metric. However, the principal assessment outcome was based on the more recently developed proportion failing to spawn metric. Results were integrated across surveys to determine if the assessment thresholds for recovery or halting decline were met using both ‘averaging’ and ‘probabilistic’ integrating procedures. Choice of integration procedure had minimal effect on assessment outcomes.
Here results for the assessments based on the proportion failing to spawn metric using the ‘averaging’ integration method are presented. Population recovery among a significant number of sensitive fish species was evident in the Celtic Seas, but not in the Greater North Sea (Figure 3). However, in both regions, recent trends in the number of sensitive species increasing in abundance suggest an improving situation (Figure 4). A further decline in the population abundance of sensitive fish species has been halted in both regions (Figure 5). For this assessment, confidence in the methodology is moderate, and confidence in the data is high.
Figure 3. Outcomes against the ‘population recovery’ assessment for suites of sensitive species defined by the proportion failing to spawn sensitivity metric sampled by surveys carried out in the Celtic Seas and the Greater North Sea. Survey names are described in Table 1. Outcomes for regional scale integrated assessments, using an ‘averaging’ integration procedure are indicated by horizontal green (meets or exceeds assessment threshold, represented by a black dashed line) or red (does not meet assessment threshold) horizontal lines. The common indicators score is determined as an indicator value/assessment threshold.
Figure 4. Integrated assessment outcomes for population abundance recovery (where a value above 1 means the assessment threshold is being met or exceeded) derived using an averaging integration approach.
Figure 5. Outcomes against the ‘halt further population decline’ assessment for suites of sensitive species defined by the proportion failing to spawn sensitivity metric sampled by surveys carried out in the Celtic Seas and the Greater North Sea. Outcomes for regional scale integrated assessments, using an ‘averaging’ integration procedure are indicated by horizontal green (meets or exceeds assessment threshold, represented by a black dashed line) or red (does not meet assessment threshold) horizontal lines. The Common Indicators Score is determined as an indicator value/assessment threshold.
Trend assessment
Assessment outcomes suggest that the decline of the abundance of fish species sensitive to fishing mortality has been halted since 2010.
Further information
This section presents the results of assessments using both sensitivity metrics to demonstrate that assessment outcomes were not affected by the choice of sensitivity metric.
Primary assessment: Assessment thresholds related to population recovery
The sensitive species indicator-level assessment threshold was met in just one Greater North Sea survey regardless of the sensitivity metric used. In the Celtic Seas, the sensitive species indicator-level assessment threshold was met in four surveys using the average life-history trait sensitivity metric, and in five surveys using the proportion failing to spawn sensitivity metric. Probabilistic integration was feasible for both the Celtic Seas and Greater North Sea regions where, with six surveys operating, if assessment thresholds are met in two or more surveys in each region there is also a significant departure from the binomial distribution at p<0.05. In the Celtic Seas region, the sensitive species indicator-level assessment threshold was met in four surveys using the average life-history trait sensitivity metric (Table 2) and in five surveys using the proportion failing to spawn sensitivity metric (Table 3). In both cases, the probability of these results occurring by chance was p < 0.0001, so both probabilistic integrated assessment outcomes represented significant departures from the binomial distribution. In the Greater North Sea region, regardless of the sensitivity metric used, the sensitive species abundance indicator met its sensitive species indicator-level assessment threshold in only one survey (Tables 2 and 3), and this was not a significant departure from the binomial distribution.
Table 2. Assessment results from the average life-history trait sensitivity metric and probabilistic integration. For more details on the information in each column, see the Assessment Method section above.
|
Region |
Survey |
Number of “sensitive” species |
indicator level assessment values |
assessment value met (yes/no) |
common-scale indicator score |
regional average integrated assessment outcome |
||
|
sampled |
meeting 50 % of years criterion |
meeting metric-level assessment values in final year of survey |
||||||
|
Celtic Seas |
CSEngBT3 |
27 |
16 |
4 |
9 |
no |
0.444 |
0.985 |
|
CSIreOT4 |
61 |
31 |
16 |
16 |
yes |
1.000 |
||
|
CSNIrOT1 |
26 |
17 |
12 |
10 |
yes |
1.200 |
||
|
CSNIrOT4 |
25 |
17 |
11 |
10 |
yes |
1.100 |
||
|
CSScoOT1 |
40 |
21 |
15 |
12 |
yes |
1.250 |
||
|
CSScoOT4 |
44 |
22 |
11 |
12 |
no |
0.917 |
||
|
Greater North Sea |
GNSEngBT3 |
25 |
14 |
6 |
9 |
no |
0.667 |
0.781 |
|
GNSFraOT4 |
29 |
19 |
11 |
11 |
yes |
1.000 |
||
|
GNSGerBT3 |
11 |
6 |
2 |
5 |
no |
0.400 |
||
|
GNSIntOT1 |
49 |
29 |
14 |
15 |
no |
0.933 |
||
|
GNSIntOT3 |
46 |
31 |
13 |
16 |
no |
0.813 |
||
|
GNSNetBT3 |
26 |
13 |
7 |
8 |
no |
0.875 |
||
Table 3. Assessment results from the proportion failing to spawn sensitivity metric and probabilistic integration. For more details on the information in each column, see the Assessment Method section above.
|
Region |
Survey |
Number of “sensitive” species |
indicator level assessment values |
assessment value met (yes/no) |
common-scale indicator score |
regional average integrated assessment outcome |
||
|
sampled |
meeting 50 % of years criterion |
meeting metric-level assessment values in final year of survey |
||||||
|
Celtic Seas |
CSEngBT3 |
33 |
22 |
6 |
12 |
no |
0.500 |
1.003 |
|
CSIreOT4 |
69 |
37 |
19 |
18 |
yes |
1.056 |
||
|
CSNIrOT1 |
32 |
22 |
15 |
12 |
yes |
1.250 |
||
|
CSNIrOT4 |
31 |
22 |
12 |
12 |
yes |
1.000 |
||
|
CSScoOT1 |
49 |
28 |
16 |
14 |
yes |
1.143 |
||
|
CSScoOT4 |
49 |
28 |
15 |
14 |
yes |
1.071 |
||
|
Greater North Sea |
GNSEngBT3 |
33 |
16 |
6 |
9 |
no |
0.667 |
0.841 |
|
GNSFraOT4 |
35 |
25 |
13 |
13 |
yes |
1.000 |
||
|
GNSGerBT3 |
19 |
9 |
4 |
6 |
no |
0.667 |
||
|
GNSIntOT1 |
58 |
36 |
17 |
18 |
no |
0.944 |
||
|
GNSIntOT3 |
57 |
42 |
17 |
20 |
no |
0.850 |
||
|
GNSNetBT3 |
33 |
21 |
11 |
12 |
no |
0.917 |
||
Integrated assessment outcomes derived from the averaging integration were more conservative. The integrated assessment gave an unacceptable outcome for the Greater North Sea regardless of the sensitivity metric used. The integrated assessment gave an acceptable outcome for the Celtic Seas only when using the preferred proportion failing to spawn sensitivity metric.
Trends for any one survey derived from the two different sensitivity metrics were almost identical. For both metrics, no trend or an increasing trend was observed more frequently than a declining trend (Figures 6 and 7). Although six surveys operated in both the Celtic Seas and the Greater North Sea regions, data were not available from all surveys in all assessment years between 2010 and 2016. Probabilistic integration was feasible for assessment years 2010 to 2015 when, in both regions, at least five surveys operated. In 2016, data were only available for assessment from one Celtic Seas survey and from two surveys in the Greater North Sea (see Figure 8). With so few surveys, obtaining an acceptable outcome would be extremely unlikely. Although the number of surveys operating in each year and in each region varied, ,each individual survey-specific sensitive species abundance indicator value still had a probability of p=0.05 of meeting its sensitive species indicator-level assessment threshold, and since the number of surveys generating data in each year and in each region was known, the binomial distribution could still be used to determine the number of surveys where, if the sensitive species indicator-level assessment threshold met, would constitute a significant departure from the binomial distribution at p<0.05.
Figure 6. Trends in the sensitive species abundance indicator against the primary recovery assessment using the average life-history trait sensitivity metric. Using the average life-history trait sensitivity metric to define sensitive species for each survey time-series, trends are shown in the sensitive species abundance indicator (the number of sensitive species for which their species-specific abundance metric met or exceeded its recovery-related species-specific abundance metric-level assessment level (grey dashed line) in successive assessment years from 2010 to the last available data. Colour coding distinguishes surveys operating in different regions: Celtic seas, yellow; Greater North Sea, purple.
Figure 7. Trends in the sensitive species abundance indicator against the primary recovery assessment using the PFS sensitivity metric. Using the proportion failing to spawn sensitivity metric to define sensitive species, these plots show trends in the sensitive species abundance indicator (the number of sensitive species for which their species-specific abundance metric met or exceeded its recovery-related species-specific abundance metric-level assessment value (grey dashed line) in successive assessment years from 2010 to the last datum. Colour coding distinguishes surveys operating in different regions: Celtic seas, yellow; Greater North Sea, purple.
Figure 8. Proportion of surveys where the sensitive species abundance indicator met the sensitive species indicator-level assessment value for the primary recovery assessment. Trends in the proportion of surveys in the Celtic Seas (yellow) and Greater North Sea (purple) regions where the sensitive species abundance indicator met its population-recovery related sensitive species indicator-level assessment value in each assessment year from 2010 to 2016. Numbers within each histogram bar indicate the number of surveys providing data for analysis. Light grey bars and dark grey bars indicate non-significant departures and significant departures respectively from the binomial distribution. The probabilities of observing each significant departure are shown above the histogram bars.
Results were similar regardless of the choice of sensitivity metric. In the Celtic Seas, the number of surveys where the sensitive species abundance indicator met its sensitive species indicator-level assessment threshold was significantly higher than would be expected by chance in all years from 2010 to 2015, when based on the average life-history trait sensitivity metric, or from 2011 to 2015 if using the proportion failing to spawn sensitivity metric. Both metrics suggest an increasing trend in the fraction of surveys showing recovery in population abundance among a significant number of sensitive species. In the Greater North Sea, evidence of recovery in a significant number of sensitive species’ populations has emerged only in more recent years. For both metrics, and in both regions, the result was not significant in 2016, but this was largely down to the small number of surveys (n=3) for which 2016 data were available (Figure 8). Use of an averaging integration method gave similar integrated assessment outcomes to the probabilistic integration method at the regional scale, recovery is apparent in the Celtic Seas region, but the integrated recovery assessment threshold was not achieved in the Greater North Sea (Figure 9).
Figure 9. Integrated assessment outcomes for population abundance recovery (where a value above 1 means the assessment value is being met or exceeded) derived using an averaging integration approach for the average life history-trait metric (ALHT, left-hand plot) and proportion failing to spawn metric (PFS, right hand plot). The evidence supporting recovery among significant numbers of sensitive species in a significant number of surveys is uncertain. The regional scale integrated assessment outcome varied depending on the sensitivity metric used to identify the suite of sensitive species, and on the type of integration method used. There was more convincing evidence of recovery in the Celtic Seas compared to the Greater North Sea where evidence of recovery was scarce.
The evidence supporting recovery among significant numbers of sensitive species in a significant number of surveys is uncertain. The regional scale integrated assessment outcome varied depending on the sensitivity metric used to identify the suite of sensitive species, and on the type of integration method used. There was more convincing evidence of recovery in the Celtic Seas compared to the Greater North Sea where evidence of recovery was scarce.
Secondary assessment: targets related to halting further population decline
Regardless of the sensitivity metric used, a probabilistic integrated assessment confirmed that further decline in sensitive species population abundance had been halted in both the Celtic Seas and Greater North Sea regions. In the Celtic Seas, sensitive species abundance indicator-level assessment thresholds were met in four of the six surveys regardless of the sensitivity metric used; a highly significant departure from the binomial distribution at p<0.0001 (Tables 4 and 5). In the Greater North Sea, sensitive species abundance indicator-level assessment thresholds were met in five surveys when the Average Life-history Trait sensitivity metric was used (Table 4), and in three surveys when the Proportion Failing to Spawn metric was used (Table 5). Both results represented significant departures from the binomial distribution at p<0.00012 and p<0.0022, respectively. An averaging integration approach gave slightly different results. When the Proportion Failing to Spawn sensitivity was used, an acceptable assessment outcome was achieved in both regions (Table 5), but when the Average Life-history Trait sensitivity metric was used to identify sensitive species, the assessment outcome was not acceptable for the Greater North Sea. However, the sensitive species abundance indicator-level assessment threshold was missed by only a very small margin, primarily as a result of a single survey giving a poor assessment result (Table 4). Trends for any one survey derived from the two different sensitivity metrics were almost identical (Figure 10 and Figure 11).
Table 4. Assessment results based on using the average life-history trait sensitivity metric and averaging integration to define the suite of sensitive species in fish communities sampled by 12 groundfish surveys. For more details on the information in each column, see the Assessment Method section above.
|
Region |
Survey |
Number of “sensitive” species |
indicator level assessment values |
assessment value met (yes/no) |
common-scale indicator score |
regional average integrated assessment outcome |
||
|
sampled |
meeting 50 % of years criterion |
meeting metric-level assessment values in final year of survey |
||||||
|
Celtic Seas |
CSEngBT3 |
27 |
16 |
13 |
14 |
no |
0.929 |
1.014 |
|
CSIreOT4 |
61 |
31 |
26 |
26 |
yes |
1.000 |
||
|
CSNIrOT1 |
26 |
17 |
14 |
15 |
no |
0.933 |
||
|
CSNIrOT4 |
25 |
17 |
15 |
15 |
yes |
1.000 |
||
|
CSScoOT1 |
40 |
21 |
21 |
18 |
yes |
1.167 |
||
|
CSScoOT4 |
44 |
22 |
20 |
19 |
yes |
1.053 |
||
|
Greater North Sea |
GNSEngBT3 |
25 |
14 |
13 |
13 |
yes |
1.000 |
0.991 |
|
GNSFraOT4 |
29 |
9 |
17 |
17 |
yes |
1.000 |
||
|
GNSGerBT3 |
11 |
6 |
6 |
6 |
yes |
1.000 |
||
|
GNSIntOT1 |
49 |
29 |
26 |
24 |
yes |
1.083 |
||
|
GNSIntOT3 |
46 |
31 |
29 |
26 |
yes |
1.115 |
||
|
GNSNetBT3 |
26 |
13 |
9 |
12 |
no |
0.750 |
||
Table 5. Assessment results based on using the proportion failing to spawn sensitivity metric and averaging integration to define the suite of sensitive species in fish communities sampled by 12 groundfish surveys. For more details on the information in each column, see the Assessment Method section above.
|
Region |
Survey |
Number of “sensitive” species |
indicator level assessment values |
assessment value met (yes/no) |
common-scale indicator score |
regional average integrated assessment outcome |
||
|
sampled |
meeting 50 % of years criterion |
meeting metric-level assessment values in final year of survey |
||||||
|
Celtic Seas |
CSEngBT3 |
33 |
22 |
17 |
19 |
no |
0.895 |
1.006 |
|
CSIreOT4 |
69 |
37 |
32 |
30 |
yes |
1.067 |
||
|
CSNIrOT1 |
32 |
22 |
19 |
19 |
yes |
1.000 |
||
|
CSNIrOT4 |
31 |
22 |
18 |
19 |
no |
0.947 |
||
|
CSScoOT1 |
49 |
28 |
26 |
24 |
yes |
1.083 |
||
|
CSScoOT4 |
49 |
28 |
25 |
24 |
yes |
1.042 |
||
|
Greater North Sea |
GNSEngBT3 |
33 |
16 |
16 |
14 |
yes |
1.143 |
1.005 |
|
GNSFraOT4 |
35 |
25 |
20 |
21 |
no |
0.952 |
||
|
GNSGerBT3 |
19 |
9 |
8 |
9 |
no |
0.889 |
||
|
GNSIntOT1 |
58 |
36 |
31 |
30 |
yes |
1.033 |
||
|
GNSIntOT3 |
57 |
42 |
40 |
34 |
yes |
1.176 |
||
|
GNSNetBT3 |
33 |
21 |
15 |
18 |
no |
0.833 |
||
Figure 10. Using the average life-history trait sensitivity metric to define sensitive species, these plots show trends in the sensitive species abundance indicator: the number of sensitive species whose species-specific abundance metric met or exceeded its halt-further-population-decline related species-specific abundance metric-level assessment threshold
Figure 11. Using the proportion failing to spawn sensitivity metric to define sensitive species, trends in the sensitive species abundance indicator: the number of sensitive species for which their species-specific abundance metric met or exceeded its halt-further-population-decline related species-specific abundance metric-level assessment threshold (grey dashed line) in successive assessment years from 2010 to the last datum. Colour coding distinguishes surveys operating in different regions: Celtic seas, yellow, and the Greater North Sea, purple.
At the regional scale, sensitive species abundance indicator-level assessment thresholds were achieved in a highly significant proportion of the six surveys operating in the Celtic Seas in all years since 2010, regardless of the sensitivity metric used, and in a highly significant fraction of the six surveys operating in the Greater North Sea in all years when the proportion failing to spawn sensitivity metric was used, and in all years except 2010 and 2013 when using the average life-history trait metric (Figure 12). An averaged integrated assessment of individual survey assessments generally produced an acceptable status outcome in all years since 2010 at the regional scale. Averaged confidence interval values were around, or just above, the target value of 1. Whenever a region trend dropped below this target value, it did so by only the smallest of margins (Figure 13).
Figure 12. Trends in the proportion of surveys in the Celtic Seas (yellow) and Greater North Sea (purple) regions where the sensitive species abundance indicator met its halt-further-population-decline related sensitive species indicator-level assessment threshold in each assessment year from 2010 to 2016. Numbers within each histogram bar indicate the number of surveys providing data for analysis. Light grey bars and dark grey bars indicate non-significant departures and significant departures respectively from the binomial distribution, respectively. The probabilities of observing each significant departure are shown above the histogram bars.
Figure 13. Integrated assessment of halt-further-population-decline derived using an averaging integration approach for average life-history trait (ALHT) and proportion falling to spawn (PFS) metrics. Values above 1 (shown by dashed lined) mean the assessment threshold is being met or exceeded.
The information regarding the assessment (using the last datum in each survey time series) contained in Tables 2-4 is summarised in Figure 14. This provides assessment results for each survey in each region, using both sensitivity metrics to define the suites of sensitive species, against both the ‘population recovery’ and ‘halt further population decline’ assessments, and based on the ‘averaging’ integration procedure.
Figure 14. Outcomes against both ‘population recovery’ and ‘halt further population decline assessments’ for suites of sensitive species defined by both the average life-history trait and proportion failing to spawn sensitivity metrics sampled by surveys carried out in the Celtic Seas and Greater North Sea. Outcomes for regional scale integrated assessments using an ‘averaging’ integration procedure are indicated by horizontal green (meets or exceeds assessment threshold, represented by a black dashed line) or red (does not meet assessment threshold) horizontal lines. The common indicators score is determined as an indicator value/assessment threshold.
The majority of the assessment method has been published in peer-reviewed literature, however, some new elements have not. Therefore, the confidence in the method is moderate. There are no significant data gaps, and there is sufficient spatial coverage, therefore, the confidence in the data is high.
Conclusions
- The UK target has been met in the Celtic Sea but not in the Greater North Sea. Evidence of recovery was compelling in the Celtic Seas, but in the Greater North Sea, the number of sensitive species increasing in abundance was insufficient to meet the assessment threshold.
- Evidence to support a halt in decline of the abundance of fish species sensitive to fishing mortality is clear. Assessment outcomes suggested that decline has been halted since 2010. These conclusions are robust regardless of which sensitivity metric is used to define suites of sensitive species and the choice of integration method.
- When considering all areas assessed (the Greater North Sea and the Celtic Seas), evidence to support the case that significant recovery had been achieved in the population abundance of sensitive species was unclear. The assessment outcomes were influenced by which sensitivity metric was used to identify the suites of sensitive species in each survey, and also by the type of integration method applied to derive integrated assessment outcomes from the individual survey assessments.
Knowledge gaps
Knowledge gaps for this assessment are:
- The availability of suitable population dynamics models to support the setting of absolute abundance targets for sensitive fish species.
- The effects of warming seas on the scope for population growth and potential for population recovery among large-bodied sensitive fish species.
Further information
The lack of historical data precludes the use of absolute assessment thresholds. The absence of historical survey data at times early enough to be considered suitable as reference periods precludes the use of an empirical approach to set assessment thresholds for the absolute abundance of sensitive fish species. Most of which are known to have become depleted well before the onset of any of the currently extant surveys. For this assessment, a trends-based approach has been adopted, but this can only ever be a temporary stop-gap method. Once such trends-based assessment thresholds are achieved, then the need for absolute abundance targets will become paramount because the continuous increase in any species is biologically impossible. At some point, each species’ population carrying capacity will be reached, and resource limitation will cap any further population growth. In the absence of empirical data to support the setting an absolute abundance assessment threshold, populations dynamics models parameterised to represent specific fish communities could provide the only viable alternative. Part of the motivation behind the development of the proportion failing to spawn sensitivity metric is that the fraction of any cohort that survives to spawn at least once is a key variable in any population dynamics model.
A further knowledge gap relates to the effects of climate change on the potential for depleted sensitive species populations to recover. In many organisms, individuals in colder environments grow more slowly but are larger as adults. This widespread pattern is embodied by two well-established rules: Bergmann’s rule, which describes the association between temperature and body size in natural environments, and which gives rise to gradients of increasing body size with latitude, and the temperature-size rule, which describes reaction norms relating temperature to body size in laboratory experiments (Anguilletta and Dunham, 2003). Large ultimate body size is a key life-history trait determining the ‘sensitivity’ of fish species to fishing mortality, and these temperature-size rules imply that warming seas might well inhibit population growth and reduce the potential for population recovery among large-bodied sensitive species.
References
Angilletta MJ, Dunham AE Jnr (2003) ‘The Temperature‐Size Rule in Ectotherms: Simple Evolutionary Explanations May Not Be General’ The American Naturalist, 162(3):332-342 (viewed on 7 January 2019)
European Commission (2008) ‘Directive 2008/56/EC of the European Parliament and of the Council of 17 June 2008 establishing a framework for community action in the field of marine environmental policy (Marine Strategy Framework Directive)’ Official Journal of the European Union L 164, 25.6.2008, pages 19-40 (viewed 21 September 2018)
Fish MG, Miller TJ, Fogarty MJ (2001) ‘Estimation and analysis of biological parameters in elasmobranch fishes: a comparative life history study’ Canadian Journal of Fisheries and Aquatic Sciences, 2001, 58(5): 969-981 (viewed on 8 January 2019)
Gislason, H, Pope, JG, Rice JC, Daan, N (2008) ‘Coexistence in North Sea fish communities: implications for growth and natural mortality’ ICES Journal of Marine Science, 65(4):514–530 (viewed on 8 January 2019)
Greenstreet, SPR, Spence FB, Shanks AM, McMillan JA, (1999a) ‘Fishing effects in northeast Atlantic shelf seas: patterns in fishing effort, diversity and community structure. II. Trends in Fishing effort in the North Sea by UK Registered vessels landing in Scotland’ Fisheries Research, 40(2):107-124 (viewed on 8 January 2019)
Greenstreet, SPR, Spence FB, McMillan JA, (1999b) ‘Fishing effects in northeast Atlantic shelf seas: patterns in fishing effort, diversity and community structure. V. Changes in structure of the North Sea groundfish species assemblages between 1925 and 1996’ Fisheries Research, 40(2):153-183 (viewed on 8 January 2019)
Greenstreet, SPR, Rogers SI (2000) ‘Effects of fishing on non-target fish species’ in ‘Effects of Fishing on Non-Target Species and Habitats: Biological, Conservation and Socio-economic Issues’ (editors: Kaiser MJ, de Groot B), Blackwell Science, Oxford, UK, pages 217-234
Greenstreet SPR, Rogers SI (2006) ‘Indicators of the health of the North Sea fish community: identifying reference levels for an ecosystem approach to management’ ICES Journal of Marine Science, 63(4):573–593 (viewed on 8 January 2019)
Greenstreet, SPR, Holland, GJ, Fraser, TWK, and Allen, VJ (2009) ‘Modelling demersal fishing effort based on landings and days absence from port, to generate indicators of “activity”’ ICES Journal of Marine Science, 66: 886–901 (viewed on 8 January 2019)
Greenstreet SPR, Rogers SI, Rice JC, Piet GJ, Guirey EJ, Fraser HM, Fryer RJ (2011) ‘Development of the EcoQO for the North Sea fish community’ ICES Journal of Marine Science, 68(2):1–11 (viewed on 8 January 2019)
Greenstreet, SPR, Fraser, HM, Rogers, SI, Trenkel, VM, Simpson, SD, and Pinnegar, JK (2012a) ‘Redundancy in metrics describing the composition, structure, and functioning of the North Sea demersal fish community’ ICES Journal of Marine Science, 69(1): 8–22 (viewed on 8 January 2019)
Greenstreet SPR, Rossberg AG, Fox CJ, Le Quesne WJF, Blasdale T, Boulcott P, Mitchell I, Millar C, Moffat CF (2012b) ‘Demersal fish biodiversity: species-level indicators and trends-based targets for the Marine Strategy Framework Directive’ ICES Journal of Marine Science, 69(10):1789–1801 (viewed on 7 January 2019)
Greenstreet SPR and Moriarty M (2017a) ‘OSPAR Interim Assessment 2017 Fish Indicator Data Manual (Relating to Version 2 of the Groundfish Survey Monitoring and Assessment Data Product)’ Scottish Marine and Freshwater Science, Vol 8 No 17, 83pp DOI: 107489/1985-1 (viewed on 7 January 2019)
Greenstreet SPR and Moriarty M (2017b) ‘Manual for Version 3 of the Groundfish Survey Monitoring and Assessment Data Product; Scottish Marine and Freshwater Science, Vol 8 No 18, 77pp DOI: 107489/1986-1 (viewed on 7 January 2019)
Hobday AJ, Smith ADM, Stobutzki IC, Bulman C, Daley R, Dambacher JM, Deng RA, Dowdney J, Fuller M, Furlani D, Griffiths SP, Johnson D, Kenyon R, Knuckey IA, Ling SD, Pitcher R, Sainsbury KJ, Sporcic M, Smith T, Turnbull C, Walker TI, Wayte SE, Webb H, Williams A, Wise BS, Zhou S (2011) ‘Ecological risk assessment for the effect of fishing’ Fisheries Research, 108(2-3):372-384 (viewed on 8 January 2019)
HM Government (2012) ‘Marine Strategy Part One: UK Initial Assessment and Good Environmental Status’ (viewed on 5 July 2018)
ICES (2017) ‘Report of the Working Group on the Ecosystem Effects of Fishing Activities (WGECO)’ 5-12 April 2017, Reykjavik, Iceland: ICES CM 2017/ACOM:26 (viewed on 5 February 2019)
Jennings S, Reynolds JD, Mills SC (1998) ‘Life history correlates of responses to fisheries exploitation’ Proceedings of the Royal Society of London. Series B: Biological Sciences, 265 (viewed on 8 January 2019)
Jennings S, Greenstreet SPR Reynolds JD (1999a) ‘Structural change in an exploited fish community: a consequence of differential fishing effects on species with contrasting life histories’ Journal of Animal Ecology, 68:617-627 (viewed on 8 January 2019)
Jennings S, Alvsågc J, Cotter AJP, Ehrich A, Greenstreet SPR, Jarre-Teichmann A, Mergardt N, Rijnsdorp AD, Smedstad O (1999b) ‘Fishing Effect in northeast Atlantic shelf seas: patterns in fishing effort, diversity and community structure. III. International trawling effort in the North Sea: an analysis of spatial and temporal trends’ Fisheries Research, 40(2):125-134 (viewed on 8 January 2019)
Le Quesne WJF, Jennings S (2012) ‘Predicting species vulnerability with minimal data to support rapid risk assessment of fishing impacts on biodiversity’ Journal of Applied Ecology, 49:20-28 (viewed on 7 January 2019)
MacArthur RH, Wilson EO (1967) ‘The Theory of Island Biogeography’ Princeton, New Jersey: Princeton University Press (viewed on 8 January 2019)
Modica L, Velasco F, Preciado I, Soto M, Greenstreet SPR (2014) ‘Development of the large fish indicator and associated target for a Northeast Atlantic fish community’, ICES Journal of Marine Science, 71(9):2403–2415 (viewed on 7 January 2019)
Philippart CJM ‘Long-term impact of bottom fisheries on several by-catch species of demersal fish and benthic invertebrates in the south-eastern North Sea’ ICES Journal of Marine Science, 55(3):342–352 (viewed on 8 January 2019)
Piet GJ, Jenning S (2005) ‘Response of potential fish community indicators to fishing’ ICES Journal of Marine Science, 62(2):214-225 (viewed on 8 January 2019)
ICES (2016) ‘Report of the Working Group on the ecosystem effects of fishing activities (WGECO)’, 6–13 April 2016, Copenhagen, Denmark. ICES CM 2016/ACOM:25. 110 pp (viewed on 7 January 2019)
OSPAR Commission (2017) ‘Intermediate Assessment 2017’ (viewed on 21 September 2018)
Resnick D, Bryant MJ, Bashey R (2002) ‘r- and k-selected revisited: The role of population regulation in life – history evolution’ Ecological Society of America, 83(6):1509-1520 (viewed on 8 January 2019)
Rijnsdorp AD, van Leeuwen PI, Daan N, Heessen HJL (1996) ‘Changes in abundance of demersal fish species in the North Sea between 1906–1909 and 1990–1995’, ICES Journal of Marine Science, 53(6):1054–1062 (viewed on 8 January 2019)
Shephard S, Reid DG Greenstreet SPR (2011) ‘Interpreting the large fish indicator for the Celtic Sea’ ICES Journal of Marine Science, 68(9):1963–1972 (viewed on 8 January 2019)
Stearns SC (1977) ‘The evolution of life history traits: A critique of the Theory and a Review of the Data’ Annual Review of Ecology and Systematics, 8:145-71
Walker PA Hislop JRG ‘Sensitive skates or resilient rays? Spatial and temporal shifts in ray species composition in the central and north-western North Sea between 1930 and the present day’ ICES Journal of Marine Science, 55(3):392–402 (viewed on 8 January 2019)
Van Strien AJ, van Duuran L, Foppen RPB, Soldaat LL (2009) ‘A typology of indicators of biodiversity change as a tool to make better indicators’ Ecological Indicators, 9(6):1041-1048 (viewed on 8 January 2019)
Acknowledgements
Assessment metadata
| Assessment Type | UK MSFD Indicator Assessment |
|---|---|
D1 Fish Biodiversity Recovery in the population abundance of sensitive fish species | |
| Point of contact email | marinestrategy@defra.gov.uk |
| Metadata date | Thursday, August 1, 2019 |
| Title | Fisheries survey data from Research Vessels |
| Resource abstract | The Groundfish Survey Monitoring and Assessment Data Product data derived by Marine Scotland from data collected during Research Vessel surveys, co-ordinated by International Council for the Exploration of the Seas (ICES), from January 1983 to June 2017 for surveys of the northeast Atlantic shelf and marginal seas. |
| Linkage | Acquisition during Research Vessel cruises Sampling device: otter and beam trawls Manual for the data product used in the assessment Greenstreet, S.P.R and Moriarty, M. (2017) Manual for Version 3 of the Groundfish Survey Monitoring and Assessment Data Product. Scottish Marine and Freshwater Science Vol 8 No 18, 77pp. DOI: 10.7489/1986-1 Moriarty, M., Greenstreet, S.P.R. and Rasmussen, J. (2017) Derivation of Groundfish Survey Monitoring and Assessment Data Product for the Northeast Atlantic Area. Scottish Marine and Freshwater Science Vol 8 no 16, 240pp. DOI: 10.7489/1984-1 |
| Conditions applying to access and use | © Crown copyright, licenced under the Open Government Licence (OGL). |
| Assessment Lineage | The Groundfish Survey Monitoring and Assessment (GSMA) data product is a single set of fully standardised and quality assured data products for all the surveys operating in the Northeast Atlantic. |
| Dataset metadata | |
| Dataset DOI |
Moriarty, M., Greenstreet, S. 2017. Greater North Sea Dutch Beam Trawl Quarter 3 Groundfish Survey Monitoring and Assessment Data Products. DOI: 10.7489/1967-1 Moriarty, M., Greenstreet, S. 2017. Greater North Sea International Otter Trawl Quarter 1 Groundfish Survey Monitoring and Assessment Data Products. DOI: 10.7489/1922-1 Moriarty, M., Greenstreet, S. 2017. Celtic Sea Irish Quarter 4 Otter Trawl Groundfish Survey Monitoring and Assessment Data Products. doi: 10.7489/1925-1 Moriarty, M., Greenstreet, S. 2017. Greater North Sea International Otter Trawl Quarter 3 Groundfish Survey Monitoring and Assessment Data Products. doi: 10.7489/1923-1 Moriarty, M., Greenstreet, S. 2017. Celtic Sea Scottish Otter Trawl Quarter 4 Groundfish Survey Monitoring and Assessment Data Products. doi: 10.7489/1924-1 Moriarty, M., Greenstreet, S. 2017. Celtic Sea Scottish Quarter 1 Otter Trawl Groundfish Survey Monitoring and Assessment Data Products. doi: 10.7489/1957-1 Moriarty, M., Greenstreet, S. 2017. Celtic Sea /Bay of Biscay French Quarter 4 Otter Trawl Groundfish Survey Monitoring and Assessment Data Products. doi: 10.7489/1958-1 Moriarty, M., Greenstreet, S. 2017. Greater North Sea French Otter Trawl Quarter 4 Groundfish Survey Monitoring and Assessment Data Products. doi: 10.7489/1959-1 Moriarty, M., Greenstreet, S. 2017. Celtic Sea Northern Ireland Otter Trawl Quarter 1 Groundfish Survey Monitoring and Assessment Data Products. doi: 10.7489/1961-1 Moriarty, M., Greenstreet, S. 2017. Celtic Sea Northern Ireland Otter Trawl Quarter 4 Groundfish Survey Monitoring and Assessment Data Products. doi: 10.7489/10.7489/1962-1 Moriarty, M., Greenstreet, S. 2017. Celtic Sea English Quarter 3 Beam Trawl Groundfish Survey Monitoring and Assessment Data Products. doi: 10.7489/1964-1 Moriarty, M., Greenstreet, S. 2017. Greater North Sea German Beam Trawl Quarter 3 Groundfish Survey Monitoring and Assessment Data Products. doi: 10.7489/1965-1 Moriarty, M., Greenstreet, S. 2017. Greater North Sea English Beam Trawl Quarter 3 Groundfish Survey Monitoring and Assessment Data Products. doi: 10.7489/1966-1 Moriarty, M., Greenstreet, S. 2017. Bay of Biscay Iberian Coast Portugal Otter Trawl Quarter 4 Groundfish Survey Monitoring and Assessment Data Products. doi: 10.7489/1963-1 Moriarty, M., Greenstreet, S. 2017. Wider Atlantic Scottish Otter Trawl Quarter 3 Groundfish Survey Monitoring and Assessment Data Products. doi: 10.7489/1960-1 |
The Metadata are “data about the content, quality, condition, and other characteristics of data” (FGDC Content Standard for Digital Geospatial Metadata Workbook, Ver 2.0, May 1, 2000).
Metadata definitions
Assessment Lineage - description of data sets and method used to obtain the results of the assessment
Dataset – The datasets included in the assessment should be accessible, and reflect the exact copies or versions of the data used in the assessment. This means that if extracts from existing data were modified, filtered, or otherwise altered, then the modified data should be separately accessible, and described by metadata (acknowledging the originators of the raw data).
Dataset metadata – information on the data sources and characteristics of data sets used in the assessment (MEDIN and INSPIRE compliance).
Digital Object Identifier (DOI) – a persistent identifier to provide a link to a dataset (or other resource) on digital networks. Please note that persistent identifiers can be created/minted, even if a dataset is not directly available online.
Indicator assessment metadata – data and information about the content, quality, condition, and other characteristics of an indicator assessment.
MEDIN discovery metadata - a list of standardized information that accompanies a marine dataset and allows other people to find out what the dataset contains, where it was collected and how they can get hold of it.
Recommended reference for this indicator assessment
Greenstreet, S.P.R.1, Moriarty, M.1 & Lynam, C.P.2 2018. Recovery in the population abundance of sensitive fish species*. UK Marine Online Assessment Tool, available at: https://moat.cefas.co.uk/biodiversity-food-webs-and-marine-protected-areas/fish/abundance/
* Adapted from OSPAR Intermediate Assessment 2017 on Recovery in the Population Abundance of Sensitive Fish Species
1Marine Scotland
2Centre for Environment, Fisheries and Aquaculture Science