Using multiple fishery indicators

in a traffic light plotin a traffic light plot

John F. Caddy

A wide variety of uses for colour coding of indicators

are emerging in fisheries and elsewhere. Some of these

applications will be touched on briefly:

1) For simultaneously reviewing changes in the values of multiple

indicators;

2) To formulate different hypotheses on likely driving forces in a

multivariate situation;multivariate situation;

3) To display indicator performance in relation to reference points;

4) To comunicate complex information to non-technical audiences;

5) To formulate a fisheries control rule for management action using

empirical indicators and semi-quantitative information.

Two new aspects of fisheries science and management

have been emphasized since the UN Fish Stock

Agreement in 1995: Indicators and reference points

• An indicator is a measurable quantity believed to be relevant to the resource and its environment: e.g., the catch per day fished, the mean size of fish caught, the temperature of the water, or the amount of nitrogen flowing into the lake every year.

• A reference point is the value of an indicator which is believed to mark an optimal state of the fishery or stock (a TRP), or marks a change in condition of the stock or environment from a safe to a risky state of the fishery (a LRP). There is a need to apply more precautionary management measures in the future, since we can now safely admit our ignorance!

The traffic light approach is a useful way to display

multiple indicators without assuming these data series

are related

Some terminology for the first approach:2) monitoring biological, environmental or economic time series

results in an indicator.3) Groups of indicators that measure similar processes are

‘characteristics’ or ‘indices’.‘characteristics’ or ‘indices’.4) Reference points are values of indicators that represent important

changes to the fishery. 5) These can either be target reference points (TRPs) or limit

reference points (LRPs).6) LRPs are the main tools of precautionary management.7) LRPs can be outputs from models, historical values of indicators

when serious declines occurred previously, or represent agreements between the parties as to what indicator value should prompt actions, such as the start of a recovery plan.

Organizing indicators into

categories

A change in perspective over the last few years in marine

fisheries: people are now looking at a broader set of

environmental/economic/ecosystem data than before.

• Stock assessment previously involved working with Biomass, catch rates, sizes and ages, & fitting them to mathematical models to judge the state of exploitation.

• Such approaches are still valid, but we now know that • Such approaches are still valid, but we now know that ecosystem effects occur, as well as socio-economic and environmental impacts.

• Now managers are more comfortable monitoring a wider range of variables. Displaying them together helps judgements.

• A traffic light system is useful to display the data sets, and not just model output under restricted assumptions.

It may be useful to classify indicators into functional

categories – and scorings for these can be merged. It is

important however, to be able to retrieve scorings for the

separate clauses!

A science-based fisheries management system is

information-intensive – it must measure inputs to the

fishery as well as outputs. I personally subscribe to

monitoring a fishery in terms of inputs, state variables,

and outputs:

Monitoring fisheries nowadays looks at a broader range

of indicators, including ecosystem factors, environment,

and economic performance.

ADD Fig 5. from Caddy (1999) showing key factors (inside the rectangle) affecting fisheries production, and some

important ‘extrinsics’ outside, that could be monitored by indicators.

Using a ‘basket’ of indicators

• Indicators may be incorporated into a ‘basket’ of monitoring measures,

but it is ideal if each is derived from a different data set.

• Model-based, empirical indicators, and questionaire responses can be

combined in a ‘traffic-light’ information display system.

• The management response can be based on the number of key

indicators which have turned from green to either yellow or red (Caddy indicators which have turned from green to either yellow or red (Caddy

1998). Extra colours (Caddy and Surette 2004) may be added to better

monitor quantitative changes.

• The tally of green, yellow and red indicators helps ID changes, and

could be the basis for decision rules.

• Statistical analysis and modelling can be carried out in parallel with a

TL approach, and outputs incorporated into the TL.

A TL approach can use all the data that are available.

�Mathematical modelling can usually accept only a few potential indicators and has

difficulty with empirical and sample-based biological indicators (age/size, condition

factor, sex ratio, stomach contents?).

�These indicators have the advantage of being based on readily available data, they

can be calculated with minimal technical input, and give results understood and

accepted by non-technical personnel or stakeholders.

�In other words, a highly technical model-based reference point or control law will

be useful but incomprehensible to policy-makers and fishermen.

(A less precise, empirically-based reference point may receive consensus from the

fishing industry, and can lead to useful management results).

MOST IMPORTANT! Carrying out a multi-variable Traffic Light plot prior to a

specific modelling exercise will help to indicate which types of variables are likely to

be having a dominant effect!

Deciding on TL colour boundaries

The traffic light approach helps to display multiple indicators.

Ideally, the green-yellow boundary is a precautionary (pa) RP,

and the yellow-red boundary a limit (LRP) marking onset of

dangerous conditions. These boundary values can be decided

upon as a result of modelling or empirically.

At the start of a traffic light approach, we may not have

information on reference points.

I would suggest dividing the likely range of the indicator

into 3 or 4 colour bands. Below is the scheme we used for a

30 yr series of North Atlantic landings for 55 species (Caddy

and Surette 2004).

We should use more management indicators than

those few currently used in standard stock

assessment (e.g., B, F, CPUE) :

1) Empirical relationships of resource ‘health’ need

different environmental/ economic ‘forcing functions’ to

be taken into account.be taken into account.

2) Information that is important but not fully

quantifiable can be used in a TL system in support of

management.

Northern shrimp – cluster analysis of multiple

time series of data may reveal similarities

between trends:

multi+ov ig

Ic e(J-M)

Ic e(A -J)

s ex ra tio

males 0

s iz e s ex c hange

q ov errun

ef f o r t

A bundanc e

B iomas s

quota

c atc h

males 2

Mode l c pue

multi+ov ig

males 3

males 4

Cod 3 j3kl

ma les 1

tran +pr im

-11

Use a correlation matrix from Pearson’s option of cluster analysis

to decide on variables to include in a northern shrimp traffic light

Another approach: Using the 3-yr running average of X and Y to

obtain ‘control curves’, and colouring the ‘change in phase’ to

obtain traffic light plots

Xt = (Xt-1 + Xt + Xt+1)/3

Yt = (Yt-1 + Yt + Yt+1)/3Yt = (Yt-1 + Yt + Yt+1)/3

What indicators to choose from the larger number

available?

Weighting:

One proposition is that two or more indicators derived from the One proposition is that two or more indicators derived from the

same basic data source are likely to be correlated, and perhaps

should be given a fractional weighting related inversely to the

number involved?

Some examples of TL approaches:

Black Sea fisheries

8 different modelling approaches to date have assumed different causes

for Black Sea problems! Six are listed below. They suggest having access

to a wide range of environmental/biological/socio-economic indicators:

1) The ECOPATH model assumes an exotic jellyfish impacts the pelagic fish

food web.

2) Increased nutrient inputs lead to abnormal phytoplankton blooms.

3) Pollution of incoming rivers affected planktonic productivity in the Black

SeaSea

4) Reproductive success of small pelagics is affected more by environment

than spawning stock size

5) A steady state model (ignoring the changing environment) suggests that

stock collapse was mostly due to overfishing

6) Elimination of top predators in the 1970s drove a trophic cascade affecting

all later events.

Pick variables to monitor that do not tie you down to

only one explanation of events in the fishery

�Do not tie yourself to one explanation of what is affecting the

stock until you understand it better!

�If you pick the wrong variables to monitor you may end up in

10 years with 10 yrs of irrelevant data!10 years with 10 yrs of irrelevant data!

�You need to extract the most info from your survey samples.

�You need variables that measure abundance, recruitment and

fishing pressure, but also environmental or ecological change and

socio-economic information.

INDICATOR (SPECIES): INDICATOR OF CONDITIONS BY

HABITAT (RELEVANT ACTIONS)

Environment and

productivity

Planktonic productivity, nutrients, shelf hypoxia

Mugilidae/ Mytilidae/

Venus sp.

Indicator species for uncontaminated coastal

lagoons/inshore areas (Lagoon cleanup)

Sturgeons/shads Good stocks = healthy estuarine/riverine conditions

– (low contaminants; adequate fishery escapement)

Phyllophora weed, turbot,

Rapana, Mullidae, gobies

Oxygenated shallow shelf

Rapana, Mullidae, gobies

Plankton and resident small

pelagic fish

Phyto/zooplankton/jelly predators/small pelagic

eggs and larvae? (surveys)

Migratory pelagics (Bonito,

bluefish, mackerels)

Successful migrations are an indicator of

unimpeded migratory routes

GENERAL ECOSYSTEM

INDICATORS:

Pelagic/demersal ratio; Planktivore/piscivore ratio;

Mean trophic level.

FISHING PRESSURE Mortality rates/ fleet size/ capacity / employment

ECONOMIC ANALYSIS NEEDED!

Displaying indicator series for a non-specialist audience

Landing trends for turbot are given traffic light colours for fishing mortality

from retrospective analysis (given by Prodanov et al. 1997). Colour boundaries

were decided on by comparing annual F values with an F(0.1) estimate for

turbot. (The colour code shows the rise in landings towards 2000, was clearly

due to higher fishing rates not stock recovery!).

One obvious approach to indicator development could

be to follow the approach of CITES: look at trends in

landing data, or better, survey data. In the Black Sea,

we have looked at trends in the history of resources by

dividing landing data into 4 phases:

1) A Baseline period: 1967 (when most

statistics began) – 1989.statistics began) – 1989.

2) A Period of Collapse (1989-1992) – when

the Mnemiopsis outbreak became important

3) The Recent period (1992-2002)

4) The last 5 years (1989-2002)

- Indicator values can be referred to the mean

value in 1967-89

Using a 3 colour convention, multiple indicators can be

displayed simultaneously, allowing apparent synchronies and

sequences to be identified – without making a prior

commitment to any particular causal factor.

Gulf of Mexico fisheries

An example from Gulf of Mexico fisheries. Mexican landings

developed rapidly from 1960 in a synchronized fashion (except tunas).

(Overall data ranges divided into 4 quartiles). There is no evidence here

of species interactions – either top-down or bottom-up.

Landings Year Red Grouper Algae and Shrimps Spanish and Oysters Red/ other Mullets Sardines + Sharks Others Mojarras Octopus Tunas

(all species) Sea grasses king mackerels snappers (Mugil spp) other clupeoids (Gerreidae) (Octopus spp)

1940

1941

1942

1943

1944

1945

1946

1947

1948

1949

1950

1951

1952

1953

1954

1955

1956

1957

1958

1959

1960

1961

1962

1963

1964

1965

1966

1967

1968

1969

1970

1971

1972

1973

Z 1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Looking at the effort data (next) it is clear that species interaction

was secondary to fishing effort impacts. Several different indicators

of fishing effort/investment showed similar trends over time in the

Gulf of Mexico fishery:

Time Fishing effort Fishing effort Fishing effort Financial support

(yr) (Large scale) (small scale) (finfish) (Millions of pesos)

1978

1979

1980

1981

1982

19831983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

North Atlantic and Mediterranean

fisheries

In the North Atlantic invertebrate landings have

increased following declines in finfish stocks - an

ecological interaction or a shift in industry

priorities?

Between different finfish species, ecosystem Between different finfish species, ecosystem

interraction seems a less tenable hypothesis than

the ‘whole-system impact’ of industrial fishing

plus climate change/a regime shift?

In the Mediterrean and Black Sea, there was an increase in

production in the 70’s, but many species production

declined significantly in the 1990’s:

From cluster analysis of the GRUND data set, we can recognize

different biogical assemblages made of species that vary together. Even

if information on their interrelationships is scarce, species in the same

‘cluster’ form a common indicator.

Gulf of St Lawrence snow crab

WE CAN USE THE TL APPROACH ON SIZE AND AGE DATA:

The stages (below, left) of the snow crab (Chionoecetes opilio) are

equivalent to year classes. Their abundance in trawl surveys shows a

clear cycle over time: good or poor year classes remain visible as

diagonal strips of colour. Note: A good adult yc (compopsc129)

coincides with poor recruitment of very small crabs (Pub Females).

(This might not be a SRR – C. opilio is a cannibalistic species!).

RecruitmentRecruitment88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03

Pub Female 2 2 2 1 0 0 1 2 3 2 2 2 1 0 1

PrimFemale 2 2 3 2 1 0 0 0 2 2 2 2 2 1 0

Mat Fem 1 2 3 2 2 1 1 0 1 1 2 1 2 2 1

Instar VIII 3 1 1 0 0 1 1 2 3 3 3 2 1 0 1

R3 1 2 3 2 1 0 0 0 1 2 2 2 3 2 1

R2 0 1 3 2 2 1 1 0 0 1 2 2 3 3 2

R1 0 1 2 2 3 3 2 1 1 1 1 1 1 2 2

Com Popsc12 0 0 1 1 2 3 2 2 1 1 1 1 1 2 2

Fig. 12. Recruitment indicators can be lagged so that good

and poor cohorts passing through the population show up as

vertical colour bands: i.e., year class size is determined early

on – by cannibalism? This plot also helps provide a rough

forecast of future yields, and shows decadal cycles?

Recruitment88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17

Pub Female +14Pub Female +14 2 2 2 1 0 0 1 2 3 2 2 2 1 0 1

PrimFemale +13 2 2 3 2 1 0 0 0 2 2 2 2 2 1 0

Mat Fem +12 1 2 3 2 2 1 1 0 1 1 2 1 2 2 1

Instar VIII +5 3 1 1 0 0 1 1 2 3 3 3 2 1 0 1

R3 +3 1 2 3 2 1 0 0 0 1 2 2 2 3 2 1

R2 +2 0 1 3 2 2 1 1 0 0 1 2 2 3 3 2

R1 +1 0 1 2 2 3 3 2 1 1 1 1 1 1 2 2

Com Popsc12 0 0 1 1 2 3 2 2 1 1 1 1 1 2 2

average 0 0 1 1 2 3 2 1 1 0 1 1 1 2 2 2 2 1 1 0 1 1 2 2 2 2 2 1 0 1

Frequency of ice occurrence in March, from sea ice charts over 1972-

1990. More recently there has been a warming trend and melting of

Arctic Ocean/Greenland ice, and southerly flow of cold low salinity

water down to the Grand Banks - i.e., the water has been colder off

Eastern Canada – not good for cod; better for shrimp and snow crab!.

Davis Strait Pink Shrimp : a

changing ecosystemchanging ecosystem

In trying to decide which variables to monitor in the Northern shrimp TL

system, I went through papers in a recent symposium (Orr, (ed). 2004),

and counted mentions of relevant variables, and underlined observed or

hypothesized interrelationships between them. Variables were ranked

and plotted in the histogram below:

YC strength can be established roughly without cohort

analysis by lagging numbers at age, then averaging

numbers in each cohort separately (Nfld shrimp/cod)

relative cohort strength in year - cod/male shrimp

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

1970 1980 1990 2000 2010

year

rela

tive

str

en

gth

cohort relative strength

(cod?)

cohort relative strength

(male shrimp)

Problems with time lags: Comparing shrimp catch/biomass and bottom temperature

time series for different lags before incorporating an environmental (driving) variable

into a traffic light series (-2 yr lags seemed best for shrimp predator biomasses; -6 year

lags for environmental data (Pandalus borealis is a protandric hermaphrodite -

harvested as females at age 6+).

A simple ecosystem model incorporating northern shrimp:

showing one way of setting colour boundaries in the TL

Monitoring the fishery and

displaying results

The Amoeba method is well suited to displaying the output

from questionnaires – segments that fall in the ‘red’ area are in

serious need of improvement (after Pajak 2000).

Summary of sustainability indicators from 3 sectors for three levels of indicators have

been set: Those entering the ‘Red’ zone are ‘unsafe’; the ‘Yellow’ category from 35 –

70% is ‘uncertain’ – i.e., unsatisfactory but not dangerous conditions prevail inside

the yellow circle.

Using the TL approach in

management

The TL approach can be used in a conventional

fisheries control law (�the allowable F values

decline as the biomass declines)

Fishery managers need understandable

indicator values to help them in decision

making – a suggested mechanism:

• The interface between science advice and

management decision-making should be clear cut. management decision-making should be clear cut.

• A Consideration Matrix is one option suggested

by the FRCC for an interface between scientists

and managers (see next slide)

A simple approach based on annual science evaluations requires

appropriate actions by managers when fishery indicators turn ‘red’,

‘yellow’ or ‘green’. ‘Science’ puts the fishery in one of 9 boxes each

year, and management agrees to implement the management

recommendation within that box.

IN SUMMARY:

The traffic light approach is a flexible way of

summarizing data of concern to fisheries

managers, is easily understandable by non-

technical personnel, and may also be useful for

hypothesis formulation in science, and planning hypothesis formulation in science, and planning

further multivariate scientific investigations.

THE END