Universidade de Aveiro Departamento de Biologia, 2008
Juan Pablo
Zwolinski
Estimação acústica da distribuição e
abundância de sardinha Sardina
Universidade de Aveiro Departamento de Biologia, 2008
Juan Pablo
Zwolinski
Estimação acústica da distribuição e
abundância de sardinha Sardina
pilchardus
Dissertação apresentada à Universidade de Aveiro para cumprimento dos requisitos necessários à obtenção do grau de Doutor em Biologia, realizada sob a orientação científica do Dr. Victor Manuel dos Santos Quintino, Profes-sor Auxiliar do Departamento de Biologia da Universidade de Aveiro e co-orientação do Dr. Georgios Stratoudakis, Investigador Auxiliar da Unidade de Recursos Marinhos e Sustentabilidade do Instituto Nacional de Recursos Biológi-cos (IPIMAR-INRB/IP)
o júri
presidente Prof. Dr. Ana Maria Vieira da Silva Viana Cavaleiro
Professora Catedrática do Departamento de Química da Univer-sidade de Aveiro
Prof. Dr. Amadeu Mortágua Velho da Maia Soares Professor Catedrático do Departamento de Biologia da Universi-dade de Aveiro
Prof. Dr. Ana Maria de Jesus Rodrigues
Professora Auxiliar do Departamento de Biologia da Universi-dade de Aveiro
Dr. Paul G. Fernandes
Principal Scientifc Officer, FRS Marine Laboratory, Aberdeen Prof. Dr. Pedro Conte de Barros
Professor Auxiliar da Faculdade de Ciências do Mar e do Ambi-ente da Universidade do Algarve
Prof. Dr. Victor Manuel dos Santos Quintino
Professor Auxiliar do Departamento de Biologia da Universidade de Aveiro (orientador)
Dr. Gergious (Yorgos) Stratoudakis
Investigador Auxiliar, Instituto Nacional de Recursos Biológicos / IPIMAR (orientador)
agradecimentos Quero agradecer em primeiro lugar à direcção do Instituto Nacional de Recursos Biológicos / IPIMAR por me ter dado as condições necessárias para a realização do trabalho que deu origem a este Tese. Um agradecimento especial vai para a Dr. Graça Pestana, pela sug-estão do tema e pelo apoio dado ao longo dos últimos cinco anos. Quero ainda agradecer a todos os responsáveis e colaboradores dos projectos PELASSES (EU Study 080/99), PELAGICOS e PNAB-DCR, pela realização das campanhas acústicas que suportaram os estudos apresentados nesta Tese. Quero igualmente agradecer à Fundação para a Ciência e Tecnologia pelo financiamento que me foi concedido. Estou grato ao Prof. Victor Quintino, orientador na Universidade de Aveiro, por ter apoiado desde logo a minha candidatura e pelo empenho posto para que o trabalho fosse levado a bom término.
Um agradecimento especial vai para o Yorgos pelo empenho que pôs no meu desenvolvimento científico e na amizade que demonstrou desde que comecei o meu trabalho no IPIMAR.
Quero ainda agradecer ao Dr. Paul Fernandes pela orientação valiosa e assertiva que me despendeu em pontos chave do trabalho.
Estou grato ao Dr. Vítor Marques e ao Alexandre Morais pelos trabalhos realizados antes de mim e que serviram de base para o meu estudo. Estou grato ao Dr. Paulo Oliveira e Evan Mason pelo acompanhamento e colaboração no campo da Oceanografia.
Quero ainda agradecer a todos os amigos e colegas que de alguma forma contribuíram para a conclusão deste trabalho. Em particular e sem ordem de importância, quero agradecer ao Alberto Murta, Patrí-cia Gonçalves, Susana Garrido, Pedro Costa, Rui Rosa, Laura Wise e Maria Manuel Angélico.
Toda a análise estatística e a programação utilizadas nesta Tese foram feitas no software livre R. Quero por isso fazer um agradecimento es-pecial a toda a comunidade de eses-pecialistas que desenvolvem e man-têm R como uma das melhores ferramentas estatísticas actualmente disponíveis.
Finalmente, quero agradecer à Gabriela e à minha família, pelo ânimo, pelo incentivo desinteressado e pelo apoio incondicional que põem em
A natureza proporcionou-nos um espírito curioso. Consciente da sua arte e beleza, fez-nos espectadores dos grandes espectáculos das coisas. Teria trabalhado em vão se expusesse à
solidão essas obras tão grandes e de variada beleza.
Ela quis ser contemplada e não apenas olhada. Não se limitou a erguer o homem, mas para que ele pudesse acompanhar os astros e pudesse voltar o rosto observando tudo o que gira,
fez-lhe a cabeça elevada e colocou-a sobre o pescoço flexível.
Ainda não vimos tudo o que existe, a inteligência abre-nos a via da investigação e lança os alicerces para que a pesquisa se desenvolva dos assuntos claros aos obscuros e produza algo mais antigo que o próprio mundo: de onde teriam saído os astros, qual seria o estado
do universo antes das suas várias partes se separarem, qual a razão que conduziu essas coisas confusas e ocultas, quem terá indicado o lugar de cada uma delas.
(...)
O nosso pensamento não fica satisfeito apenas com o conhecimento daquilo que vê; deseja saber sobre aquilo que não é visível. O ser destinado a investigar essas coisas tem muito pouco tempo; mesmo que reserve o tempo todo para si, que não deixe escapar nada, que chegue até à idade avançada sem que a fortuna lhe retire o que a natureza lhe deu, mesmo
assim o homem é demasiado mortal para conquistar o conhecimento dos imortais.
Palavras chave: acústica submarina, habitat, ciclos circadianos, cardumes, mod-elos aditivos generalizados
Resumo Esta dissertação integra informação sobre a ecologia, di-stribuição e abundância de sardinha (Sardina pilchardus) na costa continental Portuguesa e Golfo de Cádiz com o recurso a métodos acústicos. O material de estudo foi recolhido nas campanhas acústicas levadas a cabo pelo Instituto Nacional de Recursos Biológicos/IPIMAR entre 2000 e 2007, destinadas à monitorização do stock Ibero-Atlântico de sardinha. A dis-sertação é constituída por 4 trabalhos que visam aprofundar o conhecimento dos factores ambientais e biológicos que influen-ciam a distribuição da sardinha e consequentemente a sua ava-liação directa através do rastreio acústico. No primeiro estudo mostra-se que a típica distribuição costeira da sardinha está rela-cionada com a presença de massas de água menos salinas, mais frias e ricas em fitoplâncton e que a sardinha responde às mu-danças sazonais das massas de água na plataforma continen-tal com mudanças na sua distribuição. No segundo e terceiro trabalhos estudou-se o comportamento da sardinha em escalas que normalmente não são exploradas durante as campanhas de estimação de abundância. Nestes trabalhos revela-se como a distribuição da sardinha em pequena escala é governada pelo comportamento social, em particular a reprodução e o compor-tamento agregativo em cardumes. O último trabalho debruçou-se sobre o cálculo da abundância, empregando metodologias de modelação recentes que mostraram ser capazes de gerar esti-mativas de abundância não enviesadas e com variâncias inferi-ores às obtidas através de métodos tradicionais.
Keywords: underwater acoustics, habitat, diel cycles, schools, generalized additive models
Abstract This dissertation compiles information from acoustic surveys on ecology, distribution and abundance of sardine (Sardina
pilchardus) off the continental coast of Portugal and the Gulf
of Cadiz. The information was collected during routine acous-tic surveys for the monitoring of the Atlanto-Iberian sardine stock performed by the Portuguese Fisheries Research Institute (INRB/IPIMAR) between 2000 and 2007. The dissertation is based on four independent studies which were aimed at impro-ving the knowledge of the environmental and biological factors that affect sardine distribution and consequently estimates of abu-ndance by acoustic surveys. In the first study, the typical coastal distribution of sardine is described and the relationship between sardine presence in the cooler, less saline and chlorophyll rich waters is investigated; When the mesoscale water circulation modified the distribution of the coastal water masses, sardine responded accordingly by changing its distribution. The second and third studies focus on processes usually disregarded during routine surveying. It is shown that the small scale distribution of sardine is dictated by social behaviour, in particular spawning and schooling. The last study addresses the estimation of abun-dance: a recent statistical methodology is apllied to a large time series of acoustic surveys, providing unbiased and more precise estimates than traditional methods.
Contents
1 General Introduction 1
2 Potential habitat 15
2.1 Introduction . . . 17
2.2 Material and Methods . . . 19
2.3 Results . . . 23
2.4 Discussion . . . 37
3 Spawning behaviour 41 3.1 Introduction . . . 43
3.2 Material and Methods . . . 44
3.3 Results . . . 47
3.4 Discussion . . . 58
4 Diel Schooling behaviour 61 4.1 Introduction . . . 63
4.2 Material and Methods . . . 64
4.3 Results . . . 69
5.2 Material and Methods . . . 87
5.2.1 Acoustical and biological sampling . . . 87
5.2.2 Data analysis . . . 90
5.3 Results . . . 96
5.4 Discussion . . . 106
6 Conclusions 111
List of Figures
1.1 Schematic of an echosounder. The sequence of transmitted pulses gener-ates echotraces from fish schools and the seabed bellow. The echoes are received, amplified and displayed as an echogram. Adapted from Simmonds
and MacLennan (2005). . . 3
1.2 An echogram showing the bottom echo (the dark thick descending line), echoes from fish schools (the red/yellow/green echo-traces close to the bot-tom) and echoes from plankton (diffuse light blue signal in the upper part of the image). Horizontal spacing of the vertical lines is 0.5 nautical miles, and the numbers at the intercepts mark the vertical distance to the transducer in
metres. . . 4
1.3 ICES areas and subareas off Southwestern Europe. The Atlanto-Iberian
sar-dine stock is contained within the sub-areas VIIIc and IXa. . . 9
2.1 Survey region. The full and dashed lines represent the coast and the 200 m isobath contour respectively. The dots along parallel lines mark the acoustic
samples (EDSUs) along the transects. . . 20
2.2 Pooled distribution of acoustic energy NASC due to sardine in a) linear scale and b) logarithmic scale of positive values. Panels c) and d) illustrate re-spectively latitudinal and bathymetric distribution of samples with sardine presence by season, together with the survey total latitudinal and depth dis-tributions range. . . 24 2.3 Echogram appearance of Diffuse Backscattering Targets - DBTs (a) and
sea-sonal acoustic intensities of DBTs by EDSU in relation to depth during the
trends calculated by a loess smoother. . . 26
2.5 Quotient plots of sardine presence against environmental variables. Dashed lines limit the confidence envelope for quotients arising from random
distri-butions. . . 28
2.6 Quotient plot of sardine presence against standardized environmental vari-ables. Dashed lines limit the confidence envelope for quotient arising from
random distributions. . . 28
2.7 Standardized semivariograms of sardine presences vs GAMs deviance
resid-uals for the spring model. . . 31
2.8 Quotient plot of sardine presences as a function of the predicted values for
the spring 2006 data using a model with all surveys combined. . . 32
2.9 Fitted probability of sardine presence based on the spring and autumn GAMs
in relation to depth and region. All survey data points are included. The
superimposed line is a smoother capturing the mean trend. . . 33
2.10 Contour of sardine presence probability according to the multi-annual
mod-els applied to the surveys of the year 2000. . . 34
2.11 Contour of sardine presence probability according to the multi-annual
mod-els applied to the surveys of the year 2001. . . 35
2.12 Contour of sardine presence probability according to the multi-annual
3.1 Acoustic survey day transects extending from the Gulf of Cadiz (Spain, to the east of the Guadiana River) to the western part of the Algarve in Portugal (west of the Guadiana River) and small sampling events. Crosses correspond to 1 nmi EDSU during day sampling, the two clusters of circles correspond to 1 nmi CUFES and EDSU during the small scale survey. In both cases, small scale sampling was conducted from the east to the west starting roughly at
18:00 and lasting for approximately 12 hours. . . 45
3.2 Sea surface temperature map of 14 November 2001 for Cadiz (on the top) and Lagos (bottom) area. The dots represent the CUFES samples and arrows indicate the displacements of thermal structures observed between
succes-sive images from 13 and 14 November. . . 48
3.3 Egg distribution in space and depth in the two sampling regions. Circle diameters are proportional to the absolute maximum observation (259 eggs
m−3) which was obtained in Lagos and “x” represents CUFES samples with
no eggs. . . 49
3.4 Egg distribution in Cadiz and Lagos separated by daily cohorts. Day 0 cohort is represented by empty circles and Day 1 by gray circles. Circle diameters are proportional to the absolute maximum egg density observation. Samples
with no egg presence are not shown. . . 50
3.5 Sample semivariograms of egg data with fitted model. All models are
Gaus-sian semivariograms, apart from Day 0 in Cadiz that is a power model. . . . 51
3.6 Egg density contour separated by daily cohorts superimposed on water
den-sity anomaly calculated from in situ CUFES data. . . . 52
3.7 Comparison of day and night sampling of eggs (left) and sardine echoes (right) in Lagos. In the egg plot, circles represent the Day o cohort during the night (open circles) and on the following day (full gray circles). “X” point the samples with no eggs during the day and the dots show the nill samples during the night. In the NASC plot, empty circles represent night sardine NASC and full gray circles day-time sardine NASC. “X” represent EDSU with no sardine energy and the dots their nightly counterparts. Circle
local values. . . 55 3.9 Daily pattern of sardine schooling from selected echograms in the Lagos site
at 20:30 (top) and 9:30 (bottom). Vertical lines indicate 1 nmi. Sunrise and
sunset occurred at 7:09 and 17:23 respectively. . . 56
4.1 (a) The Portuguese coast showing the locations of the three study sites for day/night sampling; latitude and longitude are expressed in decimal form with negative values indicating longitudinal westings. The dense line repre-sents the coast line and the dashed lines is the 200 m isobath contour. Ex-panded view of (b) the northern, (c) western and (d) southern sites, with the
locations of the transects and nearest trawl hauls superimposed. . . 65
4.2 Boxplot distribution of school Mean Volume Backscattering Strengths (MVBS),
area, elongation, fractal dimension, minimum altitude and NASC relative to 1 nmi of schools yielding 95% of the cumulative schools backscatter in the northern and western sites during day and night (n=86 and 51 respectively). With the exception of log (NASC) all mean values at night were significantly
different from those by day. . . 71
4.3 Echograms at different times of the day. Sardine echograms at the northern
site at (A) 7:50 and (B) 22:40. Two consecutive passages at the western site at (C) 20:35 and (D) 20:51 show schools expanding and descending at dusk. Fish schools close to the seabed at the southern site before sunrise at (E) 6:00
and pelagic schools at the same site after sunrise at (F) 8:30. . . 73
4.4 Boxplot distribution of school Mean Volume Backscattering Strengths (MVBS),
area, elongation, fractal dimension, minimum altitude and NASC relative to 1 nmi of schools yielding 95% of the cumulative schools backscatter in the southern site during day and night (n=258 and 150 respectively). With the exception of log (NASC) all mean values at night were significantly different
4.5 Evolution of school descriptors during daylight transition periods. Local time of sunset (SS), sunrise (SR), civil (CT) and nautical (NT) twilight limit are shown. With a clear sky, the civil twilights mark day/night transitions and a total absence of sunlight occurs within the nautical twilights. Note that no sampling was conducted between 23:30 and 05:30. The trend line is the result of a loess fit, with a span of 0.5 and weights proportional to school
log(NASC). Circle diameters are proportional to the school’s log(NASC). . 75
4.6 Evolution of school descriptors along one daily cycle at the southern site. The vertical line marks the local civil twilight (CT) defining day/night tran-sition. The trend line is the result of a loess fit, with a span of 0.5 and weights proportional to log(NASC). Circle diameters are proportional to the school’s
log(NASC). . . 77
5.1 Map of the survey area, extending from the northern Portuguese border to Cape Trafalgar in the Gulf of Cadiz. Full line represents the coast line, dashed line marks the 200 m depth contour and the parallel lines represent the acoustic transects. The boundaries between strata occur at the Nazaré
Canyon and Cape São Vicente. . . 88
5.2 Building a NASC surface: (a) raw data superimposed on the trimmed pre-diction grid; “x” mark zero NASC and circles are proportional to square root values. Panels (b) and (c) show respectively the predicted presence and acoustic density surface. Panel (d) shows the mean NASC surface obtained by the product of surfaces (b) and (c). NASC contours are in square root scale. 92 5.3 Schematic representation of the GAM and trawl allocation procedure to
esti-mate numbers and biomass per length class with respective confidence inter-vals. The process starts at the top of the scheme with the scrutinized sardine
NASC and the length distribution from trawl samples. . . 95
5.4 Model diagnostics for the autumn 2001 North model showing a good fit (up-per graphs) and spring 2005 North model in which spatial correlation was still visible in the residuals (lower graphs). From the left to the right: stan-dardized semivariogram (empirical and fitted theoretical) of log-transformed data, standardized semivariogram of deviance residuals and histogram of de-viance residuals. . . 99
retical semivariogram (pure nugget effect). . . 100 5.6 Comparison of the modelling and non-modelling estimates for: (a) area of
occupancy, estimated by multiplying the proportion of positive samples and total grid area (x-axis) versus GAM estimated area (y-axis) and (b) total sardine backscattering area estimated as the product of the arithmetic mean and grid area (x-axis) versus GAM estimated backscattering area (y-axis). The solid line is the 1:1 line. . . 100
5.7 Representation of the quantile-based 95% confidence intervals for the total acoustic energy obtained from GAMs in (a) logarithmic and (b) linear scales shown as boxplots. The bounding-lines show the confidence intervals for the mean-based estimator assuming iid data after equation 5.2. . . 101
5.8 Prediction grid from the autumn 2001 northern stratum divided in the regions
of influence of the 8 trawl stations. The corresponding length frequency distributions are represented in the histograms. . . 102
5.9 Variability of the length frequency distribution of the estimated population
due to trawl sampling at the northern stratum autumn 2001 survey. Panel (a) shows the the length distribution using all available trawls and (b) through (i) show the results excluding one trawl at a time. This exercise revealed that trawl number 2 in Figure 5.8 by being in a hot-spot of acoustic density (Figure 5.2) was the most influential trawl to the estimated length frequency distribution. . . 104 5.10 Length frequency distribution of the estimated population at the northern
stratum autumn 2000 survey in (a) numbers and (b) biomass. The thick con-tinuous lines represent the routine estimate for assessment. The doted lines mark the confidence boundaries accounting for acoustic and trawl sampling error, constructed by joining the 99% quantiles for each individual 0.5 cm length class and the dashed lines represents the median of the same distribu-tion. . . 105
6.1 Representação de uma onda sonora contínua no intervalo de 1 s. A onda sinusoidal representa a pressão relativa ao longo dos 5 ciclos. Cada ciclo inicia-se e termina à pressão ambiente (indicada pela linha horizontal) e in-clui um máximo e um mínimo consecutivos. Retirado de (Johannesson and Mitson, 1983). . . 138 6.2 Velocidade do som na água em função da temperatura e salinidade. Retirado
de (Johannesson and Mitson, 1983) . . . 140 6.3 Um ecograma mostrando o eco do fundo (a linha escura descendente), ecos
de cardumes de peixes (as marcas vermelhas, amarelas e verdes perto do fundo) e ecos de plâncton (marcas difusas na parte superior da imagem). O espaçamento horizontal das linhas verticais é de 0.5 milhas náuticas e os valores nas intercepções das linhas verticais e horizontais marcam a distância ao transdutor em metros. A cor dos pixeis está indexada a uma escala da intensidade do eco. . . 142
List of Tables
2.1 Basic statistics of the environmental variables obtained by the CUFES sam-pler. Temperature is given in C, Salinity in psu and fluorescence in mV. N is the number of EDSUs. Coefficient (cv) of variation calculated as sample standard deviation divided by sample mean. 1st and 3rd Q are the respective
quartiles of the sample distribution. NA stands for not-available. . . 27
2.2 Loadings from the Principal Component Analysis applied to the standardized
environmental data matrix. . . 29
2.3 Summary of GLM coefficients by season and total % dev.explained. All coefficients were statistically significant (p<0.01). . . . 30 4.1 Summary of the trawl haul composition by species used for species
iden-tification and obtaining biological information at the three sites. “Trawl” refers to the trawl haul name which can be cross referenced with the locations shown in Figure 4.1. Species are sardine (Sardina pilchardus), bogue (Boops
boops), chub mackerel (Scomber japonicus), mackerel (Scomber scombrus)
and horse mackerel (Trachurus trachurus). Weight is expressed in kilo-grams, number is the number of individual fish, mean length in cm and % acoustic energy is the percentage of acoustic energy per species following
the method of Nakken and Dommasnes (1975). . . 67
4.2 Statistical summary of school descriptors by area and time of day. Number refers to the number of schools retained with the low NASC threshold shown on the line bellow (threshold at which the schools contributed to 95% of the total school scattering). . . 70
ber of nonzero samples. Family is the statistical distribution used; QB -quasibinomial, NB - negative binomial, G - Gamma. The number in
brack-ets following NB is the estimated theta-parameter. . . 96
5.2 Total acoustic scattering due to sardine by region and survey in m2/10000.
In the GAM field, the point estimates corresponds to the integration of the fitted surface, and the 0.5, 0.025, 0.975 are the quantiles resulting from the simulations. The point estimate in the Mean field corresponds to the sample mean raised to the stratum area and 0.025 and 0.975 are the confidence limits
obtained by equation 5.2. The Delta colum only has point estimates. . . 98
5.3 Estimates of total number, biomass and mean weight for all data sets. point
stands for the point estimate with corresponding relative standard error (RSE) in brackets. The columns 0.025 and 0.975 the respective quantiles of the combined GAM simulations and trawls’ jackknife realizations. The rows la-beled as “Ass.” correspond to the values provided to ICES for assessment; “NA” stands for not-available. . . 103
Chapter 1
General Introduction
Underwater acoustics
Compared to electromagnetic waves, e.g., light and radar waves, sound waves travel effi-ciently underwater and can be scattered by any object whose density is different from that of the surrounding environment. These properties of sound allow the remote sensing of underwater environment with equipment generically called sonar (the acronym for Sound Navigation and Ranging). The invention of sonars might be attributed to Paul Langevin and colleagues, who during the First World War started to develop an acoustic submarine detector. In 1918, they were able to transmit an acoustic signal and receive the echo from a submarine for the first time. Roughly ten years after Langevin’s success, the Japanese re-searcher Kimura published a work in which he showed that fish could also interfere with the propagation of sound in the water, thus being prone to detection by sonars (Simmonds and MacLennan, 2005). This finding permitted the use of acoustics in fish detection in the wild and in the years that followed, the use of sonars rapidly increased in commercial fisheries and science applications (see Fernandes et al., 2002).
The earliest approach to quantitative use of sonars in a fisheries context was echo-counting (Cushing, 1964), a technique that consists in counting the acoustic echoes, assuming that they arise from single targets. However, this is hardly the case for schooling fish, and echo-counting proved to be of limited use for most marine pelagic species. So, it was not until the addition of correction factors (i.e., time varied gain - TVG) and the invention of the echo-integrator by Ingvar Hoff (Dragesund and Olsen, 1965) that quantitative assessment of marine fish populations was possible. The principle of echo-integration, later rectified by
Scherbino and Truskanov (1966), states that the integral of the intensity of the echo in a given volume of water compensated for transmission losses, is proportional to the concentration of fish. When a number of acoustic signals are transmitted by a moving ship or acoustic platform, integrating the echoes over depth and time results in a quantity that is proportional to the number of fish per surface area. This value can then be raised to the total area of sampling, providing an estimate of total fish abundance. Despite the great technological improvement that occurred in acoustic devices since the time of Hoff, the principle of echo-integration continues to form the basis for the fisheries-independent estimates of abundance that support catch-at-age assessment models of many important pelagic species world-wide (Simmonds and MacLennan, 2005).
The echosounder
The primary instrument for acoustic estimation of abundance of aquatic organisms is the sci-entific echosounder - a particular type of sonar with a vertically directed narrow beam and with the precision necessary for scientific purposes. An echosounder is typically made of five basic components: timer, transmitter, transducer, receiver, and display (Figure 1.1). The transmitter produces a voltage signal that is converted by the transducer to a burst or pulse of sound (ping) that propagates through the water column. The pings are discretized into samples with lengths in the order of centimetres, from which the acoustic data values are retrieved. Echoes from targets in each sample, e.g., fish or seabed, are received by the trans-ducer, converted back to an electrical voltage and amplified. By knowing the speed of sound, the time delay between the transmission and reception provides a measure of the target’s dis-tance to the transducer. The time varied gain - TVG, amplifies the acoustic signal according to the time delay in order to compensate for the decrease in intensity of the sound that occurs with increasing distance. After a ping has been processed a new ping is emitted and the process is repeated periodically; the ping emission or pulse rate and the pulse duration are selected as a function of range, size of the organisms to sample and physical properties of the transducer. The succession of pings is plotted on the display, creating a 2-dimensional image (echogram) of the acoustic data underneath the transducer (Figure 1.2). The y-axis of the echograms represents depth and the x-axis represents time, that in the case of a moving acoustic platform is related to the distance covered. The color of each pixel is indexed to a color scale representing the acoustic intensity. Modern systems provide high resolution ping-by-ping geo-referenced data that can be stored for post-processing and analysis. A common
CHAPTER 1. GENERAL INTRODUCTION
Figure 1.1: Schematic of an echosounder. The sequence of transmitted pulses generates echotraces from fish schools and the seabed bellow. The echoes are received, amplified and displayed as an echogram. Adapted from Simmonds and MacLennan (2005).
Figure 1.2: An echogram showing the bottom echo (the dark thick descending line), echoes from fish schools (the red/yellow/green echo-traces close to the bottom) and echoes from plankton (diffuse light blue signal in the upper part of the image). Horizontal spacing of the vertical lines is 0.5 nautical miles, and the numbers at the intercepts mark the vertical distance to the transducer in metres.
scientifically proposed format to store acoustic data - the hydroacoustic or .HAC file format, ensures that the most important variables of the raw acoustic data are digitized and saved in a standard format (ICES, 2004a).
Frequency and scattering of sound
The frequency of a sound wave is the rate of oscillation of the relative pressure, i.e., the number of peaks or lows of pressure occurring at a certain time interval, typically 1 second. Sonars used for biological purposes produce high frequency sounds, typically between 10 and 420 kHz for fisheries applications (Simmonds and MacLennan, 2005). Frequencies higher than 420 kHz have a limited sampling range due to the high absorption by water and are usually reserved for high resolution and low range applications suitable for plankton or physical studies (Wiebe et al., 1997; Warren et al., 2003)
Knowledge of the scattering properties of the targets at the frequency used is essential to obtain quantitative estimates of density. The calculations involved in abundance estimation require the knowledge of the average echo return of the organisms being sampled. The latter is formally known as backscattering cross-section - σbs, a quantity related to the proportion
CHAPTER 1. GENERAL INTRODUCTION
cross-section of marine organisms is frequency-dependent. In the case of fish the swimblad-der, when present, produces the highest proportion of backscatter (Foote, 1980). For most fish species σbs relates to fish length (L) by the relationship
σbs = 10
m log10(L)+b
10
where the parameters m and b are obtained either theoretically or experimentally. For clu-peoids and many other families of fish, m is accepted to be 20 so b is the only parameter to be determined. The parameter b has been derived for most species that are routinely as-sessed by acoustics in experiments with dead, stunned or free-swimming fish. Due to the relatively low values of σbs in fish that require the use of many decimal places, σbs is most
commonly expressed in its more appealing logarithm version called the target strength (TS). Target strength relates to σbsby T S = 10 log10(σbs), which gives rise to the commonly used
TS/Length relationship T S = m log10(L) + b.
When estimating the abundance of fish in the wild the average σbs of the population is
ob-tained from averaging the individual σbsfrom fish in representative trawl samples. When fish
are randomly distributed within the volume swept by the echosounder, the principle of lin-earity (Foote, 1983) states that the echo-integral within the volume is the sum of individual
σbs. Most of the times, the echo-integral by unit of volume is of little use and instead it is
normally expressed by unit of surface area typically by the nautical area scattering coefficient
- NASC or sA(MacLennan et al., 2002).
Quality of acoustic estimates
The estimation of the mean acoustic density of the target species in a given area can be performed by a regular or stratified regular sampling scheme, i.e., transects placed evenly across the area or strata, usually in the direction of higher environmental gradient. The survey is conducted along the transects, which are discretized into smaller units from which the acoustic measurement are averaged to give one sample. The length of these units is called Elementary Distance Sampling Unit - EDSU (MacLennan et al., 2002). The scrutinized echoes of the species, i.e., the echoes attributed to the species or groups of species of interest,
area A can be converted to fish numerical density - ρAby
ρA=
hsAi
4πσbs
,
and abundance - N, can ve calculated by raising ρAto the sampling area by N = ρA× A. As
in any estimation procedure, the fact that only a minor fraction of the pelagic environment is being sampled, means that acoustic estimates do not arise without error. However the means for calculating and reporting total uncertainty in acoustic estimates has only recently been acquired (Demer, 2006). Total uncertainty of acoustic estimates comprises measurement error (e.g., errors in the system calibration due to changes in water characteristics) and sam-pling error (e.g., variability due to acoustic and trawl samsam-pling). For some surveys however, measurement error tends to be very small and diluted over the survey, and total uncertainty is dominated by sampling error (Demer, 2006).
One of the problems with acoustic sampling through regular sampling designs is that the variance of total backscattering area (proportional to fish number) can not be calculated directly because acoustic samples are spatially correlated (Rivoirard et al., 2000). The vari-ance of the mean acoustic density over a field can be obtained by geostatistical methods (Rivoirard et al., 2000; ICES, 2004c), however, these rely on the estimation of the semivari-ogram function that might be difficult to estimate properly with zero-inflated, highly skewed data typically arising from acoustic surveys (Chilès and Delfiner, 1999). The shape of the statistical distribution of acoustic data reflects the spatial organization of the fish that is in turn the result of a hierarchical system of spatial organization; at larger scales it is driven by the environment and at lower scales it is dominated by the fish social behaviour (Mackinson et al., 1999; Maravelias, 2001; Fréon et al., 2005).
The randomness and noise naturally associated with acoustic data require the development of general estimation methods that are able to capture the signal while accommodating the noise within reasonable statistical assumptions. Alternative approaches to geostatistics have been used in the modelling of phenomena with similar types of statistical distributions to that of acoustics surveys. The most common are generalized linear and additive models (Borchers et al., 1997a; Barry and Welsh, 2002). In relation to traditional estimates, precision could be improved by the inclusion of non-linear relationships between the covariates and the response variable (Borchers et al., 1997a). Additionally, generalized additive models use smoothing techniques that are equivalent to co-kriging - a geostatistical interpolation
CHAPTER 1. GENERAL INTRODUCTION
technique (Cressie, 1991; Dubrule, 2004), but with the advantage of allowing the inclusion of a wide range of distributions for the data. These include the binomial distribution, for binary data and the Poisson, Gamma and Negative Binomial to be used in cases in which the data exhibit a skewed distribution and proportional effects (Venables and Ripley, 2002; Wood, 2006), normally found in acoustic data.
Sardine and acoustic surveys in Portugal
Fisheries acoustic surveys in Portugal were initiated in the early 1980s to support the as-sessment of its most important fishery resource: sardine (Sardina pilchardus). The sardine fishery in Portugal dates back to Roman times, as sardine was the primary source for fish salt-ing in the small production plants existsalt-ing in coastal villages (Tavares da Silva and Soares, 1993). The fishery has maintained its importance since then and still has social and eco-nomic importance. Despite the low market value of sardine, the relatively large catches (sar-dine catches comprise around 40% of total Portuguese landings by weight) provides work for approximately 6000 people working directly in the fishing fleet or the canning industry. Sardine is consumed fresh during summer months and approximately half of the catches are directed towards the canning industry that supplies both internal and external demand. Sardine is a small pelagic clupeoid that can be found in the North Atlantic eastern conti-nental margin, from Senegal to the British Isles (Parrish et al., 1989; Lluch-Belda et al., 1989). There are also sardine populations in the Mediterranean and adjacent seas, but they are smaller than those of the Atlantic. Sardine exhibits regional characteristics in many biological aspects such as feeding, reproduction and morphology. This partly explains the species’ success in colonizing contrasting environments that range from the oligotrophic Mediterranean Sea (Somarakis et al., 2006) to the highly productive waters of the Canary Upwelling System (Lluch-Belda et al., 1989). Sardine is an omnivorous planktivore that has the ability to ingest particles as small as 5 µm and in excess of 1.5 mm, changing between non-selective filter feeding to the visual predatory type of particulate feeding according to prey size (Garrido et al., 2007). As a result, sardine diet is varied and dependent on the local and seasonal availability of prey, which include phytoplankton, micro-zooplankton, macro-zooplankton and even fish eggs (Varela et al., 1988; Garrido et al., in press). Sardine diet also varies with age: as the filtering apparatus develops, phytoplankton becomes progres-sively dominant in its diet (Bode et al., 2003).
Off Portugal, sardine can grow up to 14-15 cm during their first year of life, corresponding to approximately 60% of their maximum length in this region (Pestana, 1989). Life expectancy in the same area is typically around 7 years; however in northern locations (e.g., western Channel), individuals up to 14 years old have been encountered (Silva et al., in press). Typ-ically, sardine mature during the first or second year of life (Silva et al., 2006). Similarly to other clupeoids, sardine is an indeterminate serial spawner, i.e., the annual fecundity is not fixed at the beginning of the spawning season and the standing stock of oocytes in the ovary is continuously replaced by new developing oocytes that are released in batches dur-ing discrete spawndur-ing events occurrdur-ing throughout the season. Spawndur-ing events occur on a daily basis, within a restricted time window of a few hours around sunset (Zwolinski et al., 2001). These events involve a fraction of the spawning population that typically oscillates around 10% during the peak spawning season (Quintanilla and Pérez, 2000; ICES, 2006a). The spawning season might last more than six months in some sardine populations, but de-creases significantly towards the northern limit of sardine distribution (Stratoudakis et al., 2007). The peaks of spawning in the various locations where sardine are present are out of phase, probably synchronized with local oceanographic events in order to match spawning with increased productivity or to favour retention of early life stages in places propitious to their development (Santos et al., 2004; Coombs et al., 2006).
Early work on the population structure of sardine in Atlantic waters, revised by Parrish et al. (1989) suggested the existence of four stocks in the north-eastern Atlantic, evenly split by African and European waters. The European stocks were: the septentrional Atlantic stock, that occurred from the North Sea to the Cantabric coast of Spain, and the Iberian Atlantic stock that occurred from the Cantabric coast to the Straits of Gibraltar. The same
stock delimitation was adopted by ICES1 in its assessment areas VIIIc and IXa (Figure 1.3)
to manage the combined Portuguese and Spanish sardine fishery in Atlantic waters. The Atlanto-Iberian sardine stock has been shown to be dependent on the success of recruitment in three key locations: the Northern Portuguese shelf, the coastal region in the vicinity of the Tagus Estuary and the Gulf of Cadiz. The first area has been acknowledged as playing a key role in the dynamics of the Iberian sardine (Muiño et al., 2003a; Silva, 2007). Recently, doubts about the unity and the isolation of the Ibero-Atlantic stocks were raised by the detec-tion of differences in morphometric characteristics, age structure and populadetec-tion dynamics
1International Council for the Exploration of the Sea, ICES is the organization that coordinates and
pro-motes marine research in the North Atlantic. This includes adjacent seas such as the Baltic Sea and North Sea. For more information, refer to www.ices.dk.
CHAPTER 1. GENERAL INTRODUCTION
(Silva, 2003; Silva et al., 2006) in the stock, and by the possibility of population mixing with adjacent stocks as suggested by the continuity of sardine eggs and adults across boundaries (Bernal et al., 2007; ICES, 2006a).
Figure 1.3: ICES areas and subareas off Southwestern Europe. The Atlanto-Iberian sardine stock is contained within the sub-areas VIIIc and IXa.
The combined Spanish and Portuguese landings puts the Atlanto-Iberian fishery as the se-cond highest after the one existing off Morroco. In the last 60 years, annual Iberian landings have exceeded 100 000 tonnes and reached a maximum value of 246 000 tonnes in the 1960s (ICES, 2006a). In the recent past, a succession of low annual recruitments during the period 1993-1999 associated with low values of the spawning stock biomass contributed to a progressively lower catches to historical minima. The fishery recovered after the high recruitment of 2000 and continued after another strong recruitment in 2004 that contributed to the present high values of the stock (ICES, 2007). Apart from a minimum landing size of 11 cm there are no other global restrictive measures for the Atlanto-Iberian sardine fishery. However, effort and catch limitations have been gradually enforced at the national level since
1997 as a response to the decline of the stock and fishery in the mid-1990s (ICES, 2007). These local regulations include maximum numbers of days at sea, a temporal closure of the fishery on the Northern Portuguese coast and daily limits of landings per boat and fishermen’s associations.
Acoustic surveys targeting the Iberian sardine stock were initiated during the early 1980s with the aim of providing fisheries independent estimates of numbers at age to tune assess-ment models (Pestana, 1989). After some years of interruptions and adjustassess-ments, the surveys attained their current format in 1998 at the Planning Group for Acoustic Surveys in ICES sub-areas VIIIc and IXa (ICES, 1998). Since then, there have typically been three surveys conducted each year in Atlantic Iberian waters: a survey targeting the Northern part of the stock performed by the Spanish Institute of Oceanography (IEO) in late spring and two sur-veys conducted off Portugal and Gulf of Cadiz by IPIMAR in autumn and early spring (ICES, 2006a). Since the benchmark assessment of sardine in 2006 (ICES, 2006b), only the time se-ries of the coordinated spring surveys has been used as an index of sardine biomass, leaving the autumn Portuguese survey to produce estimates of the annual recruitment strength. The sampling area of the coordinated Spanish and Portuguese surveys in spring extends from the French/Spanish border in the Bay of Biscay to Cape Trafalgar in the southern Gulf of Cadiz. The northern region, down to the Portuguese/Spanish border has been undertaken by IEO while the remaining area has been covered by IPIMAR (ICES, 2006a).
The need to combine Portuguese and Spanish surveys required the adoption of similar prac-tices regarding acoustic sampling and data handling. Until 1997 acoustic sampling was performed around the clock while trawl stations for the identification of echo-traces were performed mainly during the day. However, echogram scrutiny at night progressively be-came more difficult and prone to error, especially in southern Iberia, as a result of changes in the pelagic community (ICES, 1998). In such locations echogram scrutiny could only be performed from data collected during the day, when the fish were usually densely aggregated and easier to classify. To maintain sampling consistency across the survey area, it was de-cided that acoustic surveying thereafter would only be performed during the day. The change in sampling scheme caused an increase in the duration of the surveys and consequently an increase in their costs. After the changes, the evaluation of the precision of the new sampling scheme was not performed, mainly because the methods for assessing uncertainty of acoustic surveys were not available. However, even today, the methods used to calculate the variance of acoustic surveys have not achieved widespread consensus and instead of a commonly
CHAPTER 1. GENERAL INTRODUCTION
internationally agreed protocol for the calculation of variance, there are several alternative methods that rely on different assumptions and consequently produce distinct results (ICES, 2005a). Furthermore the models used for the assessment of sardine do not incorporate the variance of the estimates.
In recent years, acoustic surveys worldwide have enlarged their original scope of obtaining estimates of fish abundance, and are now considered as most complete tools for ecosystem research (Massé and Gerlotto, 2003; Horne, 2005). A variety of research and monitoring activities have been included in acoustic surveys in order to improve the understanding of the environmental mechanisms responsible for the availability and distribution of the fish populations (Neváres-Martínez et al., 2001; Bertrand et al., 2003; Massé and Gerlotto, 2003; Petitgas et al., 2006). Back in the 1980s, the supporting project for Portuguese acoustic sur-veys had already acknowledged the importance of studying the ecological processes behind sardine dynamics, by including in their objectives “the study of distribution and aggregation habits of sardine off Portugal in relation to its life cycle and oceanographical conditions” (Pestana, 1989). Indications about the linkage between hydrography and the distribution of pelagic species as well as the behaviour of sardine can be found scattered in reports of the ongoing surveys that took place during the 1980s and 1990s. Unfortunately, very few of these studies have become publicly available in the mainstream scientific literature, partly because some of the techniques and equipment needed for a more insightful approach were not available at that time.
The need to study the spatial organization and the abundance of sardine stock within an envi-ronmental framework was boosted in the year 2000, under the EU study project PELASSES - Direct Abundance of Estimation and Distribution of Pelagic Fish Species in the North East
Atlantic Waters; Improving Acoustic and Daily Egg Production Methods for Sardine and Anchovy. The project’s main aim was the improvement of the acoustic estimation for sardine
and anchovy, the two most important pelagic fish species in South West Europe. The project was set up to solve some of the problems that arose with the extinction of Planning Group for Acoustic Surveys in ICES areas VIII and IX such as: the difficulty of echogram scrutiny; the masking of fish echo-traces by those of plankton; and the presence of environmental conditions affecting the distribution and abundance of fish (ICES, 2001). To address these issues, better post-processing analysis tools and a larger collection of auxiliary environmen-tal data were necessary. Therefore, MOVIES+ (Weill et al., 1993), a tool for acquisition, archiving and processing of echosounder data was purchased and became standard
equip-ment for subsequent acoustic surveys. MOVIES+ opened a new range of possibilities for acoustic data storage and processing, allowing better quality control (e.g., bottom correction, noise extraction) and post-processing of acoustic data (e.g., school or plankton extraction, echo-integration) improving the overall quality of acoustic estimates. To characterize the environment, IPIMAR’s research vessel (NI-NORUEGA) was equipped with a continuous underway fish egg sampler - CUFES (Checkley et al., 1997). CUFES allows regular log-ging of geo-referenced environmental data (salinity, temperature and fluorescence due to chlorophyll-a) taken from the top of the water column (3 m depth) while the ship is cruis-ing along the survey track. Ichtyoplankton samples are also taken continuously, providcruis-ing contiguous samples that can be matched to acoustic samples to help in echogram scrutiny (ICES, 2001).
Besides acoustics, the PELASSES project also aimed to improve the estimation of sar-dine biomass through the daily egg production method - DEPM (Stratoudakis et al., 2006). DEPM, a direct estimator of the spawning stock biomass for serial spawners, was initiated in Portugal and Spain in 1988 and has been performed tri-annually since 1999. The underlying principle of DEPM is that the production of eggs in a region is proportional to the spawning stock biomass. By calculating the rate of total daily egg production together with biological parameters from the adult fish (e.g., fecundity, the proportion of spawning fish) it is possible to work backwards and derive the spawning stock biomass. Despite providing unbiased esti-mates of the spawning stock biomass, DEPM is fairly imprecise (Stratoudakis et al., 2006). Part of the lack of precision of DEPM arises from the patchy distribution of the eggs, that is in turn, a direct consequence of the spatial distribution of the spawners (Smith, 1973; Curtis, 2004). Therefore, understanding the dynamics of fish during the spawning events might be of great value to improve sampling design and estimate precision of both eggs and acoustic surveys.
Rationale and structure of the thesis
The series of Portuguese acoustic surveys performed since the year 2000 has produced a large amount of good quality data with valuable information about the distribution of sardine and its relation to the pelagic environment. The data available include ping-by-ping digi-tized acoustic data, a large data-base of the trawl samples used for echo-trace and fish size characterization and high-resolution concurrent environmental sampling from CUFES. The
CHAPTER 1. GENERAL INTRODUCTION
aim of this thesis is to make use of the additional information that has been collected in Por-tuguese acoustic surveys since 2000 in order to better understand the ecology and behaviour of sardine and to improve its acoustic estimation. Among the main ecological questions are the relationship between sardine distribution and the water masses, the nature of their aggregative behaviour and the diel cycle of schooling. These in turn, will aid in the identifi-cation of better sampling procedures and potentially improve the techniques used to model the species’ abundance for assessment purposes.
The material that composes the main body of this thesis is separated into four stand-alone chapters written in the form of scientific papers, each of which contains a respective intro-duction, material and methods, results and discussion. In Chapter 2, a series of acoustic surveys in which detailed information on the aquatic environment was gathered concurrently with the acoustic sampling, were used to improve the understanding of the processes that regulate sardine distribution at scales of the order of 10s to 100s of km off Western Portugal. Multivariate statistics and generalized additive models were used to test the significance of the relationships obtained and to construct maps of sardine potential habitat. The results are then discussed in the light of recent knowledge about the meso-scale circulation off Western Iberia. Chapters 3 and 4 deal with sardine distribution at a spatial scale lower than that of routine acoustic surveys in order to clarify the organization of sardine at scales dominated by social interactions. In Chapter 3, a small scale study has been set up to describe the behaviour during two spawning events using two sources of information: eggs and acoustic data. In Chapter 4, three experiments were conducted to characterize the diel patterns of sardine schooling behaviour and vertical migrations, that have been suggested to be atypical among the clupeoids. In it, sardine schools extracted from echograms of the daily cycle along var-ious points of the Portuguese continental shelf were analyzed to derive the common pattern of sardine behaviour off Portugal. Chapter 5 deals with the primary goal of the Portuguese acoustic surveys performed by IPIMAR, that is to provide estimates of abundance. A recent statistical methodology was implemented within a framework aiming to provide spatially indexed estimates of fish number at length with respective confidence intervals. The gener-ality of the method was tested by applying it to 18 data sets with acoustic data exhibiting various levels of abundance. The thesis is then followed by a chapter containing the general conclusions of the work. Additionally there is small appendix (in Portuguese) aiming to provide the reader with basic information about underwater acoustics and the nomenclature used throughout the thesis.
Chapter 2
Sardine potential habitat and
environmental forcing off western
Portugal
∗
∗Submitted as:
Zwolinski, J., Oliveira, P. B., Morais, A., Quintino, V., Stratoudakis, Y. Sardine potential habitat and environ-mental forcing off western Portugal. Submitted to Continental Shelf Research
Abstract
Relationships between sardine (Sardina pilchardus) distribution and the environment off western Por-tugal were explored using data from seven acoustic surveys (spring and autumn of 2000, 2001, 2005 and spring 2007). Four environmental variables (sub-superficial salinity, temperature, chlorophyll content and planktonic acoustic backscatter over the water column) were related to the acoustic pres-ence of sardine. Univariate quotient analyses revealed sardine preferpres-ences for waters with higher chlorophyll content, lower temperature and salinity and lower planktonic scattering. Regression of sardine presence on the principal component rotated environmental variables confirmed quantitatively the above trends. These results are in line with a sardine diet based mainly on phytoplankton and small zooplankton that usually concentrate in productive, cooler and less saline coastal waters. Generalized additive models were used to construct multi-annual synoptic relations to map sardine potential habi-tat (SPH), revealing a clear seasonal effect in the bathymetric and along-shelf extension of SPH off western Portugal. During autumn, SPH occupied a large part of the northern Portuguese continental shelf but was almost absent from the southern region, while in spring SPH extended further south but was reduced to a narrow band of shallow coastal waters in the north. This seasonal pattern agrees with the spatio-temporal variation of primary production and oceanic circulation off western Iberia. The northerly concentration of SPH during autumn can be linked to the onset of the downwelling season that coincides with the settlement of oligotrophic waters in the narrow southern shelf and the maintenance of higher production associated to nutrient input from river in the wider northern shelf. Shelf-wide spring phytoplankton blooms provide a suitable habitat for sardine across western Iberia, but the penetration of the Iberian poleward current onto the outer shelf of northwestern Iberia constrains spring production (and SPH) to the inner shelf.
2.1. INTRODUCTION
2.1 Introduction
Small pelagic fish (sardines and anchovies) play a key ecological role in coastal ecosystems, transferring energy from plankton to upper trophic levels (Cury et al., 2000). Their relatively low position in the marine food web, together with a short life-span and a reproductive strat-egy of producing large quantities of pelagic eggs over an extended spawning season, render small pelagic fish (SPF) greatly dependent on the environment (Bakun, 1996). At scales spanning from days to weeks and from 10s m to 10s Km, the distribution of SPF largely responds to environmental stimuli (Mackinson et al., 1999; Maravelias, 2001; Robinson, 2004; Barange et al., 2005; Planque et al., 2007). However, at any given time, the envi-ronmental factors that drive SPF distribution can be confounded with biological constrains like population size or demographic structure. As a result, understanding the environmen-tal conditions that drives the distribution of SPF by defining its potential habitat requires information over large spatial and temporal scales (Mackinson et al., 1999; Planque et al., 2007). When observed species distribution can be linked to environmental variables over a range of conditions (such like the data obtained by a series of synoptic monitoring surveys), then statistical relationships can be established to describe the environmental conditions that modulate potential habitat (Guisan and Zimmermann, 2000).
Sardine (Sardina pilchardus) can be found throughout the North Atlantic eastern continental margin from Senegal to the British Isles and in the Mediterranean and adjacent seas (Par-rish et al., 1989). It is commercially exploited across its distribution range, with the most important fisheries occurring in upwelling areas. Sardine is an eurithermic and eurihaline
clupeoid that inhabits waters with temperatures ranging from 8 to 22◦ C (Coombs et al.,
2006) and salinities from 30 up to 38 psu (Coombs et al., 2006; Giannoulaki et al., 2005). Similar to other clupeoids, sardine are opportunistic omnivorous feeders, well adapted to the variable food sources available in upwelling systems (Bode et al., 2004; Somarakis et al., 2006; Garrido et al., 2007). In Portugal (Figure 4.1) sardine is the most important marine resource being mainly fished in coastal waters by purse-seiners (Stratoudakis and Marçalo, 2002). The majority of the portuguese sardine landings is due to the purse seine fleet op-erating North of Lisbon (ICES, 2006b) that is an area of high adult abundance, as well as a key recruitment area for the Atlanto-Iberian sardine stock (Carrera and Porteiro, 2003). South of Lisbon commercial catches and estimates from acoustic surveys show that sardine abundance is low when compared to the North.
The Iberian Peninsula is situated at the northernmost limit of the Northern Atlantic Up-welling Region. Off western Iberia, upUp-welling favourable winds occur more consistently between April and October (Fiúza et al., 1982) but short upwelling episodes may occur all year round (Santos et al., 2004). Along the western Portuguese continental shelf, water char-acteristics show a gradual north/south trend, with colder, less saline and nutrient richer in the north and warmer, saltier and nutrient poorer waters in the south (Peliz and Fiúza, 1999). In the northern coast, a low salinity buoyant plume (the Western Iberian Buoyant Plume -WIBP) is recurrently observed in the inner-shelf owing to the higher freshwater runoff in the north. The WIBP has been identified as an important feature for retention and growth of early stages of small pelagic fish (Santos et al., 2004). Also, due to enrichment from river runoff, productivity in coastal waters of the northern portuguese shelf is relatively high dur-ing winter, contrastdur-ing with the more oligotrophic conditions in the narrow continental shelf south of Lisbon (Peliz and Fiúza, 1999). The whole western portuguese continental shelf is also rich in temporary oceanographic events that reshape and confound the large scale oceanographic patterns described above (Relvas et al., 2007).
Following the hypothesis that sardine meso-scale spatial distribution is driven by environ-mental forcing, we aimed to describe the environenviron-mental conditions suitable for sardine pres-ence off Western Portugal. Sardine distribution obtained during 7 acoustic surveys (spring and autumn of 2000, 2001, 2005 and spring 2006) was used to explore relationships with a subset of environmental variables obtained in situ. These were sub-superficial salinity, temperature and fluorescence due to chlorophyll-a obtained with a continuous underway fish egg sampler - CUFES (Checkley et al., 1997) and planktonic acoustic scattering at 38 kHz integrated over the water colum. Generalized linear and additive models were built in order to obtain quantitative information on the significance of the relationships between sardine distribution and the environment. Finally, the multi-annual synoptic relationships were used to map sardine potential habitat during each survey. The observed relationships between the pelagic environment and sardine presence are discussed in light of current knowledge of sar-dine ecology. The seasonal changes of the spatial extent of sarsar-dine potential habitat are put into the context of what is known about water circulation off Western Iberia.
2.2. MATERIAL AND METHODS
2.2 Material and Methods
Data collection
The study took place in the western Portuguese continental shelf (Figure 2.1), from the
Por-tuguese/Spanish border (river Minho- 41◦54.4’N, 008◦54.5’W ), to the Cape São Vicente
(southwest Portugal at 37◦01.45’N, 008◦59.71’W). Three distinct areas were considered in
the analysis based on the geomorphology of the shelf and historical records of sardine dis-tribution. The Northern region, from the northern Portuguese border to the Nazaré Canyon, is characterized by a wide (50-60 Km) soft-bottom continental shelf with intense freshwa-ter runoff, mainly from the rivers Minho, Douro and Mondego; It is an area of high sardine abundance and recurrent recruitment (Carrera and Porteiro, 2003; ICES, 2006b). The Central region, from the Nazaré Canyon to the Setúbal Canyon, is characterized by a wide promon-tory of rocky bottom at its northernmost section, followed by a relatively narrow shelf influ-enced by river runoff from the Tagus and Sado estuaries. Adult sardines are ubiquitous in this region while recruits are frequently found between the Sado and Tagus rivers. Finally, the Southern region, extending south of Sado river to Cape São Vicente, is characterized by a narrow (25 Km), rocky-bottom and a steep continental shelf with little influence from river runoff. Compared to the other two regions sardine presence is less frequent in the Southern region and, when occurs, only adult fish are encountered. Acoustic data were obtained dur-ing 3 bi-annual acoustic surveys (March and November 2000, March and November 2001, April and November 2005) and a spring acoustic survey from the year 2006. Acoustic sur-veys off Portugal are performed systematically under international coordination in order to provide estimates of sardine abundance at age off the Iberian Peninsula for assessment pur-poses (ICES, 2006a). All surveys used in this study followed the same regular design with an inter-transect distance of 8 nautical miles (nmi) that covered the continental shelf between the 20 and 170/200 m isobaths (Figure 4.1). Acoustic surveying was limited to day-light hours (ICES, 1998) and vessel speed was targeted to be 10 knots. Acoustic sampling was performed with a Simrad EK500 echosounder, operating through a hull-mounted 38 kHz split beam transducer with a 7o×8◦beam angle, emitting pulses 1 ms long at a rate of 1 s−1.
The echosounder was calibrated prior to each survey by means of a copper sphere accord-ing to the standard procedure of Foote et al. (1987). The echograms produced by the EK500 were digitally stored and posteriorly processed with MOVIES + software (Weill et al., 1993). Fishing for echo-identification and sardine biological sampling was performed through pelagic
−10.5 −10.0 −9.5 −9.0 −8.5 −8.0 37 38 39 40 41 42 Longitude (° W) Latitude (° N) Douro river Mondego river Nazaré Canyon Tagus river Sado river
Cape São Vicente Setúbal Canyon Northern
Central
Southern
Minho river
Figure 2.1: Survey region. The full and dashed lines represent the coast and the 200 m iso-bath contour respectively. The dots along parallel lines mark the acoustic samples (EDSUs) along the transects.
2.2. MATERIAL AND METHODS and demersal trawling. Due to the conspicuousness of sardine schools and to the higher abundance of sardine in comparison to other pelagic species over the Western Iberian shelf (Zwolinski et al., 2007), sardine echo-identification was mainly performed by visual inspec-tion of the echograms. When sardine schools could not be clearly distinguished from other species, the integral due to sardine was obtained by partition of the total fish echo-integral based on the species composition of the corresponding trawl hauls (Nakken and Dommasnes, 1975). Planktonic echoes other than those of fish schools or school-like aggre-gations, were classified as diffuse backscattering targets (DBT). Contrary to fish schools that present sharp contours and strong contrast to background levels (Reid, 2000), DBT echoes
were characterized by a smooth and relatively low volume-backscattering strength (Sv)
dis-tribution with very diffuse boundaries. echo-integration for sardine and DBT was performed
using 1 nmi elementary distance sampling units EDSU) with a low Sv threshold of -60 dB.
This threshold is currently used for sardine estimation and when used for DBT excludes backscattering from physical sources such as turbulence or oceanic microstructures (Good-man, 1990); this threshold is most likely to attribute DBT echoes to aggregations of highly reflective planktonic organisms (Stanton et al., 1996).
A hull-mounted continous underway fish egg sampler - CUFES (Checkley et al., 1997) op-erating simultaneously with the acoustic data collection recorded oceanographic variables (salinity, temperature and fluorescence due to chlorophyll-a) from sub-superficial waters (3 m depth) by means of conductivity/temperature sensors and a fluorimeter coupled to the intake pipe of the CUFES collector. CUFES data were averaged every 18 minutes corre-sponding to a 3 nmi transect at the speed of 10 knots) and their values were posteriorly associated to the nearest acoustic EDSU. The CUFES variables and the nautical area scatter-ing coefficient - NASC (MacLennan et al., 2002) originatscatter-ing from DBT comprised the set of environmental variables used to characterize sardine habitat.
Statistical analysis
The environmental variables were scaled to zero mean and unit standard deviation. Fluores-cence (proportional to chlorophyll a concentration) and DBT (NASC+1) were log-transformed prior to standardization to reduce the positive skewness of the raw data. Sardine preferences in relation to the scaled environmental variables were analyzed by quotient analysis (Lluch-Belda et al., 1991) using the routines available in the library shachar (ICES, 2004d) for R software (R Development Core Team, 2005). Quotient analysis is particularly useful for
presence/absence data, providing a measure of the relative number of presences across the whole frequency distribution of the predictor (Bernal et al., 2007; Ibaibarriaga et al., 2007). After dividing the predictor values into a series of i classes constructed analogously to a histogram, the quotient Qifor the ith class is given by
Qi = yi/ P iyi ni/ P ini (2.1)
where yi is the sum of the dependent variable on the predictor class i,
P
iyi is the sum of
the y across all classes, niis the number of samples in the ith class, and
P
ini is the overall
number of samples. Quotients higher than 1 suggest preference while lower than 1 indicate avoidance. The functions for quotient analysis also include a resampling routine for the calculation of confidence intervals for the null hypothesis of even distribution, i.e., Qi = 1.
The correlation between the environmental variables was addressed by principal component analysis - PCA (Venables and Ripley, 2002) on the survey standardized data matrix. The resulting vectors of scores arising from the PCA rotation were used within a generalized linear model - GLM (Venables and Ripley, 2002) aiming to test quantitatively the effect of the environment on sardine distribution, either by using the whole data combined into a single model (global model) or by splitting the data according to the time of the survey (seasonal models). The models were built by adding one vector of scores at a time, starting from the 1st pricipal component (PC) and ending when the analysis of deviance showed no increase in significance of the model (significance level α = 0.05). GLMs used a binomial error distribution with a logit link function when the response was sardine presence or a logarithmic link function with negative binomial distribution when the response variable was sardine NASC.
Generalized additive models - GAMs (Wood, 2006) with distributions similar to those of the GLMs were built in order to construct maps of sardine potential habitat. Potential habi-tat was expressed as the expected probability of sardine presence, calculated by applying the multi-annual synoptic relationships obtained by the GAMs to the environmental con-ditions observed in a given survey. The explanatory variables were set either as single or multiple-dimensional smoothers according to the correlation structure inferred from the PCA. To avoid over-fitting, the final number of total degrees of freedom of the model was kept bellow 10% of the number of samples when working with density data or 10% of the number of the least represented class in the case of presence/absence data. Model adequacy
