4 Results

4.1 Coverage

4.1.1 Geographical coverage

Should probably include a map here of the countries that we have included. Here is a list instead to start with

A complete set of trait and Aquamap data (allowing the calculation of climate hazard) was gathered for 147 species caught in European waters, representing 90.3% of the total value of fish and shellfish landings in Europe. Catch data was available covering 26 countries in Europe: country-wise coverage was at least 78% by value and typically above 90%.

When combined with catch data covering 23 FAO subareas, this resulted in a total of 1300 potential “stocks”. However, many of these stocks made relatively minor contributions to the total catch of the species: stocks comprising less than 5% of the total catch of that species were therefore filtered out, leaving 523 stocks in the analysis.

4.1.2 Fleet Coverage

We should also check the coverage by fleet segment. We are able to do the full analysis for 404 fleet segments, although the coverage of some of these units can be pretty poor.

Nevertheless, we still cover 75% or more of the landings value for more than 70% of fleets. We should probably filter this at some point to focus on the most important and/or best covered fleets.

Number of fleet segements: 358

4.2 Biological metrics

4.2.1 Species-specific metrics

As a cross check that we have implemented the coversion from these metrics to a hazard, we plot one against the other, to ensure that the trend is as intended

So, longer-lived species have a higher hazard score. Tick.

So species with a higher habitat-specificity have a higher hazard. Tick.

4.2.2 Stock-specific metrics

Small stocks are a potential problem in this analysis. We first check the distribution of stock sizes (expressed as value of landings) relative to the total size (value) of the species.

We can therefore see that there are an awful lot of stocks that don’t contribute much landigns value - is is a natural consequence of the way that we have defined a stock in this case. We choose to filter out all stocks that have a landings value less than 5% of the species total.

This leaves us with 523 stocks. Looking at the distribution of TSM’s of these remaining stocks.

And cross checking the hazard implementation.

Low TSMs => higher hazards. Tick. The stripping rises due to the categorical natural of the other metrics.

4.2.3 Species-level hazard

We can look at our biological analysis by integrating the hazard back up to the species level by weighting by the value of landings of each stock.

Looking at the top and bottom ten species

FAO_3A_code FAO_EN hazard rank
WHE Whelk 0.8828335 1
DEC Common dentex 0.8790946 2
KLK Smooth callista 0.8434970 3
FOR Forkbeard 0.7948277 4
OYF European flat oyster 0.7771141 5
SCE Great Atlantic scallop 0.7704106 6
HKX Hakes nei 0.7291516 7
PRR Parrotfish 0.7263368 8
SVE Striped venus 0.7242199 9
MEG Megrim 0.7199062 10
OCC Common octopus 0.2299293 139
HER Atlantic herring 0.2271872 140
SQI Northern shortfin squid 0.2237962 141
SPR European sprat 0.2159665 142
SQC Common squids nei 0.2020260 143
SQM Broadtail shortfin squid 0.1988565 144
OCZ Octopuses nei 0.1905851 145
BLT Bullet tuna 0.1679663 146
SSH Scarlet shrimp 0.1310705 147
BON Atlantic bonito 0.1093046 148

4.3 Fleet metrics

4.3.1 Fleet hazard metrics

Fleets with the top and bottom 10 hazard scores

Table 1: Top and botton 10 fleets by hazard
fs_name value.covered total.val.landings prop.missing hazard
EST A27 TM1218 860437.0 860437.0 0.0000000 0.0550035
LTU A27 TM2440 ° 13475876.0 13475876.0 0.0000000 0.0588392
EST A27 TM2440 ° 48072014.0 48072264.0 0.0000052 0.0627536
POL A27 TM2440 ° 73972680.3 75163367.3 0.0158413 0.0677118
FIN A27 TM2440 ° 107055789.0 107065174.7 0.0000877 0.0704230
FIN A27 TM1824 30019850.0 30019850.0 0.0000000 0.0705229
FIN A27 TM1218 ° 10807913.8 10983806.6 0.0160138 0.0742556
EST A27 PG1012 7613725.0 7630744.0 0.0022303 0.0761999
PRT A27 FPO0010 1681210.0 1741082.0 0.0343878 0.0825611
PRT A27 FPO1012 1086829.0 1139708.0 0.0463970 0.0863356
BEL A27 PMP1824 ° 473171.6 495884.6 0.0458029 0.8846257
FRA A27 DRB0010 1406833.8 8547518.7 0.8354103 0.8850338
GBR A27 DRB0010 2022678.0 2657277.3 0.2388156 0.8903896
FRA A27 FPO1012 8163528.9 8435296.8 0.0322179 0.8913679
IRL A27 FPO0010 4408690.9 5406757.3 0.1845961 0.9163961
ESP A27 DRB1012 291963.2 293883.4 0.0065339 0.9236067
ESP A27 DRB1218 2284353.2 2285575.2 0.0005347 0.9291188
GBR A27 DRB1012 1972195.2 2077496.5 0.0506866 0.9356896
IRL A27 DRB2440 ° 2004905.0 2004905.0 0.0000000 0.9635988
ITA A37 DRB1218 ° 17394834.4 17772573.7 0.0212541 0.9687215
Distribution of hazard metrics

Figure 6: Distribution of hazard metrics

4.3.2 Fleet exposure metrics

We use the Shannon diversity and the Simpson Dominance metrics to characterise the diversity of species that a fishing fleet catching. The distribution of these metrics is as follows.

Fleetwsie Shannon Diversity index

Figure 7: Fleetwsie Shannon Diversity index

Fleetwise Simpson Dominance

Figure 8: Fleetwise Simpson Dominance

A correlation between these two metrics is expected.

Relationship between Shannon Diversity H’ and Simpson’s Dominance D of fishery landings, for EU fleet segments in 2016

Figure 9: Relationship between Shannon Diversity H’ and Simpson’s Dominance D of fishery landings, for EU fleet segments in 2016

Clearly there is a strong relationship between Shannon diversity of fishery landings and Simpson’s dominance (D), although this is not linear (figure 1).

We combine the two metrics together into a into exposure metrics. Checking that we have done this correctly:

Exposure vs Shannon Index

Figure 10: Exposure vs Shannon Index

So fleets with higher diversity have lower exposure. Tick. And fleets that are dominated by a few species have a higher exposure, which is what we want.

Plotting the list of fleet diversity.

Least diverse fleets

Table 2: Least diverse fleets
fs_name sum.prop sum.value shannon.H simpsons.D n.species exposure
IRL A27 DRB2440 ° 1 2004905 0.0000000 1.0000000 1 0.9952381
MLT A37 PS2440 1 360500 0.0000000 1.0000000 1 0.9952381
PRT A37 FPO2440 1 116207 0.0000000 1.0000000 1 0.9952381
ESP A27 DRB1218 1 2285575 0.0049305 0.9989311 3 0.9928571
DEU A27 TBB1012 ° 1 61863 0.0058449 0.9986430 3 0.9904762
ROU A37 PMP1218 ° 1 4157697 0.0214070 0.9944113 10 0.9880952

Most diverse fleets

Table 3: Most diverse fleets
fs_name sum.prop sum.value shannon.H simpsons.D n.species exposure
ESP A37 PMP0612 1 4814591 3.743704 0.0589389 347 0.0261905
FRA A37 DFN0612 1 1337565 3.744439 0.0373679 203 0.0071429
ESP A37 DTS2440 1 5647283 3.841203 0.0431922 476 0.0095238
ITA A37 PGP0612 1 21059018 3.854408 0.0363553 135 0.0047619
ESP A37 DTS1218 1 4437824 4.033915 0.0326257 701 0.0023810
ESP A37 DTS1824 1 11824853 4.194966 0.0263463 746 0.0000000

Catch diversity in 2016 ranged from zero (where a fleet caught a single resource) to 4.19, where a multitude of different fish and shellfish species were targeted. The lowest diversity of catches was observed for the fleet segments IRL-A27-DRB2440, MLT-A37-PS2440 and PRT-A37-FPO2440, fishing exclusively on Great Atlantic scallop (Pecten maximus), Chub mackerel (Scomber colias) and Striped soldier shrimp (Plesionika edwardsii) respectively. By contrast, the highest diversity of landings was observed for ESP-A37-DTS1824, with catch records for 746 different species.

4.3.3 Fleet vulnerability metrics

Visualisation of the distribution of metrics
Employment per vessel

Figure 11: Employment per vessel

This is a potentially problematic metric, in retrospect, as it is really a metric of vessel size…

QUESTION: Should we retain it? How should we normalise it if we keep it?

Value of landings

Figure 12: Value of landings

Hmmm. Similar problem.

Average wage per FTE

Figure 13: Average wage per FTE

Looks like a couple of outliers there that need to be polished up…

Net profit margin

Figure 14: Net profit margin

Hmmm. A rather biased distribution. Lets crunch this figure down a bit…

Net profit margin 2

Figure 15: Net profit margin 2

Outliers aside, this is not a bad distribution.

QUESTION: Shall we rescale it to +/-50, and trim the rest…?

For the sake of this analysis, we have only based vulnerability on the Net Profit Margin, while we decide whether to include the rest..

Looking at the total vulnerability distribution.

Vulnerability distribution

Figure 16: Vulnerability distribution

This doesn’t look so bad, but at the same time the outliers probably don’t help things, and should be fixed.

Checking the correlations with the metrics against vulnerability
Correlation check

Figure 17: Correlation check

Which highlights the problems quite nicely… There is also some issues here with the directionality that we need to fix.

I think we probably need to rethink these metrics. Net profit margin is a very useful metric, but I’m not so sure about the others, particularly as they scale with vessel size, which I’m not sure we realy want…

4.3.4 Fleet risk

Note that the results here are a bit meaningless until we get the vulnerability under control, but the visualisations will be the same, so we can look at and discuss how we want to approach these.

Firstly, the top and bottom 10

Table 4: Top and bottom 10 fleets by risk
fs_name country area gear length geo total.val.landings prop.missing hazard exposure vulnerability risk country_name country_2A
DNK A27 TM40XX DNK A27 TM 40XX 363359393.3 0.007 0.050 0.536 0.020 0.000 Denmark DK
ITA A37 PGP0006 ITA A37 PGP 0006 5721461.6 0.459 0.573 0.015 0.042 0.003 Italy IT
PRT A27 DFN1012 PRT A27 DFN 1012 356623.0 0.390 0.449 0.032 0.154 0.006 Portugal PT
PRT A27 DTS0010 PRT A27 DTS 0010 381875.0 0.275 0.491 0.141 0.025 0.008 Portugal PT
ESP A37 DFN0612 ESP A37 DFN 0612 630328.7 0.477 0.578 0.027 0.081 0.011 Spain ES
ESP A37 DRB1218 ESP A37 DRB 1218 167984.4 0.460 0.112 0.387 0.213 0.014 Spain ES
PRT A27 PGP0010 PRT A27 PGP 0010 9661098.0 0.206 0.323 0.176 0.230 0.017 Portugal PT
ESP A27 PS2440 ESP A27 PS 2440 34961229.8 0.174 0.146 0.333 0.258 0.020 Spain ES
PRT A27 TBB0010 PRT A27 TBB 0010 263508.0 0.439 0.367 0.149 0.249 0.022 Portugal PT
ITA A37 PMP1218 ° ITA A37 PMP 1218 ° 449075.6 0.242 0.186 0.444 0.148 0.025 Italy IT
IRL A27 TM0010 ° IRL A27 TM 0010 ° 114528.2 0.069 0.134 0.916 NA NA Ireland IE
IRL A27 TM1218 ° IRL A27 TM 1218 ° 447382.0 0.607 0.221 0.692 NA NA Ireland IE
IRL A27 TM2440 IRL A27 TM 2440 31648490.3 0.067 0.087 0.429 NA NA Ireland IE
IRL A27 TM40XX IRL A27 TM 40XX 125299317.0 0.067 0.067 0.613 NA NA Ireland IE
POL A27 DTS40XX POL A27 DTS 40XX 6263477.9 0.012 0.266 0.896 NA NA Poland PL
POL OFR TM40XX POL OFR TM 40XX 53086199.0 0.635 0.826 0.769 NA NA Poland PL
PRT A27 DTS1012 PRT A27 DTS 1012 269941.0 0.392 0.591 0.117 NA NA Portugal PT
PRT A27 PGP1824 PRT A27 PGP 1824 167591.0 0.188 0.094 0.489 NA NA Portugal PT
PRT A27 TBB1012 ° PRT A27 TBB 1012 ° 268783.0 0.611 0.243 0.313 NA NA Portugal PT
PRT OFR HOK2440 IWE° PRT OFR HOK 2440 IWE° 4408259.0 0.766 0.923 0.600 NA NA Portugal PT

Now, viewing by vessel size

By country and fishing area.

QUESTION: I’m not quite sure how whether to retain the other fishing region fleets, or just keep it to the FAO27 and FAO37 areas - we don’t have very much biological information outside these regions.

QUESTION: I can also plot this as a map, based on the median value…

Fleetwise climate risk for the 23 EU coastal nations

Figure 18: Fleetwise climate risk for the 23 EU coastal nations

TODO: Ireland needs to be investigated - it looks like it is missing some of the flet economic data => no vul => no risk.

TODO: I will separate France and Spain into Atlantic and Mediterranean fleets as well on this map

And by gear. Looks like quite some variability here.

A table of gear types, sorted by median risk.

Code Description Risk
DRB Dredgers 0.845
PGO Vessels using other passive gears 0.706
TBB Beam trawlers 0.661
FPO Vessels using pots and/or traps 0.623
MGO Vessel using other active gears 0.585
PMP Vessels using active and passive gears 0.583
DFN Drift and/or fixed netters 0.574
MGP Vessels using polyvalent active gears only 0.487
HOK Vessels using hooks 0.466
PGP Vessels using polyvalent passive gears only 0.450
PS Purse seiners 0.380
DTS Demersal trawlers and/or demersal seiners 0.375
PG Passive Gears 0.331
TM Pelagic trawlers 0.276

4.4 Regional Metrics

4.4.1 Regional hazard

4.4.2 Regional exposure

Similar relationships between the catch dominance and catch diversity are seen at the regional levels as are seen for the fleets.

Check on the scaling and tendency

Plotting the exposure score geographically

Note the clear north-south gradient here. Several countries are still missing from this analysis, as we don’t have data resolved by NUTS regions for them (e.g. Norway, Finland, parts of Sweden, SE Europe). We need to find a strategy how to deal with this.

4.4.3 Regional vulnerability

Currently this is only based on GDP per capita. Other metrics can be added. Plotting the distribution of values.

Checking the scaling and directionality

Distribution map.

4.4.4 Regional risk

We calculate risk as the average of the hazard, exposure and vulnerability scores. This may need to be improved. The top and bottom ten regions by risk are then

Table 5: Top and bottom 10 regions by risk
region hazard exposure vulnerability risk country
IE05 0.490 0.146 0.000 0.000 IE
NO 0.135 0.534 0.031 0.010 NO
ITI4 0.394 0.049 0.286 0.020 IT
UKM5 0.221 0.466 0.051 0.031 UK
NL32 0.115 0.612 0.041 0.041 NL
NL33 0.067 0.592 0.112 0.051 NL
DE50 0.096 0.621 0.071 0.061 DE
FRE1 0.173 0.087 0.561 0.071 FR
FRG0 0.279 0.126 0.418 0.082 FR
ITI1 0.413 0.019 0.429 0.092 IT
UKE1 0.942 0.718 0.510 0.969 UK
UKM8 0.990 0.903 0.296 0.980 UK
RO 0.298 0.961 0.990 0.990 RO
UKC1 0.971 0.835 0.653 1.000 UK
BE25 0.250 0.563 NA NA BE
EE00 0.010 0.854 NA NA EE
EL30 0.558 0.194 NA NA EL
IS 0.625 NA NA NA IS
LT02 0.885 0.845 NA NA LT
LV00 0.038 0.864 NA NA LV

Putting it on a map

Putting it up as a boxplot

Interesting results. Lots of details there to discuss!