a productivity-susceptibility risk analysis for the

A productivity-susceptibility risk analysis for

the effects of fishing on Arctic Char (Salvelinus

alpinus) stocks from the Nunavut Territory

Marie-Julie Roux* and Ross F. Tallman

Fisheries and Oceans Canada

Central and Arctic Region

*Present address :Current Address: National Institute of Water

and Atmospheric Research , 301 Evans Bay Parade, Greta

Point, Wellington, New Zealand

paucity of data

too large a place

too many stocks

Background

Problems

(�) “what needs to be done to address priority gaps”

195 Arctic Char350 FW and Anadromous

paucity of data

too large a place

too many stocks

prohibitive time and financial costs to gather standard

assessment data

$

Background

Problems


paucity of data

too large a place

too many stocks

prohibitive time and financial costs to gather standard

assessment data

$charr

diversity

makes it difficult to generalize scientific information for the

species and related management plans/actions

Background

Problems


charr diversity

Objective

(�) “for addressing of gaps for the scientific understanding and management of Arctic charr fisheries

charr modelling

solutions

from Gaps to Solutions

Life history traits determine stock productivity

life history traits

age at maturity (R) fecundity (R)growth rate (G)survival (M)

R = reproductive capacityG = growth

M = mortality = 1-survival

elements of productivity

(productivity ≈ resilience)

Conceptual approach

life history traitsstable population:

R + G = M

traits are in balance and the population persists

R G M

population trend = nil (0)

age at maturity (R) fecundity (R)growth rate (G)survival (M)


M = mortality = 1-survival


(productivity ≈ resilience)

Conceptual approach

Life history traits determine stock productivity

Conceptual approach

Charr modelling using life history traits

life history traitsAM = age at maturity F = fecundityGR = growth rateZ = instantaneous mortality


M = mortality


R MG

F AM GR Z

Conceptual approach



M = mortality


R MG

F AM GR Z

H

H = harvest rate(management history and population response information)


Conceptual approach



M = mortality


R MG

F AM GR Z H

H = harvest rate(management history and population response information)

explore and identify sustainable H rangeR + G – M > 0

a model using life history traits and harvest rates as input parameters to

predict population trends


Conceptual approach

• demonstrate the extent of inter-population variability in life-history traits and how this can be used for evaluating the relative vulnerability of Arctic charr stocks to fishing activities (SEMI-QUANTITATIVE RISK ANALYSIS)

• model development and testing (FULL QUANTITATIVE ANALYSIS)

phase 1 :

phase 2 :


R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

exposure

Risk assessment using life history traits

Productivity-Susceptibility AnalysisPSA

a relative (as opposed to absolute) risk assessment method

• its ability to recover from increased mortality(productivity≈resilience) (more productive = less vulnerable)

• the extent of the impacts due to fishing activities(susceptibility≈exposure) (more exposed = more vulnerable)

based on the principle that population vulnerability to fishing activities depends on:

PSA introduction

Productivity-Susceptibility AnalysisPSA

PSA introduction

•Australian Northern Prawn fishery by-catch spp.(see Stobutzki et al. 2001)

• recently proposed as a semi-quantitative step in a hierarchical ecological risk assessment for the effects of fishing (ERAEF) framework by the Marine Stewardship Council (MSC eco-certification body) (see Hobday et al. 2007)

• currently being evaluated for use in determining the vulnerability of U.S. fisheries by NOAA (see Patrick et al. 2009)

not a new method:

�but has never been experimented on a single-species basis

Risk assessment using life history traits

Productivity-Susceptibility risk assessmentPSA methods

STEP 1: define and estimate productivity and susceptibility attributes

Productivityattributes

Susceptibilityattributes




historical data base for charr from across Nunavut (1971-2003)

HUDSON

BAY

BAFFIN SEA

ARCTIC OCEAN

PACIFIC

OCEAN

ATLANTIC OCEAN

CANADA

Nunavut

HUDSON

BAY

BAFFIN SEA

ARCTIC OCEAN

PACIFIC

OCEAN

ATLANTIC OCEAN

CANADA

Nunavut

97 anadromous stocks (5.5” (139.7 mm) gillnet studies)

Productivity-Susceptibility risk assessmentPSA methods

PSA methods


age at first maturityyounger age at first maturity =

more productive

individual growth ratefaster growth = more productive

lifespanshort-lived = more productive

mortalityhigher mortality = more productive


estimation

first age group with mature fish (all data pooled)

or approximated from a linear model using max

age as predictor

Brody (K) growth coefficient calculated by fitting von Bertalanffy growth curves to fully recruited age groups* (all 139.7mm gillnet data pooled)

maximum age observed in the sample(all 139.7mm gillnet data pooled)

annual mortality estimated from catch curves (catch at age plots)(all 139.7mm gillnet data pooled)

Productivity-Susceptibility risk assessment

PSA methods



availability

selectivity

likelihood that the stock will encounter fishing gear

overlap between fishing effort and stock distribution

potential for the gear to capture/retain fish

estimation

distance from nearest human community

ranked high for anadromous stocks

ratio of mean fork length over mesh size (in mm)

encounterability


PSA methods

STEP 2: rank individual productivity attributes for data quality

Estimate was approximated using life-history information.

Sample size is <100 AND other conditions a priori determined for an estimate to be reliable are not met.

5

OR – Sample size is >100 BUT other conditions a priori determined for an estimate to be reliable are not met.

- Sample size is <1004

OR – sample size is >300 BUT other conditions a priori determined for an estimate to be reliable are not met.

- Sample size is <300 but >1003

- Data is not recent and unlikely to reflect current state environmental conditions.

- All conditions a priori determined for the estimate to be reliable are met.

- sample size is large (≥300).2

- Data is recent and is likely to reflect current state environmental conditions.



ConditionsData Quality Score

Estimate was approximated using life-history information.

Sample size is <100 AND other conditions a priori determined for an estimate to be reliable are not met.

5

OR – Sample size is >100 BUT other conditions a priori determined for an estimate to be reliable are not met.

- Sample size is <1004

OR – sample size is >300 BUT other conditions a priori determined for an estimate to be reliable are not met.

- Sample size is <300 but >1003

- Data is not recent and unlikely to reflect current state environmental conditions.



- Data is recent and is likely to reflect current state environmental conditions.



ConditionsData Quality Score

data quality scores = a measure of uncertainty


PSA methods

STEP 3: rank productivity and susceptibility attributes into tiers corresponding to different levels of risk



age at first maturityindividual growth rate

lifespanmortality

encounterabilityavailabilityselectivity

*DQ

Productivity

Risk

1=High 3=Low2=Moderate

1=Low 2=Moderate 3=High

Susceptibility

Risk

1=Low 3=High2=Moderate


determine a range of values for each attribute

divide this range into thirds:


PSA methods



*DQ

0.51-0.660.35-0.500.17-0.340.660.17Mortality

12-1718-2324-313112Lifespan (Agemax)

0.128-0.1750.080-0.1270.028-0.0790.1750.028Growth rate (K)

3-56-89-12123Age at first maturity

HIGH = 3MODERATE = 2LOW = 1MaxMin

Productivity Tiers and ScoresObserved RangeAttribute

0.51-0.660.35-0.500.17-0.340.660.17Mortality

12-1718-2324-313112Lifespan (Agemax)

0.128-0.1750.080-0.1270.028-0.0790.1750.028Growth rate (K)

3-56-89-12123Age at first maturity

HIGH = 3MODERATE = 2LOW = 1MaxMin

Productivity Tiers and ScoresObserved RangeAttribute

*only stocks with values corresponding to data quality scores ≤3 were used to define the observed range


PSA methods




encounterability scores

≥ 201151-160101-11051-60≤ 10Distance from community (km)

1 = Low1.52 = Mod2.53 = HighEncounterability scores

≥ 201151-160101-11051-60≤ 10Distance from community (km)

1 = Low1.52 = Mod2.53 = HighEncounterability scores

PSA methods



selectivity scores

4.91-5.554.26-4.903.60-4.255.553.60772501

HIGH=3MODERATE=2LOW = 1MAXMINMAXMIN

Gear Selectivity Tiers and ScoresObserved range(Mean FL / Mesh size)

Observed range(mean FL)

4.91-5.554.26-4.903.60-4.255.553.60772501

HIGH=3MODERATE=2LOW = 1MAXMINMAXMIN

Gear Selectivity Tiers and ScoresObserved range(Mean FL / Mesh size)

Observed range(mean FL)


PSA methods



lacustrine -medium size lake fisherylacustrine -large lake fishery

anadromous - river mouth fisheryanadromous - medium size lake fishery anadromous - large lake fishery

high=3moderate=2low=1

Availability tiers and scores

lacustrine -medium size lake fisherylacustrine -large lake fishery

anadromous - river mouth fisheryanadromous - medium size lake fishery anadromous - large lake fishery

high=3moderate=2low=1

Availability tiers and scores

availability scores


PSA methods

STEP 4: plot average productivity-susceptibility scores and estimate vulnerability as the Euclidean distance from the origin

average Productivity score (P) average Susceptibility score (S)

Vulnerability (V)

V = √(P - 3)2 + (S – 1)2)

Productivity attributes Susceptibility attributes

Susceptibility

Risk



Susceptibility

Risk





Productivity

Risk



Productivity

Risk





(Euclidean distance from origin)

1.01.52.02.53.0

Productivity

1.0

1.5

2.0

2.5

3.0

Su

sce

ptibili

ty

(High) (Low)

(Low

)(H

igh)

High ProductivityLow Susceptibility= least vulnerable

Low ProductivityHigh Susceptibility= most vulnerable

1.01.52.02.53.0

Productivity

1.0

1.5

2.0

2.5

3.0

Su

sce

ptibili

ty

(High) (Low)

(Low

)(H

igh)



*DQ


PSA methods

STEP 5: average data quality scores into a data quality index

average Productivity score (P) average Susceptibility score (S)

Productivity attributes Susceptibility attributes

Susceptibility

Risk



Susceptibility

Risk





Productivity

Risk



Productivity

Risk





1.01.52.02.53.0

Productivity

1.0

1.5

2.0

2.5

3.0

Su

sce

ptibili

ty

(High) (Low)

(Low

)(H

igh)



1.01.52.02.53.0

Productivity

1.0

1.5

2.0

2.5

3.0

Su

sce

ptibili

ty

(High) (Low)

(Low

)(H

igh)



*DQ

High data quality

Medium data quality

Low data quality

≤22.1-3.5≥3.6

HIGH (H)MEDIUM (M)LOW (L)

overall DATA QUALITY Index

≤22.1-3.5≥3.6

HIGH (H)MEDIUM (M)LOW (L)

overall DATA QUALITY Index


PSA results


1.0

1.5

2.0

2.5

3.0

Susc

ep

tibili

ty(L

ow

)(H

igh)

1.01.52.02.53.0

Productivity(High) (Low)

1.0

1.5

2.0

2.5

3.0

Susc

ep

tibili

ty(L

ow

)(H

igh)

1.01.52.02.53.0


High data quality

Medium data quality

Low data quality

Kitikmeot Region

Less Vulnerable(1.27) Arrowsmith River(1.27) Kellett River(1.28) Nakyoktok River(1.28) Perry River(1.47) Becher River

More Vulnerable(2.06) Freshwater Creek(2.07) Paliryuak River(2.08) Hayes River(2.24) Jayco River(2.30) Abernethy River(2.46) Lord Lindsay Lake

Less Vulnerable stocks

Vulnerable stocks

More Vulnerable stocks

PSA results


High data quality

Medium data quality

Low data quality

More Vulnerable(2.05) Ross Inlet

Kivalliq Region

Susc

eptib

ility

1.0

1.5

2.0

2.5

3.0

(Low

)(H

igh)

1.01.52.02.53.0


Susc

eptib

ility

1.0

1.5

2.0

2.5

3.0

(Low

)(H

igh)

1.01.52.02.53.0


1.0

1.5

2.0

2.5

3.0

(Low

)(H

igh)

1.01.52.02.53.0


(1.09) Stony Point Area(1.10) Barbour Bay(1.10) Robinhood Bay(1.15) Lorillard River(1.18) Gore Bay Area(1.18) Hanway River(1.19) Steep Bank Bay

(1.23) Pistol Bay(1.36) Chesterfield Inlet(1.36) Rankin Inlet Area(1.36) Ranger Seal Bay

Less Vulnerable


Vulnerable stocks


PSA results


High data quality

Medium data quality

Low data quality

More VulnerableLess Vulnerable

1.0

1.5

2.0

2.5

3.0

Susc

eptib

ility

(Low

)(H

igh)

1.01.52.02.53.0


1.0

1.5

2.0

2.5

3.0

Susc

eptib

ility

(Low

)(H

igh)

1.01.52.02.53.0


Baffin High Region

(1.23) Ikalukjuaq Lake and River(1.30) Tasirululik Lake(1.41) Kipisa Lake(1.41) Tugaat River(1.42) Ava Inlet(1.42) Nettilling Lake

(2.14) Ijaruvung Lake(2.14) Kukaluk River(2.15) Ikikeesarjuq Lake(2.15) Stanwell-Fletcher Lake(2.20) Tarsiujuak Arm(2.23) Magda River(2.29) Gifford River

(2.30) Ivisarak Lake(2.30) Koluktoo Bay(2.30) Naulingiavik Lake(2.35) Hall Lake(2.39) Rowley River(2.41) Ikpikiturjuaq Lake(2.44) Neergaard Lake

(2.44) Ravn River(2.50) Hall Beach Area(2.55) Windless River(2.56) Fury and Hecla Strait


Vulnerable stocks


Rankin Inlet

Gjoa Haven

Cambridge BayKugluktuk

Kugaaruk

Igloolik

Hall Beach

Taloyoak

Arviat

Kimmirut

Iqaluit

Pangnirtung

Pond Inlet

Clyde River

Arctic Bay

Repulse Bay

Chesterfield Inlet

Rankin Inlet

Gjoa Haven


Kugaaruk

Igloolik

Hall Beach

Taloyoak

Arviat

Kimmirut

Iqaluit

Pangnirtung

Pond Inlet

Clyde River

Arctic Bay

Repulse Bay

Chesterfield Inlet

PSA results


Less Vulnerable stocks (more sustainable fisheries)

Vulnerable stocks (sustainable fisheries)

More Vulnerable stocks (less sustainable fisheries)

Rankin Inlet

Gjoa Haven


Kugaaruk

Igloolik

Hall Beach

Taloyoak

Arviat

Kimmirut

Iqaluit

Pangnirtung

Pond Inlet

Clyde River

Arctic Bay

Repulse Bay

Chesterfield Inlet

Rankin Inlet

Gjoa Haven


Kugaaruk

Igloolik

Hall Beach

Taloyoak

Arviat

Kimmirut

Iqaluit

Pangnirtung

Pond Inlet

Clyde River

Arctic Bay

Repulse Bay

Chesterfield Inlet

PSA results





• 15% of fisheries examined• 45% of more sustainable fisheries

Rankin Inlet

Gjoa Haven


Kugaaruk

Igloolik

Hall Beach

Taloyoak

Arviat

Kimmirut

Iqaluit

Pangnirtung

Pond Inlet

Clyde River

Arctic Bay

Repulse Bay

Chesterfield Inlet

Rankin Inlet

Gjoa Haven


Kugaaruk

Igloolik

Hall Beach

Taloyoak

Arviat

Kimmirut

Iqaluit

Pangnirtung

Pond Inlet

Clyde River

Arctic Bay

Repulse Bay

Chesterfield Inlet

PSA results


• 22% of fisheries examined• 60% of less sustainable fisheries




PSA implications


over a broad geographic landscape:

• PSA analysis allows us to distinguish between regions characterized predominantly by less or more vulnerable Arctic charr populations

prevalence of more vulnerable stocks = greater risk of overfishingprevalence of less vulnerable stocks = lower risk of overfishing

Regional differencesin stock vulnerability

PSA results


High data quality

Medium data quality

Low data quality

Cambridge Bay Area


Vulnerable stocks


1.01.52.02.53.0

Productivity

1.0

1.5

2.0

2.5

3.0

Susc

eptib

ility

(High) (Low)

(Low

)(H

igh)

Jayco

Paliryuak

Ellice

Perry

Halovik

LauchlanEkalluk

Kulgayuk

1.01.52.02.53.0

Productivity

1.0

1.5

2.0

2.5

3.0

Susc

eptib

ility

(High) (Low)

(Low

)(H

igh)

Jayco

Paliryuak

Ellice

Perry

Halovik

LauchlanEkalluk

Kulgayuk

Yield Calculationslog L∞= 0.044 + 0.9841 log Lmax (Froese & Binohlan2000)

K = -ln (1-Lm/L∞) / (tm - t0) (Froese & Binohan 2003)

M = 1.5K F = Z – M – Z calculated from catch curve (Jensen 1996)

Biomass = Yield/F

Max Sustainable Production = 0.33 x Z x Biomass

Estimation of Maximum Sustainable Production

General conclusion


• Among population Variability in life history traits is a characteristic of Arctic charr populations in Nunavut

General conclusion


• Life history traits for a stock can be determined from a single sample of ≈ 200 fish (intermediate data quality) or approximated using life history theory


General conclusion



R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

• Life history traits can be used together with a measure of exposure (susceptibility attributes) to determine the relative vulnerability of Arctic charr stocks to fishing activities over a broad geographic landscape and within smaller areas.

exposure


General conclusion



R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

soon:

• Life history traits can be used in a quantitative model to explore and identify sustainable harvest rates for a stock and thereby facilitate the formulation of scientific advice for arctic charr in the CNA region.

• Life history traits can be used together with a measure of exposure (susceptibility attributes) to determine the relative vulnerability of Arctic charr stocks to fishing activities over a broad geographic landscape and within smaller areas.

exposure


charr diversity

(�) “for addressing of gaps for the scientific understanding and management of Arctic charr fisheries

life history traits

solutions

R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

R MG

F AM GR Z

exposure

relative risk assessment full quantitative analysis

General conclusion

Gaps Solutions

Questions ?

Acknowledgments

Theresa Carmicheal DFO

YK area office

DFO Nunavut Implementation Fund

Nunavut Wildlife Management Board

a productivity-susceptibility risk analysis for the

Documents