NOT TO BE CITED WlTIIOUT PRIOR REFERENCE TO nIE AunIORS
IßtematiOnal Council fontbe 01 b 11 0 t h ~ k
Exploration of tbe Sea
' I/Iehml, Kar;l\\f.'~
.,., '.' . . "
I"
Anacat Fish Committee
C.M 1995/ M:20 Rer CJ>
L~UENCE OF HvnROLöGlCAL A.~ CUMATIC CONDmONS ON DAILY CATCH PER IDlIT
EFFORT OF ATLANTIC SALMON (SAl..\tOSAL.AR
OFTHE ADOUR RIVER, fRANcE
Aüthon: Fran~ois.XaVier CUENriE
Jacques BAriIA (3)
•
(1),
L.) WlTII DRIFf GILLNET AT mi MOiirH
Patnck PRo UZET
.
(1),
Jean·Pierre MARTINET
(2),
Adresses: (1) Institut Franrrais de Recherche pour l'Exploitation de la Mer, LabOratoire
d'Ecologie Halieutique, station d'Hydrobiologie, B.P. 3, 64310 Saint·pee·sur-Nivelle,
France.
(2) InStitUt Natiollal de la Recherche Agronomique, Laborntoire drEcologie des Poissons,
Stition d'Hydrobiologie, B.P. 3, 64310 Saint-Pee-sur-Nivelle, Fränce.
(i) lIiStitut NationaJ de la Recherche Agi-onomique, Station de Bioni~tne et d'Inteliigence
Artificielh~, B.P. 27, 31326 Ccistanet.Tolosan Cedex. France.
AbStract
Influence. of river flow, tidal coefficient and wind dirCction on Atlantic saImon (So/mo
solar L.) cUiily catch per unit effort (CPUE) at the mouth ofthe Adom river were 3.ruilysed
by the Correspendence Analysis and by the. GenefaIiZeci Linear MOdel. Catch arideffort
data were provided by logbooks from commercial fishermen. Scale reading of a stratified
sampie of the catch allowed partitioning of the fishing season (march.july) rntwo periods,
correspOndirig totwo different salIit0n types: multi·sea-winter salriion (niarch-may) arid
one.sea-Winter salmon or grilse (jüDe-july).,For both periods, hydrological contl'aSis (high
river flow combinaioo with spnng tide) and sea Winds contiibuted to high-level CPUE of
salmon. However, relätive. influence of the hydrological conditions differed according io
salmontype. Tidal coefficieni was
sourCe of variation ofCPUE (in numtier of
fish caught per fisherinan) of multi.sea:.Winter . salmon, with tidal flow and sea wirid as
secondäry sources. As for grilse, river flow, which Was lower thari in spring, apPeared to be
more critical: CPUE level. calculated as the deviation betWeen daily CPUE and anormal
trend (in iiuniherof fish caught per fisherman), increased with increasmg river flow ; tidal
flow and wind direction apPeared to be less influent.
apnmaI)r
Kerwords
"
Sei/mo solar, Atlantic salmon, Salmonids, anadromous migratio~ C~ltch. drift gillnet,
river flow. tidal flow, wind directiön, correslxuldence arialysis, gerierillized linear model.
.'
I.
lNTRODUCfION
lnfluence of environmental factors on anadromous migration of AtlaIirlc salirion in
estuaries arid rivers has been analysed by many aiithors. ReviewS can be found in BANKS
(1969) and JONSSON (1991). However, most ofthose"stildies rely
• use of traps or automatie counters set on fish-passes or obstacles (JACKSON &
HOWIE, 1967; SWAIN & CHAMPION, 1968 ; ALABASTER., 1970; HELLAWEU. el al., 1974 ;
JENSEN el a/., 1986 ; JONSSON er al., 1990)
.... .
• acoustic or radioelectnc tiacking in estwiries(STASKO, 1975 ; BRAWN, 1982 ;
POlTER, 1988 ; PRrEDE er a/., 1988 ; SOLo~ioN & pori'ER; 1988), in riverS (HAWKINS &
SMI1H, 1986; WEDB, 1989), or near sPaWning areas (WEBB& HAWKINS, 1989).
Tbus, they provide information on individual behaviour of small numbers of fishes, or
on obstacle passing. Moreover, these studies often cover only a shoit perlod of thC year, or
inelude all recruitment periods of the ditTerent sea-age-groups without diStinction betWeeri
salmon types.
For example, SMIllI (1990) stUdied coniinercial net S8!nion eatchCs at the
Aberdeenshire Dee mouth. He showed sigriificant rie~ve linear correlatioDS between
daily values of cateh and river flow, but did not take into account variationS in fiShiilg
etTort or in relative abundance of theditTerentsea-age groups around the year. .... .
.
Our study considered the first phase of the ariadrOmoUs riii8iation, Le. the.entry in the
estuary of the Adour river (South-West of FraDCe). We usC:d daily c3.tch Per UDit effort
(CPUE) with drift gillnet as an index of satmon abundänce. We an8lySed covUiatiODS of
daily CPUE and some hydrological arid cliInatic faetors (tidal flow, ri",er flow, wiIid
direction), during the recruitment pericids ofthe two main Salnion types (on~ and tWO;.Seawinter salmon) in the fishery at the river
on:
•
mouth.
II. SITUATION AND CARACfERISTlCS OF mE SURVEY AREA
Tbe Adour esnWy, th.e mouth of wmch issitWited in Bayonne (fig. 1), is the
interference zone between the flow of the Adom-Gaves-Nivc hydrographie System arid the
Atlantic tide.
We USed catch and effort data and environmental datä on the Adourriver moUth during
the years 1988, 1990, 1991 and 1992. According to the fismng days, the number offishing
boatS in the fishery under study vaned between tWo and tWelve.
A. HYDROLOGICAL CONDmONS
River flow is relarlvely high in the estuaririe p3it ofthc AdOur basin (bCtWcen 100 and
450 m3/s accordirig to seasonS), maintained as a result of the contributioii of rivers With
varied hydrological
(FIsCHER, 1930; PARDE. 1968; PAGNEY. 1988).
During the years 1988 to 1992, the yearly mean daily flow at the
mouth Was
3
3
around 240 m /s, with extremal monthly valucs of 56 and 720 m /s. Dunng sPß,tes, meso
daily flow could reach 2000 m3/s.
patterns
river
Tbe fishing zone under study correspcirids to the entrance to the commereial harb()ur of
Bayonne. 115 main characteristics are:
.
• the artificial narroWing ofthe river by dykes: river width is Mound ISO m between
dykes at the river mouth, against 700 m around Saint-Bernard sand bank, some 300 m up
ri~:
• a depth maintained between
dredging (ANON., 1991).
9 arid
.
15 m (zero-Icvel of seä charts) by reguI3.r
2
..."'
•
,-
The fall oftbe river, combined with the great depth, leads to high speeds ofwater (tab.
I).
,.
'.'
J:.
. '".,
B. TIDAL CO:\TDmONS
Tidal phenomenons iri tbe Adour. esrilary have been deseribed arid quaniified by
Duration of tidal Period is 12 h 25 min. At, the nver mouth. ebb and
flow have the same duration, and tide height varies betWeen 1.35 ni in neap tides lind 4.20
ni in spririg tides. During tides of maximum strength., around 30 inillionS eubic mettes of
sea water get in and out of the Adour estUalY.
LESBORDES (1979).
III. ~lATERIAL ANÖ METHODS
A. COLLECTIONOFCATCH At'l> EFFORT DATA
Current data on commercial fisheries in the Adour estuary, and p3.rticularlY ori salmori
fishing (tab. 2) come from:..
. . '
• iridividual daily logbooks, indicating fishing zone, fishing effort, catch by sPecies
in weight arid/or number ~
..
• and a sampling 01' the biometrie and demographie charaeteriSties of the eatches:
weight, length, scäles and blood for age and sex determination.
Using the individual logbooks, we could discrimiriate the pOteritial fishirig days into
three groups within the legal fishing season: days with null fishing dfoit, days with non;'
zero fishing effort and null eatch. and days with rion-zero catch.
.
Our study used observationS and informations eollected fOf the secorid and third groups
01' fishing days.
Levels of catch and fishing effort were very low in 1989, and many d3ys ofthe fishing
season saw no fishing etTort at all. So this year was excluded from OUf . anaIysis.
Observations for the other foUr years (1988, 1990, 1991, 1992) amount to a totil of 346
fishirig days, including fishing days with zero catch.
.
In this study, the daily CPUE was calculated by dividing the tot3l mimber of fish caught
b\' the nurnber of fishermen having fished that day.
B. AGE DETERMINAnON FOR SALMON CATCH
Two methods were ilsed:
• scah~-reading, for individual ageing of fish
whicn seale
werecollected.
according to a gerierie methodology (ANON., 1984) adapted to the southem area of Atlantic
salrnon distribution (BAGLINIERE, 1985). Thanks to the time-straiifieation of the sampling,
the results obtained \vith this method provided an age-weight key for the different periods
ofthe tishing season ;
• this age-wdght key allowed the asslgitment of a most probable sea-age to fish for
which only individUal weight and catch date \~'ere knoWn. This method was proven
consistent \vith scale-readirig on test sampies (CUENDE & PROUZET, 1992).
on
sampie
J
c.
PARTITIONING OF TIIE FISHJNG SEASON
Age detennination of catches aimed at dividing the fishing season intotwo periods
corresporiding to the recruitrnent of the t\vo major types of salmon in the fishery at the
mouth of the river: multi-sea-winter salmon (mainly 2-sea-winter salmon), and one-seav.inter salmon or grilse.
For each fishing year, we detennined the approximate date until which 2-sea-winter
sahnon are largely the most numerous type and after which grilse become the salmon type
\\ith the largest relative importance.
This allowed us to take into account the possible disproportion betWeen abWldance
levels of the salmon types, as weil as differences in prevalent hydrological and climatic
conditions.
D.
ENVIROm1ENTAL VARIABLES
The astronomie tidal coefficient (TC) was provided by the Freneh Hydrographie and
Oceanographie Marine Service (Service Hydrographique et Oceanographique de la
Marine). It is expressed as a fraction (in hundredths) of the mean spring tide heigths in
March and September in Bayonne harbour. Values range benveen 20 and 120.
River flow (RF) data, in m 3/s, were provided by gauging stations on the Adaur river änd
its tributaries.
The National Meteorologieal Service provided data on wind direction (WO) from
Biarritz airport monitoring station. Values are expressed in tens of angle degrees,
according to the following convention: north = 0, east = 9, south = 18, west = 27.
E. A~ALYSIS
OF COVARIATIONS OF EWIROmlENTAL VARIABLES M!'D CATCH PER UNIT
EFFORT
Separate analyses were condueted for eaeh of the two salmon types.
I. STA175nCALMETHODS
a) Correspondence analysis
A multiple correspondnce analysis (BENZECRI. 1973 ; JA.;.\ffiU, 1989) was used to
describe covariations of hydrological (RF, TC) and climatie (WD) faetors and salInon
catch. Lines of the observation matrix correspond to tishing days, and colurnns to chisses
of catch and environmental variables.
b) Generalized linear model
The generalized linear model (MCCULLAGH & NELDER, 1989) was used to qUantifythe
etfects of hydrological and climatie conditions on salmon catch per unit etfort with drift
gillnet. This statistical technique does not belong to the traditional regression technique
group. which are design 10 s1udy cominuous, or a1 leaSt numerical, variables (TREXLER&
TRAVIS, 1993). The classical linear mOdel postulates that the error terins are nonnally
distributed, \'with zero mean and constant variance. Yet, tor many types of data. variance
changes with the mean. ResPonse-variable reexpression models sutTer from severa~
drawbacks. For example: loss of familiarity with the reexpressed variable. tninsfonnation
not detined at the boundaries of the sam pie space.
The generalized linear model is an alternative response, a reparametrization inducing
linearity and allo\'Ying non constant varümce. It requires a link furiction describing rehitioris
between the mean and the linear predictors, arid a vanarice furietion describing dependerice
ofvariance upon mean (HASTIE & PREGmON, 1992).
Wheri dealing with contingency tables, the generalizoo linear mOdel can be used io
model the probability distribution ofa ciltegorical variable, or a fUnction ofthis probability
distribution, as a fiinctiori of set of categorical explanatory variables. Such an approach is
presented in TENENHAUS er al. (1993).
Taking into account the numberof our observations (1 obserVation =, I fishing day), we
buHt. three-way contirigency tables, with two environmental . explanäiory variables
(hereafter noted A and B) arid a salmon catch explainOO variable (noted C). Each eritIy of
the thi-ee:way ta~les was the riumber "ijk. o~ fishi~g days for, which .~"e observed the
combinauon of levels i,} and k for the categoncal vanables A, B and C respectively.
The to13l number of observations for agiven combination AxB (ni) being fixed, cell
counts on the same row of the table are not independent. ThiJs, data can beanalysed ilsing
a multiriomial model (PLACKETI, 1981). A log-linear model was used to estimate the
parameie~ o~ the rriultinomial mode~ an~ the conditiorial pr?ba~ilities Pkij to obSer:v~ the
level k of vanable C under the combmatlOn of level i of vanable A and level} of vanable
8.
Stiuiirig from a minimum model [A.B + C.(A+B)]~ interactions betWeen variables were
successively introduced. We selected the only predictors inducing a sigmficant decreaSe of
the deviance. The deviance (dev) is a statistic allowirig comparison
the likelihood
01' the model wider shidy arid that of the saturiited mOdel (i.e. explaining perfectly the
observed proportions nijk I liij):
.
..
. . . .. .'
dev = -2 Log [~(partial model) / ~ (satilrated model)]
;''v'here <b representS the likelihood (TBo'ENHAUS er eil., 1993).
between
When the numbers of observations are high, the differences between two successive
values of deviance are approxim.itely chi-square distribiJtOO (McCuu.AGH & NELDER,
1989).
•
·,
2.
PARTmONlNG OF VARIABLES INTO LEVELS
a) Catch per unit effort variables
Division ioto faet6r levels \vas made separately tbreach year and for each saißlon ~.
Thus, we could take into accourit both the mean catch level for a giveri year and tne
seasonal variation of aburidance beiween the salmon t}rpes.
( 1) Multi-sea-\..inter salmon
,
.
We assumedthat MSW tish abtindarice
uniformly distiibuted along ihe period of
time corresponding to their maximum recruitment. and that variations in CPUE were
mainly related to environmental factors, paniculafly those under study. Thc: CPUE variable
division into levels was made according to quaniles ("low", "medium", "high", arid "very
high" catch levels. coded as CPUEI to CPUE4).
was
5
(2)
Grilse
Observations on the Adour river as well as on neighbOunng rivers (Nivelle river,
Bidassoa river) lead us to assume that the arrival of giilse at the Adcur eStuar)r mouth
followed a normal distribution aIong their recruitment period. Thus, entry of grilse in the
estuary and CPUE were assumed to follow a normal trend. Deviatioris from that trend were
assumed to be mainly related to environmental factors.
Estimators for mean J.l and standard deviation a of the nonnal distribution were
respectively calculated, for each year, as the median day of the grilse recrilitinent periOd
and the sixth part ofthis recruitment period duration, since iritervaI [Jl-3a; Jl+3a] coniains
99.72% ofthe individuals in a normal distribution (SOKAL & ROHLF, 1969).
We defined a new variable, coded as GRI, as the deviation betweeri the observed CPUE
for a given day and the nonnal value calculated for this same day.
Values of this GRI variable were divided irito levels according to qWirtiles ("low",
"medium", "high", and "very high" catch levels, coded as GRIt to GRI4).
b) Environmentsi variables
Data sets of river flow (RF), tidal coefficient (TC) and wind direction (WO) were
splitted ioto levels with equal numbers (tab. 3) fcr each recruitnient period.
Low (TC1) and high (TC3) tidal coefficient levels correspond to neap tides (TC~5)
and spring tides (TC~90) respectively.
Classes of wind direction were designed to reflect wind iIlf1uence in coricentratmg or
dispersing fresh water in the coastal zone near the river mouth and its contribution to the
mixing of superticiallayers of \vater.
IV.
RESULTS
A.
RECRUIDIENT PATTERN OF DIFFERENT SAL\fON SEA-AGE GROUPS INTIffi FISHERY
By-catch of Atlantic salmon are scarce out of the salmon legal fishing season, even
during the sea larnprey (Petromy=on marmus) in FebruarY. or during the catch periOd of
other marine species in August and September. Moreover, few salmon were caught before
mid-March. For the years under study, transition between mulii-sea;'winier salmon periOd
and grilse period lasted arotind ten days. The recruitment 01' MSW fish begari by IllidMarch and ended during the t1rst days of June. Grilse entered the fishery by the end of
May. Assuming the nonnality ofthe grilse recruitnient pattern, the mode ofthe distribution
occured in the Jast days of June (except in 1988: mid.;june), and the standard deviation
varied between 10 to 16 days.
6
•
8. EmCTS OF HYDROLOGICAL AND CLL\lAl1C FACTORS ON CPuE
.........
'
AIULn-SEA-ft7NTER SALHON SEAS(JN
I.
a) Desci-iptive approach
The CPUE variable had a high contribution to axes 4 and 5, and a smaller one to axes I
arid 2 (tab. 4).
.
Projectioris of variable chi.sses iri factorial plane 1-2 (fig. 2) indicated that favourable
conditions for catches of MSW salmon (CPUE3;.CPUE4) were tides With high coefficient
(TC3) in conjunctiori with high river flows (RF3).
the oppOsite, wCak movements of
sea water (TCI-TC2) and fresh \väter (RF1;.RF2) seemed to induce low or medium catch
levels (CPUEl;.CPUE2). However, the river flow appeared tobe only ä seCoridaiy source
of irifluence: classes of the CPUEvariable were poorly discriminated on axis 3, to which
the RF variable had a strong contnbution (tab. 4).
Associations \vere aloso noticed between offshore winds (WD 1) and the lowest catch
level (CPUEI), and between west-nonh-westerly onshore windS (WD4) arid the highest
catch level (CPUE4).
On
•
b) Quantitative approach
(I)
•
Tidal coefficient and river flow
Interaction betWeen cPilE and tidal coerflcient \wS the only significant one (tab.. 5).
MaXimuni esiimated conditional probability was only 0.535, indicating that the effects of
environmental factors under study were not very strong. Neverthetess. when displayed with
tide levels ärnanged in ascending order, the matrix of estimated conditional probabilities
showed a general trerid: the probability to get "high" or very "high" catch levels increased
\vith increasing tidal coefficient (jig. 3). High river flow actoo as a secondaiy positive
factor (though not significant), panicularly in case of lo\v tidal coefficient (TCI-RF3
combinaiion for example).
.
.
These results appearoo to confirm the descriptive approach by correspOndence anatYsis.
Moreover,they provided a hierarchy among relative inflence of sea water and fresh water
movements.
.
(2)
Tidal coetlicient and wind direction ,
, The deviance analysis of the model including iidal coefficierit (TC) and \'~ind direction
(WD) aS explanatoly variables showed 00 sigruficant effect (tab. 6).
-,
GRJI.SE SEASON
a) DeScriptive approach
Factonal plane 1-2(fig. 4) showed a increase of relative catch level (GRI) \Vith
increasing river flow (RF) arid tidal coefficient (TC). Ho\vever, variable TC only had a low
contributiori to axes 4 arid 5, unlike variable GRI (tab. 7).
Influence 01' wind direction (WO) could be understod as beuer catch (GR13-GRI4)
under windS from west to nonh (WD3-WD4), arid lower catch (GRII) wider winds from
south-south-east to west-söi.ith-west (WD2).
7
.'
b) Quantitative approach
(1)
Tidal coefficient and river flow
Unlike the case of multi-sea-winter salmon, the only significant interacrion (tab. 8) was
that ofriver flow with relative catch index (RF x GRl).
EfTects 01' environmental factors under study on salmon catch were poorly marqked
(maximum estimated conditional probability 0.426). Figure 5, with river flow l~els
arranged in ascending order shows that probability of higher catch levels improved with
increasing river flow, while tidal coefficient plays a minor role.
River flow and wind direcrion
No significant efTect appeared in the analysis of deviance (tab. 9).
(2)
•
V. DISCUSSION
A. USE OF CATCH AND EFFORT DATA
1. ADVANTAGES
The use of catch per unit effort data as an index of abundance ofTers several advantages
when compared to other methods:
• the observation period is the whole drift gillnet fishing season, and lasts over
several months. It covers almost the entire period of Adour salmon return to their horne
nver ~
• the observations consider a large number of fish, unlike studies based on acoustic
or radio-goniometrie traeking ;
• this method does not present the drawback of the delaying effect met in obstacle
passing studies, during which salmon ean wait for favourable conditions for weeks ;
• finally, unlike rod-and-line fishing, net fishing does not hinge on the agressive
behaviour of salmon, but only on his movementS in the estuary.
2.
DRAWBACKS
However, epVE index has its drawbacks:
• drift gill net ooIy takes plaee during low slack water and flood tide, therefore no
observation is possible during the other half of the tidal cycle. Nevenheless, different
studies using tracking showed that movements against tidal currents are rare in coastal and
estuarine waters (STASKO. 1975 ; HAWKlNS er al., 1979 ~ POITER, 1988 ; PRIEDE er a/.,
1988);
• the main problem \..ith epVE index is not being able of distinguishing the part of
the variability 01' the epUE due to fish availability variations from that originating in
catchability variations. This demonstrates the necessity of in silu studies to estimate the
etTect of each factor.
g
,,"
•
B. EmCTS OF HYDROLOGICAL Mn CLTh1AnC FACTORS ON CPuE
The results obtained v.ith the correspondence arialysis (around 60% ofinertia explained
by the first 5 axes) and with the generalized linear model (maximum probabilities lower
than 0.55) indicated that the relations appearing between salmon catch and the stUdied
environriierital varüitioDs were only general trendS. This led to thinking tfuit other factors
atTected this phenomenon.
,
HO\\'ever, for ooth salmon types (MSW and 1SW fish), high catch levels apj>eafed to be
assocüited,,1Ugh river flow and high tidal coefficient, with sea winds playing a less
significanfj5irt. Relative influence of factors varied according to the salmein type.
I. INFLUENCEOFAA-mflOW
as
•
In the review by BANKS (1969), river flow was most frequently mentiomied
a
controlling factor for salmonid anadromoüs migration. Spates are said to iriduce Atlantic
salmon concentration in coastal waters, near the river mou~ the änädieimous migration
taking place during the fall of water after the freshet (Hum-s~i:AN, 1939, 1948 ; HAYES,
1953; HARlUMAN, 1961 ; SWAIN& CHMWION, 1968).
.
Spates induce physical and cheinical rriOdifications of estlllirine water quälity. These
of effect due
modificaiieiris being gerierally simultaneous, determinirig the respeCtive
to each factor results difficult.
As a consequence, contradictory results were obtained when trying to cl.etermine the
fange of river flo\v values favourable to anadromous migration. In the river Coquet.
salmon,appeared to inigrate in tlows suPerior to the, mean avalaibleflow, (ALABASTER,
1970).The situation was opposite, in the river Frome (HELLAWEU et al., 1974).
High nver tlow or a rising of the river flow seemed to have a positive effect on:
• catch abundance (ALABASTER, 1970; STEWART, 1969);
,
• obstacle passing(ALAßASTER, 1970; JENSEN et al., 1986; JONSSON et ai.; 1990);
• inoverrients, as morutored by trackirig techIliques (POTTER, 1988 ; P1UEDE et al.,
1988; WEBB. 1989; WEBB & HAWIcrNS, 1989).
.
However, vanations of river flow are riot sufficient in exPlaining a salffion ascent.
HEllAWELL (1976) showed that river flow variations \"'ere only a secondaly factor, the
riüiiri factof being theseason ofthe year. But this coUld result from nottilkiriginto account
differerice in aburidance, and thus in availability, of the different salmori t)rpes: river flow
conditions showed as most favourable to salmon anadromeius migration correspond to
those observed dUriiig the recruitment period of the most abundant sea-age group.
'. Differences in response could be due to differences between hydrologicälpllttems of
the rivers under study. Moreover, some studies, in particular those using tracking
techßiques. are carried over during short penods only, uriIike other studies lasting over
eritire years. Ranges of available flow and related effects are thus difficult to compare.
EfTects of the Adour nver tlow on salmon catch are more prominent during the grilse
recruitment period ttia11 duririg the MSW salmon recruitßtent period. Differences in
nver flow could ex-plained this obserVation: nver flow is much lower in
available
J une arid J uly than in the beginning of the fishing season. while ranges of tieW coefficient
are similar (lab. 3). Freshwater tlow could be a limiting factor for grilse ascent.
Part
mean
a) Final orientatiön
High nver tlow at the beginning. of sping could erihance hoine river recognitiori by
salmon. As a m~r of fact. the impomince of olfaction iri guiding the salrriori dunng its
return migration has been emphaSized in the hypothesis of oltactive precociouS
9
.'
~
•
impregnation offry or smolt (HARDEN JONES, 1968; HA.S~ 1971 ; HANse.. er al., 1989),
as weIl as in the hypothesis of a innate genetic capacity to recognize specific pheromones
(NORDENG, 1977, 1989; STABELL, 1984).
Yet, experimentS by HANSEN er al. (1993) lerid support to the hypothesis of an active
search of tbeir horne river, appearing not to be based on a genetically fIXed rnemory of
specific pheromones. This hypothesis, elose to that of S~nm er al. (1980), is in contrast
with suggestions by SAILA & SHAPPY (1963) or J At\fON (1990) on return migration being a
random trial and error process.
.
When in coastal waters elose to their horne river, sahnon would .look for visual or
olfactive guidarice (HARDEN JONES, 1968 ; HASLER & SCHOLZ, 1983 ; HANsEN er al.,
(993). The~ in the estuary, they would wait for conditions propitious to their ascent
(JONSSON
er al., 1990).
By greatly increasing presence of characteristical olfactive elues in coastal waters,
spates could contribute to attracting salrnon towards their horne river mouth.
b) Anadromous migration snd osmoregulatlon
When changing from salt water to fresh water, salrnon tindergoes irnportant
modifications of its osmoregulation proeesses. This requires a progressive adaptation,
made easier by a low saliriity gradient in waters close to tbe estuary mouth. The mixing of
sea and river waters during spring tides arid spates probably coniributes to ci better
transition of fish between the two media. This häs also been observed for the alIis shad
(Alosa alosa) in the same estuary (PROUZET er al., 1994a).
c) TemperatUre snd dissolved gases
An increased river tlow. particularly dunng the spates ofthe Gaves riverS(tributaries of
the Adour river runriing down from the Pyrenees moutain range), can induce a decrease in
water temperature in and around the river mou~ as weH as ari increase of dissolved
oxygen concentration.
A decrease of temperatlire bäs a Pösitive etTect on salinon ascent, as observed dUring
artificial freshets bv MENZIES (1939) and SruART (1962), arid in uricontrolled conditions
byPOTTER (1988), PRrnnE er al. (1988) arid ALABASTER. (1990).
On the other side, hypoxia can inhibate sahnon ritovements in äri estuary. In anaerobic
conditions, salrnon muscles produce lactic acid, iriducing a decreaSe in pH, arid a
subsequent reductton of the blOOd oxygen can)ing capacity. Dissolved ox)rgen
requirements defined as optimal for Atlantic sahnon Valy greiltly (ALABAsTER. & GoUGH,
1986 ; PRlEDE er ai., 1988).
A freshet cari thus coritribute to attiacting salmon by improving estiiarine water qi1alit},
particularly by increasing the dissolved oxygen concentration (CURRAN & HENDERSON,
1988 ; PRlEDE er al., 1988).
In the Adour esruarY, such an improvement could occur in June or July, during the
grilse recruitrnent period. As a matter of fact, water quality could play ä.n important Part
during this period, becailse of the generaIly low river flow and the rise iri temperaiU.re in
coastal waters (uj> to 22-23°C). However, by lack of dissolved oxygeri meaSuremerits, \ve
were not able to draw ci conclusion on this particular point.
10
•
..
..,
INFLUENCE OF TlDAL FLOW
'~
•
,.'
....
, According to MENms (1931) and HAYES d953), spates coritnbute to salmon ascent in
thc nver zone under tidal influence. MlLLs (1968) suggested thai they have no signifieant
etTeet outside tidal waters.
Tracking experiments in coastal or estuaririe ....Clters (STASKO, 1975 ; HAWKINS et uf.,
1979 ; POTTER, ,1988 ; PRIEDE et uf., 1988) showed salrrion movemelus with tidal currents,
in a way named tidaf e.xcurSlOn behaviour by SOLO~{QN & POTIER (1988). AScents have
been said to be stimulated by high tidal eonditions (SWAIN & CHAMPION, 1968) ~lnd by
iriereasing tidal amplitUde (HAYES, 1953). Ori the opposite, in the Luneesiuary, STEWART
(1973) observed that sahnon eateh and ascents increased when tidal amplitUde deereased.
At the mouth of the Adour esiuary, tidal amplitude has a prevalerit influence on sahnori
eateh dUririg spring months. During this perio~ river flow is not a liminng faetor änd the
strong lides make upstream migi'ation easier for salmon.
.
.
Moreover. these tides allow a gi'eater overlapping of sea and rivefi waters, favoirrable to
;he progressive adaptation of amphihaline fish to salinity chiiriges.
.3.
IIVFLUENCE OF W1ND
rnfluenee of\vind on salm<>n tateh i5 known in:in empincal Way by fishermen (STASKO
up river had a positive etTeci
on salm<>n ascent in the Sevem river. Studies by HUNTSMAN (1939) arid HAYES (1953)
sho\ved that onshore winds eoneentrated salmon near the river mouth and in the estullry
and affeeted nver aScent.
Yet wind seems to have only a rilinor iritluenee on salrrion catch, when coinpared to
river flow or tide conditions. Onshore \vinds contribute to freshwater concentration along
the eoast. But in narrowchannels like the, one at the mouth of the Adou..r estu3.ry, the sea
,..indS create swell etTects that make driftiu:t fishing difficult, paiticularly with the kind of
small fishing hoats Used in tl1at zone.
et al., 1973). DAY (1887) observed thai strang winds blowing
•
C. FURTHER RESEARCH
a
Designing contingency tables with more than 1\\'0 explanatoryvaiiables would require
lärge increase ofthe number of observations. to avoid table entriesclose or equal to zero.
rn the Adour esiUarY, each salmon tishing year comprises around 160legaIly open
tishing days (trom end of Februar)r to end of July)., Yet, in Febniary arid March, arid
sometimes weIl into April, commercial fishing etTort is rather ilimed at sealarnprey, in a
different river zone, further upstream (PROUZET et clf., 1994b). This reduees the mimber of
salillOn tishing days with driftnet at the möuth of the estuary. Moreover. obServations on
salmori fishing
divided irito two periods cörrespOnding tö salmon types.
The possibilitY of a sign{ficani augmentation of the number of observations can only be
contemplated on a lang tenn.
are
11
:
I·
REFERENCES
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A..'l'O~., 1984. - Atlantie salmon seale reading. Report 01 the Atlantie salmon seale reading
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fu"lON., 1991. - Plan du port de Bayonne. Direction Departementale de IEquipement des
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& B. JONSSON (Ed.), Sa/mon migration and distribution symposium. School of Fisheries,
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HANSEN L.P., N. JONSSON & B. JONSSON, 1993. - Oceanic migration in homing Atlantic
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HAsLER AD. & AT., SCHOU, 1983. - Olfactory Imprinting and Homing in Salmon.
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HELLAWELL J.M., 1976. - River management and the migrato(y behaviour of saLmonids.
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AG., 1939. - Salmon tor angling in the Margaree' River. Bul/. Fish Res. Bd
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AG., 1948. - Fteshets and fish. Trans. Am. Fish. Soc., 75, 257-266.
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JAMON M., 1990. - A reassessment of the random hypothesis in the ocean migration of
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JENSEN A.l, T.G. HEGGBERGET & B.O. JOHNSEN, 1986. - Upstream migration of adult
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LESBORDES Ch., 1979. - Le regime de la maree clans l'Adour. Bulletin de la Soeiete de
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MCCULLAGH P. & J.A. NELDER, 1989. - Generali=ed Linear Atfodels. Monographs on
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MENZIES W.J.M., 1939. - In: MOULTON F.R. (Ed), Conferenee on salmon problems.
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p.
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south-we~t
PRlEDE LG., J.F. De L.G. SOLDE, J.E. NOIT, K.T. O'GRADY & D. CRAGG-HINE, 1988. Behaviour of adult Atlantic salmon, Salmo salar L., in the estuary of the River Ribble in
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14
....
•
. ,
PROUZET P., J.-P. MARTINET & J. BADIA.. 1994 a. - Caracterisation biologique et variations
des captures de la grande alose (Alosa a/osa) par unite d'effort SuT le fleuve Adour,
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•
SMITI-I G.W., 1990. - The relationship between river flow and net catches of salmon
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15
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Rep.. 40, 41 p.
......
•
16
•
Table 1 - Comparisons of river falls near tbe moutbs. and ebb aod Bow tide speeds in tbe Adour. Loire
and Garonne estuaries (ANON•• 1981 ; LESBORDES, 1979).
river fall
tidal flow speed
(m/s)
(crn/km)
low slack water
Adour
3.5
Garonne
Loire
•
2
high slack water
6-8
10-12
5
15
4-6
6-10
Table 2 - ~umber of salmon caugbt by the Adour river drift gillnet fisheries. by year and by sea-.gegroup. The numben of fish used for age determination is given between parentbesis.
1988
1990
1991
1992
One-sea-winter salmon
551 (77)
1027 (209)
726 (163)
1506 (126)
Multi-sea-winter salmon
1748 (435)
473 (147)
374 (74)
924 (141)
Table 3 - Definition and extreme values of hydrologieal and climatie variable levels.
•
,'"
variable
code
daily
nver tlow
(m 3/s)
RF
tidal
coefficient
(hundredths)
TC
wind
direction
(tens 01'
.mgle degrees)
levels
RFI:
RF2:
RF3:
TC 1:
\\in
Tc2:
rc3:
WDI:
\\n2:
I,l,n3:
\\n4:
low
moyen
high
low
moyen
high
N to SSE
SSEto WSW
WtoWNW
WNWtoN
mu1ti-sea-winter salmon
lowest
highest
102.3
245.2
274.6
one-sea-winter salmon
lowest
highest
245.1
274.5
1065.4
55.4
102.8
210.6
102.7
210.5
890.7
78
S9
77
107
38
61
75
60
74
107
0
16
15
26
0
16
31
30
35
15
26
30
35
'.,
.)-
60
:7
27
31
17
Table 4 - MulMes-winter salmOB. Eigen values associated with factorial nes, relative contributions of
variables to factorial nes, and coefficieDt5 of variable dasses in the linear equatioDl of the nes.
axis 1
axis2
axis3
axis4
axis5
0.36
0.32
0.30
0.27
0.26
CPL'E
19.1
25.7
24.6
30.6
16.7
46.5
12.7
24.1
59.2
7.6
26.0
7.2
0.7
4.3
51.5
43.5
14.5
19.2
22.6
43.7
RFI
RF2
RF3
-0.463
-0.757
1.230
-0.960
-0.057
1.037
-1.505
2.120
-0.584
-0.006
0.204
-0.198
0.707
0.338
-1.059
Tcl
Tc2
Tc3
-0.585
-0.868
1.390
1.816
-1.541
-0.162
-0.723
0.031
0.635
0.031
-0.520
0.482
-0.615
-0.656
1.209
INDI
\VD2
\VD3
WD4
-1.719
0.696
0.669
0.192
0.385
-1.050
0.743
0.137
-1.347
-0.398
0.518
1.545
-0.025
0.925
1.098
-2.717
0.397
0.915
-1.545
0.138
epUEI
cPUE2
CPUE3
CPUE4
-1.720
-0.136
0.238
1.429
0.973
-1.489
0.815
-0.257
0.891
0.097
-0.500
-0.378
1.657
-0.148
-1.943
0.655
0.759
-2.241
1.114
0.359
Eigen values
RF
TC
relative
contributions
\\'0
(%)
coefficients
in the
linear
equation
•
Table 5 - Multi-sea-winter salmon. Fitness statistics for a sequence of models. DeviaDc:e (dev), degrees
of freedom (df), variation of deviance (Delev), variation of degrea of fl'ftdom (DelI). XJ statistic (i,a:
significant at 0.05 ; DS: not significant••
predietors
dev
df
minimum model
30.85
24
+ RF.CPUE
::.44
18
-TC.CPUE
9.92
12
..... RF.TC.C·
:~
Ddev
Ddf
XZ
8.41
6
ns
12.52
6
••
9.23
12
os
0
18
•
Table 6 - Multi-sea-winter salmon. Fitness statistics for a sequenc:e of models. Devianc:e (dev), degrees
of freedom (df), variation of devianc:e (Ddev), variation of degrees offreedom (Ddf), Xl statistic (ns: not
signiticant).
predietors
dev
df
minimum model
:6.67
33
... TC.CPUE
16.41
27
... WD.CPUE
13.30
18
.,. TC.\\iD.CPUE
0
Ddev
Ddf
X2
iO.26
6
ns
3.11
9
os
13.30
18
ns
Table 7 - One-sea-winter salmon. Eigen values assoc:iated witb fac:torial axes, relative contributions of
variables to factorial axes, and coeftic:ients of variable dasses in tbe ÜDear equatioDs of tbe axes.
axis I
axis2
axis3
axis4
axis5
0.34
0.32
0.31
0.27
0.25
RF
35.2
TC
GRl
21.0
21.2
22.7
10.0
37.2
21.0
31.8
30.1
23.3
42.0
4.6
14.6
2.1
31.1
52.1
2.3
32.3
61.3
RFI
RF2
RF3
0.081
-1.496
1.413
0.668
0.169
-0.848
-1.530
0.921
0.632
1.066
-0.661
-0.421
0.003
-0.503
0.500
Tcl
Tc2
Tc3
0.257
-1.181
1.035
1.688
-0.765
-1001
0.724
0.574
-1.434
-0.404
0.237
0.180
0.403
-0.301
-0.108.
WD]
WD2
-0.539
0.953
0.856
-1.181
0907
-0.976
0.925
-0.866
-2.164
0.121
1.422
0.380
-0.213
-1.871
0.904
0.886
-0.734
1.733
-1.271
0.412
-0025
-0.033
-1.278
1.386
1.877
-0.112
-1.118
-0.565
-0.238
0.414
-0.572
0.422
-1.296
2.365
-1.080
0.077
0.823
1.966
-0.445
-2.270
Eigen values
relative
contributions
(%)
•
coerlicients
in the
linear
equation
\VD
\vD3
\1,1)4
(,RIl
tiRI2
(JRIJ
(iRl4
4.2
"
19
Table 8 - OnMU-winter salmon. Fitness statistics ror a sequeace of models. DeviaDce (dev), degrees or
freedom (dt), variation of deviance (Delev), variation of degrees or freedom (DelI), Xl statistic (*-:
!igniflCant at 0.05 ; ns: not significant).
Table 9 - One-sea-winter salmon. Fitness statistics for a sequence or models. Deviance (dev), degrees of
freedom (dt), variation of dmance (Ddev), variation of degrees of freedom (Ddf), Xl statistic (ni: not
signiflCaDt).
predictors
dev
df
minimum model
29.77
33
+ RF.GRl
24.66
27
-+-WD.GRI
18.21
18
Ddev
Ddf
5.11
6
ns
6.45
9
ns
18.21
18
ns
•
+RF.WD.GRI
.~
•
20
Fipre 1. Situation map. Main rinn: Adour (Ad). Bid.... (Bi). Gave d'Oloron (GO). Gave de Pa. (GP).
Loire (1.0), NiveUe (Ni), Nive (Nv). [stuarille salmon drift giUaet (liberia (N). Im,..... daJIII (0).
Tidal saliIIe limit (I). Tidal dyaamic limit (11).
N
Ad\
iI
i
GP
I
I .:
I'\Bi - ;
I
: '
\
N"1
i
......
,I
('
!.;~~
1- I
~~
\,
\,
(
,
i
I
o
20 km
\
-..-,
'----- I,
21
'---..,
r-~~~---
------- - -----
..-e i-2. CIaI'Ia 01'
Figure 2. Multi-sea-winter sa1moo. Distribution 01' cJ.IISsa 01' variables iD radorW
inc..wing levd 01' c.3tcb per unheffort (CPUEl to CPUE4), iDmuiag river
(RF1 to
tidal coefficient (TCI to TO), ud dWes orWind diftetioa (WDl to W»4).
flOw
m), iDcreuing
C13~)
axis 2
h,
!
a
TC1
-1
Figure 3. Multi-su-winter sa.Imoa. Estimattel conditioUl probabilida to gCl leVd k ot catdl
effort (CPUE) ror levd ; 01' river ßow (RF) and levdj or tidaI ~t ([C).
per aan
Cpuc:
1
2
4
Tc
RF
3
'"J
0
3
2
1
?
.....
3
':J
-..
2
0.30
-.
.)
1
0.25
1
:]
'1
2
1
3
1
3
..
•
0.20
:~:::::::;r..: .'
I··········..·,·
.1
,
ffmfi!!img
.
0.15
..t
0.10
11=
~II
i;;;•••••••".
..............
~
"::
I
l..---'-_---...I l . . -_ _---'
,
Il·.li!~ ... !'!~~~l&!'
~.!'
•••".,'r
_
0.05
Fipre 4. One-sea-wintersalmon. Distribution of cWSa, öf,variabks in factoria) space 1-1. CIUSCS or
ioclU!ing tatcb per unit eft'ort (GRIt to GRl4), river flow (RFt to RF3), tidal cOeft"lCieot (TCt to TC3),
and daSseS orWiod dii"fCtion (WDI to WD4).
.
axis 2
(13~)
..
r
1
DTC-t
A
A
wo~
0
o ~F1
1,,111>3
RF2.
axes 1 (l4~)
-1
G-Ri 4-
e
WbZ'
~
o R F3
TC~
(~R.i .3
-1
Figure 5. One-sea-winter saimoo. Estimatect cooditional protJabUities to get level k or tatcb
(GRI) for level i ormer flow (RF) ud levelj orüdal coeffideat (Tq.
G-R
RF
•
TC
1
?
"'-
per unit eft'ort
I
3
4
3
'. 0,550,50
"'-
2
1
3
'i-:::>
'::J
.--
0,30
2
0,25
1
1
3
1
?"'-
1
1
3
:3
3
'::J
'"
0,45
0,40
0,35
0,20
0,15
....
0,10
0,05
23