Noëlle BRU Laboratoire de Mathématiques et ses Applications UMR CNRS 5132 Université de Pau et des Pays de l’Adour : [email protected] Patrick PROUZET

Noëlle BRU Laboratoire de Mathématiques et ses Applications

UMR CNRS 5132 Université de Pau et des Pays de l’Adour

: [email protected]

Patrick PROUZETIFREMER LRHA

Michel LEJEUNEUniversité Grenoble 2

As an example the Adour Estuary (Southwestern part of France)

About management groups : ICES (International Council for the Exploration of the sea): “the stock of

European eel (Anguilla Anguilla L.) is outside safe biological limit and that current fisheries are not sustainable”.

18 September 2007: the Council of the European Union adopted a council regulation (EC 1100/2007) establishing measures for the recovery of stock of European eel.

November 2007 : the IUCN (International Union for Conservation of Nature) listed the European eel on its Appendix II and considered the eel as an endangered species.

Glass eel = young eel... Passive migration… During flood tide… Mainly by night…

Colloque ASP - Boulogne sur Mer 2008 2

General context…General context…

(source : IFREMER)


In European estuaries: constant decrease is observed for several years.

Over-exploitation at the different phases of the biological cycle ?

Inland habitat loss ?Climate and ocean current changes ?

Diseases ? Degradation of continental environment and pollution ?…

General context…General context…

??


• Most methods : » Designed for a fixed stock within a given area » Geostatistical approaches» Hydroacoustics campaigns…

• A specific method : in a particular environment : from the mouth of a estuary + an area of migration ... a species with a specific migratory behavior... a method to compute an efficient estimate of glass eels abundance...

Problems…Problems…


Used to propose biomass estimates…

How and why to put together these datasets ??

About the talk...About the talk...

What are the data available to do so ?

Historical time series of professional catches

(by day, into the maritime area)

Data from scientific in situ surveys

Step 1

Step 2

Are they really good indicators of abundance ?

Step 1 of the method :

Using data from scientific in situ surveys…

Step 1 : In situ scientific surveys…

• Sampling protocol and data

• Statistical method for estimation of « observed » glass eels biomass

• Results and discussion

Step 2 : Using professional catches…• Estimate of seasonal glass eels

biomass migrating by night • Estimate of glass eels recruitment

•

« Observed » glass eels biomass estimates(source IFREMER)


All samples are assumed to be simultaneous... -9

-8

-7

-6

-5

-4

-3

-2

-1

0

1 passage 30 min( 8 passages max !!!)

…… during the flood tide….

5 min 5 min 5 min

10 min

Right Bank Middle Left Bank

Glass eel densities(in g/100m3)

ks td

s=1…S (=6) k=1,…, n(=8)

Main Push-sieve fishery







•

The Adour estuary


R

t

t

s dtdstBRB2

1

J

j

S

ssj

t

t ss SdttvtdJ

RB1 1

2

1

1

J

j

S

ssj

t

t s SdttBJ

RB1 1

2

1

1

Total biomass (in kg)for the flood tide period

through vertical region R

sB̂ jB̂ RB̂







•

J different schemes tested…


2

1

ˆˆˆ t

t sss dttvtdB

tvftd ss

We need a functional form for each term!!







•

For the current speed vs(t)…

A sinusoidal model depending on time

A log-log linear model depending on the current

speed

For glass eels density ds(t)…

An explicit form for the estimate

+An evaluation of its

variability

• to correct possible errors of measurement• to evaluate the beginning and the end of the flood tide

Finally,◦ Simple method... With sparse data...◦ Some approximations and hypothesis…◦ More surveys than results...◦ But “observed” biomasses seems

“realistics” !

Method easily adaptable for other estuaries

Exploitation rates for push-sieve are derived by days...








• Season Day Biomass (in Kg) push-sieve catches (in Kg)

98/99

12/01/1999 138 6

14/01/1999 379 8,2

22/01/1999 157 11,2

28/01/1999 42 4,5

11/02/1999 79 4,6

17/02/1999 206 14,5

17/03/1999 203 13,5

99/00

09/12/1999 1597 36,79

22/12/1999 664 21,15

05/01/2000 409 77,31

07/01/2000 821 63,64

08/01/2000 763 34,67

09/01/2000 993 12,56

12/01/2000 95 8,79

01/02/2000 1715 36,18

08/02/2000 464 1,2

00/01

14/12/2000 152 3,2

21/12/2000 134 14,7

03/01/2001 28 3

10/01/2001 97 11,5

12/01/2001 81 18,1

24/01/2001 264 64,5

26/01/2001 132 45,9

02/0303/01/2003 55 30,4

22/01/2003 82 2,6

03/0426/11/2003 80 8,2

21/01/2004 37 8,2

04/05

09/11/2004 550 10,3

18/11/2004 257 33,8

23/11/2004 37 1,5

08/12/2004 375 13

14/12/2004 198 17,6

Step 2 of the method :Using both information (« observed » estimates and professional catches)

Estimate of seasonal glass eels biomassmigrating by night

Estimate of glass eels recruitment for a fixed fishing season







•


Evolution des captures professionnelles en zone maritime et des biomasses journalières estimées lors des campagnes scientifiques in situ .

0

200

400

600

800

1000

1200

1400

1600

1800

27/10/1999 16/11/1999 06/12/1999 26/12/1999 15/01/2000 04/02/2000 24/02/2000 15/03/2000

Saison 1999/2000

TOTTAM (en kg) Biomasse (en kg)

Model ? Dependent variable : rate of exploitationExplanatory variables : catches

+ environmental variables







•

Professional catches with push-sieves

« observed » biomass

Data concerning the same glass eel run !!

Model : glm( Rate ~ TurbCod + Catches^0.646 + TideCod+

FlowCod * Moon+catches*Moon)

◦ Gamma distribution (with canonical link = inverse)◦ Coded explanatory variables :








•

TurbCod TideCod FlowCod

1 if <13 NTU 2 otherwise.

1 if <60, 3 > 86 2 otherwise

1 if <250 m3/s, 3 > 400 m3/s 2 otherwise

Degree of

freedom Deviance

Residual Degree of freedom F value Pr(F)

NULL 31

TurbCod 1 9,572 30 18,746 0,001

Catches^0,678 1 3,185 29 6,237 0,024

TideCod 2 0,939 27 0,919 0,419

FlowCod:Moon 8 7,044 19 1,724 0,168

Catches:Moon 3 1,295 16 0,845 0,489

With 32 observations…


« cleaned » values

+ A variability associated to each predicted

value of the rate of exploitation !







•

Values >100 or < 0

Predicted rate of exploitation <1,5% and catches >100kg

tide < 60 and rate of flow >600m3/s


Nov Dec Jan Feb1999 2000

10

00

20

00

30

00

40

00

50

00

60

00

*

* ****

*

*

*

Biomasse estimée sur certains joursBiomasse extrapolée sur la saisonBiomasse observée

Smoothed biomass (by gaussian kernels)

Predicted biomass + erreur

« observed » biomass + error of prediction







•

T97ˆ nightB

With a estimated standard error around 7 T


+Glass eels recruitment for a fixed season ???

Known for every night (see before) unknown

Hyp : mean value between two consecutive nights

Fishing season

Catckes with push-sievesin kg

Total catchesin kg

Biomass

in kg

Rate of exploitation

of push-sieves

Glass eels recruitment

in kg

Overall rate of exploitation

1998/1999 1,655 ~ 6,000 28,200 5.9% 53,478 11.2%

1999/2000 4,579 ~15,000 96,900 4.7% 181,354 8.3%

2000/2001 1,446 1,983+F 15,500 9.3% 32,990

2001/2002 770 2,395+F 20,600 3.7% 43,307

2002/2003 388 0,594+F 3,100 12.5% 6,231

2003/2004 1,093 2,969 8,700 12.6% 17,550 16.9%

2004/2005 1,398 7,183 27,700 5.0% 59,292 12.1%

2005/2006 686 3,531 7,870 8.7% 15,719 22.4%

average 1,502 26,070 7,85% 51,240

median 1,246 18,050 7,3% 38,149







•

Need to explicit the precision of each estimates at each step of the calculus.

The method is adaptable to other estuary but with the search of a model…

But the level of seasonal biomass is realistic !!


Conclusions and Conclusions and perspectives…perspectives…


Thanks to the numerous scientists and crew members who conducted the experimental campaigns because our analysis is based on their hard work !!!

Documents

Noëlle BRU Laboratoire de Mathématiques et ses Applications UMR CNRS 5132 Université de Pau et des Pays de l’Adour : [email protected] Patrick PROUZET