Towards spatial life cycle modelling of eastern Channel sole

B. Archambault, O. Le Pape, E. Rivot

27 mars 2014 – Agrocampus Ouest 1

Spatialization of adults ?

• So far (justified) focus on early stages

• Adult spatial structure = missing link towards full understanding of marine population functioning (e.g. adult spatial distribution at the time of spawning may condition larval supply)

• Local actions (e.g. fishing/nursery areas) may have local/global consequences depending on population spatial functioning

• Opens the way to realistic spatial scenarios with assessment of local/global impacts Ecosys. Approach

• Need to move from theoretical models to real-world case studies

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Relevance to case study : sole in VIId

• Well studied commercially important population (much scientific background,

long time series )

• Nursery-dependent species with 5 well known coastal/estuarine nurseries

• Indices of very limited early stages connectivity between regions (Rochette 2012)

• Coastal (i.e. regional) fisheries

• Limited adult movement

Solea solea

Eastern channel

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Model

Egg-to-metamorphosis IBM coupled with hydrodynamic model Hydro-climate

estimation of larval allocation amongst identified nursery areas

Juvenile habitat suitability model Habitat descriptors

juvenile abundance indices in nursery areas

Adult age structured model fishing & natural mortality

commercial and scientific catches

Hydro climate

Habitat

Fishing

Hierarchical Bayesian Modeling framework

Based on Rochette et al. 2013

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Hierarchical Bayesian Model

e.g. abundance

e.g. F Parameters

Hidden states

From Rivot, HDR

Observations e.g. indices d’abondances

Prior information

PR

OC

ESS M

OD

EL O

BSER

VA

TION

M

OD

EL

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2

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0+

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0+

1

0+

1

0+

Eggs SSB

Larvae

Nurseries

HBM : processes

𝑳𝒚,𝒊=𝝎𝒚∗ 𝑫𝒚,𝒊

larval drift/survival allocation key

Beverton Holt

𝑁𝑎+1,𝑦+1 = 𝑁𝑎,𝑦 ∗ exp(−(𝑀 + 𝐹𝑎,𝑦))

∝,𝑲

𝑁0𝑖,𝑦 = 𝑁0𝑖,𝑦 ∗ exp(−(𝑀0))

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2

3

15 Larval survival and drift

1

0+

1

0+

1

0+

1

0+

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Eggs SSB

Larvae

Nurseries

Juveniles abundance

Catches at age / AI

HBM : observations

𝑫𝒓,𝒚,𝒊~𝑫𝒊𝒓𝒊𝒄𝒉𝒍𝒆𝒕(𝑳𝑲𝒆𝒚𝒓,𝒚,𝒊) 𝒍𝒐𝒈 𝑰𝒇 ~𝑵𝒐𝒓𝒎𝒂𝒍(𝒍𝒐𝒈 𝒒𝒇 ∗ 𝑵 ∗ −𝟎. 𝟓 ∗ 𝝈𝒇𝟐. 𝝈𝒇

𝟐)

1982 to 2011

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Density-dependence in nurseries

LARVAE

Prior information

∝ Slope at origin Maximum survival rate

𝑲 Asymptotic value Carrying capacity JUVENILES

Hard to estimate

From 3D to 2D….

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Integrating a priori information on α

Archambault et al 2014. Meta-analysis of stock recruit relationships in flatfish

Log(α_HBM)

α_metanalysis transformation

VIId sole life history

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Back to regionalisation

• Definition of 3 regions 1 : Veys/Seine (1 spawning ground) 2: UK (2 spawning grounds) 3: Somme (3 spawning grounds)

• Larval retention between spawning areas and adjacent nurseries

Rochette et al. 2012 + comm. pers. Baulier Savina

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Two alternative scenarios

NON SPATIAL SPATIAL

• Juveniles from a given nursery migrate to the adjacent adult zone

• Adults reproduce in spawning areas within their respective zone

• No adult movement between zones

One panmictic population : • Juveniles migrate to the global

population

• Adult repartition at reproduction follows observed eggs map

Mixing between zones intervenes solely through (limited) larval dispersal

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+ IA spatial + Spatialized catches

Data sources

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Catches at Age True catch allocation IA BECBT IA UKCBT IA UKBTS (spatial) A

DU

LTES

IA

JU

VEN

ILES

West UK Rye Somme Seine Veys

Larval allocation key

LAR

VES

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New AI

« Spatialazing » catch data

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• True spatial catches (in weight only) since 2003 • Relatively stable ratio among regions since 2003

2 hypotheses Identical catches age structure among regions Pre-2003 spatial repartition of catches similar to

2003-2011

Ratio of catches (weight) over the 3 regions since 2003

Two alternative scenarios : consequences on larval survival and dispersal

NON SPATIAL SPATIAL

Total eggs Eggs

1

Eggs 2

Eggs 3

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1991 egg

maps

Results : SSB estimates

NON SPATIAL SPATIAL

Consistent with ICES Recent « productivity shift » between regions or model artefact ?

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Results : larval allocation

NON SPATIAL SPATIAL

Small contributions differences – higher input from Somme in spatial scenario 16

Results : nurseries contribution to population Past proportions of Age 0 per nursery sector

NON SPATIAL SPATIAL

Much higher Somme contribution to population + less variability in spatial model 17

Results : nurseries contribution to population Past production of Age 0 per region

NON SPATIAL SPATIAL

Much higher Zone 3 contribution to population in spatial model 18

Results : nurseries contribution to population parameters estimates

NON SPATIAL SPATIAL

Possible differences between past production and parameters (K is maximum production/surface) I.e. veys

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Results : estimating local potential productivities

Survival From eggs to Age 1 20

Contribution of the different data sources

Spatialized catches

True spatial catches

Constant ratio

Spatial AI (UKBTS)

No spatial catches

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Contribution of the different data sources

Spatialized catches

True spatial catches

Constant ratio

Spatial AI (UKBTS)

No spatial catches

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We need data ! Identification of data needs (e.g. past spatial catch reconstruction)

Sensitivity to observation error levels

0.0 0.2 0.4 0.6 0.8 1.0

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Estimates of K~f(CV_obs)

CV obs

KRye

Solent

Somme

Seine

Veys

CV captures =0,2

CV obs of AI (adults (3 AIS) and juvs

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Informative prior ?

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Spatialization of adults : room for improvement

• Generally, attempting to model full spatial functioning of population may help to identifity specific needs in data : e.g. on a specific sector/region, specific aspects (genetics, mark/recapture)

• In given case study, both spatial hypotheses are probably wrong, reality in between ? Easy to explore (e.g. migrations), hard to choose.

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Spatialization of adults : new opportunities

• Assessment of stock health at a finer scale implication for regional fisheries. (e.g. recent drops in Somme region catches)

• Reevaluation/precisions of habitat contributions to population renewal

• HBM = ideal framework to integrate data, processes and prior knowledge in such cases

• Rooms for spatial scenarios (e.g. restauration/degradation of habitats) = Next and last step !

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Scenarios

A2 Diminution de la qualité et/ou surface des nourriceries. Surexploitation par la pêche. B1 Préservation et/ou restauration des nourriceries, exploitation au RMD.

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