Estimating spatial and temporal variability of juvenile North Sea plaice from opportunistic data

Journal of Sea Research 75 (2013) 118–128

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Estimating spatial and temporal variability of juvenile North Sea plaice from opportunistic data J.J. Poos a,⁎, G. Aarts a, b, S. Vandemaele c, d, W. Willems c, L.J. Bolle a, A.T.M. van Helmond a a

Wageningen IMARES, Institute for Marine Resources and Ecosystem Studies, 1970 AB IJmuiden, The Netherlands Royal Netherlands Institute for Sea Research (NIOZ), 1790 AB Den Burg, The Netherlands Institute for Agricultural and Fisheries Research (ILVO Fisheries), B-8400 Oostende, Belgium d University of Antwerp, B-2020 Antwerpen, Belgium b c

a r t i c l e

i n f o

Article history: Received 14 November 2011 Received in revised form 22 May 2012 Accepted 22 May 2012 Available online 29 May 2012 Keywords: Non-uniform Sampling Generalised Additive Mixed Models North Sea Plaice

a b s t r a c t Surveys are often insufficient to accurately capture the distribution of a species in both space and time. Complementary to the use of research vessel data, platforms of opportunity can be a powerful strategy to monitor species distributions at high temporal and spatial resolution. In this study we use data from commercial fishing vessels, collecting – under the European Union data collection framework – biological data on all species that are caught and subsequently discarded. Using such discard data in combination with a systematic trawl survey, we model the spatial and temporal distribution of juvenile plaice (Pleuronectes platessa) in the central North Sea. There is a clear age-dependent difference between the commercial fishing vessel data and the research vessel data, with age 1 being the dominating age in the survey catches, while age 2 is the dominating age in the discards. The results show how immature plaice, slowly migrate from the nursery areas, westwards into the deeper regions of the North Sea. Also, the results show that during the study period, juvenile plaice gradually moved to deeper waters at an earlier age. Finally we discuss how the framework can be applied to similar opportunistic data to monitor seasonal and inter-annual migration of marine organisms, and to quantify how they may be influenced by biotic and abiotic gradients, such as temperature. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Concerns have been raised about the state of marine species and the ecosystems they live in (Halpern et al., 2008). These ecosystems are affected by climate change (Perry et al., 2005), human induced evolution (Jørgensen et al., 2007), species invasion (Molnar et al., 2008), and habitat degradation (Turner et al., 1999). A profound understanding of changes in these ecosystems requires intensive sampling in space and time to monitor the distribution and abundance of marine species. Unfortunately, sampling by research vessels is generally a logistically complex, expensive, and time consuming operation (e.g. see Schiermeier, 2008). As a result, intensive sampling in space and time for species in the marine environment is often lacking. Complementary to the use of research vessels, sampling on board commercial vessels of opportunity can be used to monitor the distribution and abundance of species. Examples are marine mammal and sea bird surveys on vessels of opportunity (Hyrenbach et al., 2007), or discard sampling programmes on board commercial fishing vessels (Borges et al., 2004). Although the sampling generally does not follow

⁎ Corresponding author. Tel.: + 31 317487189. E-mail address: [email protected] (J.J. Poos). 1385-1101/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.seares.2012.05.014

a structured survey design and may not optimally sample the variable of interest (Legendre et al., 2002), it often occurs throughout the year and may provide information on the seasonal distributional patterns of marine biota. The objective of this study is to estimate the spatiotemporal distribution of juvenile plaice (Pleuronectes platessa) using data from commercial fishing vessels combined with data from a design-based annual research-vessel survey. Plaice is caught and subsequently discarded by commercial fishing vessels. These discards contain all noncommercial species, undersized individuals, fish of poor quality, and over-quota fish (Branch and Hilborn, 2008; Catchpole et al., 2005; Poos et al., 2010). Under the European Union (EU) data collection framework, EU members are obliged to collect biological data on such discards. Since plaice is an abundant and well-studied species, it is possible to compare our distribution estimates with those from previous studies (Beverton and Holt, 1957; van Keeken et al., 2007). To meet the study's objective, we use generalised additive mixed models (GAMMs — Wood, 2006). Spatiotemporal heterogeneity in plaice catch rates are described using smooths of depth, geographical coordinates, age and date. Differences in age and sex specific retention between the discard sampling programme and the annual researchvessel survey are captured using smooth functions of age and sex, varying between the two data sources. The appealing feature of this

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modelling framework is that it is flexible and can therefore easily be extended to other species and data sources. 2. Methods 2.1. Plaice life history characteristic Plaice is a sexually dimorphic species where females grow faster than males (van Walraven et al., 2010). Adult males currently reach lengths up to 40 cm, while females reach lengths up to 50 cm. There are a number of spawning grounds in the North Sea and English channel where mature fish congregate and considerable numbers of eggs are found during the spawning season (Bailey, 1997; Harding et al., 1978; Lockwood and Lucassen, 1984; Wimpenny, 1953). The larvae drift in the southern North Sea between 60 and 90 days, after which they metamorphose and move into the shallow coastal nursery areas in the German Bight and the Wadden Sea (Bolle et al., 2009; van Beek et al., 1989; Van der Veer and Witte, 1999; Van der Veer et al., 2000). During ontogeny, plaice gradually leave the shallow nursery coastal waters and move into deeper waters offshore (van Keeken et al., 2007; Wimpenny, 1953). Plaice matures between ages 2 and 4, with males maturing younger than females (van Walraven et al., 2010). The location and timing of spawning cause an annual migration cycle, with mature individuals migrating between the feeding grounds used in summer and the spawning grounds in winter (Hunter et al., 2003). 2.2. Data collection Discard data are collected on board Dutch and Belgian commercial beam trawl vessels as part of national data collection programmes. The vessels use two (starboard and port) bottom trawl nets, each attached to a steel beam fixing the net opening. These beams are equipped with a set of long chains (the “V-net”) or a grid of chains (the “chain-mat”). The “V-nets” are used on sandy and muddy bottoms while the “chainmat” type is used on coarse sediment sea beds with boulders (Rijnsdorp et al., 2008). Both gear types sweep the seabed in front of the net (Daan, 1997). The fleets mostly target plaice and sole, with by-catches of other commercially interesting species (Gillis et al., 2008). The average trip duration is between 4 and 5 days, during which there is continuous fishing with 40 to 50 tows of 2 to 3 h (Rijnsdorp et al., 2000). The Dutch and Belgian vessels target similar species, fish under similar management regulations, with similar mesh size. However, there are some differences between the Belgian and Dutch fleet. Belgian vessels on average have lower engine power (750–1600 hp versus 2000 hp), resulting in lower fishing speeds. Also, Belgian vessels have smaller beam widths (9–12 m versus 12 m) and only use “chain mats”. Because of management regulations, both fleets operate outside the 12 nautical mile coastal zone and outside a large coastal area in the German Bight (“plaice box”; Pastoors et al., 2000). The fleet is restricted to use a minimum mesh size of 80 mm in the Southern North Sea (south of 55°/56° N). Gear selectivity experiments (van Beek et al., 1983) revealed a 50% retention for plaice at approximately 18 cm for this mesh size. The minimum mesh size in combination with the legal minimum landing size (MLS) of 27 cm for plaice results in a substantial discarding of juveniles (Aarts and Poos, 2009). The Dutch programme started in 2000, while the Belgian programme was initiated in 2004. The analysis in this study is based on data up to and including 2009 for both programmes. On average 12 (range: 4–18) trips were sampled per year. Vessels from different regions are selected to obtain a widespread spatial coverage but skippers are allowed to decline participation. This results in quasi-random sampling, where not all vessels have an equal probability of being selected. As a result, only one trip using chain mats was sampled in the

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Dutch programme, compared to 84 trips onboard vessels which used V-nets. At least 60% of the tows of each trip are sampled by scientific observers; the remaining tows are not sampled. On average, discard data are collected for 25 tows per trip. For each tow, date, starting time, duration, and geographic position at the start of the tow are recorded. The combined discards' dataset comprises of 2974 tows in 120 trips in 10 years. For the Dutch programme, total volume of discards in each tow is estimated by subtracting the landings (i.e. the commercially valuable specimens), from the total catch. The total catch is estimated prior to the sorting process. The landings are recorded by the captain and verified with auction data. From the discards, a representative sample (40 l) is taken. If plaice is extremely abundant a smaller subsample is measured. In the Belgium programme, the total weight of all species of commercial importance is determined (discards and landings separately). The length of all individual fish in the discarded part of the tow is measured, except if a species is extremely abundant. Then a smaller subsample is measured. The estimated total catch, the sample size and the sub-sampling factor are used to estimate the total number of discards per cm-size class per species in the sampled tow. The subsampling factors for the combined programmes range from 1 to 255 (median = 22). The discard data are analyzed in combination with data collected during the Dutch Beam Trawl Survey (BTS) in the period 2000– 2009. The BTS is a systematic survey covering the southern and central North Sea (Rogers et al., 1997). This survey is carried out annually, in August–September, on board two research vessels. The survey covers a larger area than the discard sampling programmes, but only data within 25 km distance of discard observations are used. The survey is carried out using an 8‐meter‐wide beam trawl, with a 40 mm mesh size net. One of the research vessels uses a flip-up rope to allow fishing on harder grounds (ICES, 2004), but this difference in the rigging of the net is disregarded in the further analysis. Tows generally last 30 min, during which a fishing speed of 4 knots is maintained. Sampling only takes place during daytime, i.e. from 15 min before sunrise to 15 min after sunset. The sub-sampling strategy usually consisted of measuring all fish of the less abundant size classes and a fraction of the more abundant size classes, resulting in two (or more) sub-sampling factors per tow. In those cases, the data were raised to the total catch by haul and the sub-sample factor was assumed to be 1. For data prior to 2002, the sub-sampling strategy was not recorded, and numbers were stored as if the entire catch was measured without subsampling. The resulting survey dataset has 927 tows in 20 trips in 10 years.

2.3. Age determination For both the discard sampling programme and the beam trawl survey, age is determined by counting otolith growth rings. In the discard sampling programmes, otoliths are collected from a random sample of 5 fish of each 1 cm length class in each trip. This results in a sex–age–length key table containing multinomial probabilities that a fish of a given length and sex (as observed in the sample) is of a given age. Because of low otolith sample size per trip, the sex– age–length key tables are estimated on a quarterly basis, without further (spatial) stratification. In the beam trawl survey, otolith collection is done similarly, but it is spatially stratified and age–length conversion is done separately for 7 different areas within the North Sea. Because some length classes are not included in the otolith samples, age cannot be determined for 1.4% of the total number of fish (n = 138,644) of the discard sampling programmes. These fish are removed from the analyses. Because the discard samples contain mainly ages 1 to 4, the statistical modelling was limited to these age classes.

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2.4. Statistical modelling 2.4.1. Response variable The raw measurements consist of the number of discarded plaice yi,l of length l observed in the subsample of tow i. Individuals of length l ranging between 1 and maximum length L belong to age a and sex s with a probability pa,s,l, which represents one element in the sex specific age–length key. Therefore, the total number of individuals of age a and sex s in the subsample is yi;a;s ¼

L   X yi;l pa;s;l :

ð1Þ

Table 1 Model components and their function in describing discard rates. f(·) stands for tensor product smooth. The covariates age, date, depth, X, and Y are continuous covariates. Cohort, sex, and source are treated as a factor. Model components

Description

cohort

Incorporates year class specific differences in discard rates, i.e. capturing annual variation in recruitment Effect of age by sex Effect of age by data source (i.e. discards or beam trawl survey) Effect of depth Effect of depth which may gradually change by age Effect of depth which may gradually change by age and in time Models variability in discard rates in space Models variability in discard rates in space and by age

f(age|sex) f(age|source) f(depth) f(depth,age)

l¼1

f(depth,age,date)

The total number of individuals of age a and sex s in the entire tow i is Y i;a;s ¼ yi;a;s f i ;

ð2Þ

where fi is the subsample factor, which is the inverse of the fraction of the total discard that is sampled. Finally we can calculate the total number of individuals of age a and sex s per unit of area, where area is the product of time T (hours), fishing speed V (km/hour), and beam width B (km); di;a;s ¼ yi;a;s f i T i

−1

Vi

−1

Bi

−1

:

ð3Þ

Ultimately we are interested in quantifying the number of plaice per unit of area (di,a,s) as a proxy for density in space, but the measurements consist of the number of individuals observed in the sample (yi,a,s). Therefore, within the model, the actual count of discarded individuals in the subsample is treated as the response variable and the log(fi− 1TiViBi) is defined as the model offset:   yi;a;s eQ uasiPoisson λi;a;s ; ϕ

λi;a;s ¼ di;a;s f i −1 T i V i Bi ¼ eηi f i −1 T i V i Bi ¼ eηi þ logðf i

−1

T i V i Bi Þ

;

ð4Þ

where η is the linear predictor, λ is the expected number of individuals in the subsample and ϕ is the dispersion parameter accounting for over-dispersion. In summary, the expected number of plaice per area (di,a,s) is modelled as e η. 2.4.2. Model structure The distribution of juvenile plaice in space and time is the result of complex biological and physical processes, interactions between behaviour, physiology, and habitat (e.g. depth, seabed) characteristics. Therefore, abundance is expected to be a non-linear function of space and time. To capture this non-linearity, smooth functions of the explanatory variables are used in Generalised Additive Mixed Models (GAMMs; (Hastie and Tibshirani, 1990; Wood, 2006) in R (R Development Core Team, 2011). The explanatory variables that can be included in our model are depth, spatial location, date, data source (discards or beam trawl survey), year class, age, and sex of individuals. The exact terms are listed in Table 1. The heterogeneous distribution of plaice in space is most important for this study. In the simplest case the spatial distribution can be modelled using a smooth function of depth. Other spatial processes, such as dispersion from the nursery grounds, seabed type preference, or temperature may influence the distribution of plaice. These processes can collectively be approximated by tensor product smooths of X and Y coordinates. In addition, the spatial distribution or depth preference may differ among ages, within or among years, or a combination of these. This can be captured by adding the corresponding age and year covariates to the depth or spatial smooth. The variable year class reflects differences in cohort strength caused

f(X,Y) f(X,Y,age)

by differences in recruitment (Aarts and Poos, 2009; Van der Veer et al., 2000). Some of the variation in the observations does not reflect the spatiotemporal distribution of plaice, but is the result of the sampling process. Because females grow faster, they are caught earlier in their life than males, but also grow out of the discarded part of the catch at a younger age. This is captured by smooth functions of age which vary by sex, allowing quantification of age specific estimates for males and females separately. Also, the difference in mesh size and fishing speed between the commercial and research vessels will result in age specific differences in retention curves. The smaller mesh size of the survey gear (i.e. 40 mm) will result in higher catches of young individuals. In contrast, older individuals caught by the commercial vessels are more likely to be part of the landed catch, and therefore will not be observed in the discards. The age specific differences between the data sources are captured by allowing the smooth functions of age to vary by data source. The functions f(.) in Table 1 are tensor product smooths with thin plate regression splines (Wood, 2006). The maximum dimensions of the bases used to represent each smooth terms were set to 4. Another source of variation in the observations not captured by the above covariates is unobserved differences among vessels. This difference among vessels can be caused by a skipper effect or unobserved gear characteristics and is expressed as within-vessel residual autocorrelation. Ignoring such non-independence in the data may lead to invalid statistical inference (Fieberg et al., 2010; Valcu and Kempenaers, 2010). Here, we account for the within vessel correlation by treating the intercept as a random effect which varies by vessel. 2.4.3. Model selection We construct a set of models and use approximate BIC (Bayesian Information Criteria) to find the best model (Augustin et al., 2009). The method is approximate because GAMMs are fitted using quasilikelihood methods, while the Poisson log-likelihood is used for calculating the BIC. All models contain a factor for the cohorts, a smooth of age by sex and source (i.e. discard sampling or beam trawl survey), a tensor product smooth of X and Y-coordinates and a smooth function of depth. Next we test whether the dependence on X and Y varies by age and whether the dependence on depth varies by age, or varies by age and year. Model diagnostics for the model favoured by the approximate BIC estimate are evaluated for response and normalised residuals versus the fitted values, and spatial autocorrelation of model residuals. 2.4.4. Model predictions Age, year and sex specific mean spatial predictions and their corresponding standard errors were generated for a regular grid

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with a 0.05° resolution. Predictions were made, such that they reflect numbers per km 2 caught during the surveys. The heterogeneous uncertainty in the predictions owing to non-uniform sampling in space and time is shown by generating standard errors of the predictions at each prediction point. Also, we show the difference in spatial predictions between the 2000 cohort and the cohorts 2003 and 2006 to illustrate changes in distributions between cohorts.

3. Results 3.1. Spatial sampling effort The spatial distribution of sampling effort in the combined Belgian and Dutch datasets reflects the spatial fishing preference of the vessels used opportunistically (Fig. 1A and B). This sampling effort is heterogeneously distributed in space and time, with the highest sampling density located in the southern North Sea. The overlap in fishing effort between the Belgian and Dutch Fleet is limited: the Belgian sampling density was highest on coarse, rocky seabed near the British coast, while the Dutch sampling density was highest on sandy substrate along the Dutch, German and Danish coast. The BTS covers both the coarse, rocky seabed near the British coast, and the sandy substrate along the Dutch, German and Danish coast (Fig. 1C). The BTS also samples the shallow coastal areas where, because of fisheries regulation, fishing is prohibited for large vessels and hence opportunistic discard data is not available. Beam trawl survey samples in the North-west corner of the study area are excluded, because no discard samples are present within 25 km.

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3.2. Sex, age, and length composition The sex ratio of the plaice in the discard samples is 45.3% and 55.7% for females and males respectively, with a standard error of 0.24% for both sexes. The length frequency distributions of the discard data and the survey data show that females are larger than males of the same age (Fig. 2). This dimorphic growth also explains part of the skewness in the sex ratio, because males will be discarded at older ages. In the survey, age 1 is the dominating age in the catches, while in the discard programmes, age 2 is the dominating age. This suggests that small individuals of age 1 were not retained by the 80 mm gear. Age 2 appears to fall completely within the discarded part of the catches. The length distribution of ages 3 and 4 are truncated at the right hand side in the discards compared to the surveys, suggesting that some of these individuals, particularly females, are of marketable size and do not appear in the samples. The percentage of zero-observations in the samples by age, sex and tow was 14.8%.

3.3. Model selection The plaice discard rates by trip reveal considerable variability among vessels. Part of the trip variability can also be accounted for by the covariates in the model. This is reflected in the change of the random effects estimates based on the model with and without the covariates. For the intercept-only model, the among-vessel variance σ2 is 1.67. The most complex model is favoured by the approximate BIC (Table 2). For this final model, the among-vessel variance is reduced to 1.24. The within vessel residual variance is 6.99. The model does

Fig. 1. Spatial distribution of sampling locations (black dots) for the Dutch discard sampling programme (A), the Belgian sampling programme (B), and the systematic beam trawl survey (C). The grey-scaled grid in (A) and (B) indicates cumulative nominal fishing effort (hours) in the sampling period by the fleet.

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Fig. 2. Length–age distributions in the discards for males (A) and females (B), and in the beam trawl survey for males (C) and females (D) for ages 1 to 4. Increasing ages are drawn in increasingly darker shading. The curves represent spline functions fitted to the age distributions. The vertical dashed line indicates minimum landing size for North Sea plaice.

not appear to exhibit any spatial autocorrelation of model residuals (Fig. 3C and D). This is probably due to the fact that vessel observations are generally close in space and time, resulting in small residual spatial autocorrelation after accounting for within vessel correlation. The final model is η ¼ b0;j þ f ðdepth; age; dateÞ þ f ðX; Y; ageÞ þ f ðagejsexÞ þf ðagejsourceÞ þ cohort   b0;j eN β0 ; σ 2

ð5Þ

Table 2 Model selection results; description of model, log-likelihood (LogL), approximate Bayesian Information Criterion (approx. BIC), and model degrees of freedom (ngam). Model

Model components

LogL

Approx BIC

ngam

1 2

Intercept … + cohort + f(age|sex) + f(age|source) + f(depth) + f(X,Y) … + cohort + f(age|sex) + f(age|source) + f(depth) + f(X,Y,age) … + cohort + f(age|sex) + f(age|source) + f(depth,age) + f(X,Y) … + cohort + f(age|sex) + f(age|source) + f(depth,age) + f(X,Y,age) … + cohort + f(age|sex) + f(age|source) + f(depth,age,date) + f(X,Y) … + cohort + f(age|sex) + f(age|source) + f(depth,age,date) + f(X,Y,age)

− 69122 − 62576

138,274 125,420

3 40.5

− 61537

123,384

84.4

− 62457

125,214

51.5

− 61379

123,089

88.3

− 62506

125,365

92.7

− 61178

122,739

130.3

3 4 5 6 7

where b0,j is the random effect of the intercept describing among trip (subscripted by j) variability, whose variance is quantified by σ2. A detailed description of the model is presented in Table 3. 3.4. Model predictions There is a significant difference (pb 0.001) between the age specific retention between the beam trawl survey and the discard sampling programme. The beam trawl survey catches more fish up to age 2 and from age 3 onwards (Fig. 4A). This difference can be up to a factor of e1.0 ≈ 2.7. Between the ages 2 and 3.5, the catches in the survey are similar to the discards in the commercial fleet. The age specific retention also differs significantly (pb 0.001) by sex, with males being more abundant in the samples than the females. This difference increases with increasing age. This difference can be up to a factor 1.5. The catch rate varies less between sexes than between the data sources, the uncertainty in the estimated effect of sex is smaller as well (Fig. 4A and B). The seasonal spatial distribution of the young plaice between the ages 1 and 4 is estimated for each of the cohorts sampled. Because data are available for the period 2000–2009 there are 13 cohorts in the data set (cohorts 1997–2008). The cohort born in 2000 is shown in Fig. 5. The youngest ages are found in the shallow coastal areas close to the Wadden Sea. As the fish grow older, the higher densities are observed in the deeper areas offshore. This continues until they are approximately 2.5 years old, after which the spatial distribution becomes more homogeneous (Fig. 5). In addition to the relative changes in spatial distribution, the overall catch rate also decreases as the fish grow older. This is most likely a result of the larger geographic region over which the fish is distributed in combination with the mortality that the cohort has experienced.

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Fig. 3. Model diagnostics for the final model. (A) Log observed values versus log fitted values, (B) normalised residuals versus log fitted, (C) spatial distribution of normalised model residuals, and (D) semi-variogram. Predictions of the random effects are accounted for in these residuals. Red circles indicate positive residuals and black circles indicate negative residuals. The semi-variance at distance 0 reflects the correlation of observations among ages and sexes within hauls.

The prediction standard errors show that the uncertainty of the estimates is largest at the “edges” of the observation space, such as the shallow and deep areas with few samples (Fig. 6). For very young ages and in the Northern and Southern region with few samples, the standard errors are also large. Low standard errors are observed where sampling effort was highest. Table 3 Final model results. Note that the cohort effect is contrasted against the 1996 cohort, which is captured by the intercept in the model. ANOVA Term

df

F-value

p-value

cohort f(depth, age, date) f(age|sex) f(age|source) f(X, Y, age)

12 53.1 3.61 3.98 54.61

202.3 13.8 94.6 217.8 90.0

b 0.001 b 0.001 b 0.001 b 0.001 b 0.001

Parameter

Estimate

Residual sd

Residual sd2

Within-vessel variance

1.112

2.644

6.992

Random effect

Additional statistics Dispersion parameter Number data points BIC Log-Likelihood

6.992 31,192 122,739 − 61178.1

Fig. 7 shows the differences in predicted catch rate (on a logarithmic scale) in the Southern North sea between 2000 and 2003, and 2007, by age and sex. The difference is estimated by the term that combines the effect of depth, age, and date in the model. When comparing the spatial predictions of the 2003 and 2006 cohort with those for the 2000 cohort, there is a significant change in the spatial distribution. Particularly for the younger ages, more individuals are found in the deeper offshore areas. This change is more pronounced for the 2006 cohort than for the 2003 cohort. For older individuals, the shift in distribution is less pronounced. 4. Discussion For management and conservation purposes, the ability to use opportunistic data to reconstruct the spatial and temporal trends in biota is invaluable, particularly where regular scientific sampling is expensive. Indeed, opportunistic platforms have already been used to sample species distributions in space and time. Examples are non-design based surveys on marine mammal and sea birds on board vessels of opportunity (Batten et al., 2006; Hyrenbach et al., 2007; Tasker et al., 1984), or discard sampling programmes on board commercial fishing vessels (Borges et al., 2008; Liggins et al., 1997; Madsen et al., 2013–this volume). The challenge is to combine different data sources into a single, flexible model framework. In this study the data stems from many different vessels, some of which (e.g. the commercial and survey vessels) have different age-specific retention curves.

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Fig. 4. Component smooth functions on the scale of the linear predictor for age by source for the beam trawl survey (A), and for age by sex for males (B). The grey shaded regions indicate the confidence bands for the smooths.

The combination of opportunistic data and annual research-vessel survey data allows reconstructing the spatiotemporal distribution of plaice using valuable fishery-dependent information. In addition, the use of data derived from the fishing vessels may aid the dialogue between different stakeholders about the changes observed in the marine ecosystem.

4.1. The distribution of juvenile plaice in space and time The results (Fig. 5) show a pattern of the gradual offshore movement of recruits from the nursery areas of the Wadden Sea, German Bight and the Wash, into deeper water. This corresponds to earlier observations in the plaice nursery areas (Bailey, 1997). The cohort effect in the model captures the large inter-annual variations in plaice recruitment reported by Van der Veer et al., 2000, 2011 related to both large-scale and local effects such as temperature, food availability, natural predation, and larval transport (Bolle et al., 2009; Fox et al., 2000; Nash and Geffen, 2012). In addition to the variability in the densities caused by interannual differences in recruitment, also the relative distribution of juvenile plaice has changed over time. Historically, individuals up to age 2 would remain in the estuaries of the shallow coastal zones (Borley and Thursby-Pelham, 1924; Thursby-Pelham, 1927, 1932). The change in spatial distribution appears to have taken place in the 1980s and 1990s, when juvenile plaice moved to deeper water earlier in life (van Keeken et al., 2007). Accordingly, 1 year old plaice have left the Dutch Wadden Sea almost completely since the late 1990s (Van der Veer et al., 2000). This study shows that also in more recent years (i.e. from cohorts 2000 to 2006) the offshore shift of juveniles (ages 1 and 2) into deeper waters continued (Fig. 7). The distribution of age 3 appears unchanged during the same period. The causes for the changes in migration patterns of juvenile plaice are unknown, but likely linked to changes in temperature, food availability, competition, or predation. The temperature in the coastal waters of the south-eastern North Sea has increased since the 1970s and 1980s (Teal et al., 2008). The offshore shift of juveniles could be a response to reduced growth rates in the coastal areas. The ambient temperature strongly affects the growth of juvenile flatfish, especially in spring (Van der Veer and Witte, 1993). In summer, food limitation becomes important (Teal et al., 2008), especially since the decrease in primary and secondary (benthic) productivity of the coastal areas from the 1980s onwards (Philippart et al., 2007). If temperatures are too high, growth rate is likely to be negatively affected, and for plaice this occurs at

temperatures above 20 °C (Fonds et al., 1992; Freitas et al., 2010; van der Veer et al., 2009). In the model, we do not account for the actual movement that causes the changes in distribution of juvenile plaice in space and time. Mature fish may swim for extended periods in midwater, where they are outside of the reach of the gear. Electronic data storage tags on mature female plaice showed that swimming occurs during migration and spawning (October to March) but that during summer, the adult females rarely left the sea-bed (Hunter et al., 2009). Such seasonality in swimming behaviour could affect the catch efficiency of the gear and thus weaken the link between catches and densities.

4.2. Methodology To understand the spatiotemporal distribution of some organisms, the use of opportunistic data is a necessity. However, there are some shortcomings in the methods and data that should be considered when interpreting the results. First, because ageing is time consuming, only a limited number of fish was used to determine the age of each measured individual. The age–length keys used for conversion of lengths to ages in the discard sampling programme were aggregated by quarter, ignoring possible spatial differences in the age-specific size distribution. The beam trawl survey age–length keys on the other hand were collected for several areas, thus including possible spatial differences in the age-specific size distribution. Another potential issue when interpreting the results is the possible inability to differentiate between correlations caused by vessel-specific differences in fishing operation or by fine-scale spatiotemporal patterns in the distribution of the study species. For example, such fine-scale patterns could result from patchy seabed structures. If data comes from one vessel sampled in a specific area, the statistical model may attribute the spatially and temporally correlated variance in the observations to vessel effect, captured by the random effect. Hence, the size of our random effect may be over-estimated. Only a more intensive sampling programme would allow disentangling the effects of vessel-specific differences in fishing operation, and fine-scale spatiotemporal patterns in the distribution of the study species. Our model results indicate over-dispersion in our count data, meaning that there is more variability in the data than expected under the assumption of a Poisson error distribution. The reason for this over-dispersion cannot be determined without more analyses. One possible reason is the treatment of the survey data, that partly had to be treated as if no sub-sampling had occurred. As a result,

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Fig. 5. Model predicted spatiotemporal distribution of density of females (left panels) and males (right panels) of cohort 2000 for 1.5 to 3.5 year old plaice in the North Sea. Note that each column of panels represents a single cohort throughout its life.

small errors that occur when counting the fish in the sub sample get amplified when the subsampling factor is applied. This does not affect the mean estimates but likely inflates the error in the estimates. The spatial model presented here uses an age-varying smooth function of latitude and longitude and an age-varying smooth function of

depth to describe the spatial distribution of plaice. More complex, finescale spatial differences may not be appropriately captured by the model, or may require a large number of parameters. Such small scale differences may reflect the species dependence on biotic or abiotic covariates, such as sediment type, currents, distance to the coast or nursery

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Fig. 6. Standard errors of spatiotemporal model predictions on the log scale for females (left panels) and males (right panels) of cohort 2000 for 1.5 to 3.5 year old plaice.

areas. Including such covariates, now approximated by the complex latitude–longitude smooth, may improve the spatial predictions and our understanding of how marine organisms distribute themselves and the ecological niche they occupy.

5. Conclusions This study illustrates how spatially non-uniform, opportunistic data on species distributions can be used within a flexible statistical

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Fig. 7. Model predicted differences in density of females between cohort 2000 and the cohorts 2003 (left panels) and 2006 (right panels) for 1.5 to 3.5 year old plaice in the North Sea.

model framework to reconstruct the spatiotemporal distribution of marine organisms. We used a well-known commercial species, plaice, sampled on board commercial fishing vessels. A similar exercise could be carried out using data on other species or from other sampling programmes. The North Sea plaice discard data from commercial

fishing vessels combined with the annual beam trawl survey reveals the gradual movement of juvenile plaice from the nursery areas towards deeper waters and we show this pattern has changed in the last decade: in recent years, juvenile plaice are more abundant in deep, offshore waters.

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Acknowledgements We thank all the observers, ship owners and crews who have contributed to the data collection that made this study possible. Adriaan Rijnsdorp, Stijn Bierman, and two anonymous reviewers provided valuable suggestions. Finally we thank Tammo Bult and Els Torreele for creating the funding opportunities for collaborating on this study. The EU MariFish and BEMO funding to JJP, ATMvH, and GA is gratefully acknowledged.

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