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Morphometric differences between sub-populations of northern shrimp (Pandalus borealis). A case study from two adjacent fjords in Iceland
Regional Studies in Marine Science (
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Morphometric differences between sub-populations of northern shrimp (Pandalus borealis). A case study from two adjacent fjords in Iceland Ingibjörg G. Jónsdóttir a,∗ , Anika K. Guðlaugsdóttir a , Hjalti Karlsson b a
Marine Research Institute, Skúlagata 4, PO Box 1390, 121 Reykjavík, Iceland
b
Marine Research Institute, Árnagata 2, 400 Ísafjörður, Iceland
article
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Article history: Received 6 November 2014 Received in revised form 19 April 2015 Accepted 27 April 2015 Available online xxxx Keywords: Northern shrimp Pandalus borealis Morphometric Discrimination
abstract Northern shrimp (Pandalus borealis) are considered to represent localized populations in inshore and offshore Icelandic areas, with limited connectivity during adult stages. Due to potentially high larval drift it is unlikely that genetic differences are observed between nearby locations. Hence other methods, like phenotypic characters, are commonly used to distinguish between populations. In this study, the population structure of 1 and 2 year old northern shrimps was studied in two adjacent fjords in northwest Iceland. Samples were collected from three different areas within one fjord and a single sample was collected in an adjacent fjord and used as an outgroup. By using morphometric analysis, with ten different body measurements, it was possible to discriminate between northern shrimp from all the four areas within the two fjords. Discriminant function analysis correctly classified 42%–79% and 41%–57% of 1 and 2 year old shrimps, respectively. Even though this does not confirm the existence of genetically distinct populations, it does reflects life history and phenotypic difference among the areas. Body morphometrics therefore proved to be a promising method to discriminate between 1 and 2 year old northern shrimps. © 2015 Published by Elsevier B.V.
1. Introduction Understanding stock structure is important when managing multi-stock commercial fisheries as different stocks may respond differently to exploitation and rebuilding. Information on stock structure is especially important for species that are under high exploitation pressures and are being managed as a homogeneous stock. Proper knowledge of the stock structure will promote measures that are likely to prevent overfishing on different subpopulations, preserve genetic diversity and help ensure sustainable exploitation of the stock (Begg and Waldman, 1999; Booke, 1981; Stephenson, 1999). Phenotypic characters such as meristics and morphometrics have commonly been used for stock identification (Ihssen et al., 1981; Swain and Foote, 1999). Phenotypic differences may indicate a prolonged separation of individuals inhabiting different environments, but not necessarily genetic differentiation (Begg and Waldman, 1999). Hence, phenotypic methods may be useful
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Corresponding author. Tel.: +354 575 2000. E-mail address: [email protected] (I.G. Jónsdóttir).
http://dx.doi.org/10.1016/j.rsma.2015.04.002 2352-4855/© 2015 Published by Elsevier B.V.
to detect differences among genetically homogeneous groups and may give indications on population structure (Begg et al., 1999). As such, phenotypic methods were shown to be useful indicators of stock structure of Atlantic cod (Gadus morhua), haddock (Melanogrammus aeglefinus) and yellowtail flounder (Limanda ferruginea) stocks in the northwest Atlantic Ocean where differences in life history parameters were generally maintained between the populations (Begg et al., 1999). In the past decades, advances in morphometric analysis have occurred, offering image analysis as an efficient and powerful technique to detect differences among groups (Cadrin and Friedland, 1999). Image analyses on body morphometrics are relatively inexpensive and the great advantage is that it is possible to analyse large numbers of samples in a relatively short time. The next step is to apply multivariate statistical approaches, like discriminant function analysis, principal component analysis and cluster analysis, which have been applied to study the morphometric differences among populations of crustacean species (Debuse et al., 2001; Konan et al., 2010; Tsoi et al., 2005). Northern shrimp (Pandalus borealis) has a circumpolar distribution and is widely distributed in the northern hemisphere (Bergstrom, 2000). It occupies both offshore and inshore systems and genetic differences were observed between these two systems
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in Icelandic (Jónsdóttir et al., 1998) and Norwegian waters (Martinez et al., 2006). Due to the important shrimp larval drift (which can be up to 500 km (Storm and Pedersen, 2003)) it is unlikely that genetic differences would be detected between locations within a single fjord system. The population structure could, however, be complicated and several isolated populations may be found within what is thought to be a panmictic population (Knutsen et al., 2015). Several non-genetic parameters have been used to study population structure of the northern shrimp, such as life history parameters, for instance length at which 50% of females are mature females (L50 ) and maximum length (Lmax ) (Skúladóttir and Pétursson, 1999), length at sex change (Charnov and Anderson, 1989), growth rate and age at first female maturity (Teigsmark, 1983). However, morphometric analysis using size and shape of individual shrimps have seldom been applied for the purposes of discrimination. There are nevertheless several studies where morphometric parameters were used to discriminate among individuals from different areas of e.g. the northern shrimp (Kartavtsev et al., 1993), the brown shrimp (Crangon crangon) (Beaumont and Croucher, 2006), and the kuruma shrimp (Penaeus japonicus) (Tsoi et al., 2005). Even though body morphometric has not often been applied to study stock structure of shrimp species, it has been used successfully to discriminate between sub-populations among sites of other crustaceans, for instance the shore crab (Carcinus maenas) (Brian et al., 2006), the European lobster (Homarus gammarus) (Debuse et al., 2001) and the American lobster (Homarus americanus) (Saila and Flowers, 1969). For management purposes it is necessary to find methods that can be used to distinguish between populations of northern shrimp. The main objective of this study was to examine whether body morphometric analyses is a useful tool in discriminating between possible sub-populations of northern shrimp. This was performed by studying the body morphometric of 1 and 2 year old shrimps from three different locations within a single fjord system. Furthermore, a single sample from an adjacent fjord was collected and used as an outgroup. 2. Materials and methods 2.1. Sampling sites Ísafjarðardjúp is the largest fjord on the Westfjord peninsula in the northwest of Iceland. The fjord is 75 km long and the width at the mouth is approximately 20 km. Ísafjarðardjúp includes many fjords and bays. The depth in the middle of the fjord is 110–130 m but closer to the coast it is 40–60 m. Ísafjarðardjúp does not have a sill near the mouth and the water exchange into the fjord is rather unrestricted, currents mainly flow into the fjord on the south side and out on the north side (Valdimarsson, personal communication). Shrimp samples from three different areas of the fjord (Fig. 1) were collected from a shrimp survey conducted in February 2013. The tow length was 2 nautical miles, and the tow speed was 2.0–2.2 knots. Arnarfjörður is on the Westfjord peninsula in the northwest of Iceland. The fjord is a two-armed, threshold fjord. It is 40 km long and the width is approximately 7 km. The maximum depth is approximately 110 m. A 10–30 m high threshold cuts across the main fjord. Like in Ísafjarðardjúp, currents mainly flow into the fjord on the south side and out on the north side (Valdimarsson, personal communication). A single shrimp sample was gained from the catch of a commercial fishing boat in the end of November 2012 (Fig. 1). All samples, from Arnarfjörður and Ísafjarðardjúp, were collected with a standard commercial shrimp bottom trawl with 40 mm squared meshes in the cod-end. As morphometric differences may be related to local environmental conditions, the mean sea surface and bottom temperatures were calculated for each area and used as a proxy for
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Fig. 1. Shrimp samples were collected from one location in Arnarfjörður (area 1; N = 571) and three locations in Ísafjarðardjúp (areas 2–4, with N = 1787, 643 and 726, respectively). Depth contours at 100 m.
temperature differences between them. Sea surface and bottom temperature were recorded for each station during an annual shrimp survey, which is a standardized survey conducted in autumn (September/October) every year since 1988 (Jónsdóttir et al., 2012). Sea surface temperature was determined using a thermometer while the sea bottom temperature was determined using a pre-calibrated trawl sensor (Scanmar) attached to the headline. During the survey, 22 stations were sampled in Arnarfjörður and 28 stations in Ísafjarðardjúp. For this present study, 3–7 stations in the nearby surrounding of the sampling location were chosen each year, leading to a total of 114 temperature recordings. The mean sea surface and bottom temperature were calculated for each area in the period from 2007 to 2012. 2.2. Individual measurements on shrimps Shrimps from each area were length measured using sliding callipers and the individuals grouped in 0.5 mm carapace length intervals. In total 3727 shrimps were length measured. Sex and maturity of all individuals were determined as described in Rasmussen (1953) and McCrary (1971). For the analysis, the same year class from each area was chosen. This was done to try to eliminate the year class effect from the analysis. However, due to difficulties in age determination of shrimps, the length distribution in each area was used to estimate the length interval for 1 and 2 year old shrimps (Fig. 2; Table 1). The modes of 1 and 2 year old shrimps were not easily distinguishable in all areas. Length frequencies were analysed for each area and the modes were estimated visually. The mode around 12–13 mm and 17–19 mm were estimated as 1 and 2 year old shrimps, respectively (Fig. 2). Northern shrimp is a protandric hermaphrodite, i.e. each individual matures and functions first as a male, and after a transitional phase, becomes a female (Shumway et al., 1985). In the study areas, the majority of the shrimps have gone through sex change when they are 2 years old. The proportion of 1 year old shrimps was very low in area 2, which resulted in great numbers of measured shrimps. In total, 749 individuals were randomly chosen for the morphometric study (Table 1). Older shrimps were not used in the study, as determination of their age from the length distribution is hard. Hence, we recognize that there may be some mixing of cohorts in the analysis. Broken shrimps, e.g. with broken telson, were excluded from the study. Shrimps were photographed using a Canon EOS 10D single lens-Reflex digital camera (sensor size 22.7 mm × 15.1 mm) and
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Table 1 Overview of sampling dates, sampling depth, length groups of each age (see determination in Fig. 2), proportion of females within age groups, number of individuals measured in each age group (N ) and number of randomly chosen shrimps that were used in the morphometric analysis. For locations see Fig. 1. Area
Date
1
Nov 24 2012
Depth (m) 55
2
Feb 5 2013
75
3
Feb 5 2013
50
4
Feb 5 2013
110
Length group (mm)
Age estimation
N
Proportion females
N morphology
13–15.5 17–19 12–14 16–18 11.5–13.5 17–18.5 13–15 18–20.5
1 2 1 2 1 2 1 2
169 402 154 1633 381 262 183 543
7.1 96.8 0 85.2 0 81.3 2.7 96.9
66 111 69 100 100 102 100 101
2.3. Statistical analyses
Fig. 2. Length distribution of male (solid line) and female (dashed line) shrimps in Arnarfjörður (area 1) and Ísafjarðardjúp (areas 2–4).
a Canon lens with a focal length of 28–135 mm. Each shrimp was placed on a white board, orientated in a consistent manner, lying with the left side up and with the head facing to the left. Furthermore, a picture was taken of the dorsal part of the telson of each shrimp. The camera was attached to a tripod and the shrimp was symmetrically lit up by four led lamps attached to each corner of the board. The aperture, shutter speed and exposure parameters were adjusted manually for each size group in every session and the white balance was automatically set. A total of ten different morphometric characters of each shrimp were measured using the ImageJ software (Abramoff et al., 2004) (Fig. 3).
Sea surface and bottom temperature were tested for normality and homogeneity of variance and no transformation was necessary. All 10 morphometric variables were used in the discriminant analysis. All variables were tested for normality and homogeneity of variance, and transformed if necessary. Leg, abdominal somite height 1 and 2 and abdominal somite length 2 and 6 were standardized by natural-log transformation. Body measurements strongly correlate with body size. Analysis of covariance (ANCOVA) was used to determine the effect of shrimp carapace length on the magnitude of the body measurement variables, with length as a covariate, and area as a factor. Where the effect of shrimp length was significant, the product of the shrimp length and the common within-group slope (b) from the ANCOVA for a given variable and age group was subtracted from the variable to create a standardized variable. After standardization, no character measurements were observed to be correlated significantly with carapace length, indicating that the transformation had removed size effects successfully. Multivariate analysis of variance (MANOVA) for all variables combined was used to test for overall differences among groups. When differences were detected, mean differences between areas for individual variables were tested with ANOVA. Tukey HSD was then used to examine individual variables in order to explain any significant differences detected by the ANOVA. The level of statistical significance was defined at 0.05. Linear discriminant analysis of the standardized data was used to discriminate between the individuals from the four different areas. This technique is used to discriminate between two or more naturally occurring groups when the identity of the individuals is known. Furthermore, it is used to identify which variables are the most important in distinguishing between areas and how well the individuals are classified to their group (Duarte Silva and
Fig. 3. Morphometric characters measured on (a) the left side of each shrimp and (b) the telson. Length of carapace (CL2), abdominal somite (ASL2, ASL6), diagonal carapace (DCL), leg (L), spike (S) and telson (T1, T2) as well as height of abdominal somate (ASH1, ASH2).
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Table 2 The first two standardized function coefficients (I and II) from the discriminant analysis for 1 year old shrimps and 2 year old shrimps. Standardized coefficients with large absolute values correspond to parameters with greater discriminating ability. Parameter
Standardized canonical discriminant function coefficient Age 1 I
Carapace length 2 Leg Abdominal somite height 1 Abdominal somite height 2 Abdominal somite length 2 Abdominal somite length 6 Telson Telson 2 Spike Diagonal carapace length
Age 2 II
−1.99 −3.87 1.57 9.47 2.94 8.78 8.01 5.96 −2.63 18.34
−1.68 10.04 −6.19 0.09 −5.61 6.44 13.46 1.73 −7.92 9.12
I 1.70 −1.70 4.08 5.64 −3.68 13.25 8.69 0.19 −6.01 −16.61
II 13.32
−8.95 −0.25 −11.45 9.50 9.29 2.58 −2.47 0.61 9.12
Fig. 4. Mean sea surface (solid line) and bottom temperature (dashed line) in Arnarfjörður (area 1) and Ísafjarðardjúp (areas 2–4). The temperature measurements were obtained from the annual shrimp survey in September/October during 2007–2012. For locations see Fig. 1.
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Stam, 1995). It creates discriminant functions which discriminate between the groups. Once the discriminant functions had been derived, classification functions were used to determine to which area each case most likely belonged. The classification accuracy was estimated with ‘‘leave-one-out’’ cross validation, where each sample was removed from the data set and was classified by means of the classification rule estimated on the basis of the remaining n − 1 samples (Duarte Silva and Stam, 1995). Prior probabilities of each group (age and area) ranged between 20% and 30%, indicating that classification procedures worked well when groups were classified at a percentage higher than expected by chance. Discriminant function analyses were restricted to common cohorts for all groups. The discriminant analysis was performed using the MASS library in the R statistical computing environment (RCoreTeam, 2014). 3. Results The mean surface and bottom temperature have been decreasing since 2009, except the mean bottom temperature in area 1 (Fig. 4). The mean sea surface temperature varied between 7.3 and 10.3 °C but it did not vary significantly between areas (ANOVA, d.f = 3,109, P = 0.202). The mean bottom temperature was in general slightly lower (5.7 to 9.3 °C) than the mean sea surface temperature (Fig. 4), and did not vary significantly between areas 2 and 4, whereas the mean bottom temperature in area 1 was significantly lower than in the other areas (Tukey HSD, P < 0.001). Using all standardized variables (Fig. 5), the overall shape differed significantly between areas for both ages (MANOVA, d.f = 3,331 (1 year old), d.f = 3,410 (2 year old), P < 0.001 for both). All the standardized variables differed significantly among areas (ANOVA, d.f = 3,331 (1 year old) and 3,410 (2 year old), all P < 0.001), except the distance to the first spike (ANOVA 1 year old: d.f = 3,331, P = 0.132, 2 year old: d.f = 3,410, P = 0.05). The discrimination analysis provided further evidence for a separation between the areas (Fig. 6). Differences were more pronounced for the 1 year old compared to the 2 year old shrimps. For the 1 year old shrimps, the first discriminant function explained 88.7% and the discriminant scores differed significantly between all four groups (Tukey HSD, P < 0.001), indicating that 1 year old shrimps from these areas are likely to remain separate. The diagonal carapace length (DCL) explained most of the variation in the first discriminant function (Table 2). The second discriminant function of the 1 year old shrimps explained 9.1%. The telson and leg lengths explained most of the variation in the second discriminant function (Table 2). The second discriminant function
Fig. 5. Medians, the 25th and 75th percentiles, and whiskers (the upper and lower 1.5 × inter-quartile ranges) of the ten body morphometric parameters (length of carapace (CL2), abdominal somite (ASL2, ASL6), diagonal carapace (DCL), leg (L), spike (S) and telson (T1, T2) as well as height of abdominal somate (ASH1, ASH2)) for 1 and 2 year old shrimps from four different areas.
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a
b
Fig. 6. Discrimination of (a) 1 year old and (b) 2 year old sampled in 4 areas in Arnarfjörður and Ísafjarðardjúp using various body measurements. For each area, the large symbol denotes the mean, surrounded by 95% confidence ellipses. For locations see Fig. 1 (area 1 (squares), area 2 (circles), area 3 (up pointing triangles), area 4 (diamonds)).
did not distinguish area 4 from 1 or 3, but the discriminant scores differed significantly between all other areas (Tukey HSD, P < 0.01). For the 2 year old shrimps, the first discriminant function explained 78.7% and the discriminant scores varied significantly between all four areas (Tukey HSD, P < 0.001). The abdominal somite length 6 and the diagonal carapace length explained most of the variation in the first discriminant function (Table 2). The second discriminant function of the 2 year old shrimps explained 18.6%. The discriminant scores of the second function varied significantly between all four areas (Tukey HSD, P < 0.001). The abdominal somite height 2 and the carapace length 2 explained most of the variations of the second discriminant function (Table 2). The two year old shrimps are therefore also likely to remain separate but the worse discrimination of the 2 year old shrimps may indicate more mixing of individuals with increased age. For 1 year old shrimps, the classification ranged between 42% and 79% (Table 3). Shrimps from Arnarfjörður (area 1) were never classified to area 4 and vice versa. The incorrectly classified shrimps from area 3 were in most cases classified to areas 1 and 2, while only 8% of the individuals were classified to area 4 (Table 3). For 2 year old shrimps, the classification accuracy was lower than for 1 year old shrimps, and ranged between 41% and 57% (Table 3). In general, the incorrectly classified individuals in area 1 were rarely classified to area 4: fewer than 10% of these individuals were classified to area 4 (Table 3). Based on the discriminant analysis and the classification accuracy, body morphometrics proved to be a promising method to discriminate between northern shrimp from different areas. 4. Discussion The results of this present study showed differences in body measurements of northern shrimp sampled from four distinct areas in two adjacent Icelandic fjords. It was possible to discriminate between shrimps (both 1 and 2 year old) among the areas and therefore northern shrimp inhabiting these areas are likely to remain largely separate, at least during the first two years of their lifespan. The differences were more pronounced for the 1
Table 3 Classification success (%) from discriminant analysis between shrimps from four different areas at the age of 1 and 2. The rows and columns are the observed and predicted areas, respectively. Bold numbers indicate classification success for correctly classified shrimps from each area. All other numbers indicate the incorrectly classified shrimps. For areas see Fig. 1. Area
1 2 3 4
Age 1
Age 2
Predicted area
Predicted area
1
2
3
4
1
2
3
4
68 7 12 0
11 42 10 14
21 18 70 7
0 33 8 79
53 13 28 2
21 41 10 29
23 13 46 12
3 33 16 57
year old compared to the 2 year old shrimp which might indicate more mixing when the shrimps grow older. Even though this does not confirm the existence of genetically distinct populations, it does reflect life history and phenotypic differences among the groups. Variations in body morphometric have not been routinely applied for shrimp species and results from those studies are conflicting: no distinct difference in morphometric traits was observed between kuruma shrimp despite being genetically distinct (Tsoi et al., 2005), while a difference was detected in brown shrimp (Beaumont and Croucher, 2006), and morphological and genetic differences were observed in northern shrimp (Kartavtsev et al., 1993). Both (1) environmental factors and (2) genetic isolation on could be the origin of differences in body shape in groups from different areas. (1) Several studies have expressed that environmental factors influence differences in morphometrics of crustacean species (Brian et al., 2006; Chow and Sandifer, 1991; Debuse et al., 2001; Dimmock et al., 2004). The only environmental factor presented in the present study showed that the bottom temperature was lower in Arnarfjörður compared to Ísafjarðardjúp. The results of the present study do not give the opportunity to determine which factors influenced the differences in morphometrics of shrimps in the present study, and highly likely that it was influenced by the interaction of various factors. (2) The planktonic phase of shrimps lasts for approximately three months (Astthorsson and Gislason, 1991; Ouellet and Allard, 2006), thus
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shrimp larvae may be transported substantial distances during that time. Estimated transport of larvae in West Greenland waters was about 3.1 km d−1 (Pedersen et al., 2002) and they may travel distances up to 500 km (Storm and Pedersen, 2003). Ísafjarðardjúp does not have a sill near the mouth and the water exchange into the fjord is rather unrestricted. Due to the flow of currents in the fjord, shrimp larvae hatching in the outer part of the fjord may flow with currents away from the hatching site and settle in the inner part of the fjord. Shrimp larvae may even flow from Arnarfjörður to Ísafjarðardjúp. Genetic differences between the areas are therefore likely to be minimal. In fact, genetic differences were not observed between areas within Ísafjarðardjúp, and not even between Arnarfjörður and Ísafjarðardjúp in an earlier study (Jónsdóttir et al., 1998). The morphometric differences observed between the areas within Ísafjarðardjúp are therefore likely to reflect differences between the local environmental conditions rather than restricted gene flow. The fact that northern shrimps from different areas within the same fjord system remain largely separate at least during the first two years of their lives as both males and then females indicate that their population structure may be defined at the time of settlement. Even though it was possible to discriminate between the areas in this present study, it does not indicate that the shrimps are localized at the same area during the whole life cycle. After settlement, shrimps may migrate from the area of settlement and the lower discrimination between the 2 year old shrimps compared to the 1 year old may indicate that more mixing occurs with age. A tagging experiment in Ísafjarðardjúp in 1971 showed that the few recaptures were recovered near the tagging location (Skúladóttir, 1985). Shrimps may nevertheless migrate some distances and migration distance of shrimps in the Gulf of St. Lawrence, which is somewhat larger than the two fjords of the present study, was estimated to be a maximum 85 km yr−1 (Simard and Savard, 1990). Migration may furthermore be influenced by gadoid species, as northern shrimp may retrieve away from areas occupied by gadoids as it was shown in Arnarfjörður (Björnsson et al., 2011). Body morphometric analysis has the advantage over other phenotypic methods, like L50 and Lmax , that a single cohort can be studied at time, while measurements from all year classes are used when most other methods are applied. This gives a good opportunity to study early life stages of northern shrimp although little information is available about the reproductive patterns and the reproductive biology of northern shrimp. Better knowledge of juveniles is important for fisheries management as the year class strength regarding northern shrimp is probably largely driven by environmental changes during the early life stages (Parsons and Colbourne, 2000; Shumway et al., 1985). The population dynamics during the first years of marine species are often poorly known, and the processes causing the interannual variations in recruitment remain largely unexplained. Therefore, information on the early life history and the origin of juveniles is needed in order to understand the spatial stock structure and the mechanism responsible for variable recruitment. As noted above, the lower discrimination observed between the 2 year old shrimps compared to the 1 year old shrimps may indicate more mixing of older shrimps. However, two other reasons may explain the lower discrimination. First of all, it may be due to the occurrence of older shrimps in the samples. When different year classes are combined in a discriminant analysis it is possible that age and year-class effects explain a part of the discrimination among the groups. It has actually been noted in various marine species, such as the Atlantic mackerel (Castonguay et al., 1991) and coho salmon (Taylor and McPhail, 1985). Studies on body morphometric should preferable concern age-determined shrimps, a parameter that is possible to obtain, as shown in
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previous studies (Kilada et al., 2012). Second, the analyses were performed using both sexes, which may have some impact on the results. In fact, morphological variation was observed between sexes, both in white shrimp (Penaeus vannamei) (Chow and Sandifer, 1991) and other invertebrates, for instance the European lobsters (Homarus gammarus) (Debuse et al., 2001). However, in the present study, the majority of 1 year old shrimps were males while the majority of 2 year old shrimps were females and therefore, the influence is likely to be minimal. Information on stock structure is central to fisheries management and it has been argued that stocks may still be treated as separate management units if genetic differences cannot be detected and stock differentiation can be shown with alternative methods. The processes which generate stock structure are driven throughout the lifespan of the individual. Therefore, in order to understand how stock structure is maintained, information on dispersal, distribution and migration of both juveniles and adults is needed. Integration of results from various methods will help to clarify factors determining the population structure (Begg and Waldman, 1999). Due to larvae drift and lack of genetic differences in small systems (e.g. fjords) alternative methods such as morphometrics are useful to study the stock structure within the system. The results of this study illustrate the potential use of inexpensive and relatively short-time morphometrics methods to study stock structure of northern shrimp before more complex and more expensive methods are applied. Acknowledgements We would like to thank the captains and crews at the vessels used for sampling and researchers at the Marine Research Institute in Ísafjörður for sampling in Ísafjarðardjúp. Furthermore, we thank Jón Páll Jakobsson, captain of ‘Andri’ for sampling of shrimps in Arnarfjörður, Teresa Silva for assistance with the ImageJ software, Sverrir D. Halldórsson and Valerie Chosson for camera support and Dr. Guðmundur J. Óskarsson, Jónas P. Jónasson and two anonymous reviewers for valuable comments on an earlier draft of the manuscript. References Abramoff, M.D., Magalhaes, P.J., Ram, S.J., 2004. Image processing with image. J. Biophotonics Int. 11, 36–42. Astthorsson, O.S., Gislason, A., 1991. Seasonal abundance and distribution of Caridea larvae in Ísafjord-deep, north-west Iceland. J. Plankton Res. 13, 91–102. Beaumont, A.R., Croucher, T., 2006. Limited stock structure in UK populations of the brown shrimp, Crangon crangon, identified by morphology and genetics. J. Mar. Biol. Assoc. UK 86, 1107–1112. Begg, G.A., Hare, J.A., Sheehan, D.D., 1999. The role of life history parameters as indicators of stock structure. Fish. Res. 43, 141–163. Begg, G.A., Waldman, J.R., 1999. An holistic approach to fish stock identification. Fish. Res. 43, 35–44. Bergstrom, B.I., 2000. The biology of Pandalus. In: Advances in Marine Biology, Vol. 38. pp. 55–245. Björnsson, B., Reynisson, P., Solmundsson, J., Valdimarsson, H., 2011. Seasonal changes in migratory and predatory activity of two species of gadoid preying on inshore northern shrimp Pandalus borealis. J. Fish Biol. 78, 1110–1131. Booke, H.E., 1981. The conundrum of the stock concept — are nature and nurture definable in fishery science. Can. J. Fish. Aquat. Sci. 38, 1479–1480. Brian, J.V., Fernandes, T., Ladle, R.J., Todd, P.A., 2006. Patterns of morphological and genetic variability in UK populations of the shore crab, Carcinus maenas Linnaeus, 1758 (Crustacea: Decapoda: Brachyura). J. Exp. Mar. Biol. Ecol. 329, 47–54. Cadrin, S.X., Friedland, K.D., 1999. The utility of image processing techniques for morphometric analysis and stock identification. Fish. Res. 43, 129–139. Castonguay, M., Simard, P., Gagnon, P., 1991. Usefulness of Fourier analysis of otolith shape for Atlantic mackerel (Scomber scombrus) stock discrimination. Can. J. Fish. Aquat. Sci. 48, 296–302. Charnov, E.L., Anderson, P.J., 1989. Sex change and population fluctuations in pandalid shrimp. Am. Nat. 134, 824–827. Chow, S., Sandifer, P.A., 1991. Differences in growth, morphometric traits, and male sexual maturity among Pacific white shrimp, Penaeus vannamei, from different commercial hatcheries. Aquaculture 92, 165–178.
I.G. Jónsdóttir et al. / Regional Studies in Marine Science ( Debuse, V.J., Addison, J.T., Reynolds, J.D., 2001. Morphometric variability in UK populations of the European lobster. J. Mar. Biol. Assoc. UK 81, 469–474. Dimmock, A., Williamson, I., Mather, P.B., 2004. The influence of environment on the morphology of Macrobrachium australiense (Decapoda: Palaemonidae). Aquac. Int. 12, 435–456. Duarte Silva, A.P., Stam, A., 1995. Discriminant analysis. In: Grimm, L.G., Yarnold, P.R. (Eds.), Reading and Understanding Multivariate Statistics. American Psychological Association, Washington, D.C, pp. 277–318. Ihssen, P.E., Booke, H.E., Casselman, J.M., McGlade, J.M., Payne, N.R., Utter, F.M., 1981. Stock identification: Materials and methods. Can. J. Fish. Aquat. Sci. 38, 1838–1855. Jónsdóttir, I.G., Björnsson, H., Skúladóttir, U., 2012. Predation by Atlantic cod Gadus morhua on northern shrimp Pandalus borealis in inshore and offshore areas of Iceland. Mar. Ecol. Ser. 469, 223–232. Jónsdóttir, Ó.D.B., Imsland, A.K., Nævdal, G., 1998. Population genetic studies of northern shrimp, Pandalus borealis, in Icelandic waters and the Denmark Strait. Can. J. Fish. Aquat. Sci. 55, 770–780. Kartavtsev, Y.P., Zgurovsky, K.A., Fedina, Z.M., 1993. Spatial structure of the pink shrimp Pandalus borealis Kröyer, 1838 from the far-eastern seas as proved by methods of population genetics and morphometrics. J. Shellfish Res. 12, 81–87. Kilada, R., Sainte-Marie, B., Rochette, R., Davis, N., Vanier, C., Campana, S., Gillanders, B., 2012. Direct determination of age in shrimps, crabs, and lobsters. Can. J. Fish. Aquat. Sci. 69, 1728–1733. Knutsen, H., Jorde, P.E., Gonzalez, E.B., Eigaard, O.R., Pereyra, R.T., Sannæs, H., Dahl, M., André, C., Søvik, G., 2015. Does population genetic structure support present management regulations of the northern shrimp (Pandalus borealis) in Skagerrak and the North Sea? Ices J. Mar. Sci. 72, 863–871. Konan, K.M., Adépo-Gourène, A.B., Ouattara, A., Nyingy, W.D., Gourène, G., 2010. Morphometric variation among male populations of freshwater shrimp Macrobrachium vollenhovenii Herklots, 1851 from Côte d’Ivoire Rivers. Fish. Res. 103, 1–8. Martinez, I., Aschan, M., Skjerdal, T., Aljanabi, S.M., 2006. The genetic structure of Pandalus borealis in the Northeast Atlantic determined by RAPD analysis. Ices J. Mar. Sci. 63, 840–850. McCrary, J., 1971. Sternal spines as a characteristc for differentiating between females of some Pandalidae. J. Fish. Res. Board Canada 28, 98–100. Ouellet, P., Allard, J.P., 2006. Vertical distribution and behaviour of shrimp Pandalus borealis larval stages in thermally stratified water columns: laboratory experiment and field observations. Fish. Oceanogr. 15, 373–389.
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Parsons, D.G., Colbourne, E.B., 2000. Forecasting fishery performance for northern shrimp (Pandalus borealis) on the Labrador Shelf (NAFO divisions 2HJ). J. Northwest Atl. Fish. Sci. 27, 11–20. Pedersen, S.A., Storm, L., Simonsen, C.S., 2002. Northern shrimp (Pandalus borealis) recruitment in West Greenland waters. Part I. Distribution of Pandalus shrimp larvae in relation to hydrography and plankton. J. Northwest Atl. Fish. Sci. 30, 19–46. Rasmussen, B., 1953. On the geographical variation in growth and sexual development of the deep sea prawn (Pandalus borealis). Rep. Nor. Fish. Mar. Investig. 10, 1–160. RCoreTeam, 2014. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. www.R-project.org/. Saila, S.B., Flowers, J.M., 1969. Geographic morphometric variation in the American lobster. Syst. Biol. 18, 330–338. Shumway, S.E., Perkins, H.C., Schick, D.F., Stickney, A.P., 1985. Synopsis of Biological Data on the Pink Shrimp, Pandalus borealis (Krøyer, 1838). NOAA Tech. Rep. No. NMFS 30. Simard, Y., Savard, L., 1990. Variability, spatial patterns and scales of similarity in size–frequency distributions of the Northern shrimp (Pandalus borealis) and its migrations in the Gulf of St. Lawrence. Can. J. Fish. Aquat. Sci. 47, 794–804. Skúladóttir U., 1985. Tagging and recapture results of Pandalus borealis (Kröyer) in Icelandic waters. ICES C. 1985/K42. Skúladóttir, U., Pétursson, G., 1999. Defining populations of northern shrimp, Pandalus borealis (Krøyer 1838), in Icelandic waters using the maximum length and maturity ogive of females. Rit Fiskid. 16, 247–262. Stephenson, R.L., 1999. Stock complexity in fisheries management: a perspective of emerging issues related to population sub-units. Fish. Res. 43, 247–249. Storm, L., Pedersen, S.A., 2003. Development and drift of northern shrimp larvae (Pandalus borealis) at West Greenland. Mar. Biol. 143, 1083–1093. Swain, D.P., Foote, C.J., 1999. Stocks and chameleons: the use of phenotypic variation in stock identification. Fish. Res. 43, 113–128. Taylor, E.B., McPhail, D., 1985. Variation in body morphology among British Columbia populations of coho salmon, Oncorhynchus kisutch. Can. J. Fish. Aquat. Sci. 42, 2020–2028. Teigsmark, G., 1983. Populations of the deep-sea shrimp (Pandalus borealis Krøyer) in the Barents Sea. Fisk. Skr. Ser. Havundersøkelser 17, 377–430. Tsoi, K.H., Wang, Z.Y., Chu, K.H., 2005. Genetic divergence between two morphologically similar varieties of the kuruma shrimp Penaeus japonicus. Mar. Biol. 147, 367–379.
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Contents lists available at ScienceDirect
Regional Studies in Marine Science journal homepage: www.elsevier.com/locate/rsma
Morphometric differences between sub-populations of northern shrimp (Pandalus borealis). A case study from two adjacent fjords in Iceland Ingibjörg G. Jónsdóttir a,∗ , Anika K. Guðlaugsdóttir a , Hjalti Karlsson b a
Marine Research Institute, Skúlagata 4, PO Box 1390, 121 Reykjavík, Iceland
b
Marine Research Institute, Árnagata 2, 400 Ísafjörður, Iceland
article
info
Article history: Received 6 November 2014 Received in revised form 19 April 2015 Accepted 27 April 2015 Available online xxxx Keywords: Northern shrimp Pandalus borealis Morphometric Discrimination
abstract Northern shrimp (Pandalus borealis) are considered to represent localized populations in inshore and offshore Icelandic areas, with limited connectivity during adult stages. Due to potentially high larval drift it is unlikely that genetic differences are observed between nearby locations. Hence other methods, like phenotypic characters, are commonly used to distinguish between populations. In this study, the population structure of 1 and 2 year old northern shrimps was studied in two adjacent fjords in northwest Iceland. Samples were collected from three different areas within one fjord and a single sample was collected in an adjacent fjord and used as an outgroup. By using morphometric analysis, with ten different body measurements, it was possible to discriminate between northern shrimp from all the four areas within the two fjords. Discriminant function analysis correctly classified 42%–79% and 41%–57% of 1 and 2 year old shrimps, respectively. Even though this does not confirm the existence of genetically distinct populations, it does reflects life history and phenotypic difference among the areas. Body morphometrics therefore proved to be a promising method to discriminate between 1 and 2 year old northern shrimps. © 2015 Published by Elsevier B.V.
1. Introduction Understanding stock structure is important when managing multi-stock commercial fisheries as different stocks may respond differently to exploitation and rebuilding. Information on stock structure is especially important for species that are under high exploitation pressures and are being managed as a homogeneous stock. Proper knowledge of the stock structure will promote measures that are likely to prevent overfishing on different subpopulations, preserve genetic diversity and help ensure sustainable exploitation of the stock (Begg and Waldman, 1999; Booke, 1981; Stephenson, 1999). Phenotypic characters such as meristics and morphometrics have commonly been used for stock identification (Ihssen et al., 1981; Swain and Foote, 1999). Phenotypic differences may indicate a prolonged separation of individuals inhabiting different environments, but not necessarily genetic differentiation (Begg and Waldman, 1999). Hence, phenotypic methods may be useful
∗
Corresponding author. Tel.: +354 575 2000. E-mail address: [email protected] (I.G. Jónsdóttir).
http://dx.doi.org/10.1016/j.rsma.2015.04.002 2352-4855/© 2015 Published by Elsevier B.V.
to detect differences among genetically homogeneous groups and may give indications on population structure (Begg et al., 1999). As such, phenotypic methods were shown to be useful indicators of stock structure of Atlantic cod (Gadus morhua), haddock (Melanogrammus aeglefinus) and yellowtail flounder (Limanda ferruginea) stocks in the northwest Atlantic Ocean where differences in life history parameters were generally maintained between the populations (Begg et al., 1999). In the past decades, advances in morphometric analysis have occurred, offering image analysis as an efficient and powerful technique to detect differences among groups (Cadrin and Friedland, 1999). Image analyses on body morphometrics are relatively inexpensive and the great advantage is that it is possible to analyse large numbers of samples in a relatively short time. The next step is to apply multivariate statistical approaches, like discriminant function analysis, principal component analysis and cluster analysis, which have been applied to study the morphometric differences among populations of crustacean species (Debuse et al., 2001; Konan et al., 2010; Tsoi et al., 2005). Northern shrimp (Pandalus borealis) has a circumpolar distribution and is widely distributed in the northern hemisphere (Bergstrom, 2000). It occupies both offshore and inshore systems and genetic differences were observed between these two systems
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in Icelandic (Jónsdóttir et al., 1998) and Norwegian waters (Martinez et al., 2006). Due to the important shrimp larval drift (which can be up to 500 km (Storm and Pedersen, 2003)) it is unlikely that genetic differences would be detected between locations within a single fjord system. The population structure could, however, be complicated and several isolated populations may be found within what is thought to be a panmictic population (Knutsen et al., 2015). Several non-genetic parameters have been used to study population structure of the northern shrimp, such as life history parameters, for instance length at which 50% of females are mature females (L50 ) and maximum length (Lmax ) (Skúladóttir and Pétursson, 1999), length at sex change (Charnov and Anderson, 1989), growth rate and age at first female maturity (Teigsmark, 1983). However, morphometric analysis using size and shape of individual shrimps have seldom been applied for the purposes of discrimination. There are nevertheless several studies where morphometric parameters were used to discriminate among individuals from different areas of e.g. the northern shrimp (Kartavtsev et al., 1993), the brown shrimp (Crangon crangon) (Beaumont and Croucher, 2006), and the kuruma shrimp (Penaeus japonicus) (Tsoi et al., 2005). Even though body morphometric has not often been applied to study stock structure of shrimp species, it has been used successfully to discriminate between sub-populations among sites of other crustaceans, for instance the shore crab (Carcinus maenas) (Brian et al., 2006), the European lobster (Homarus gammarus) (Debuse et al., 2001) and the American lobster (Homarus americanus) (Saila and Flowers, 1969). For management purposes it is necessary to find methods that can be used to distinguish between populations of northern shrimp. The main objective of this study was to examine whether body morphometric analyses is a useful tool in discriminating between possible sub-populations of northern shrimp. This was performed by studying the body morphometric of 1 and 2 year old shrimps from three different locations within a single fjord system. Furthermore, a single sample from an adjacent fjord was collected and used as an outgroup. 2. Materials and methods 2.1. Sampling sites Ísafjarðardjúp is the largest fjord on the Westfjord peninsula in the northwest of Iceland. The fjord is 75 km long and the width at the mouth is approximately 20 km. Ísafjarðardjúp includes many fjords and bays. The depth in the middle of the fjord is 110–130 m but closer to the coast it is 40–60 m. Ísafjarðardjúp does not have a sill near the mouth and the water exchange into the fjord is rather unrestricted, currents mainly flow into the fjord on the south side and out on the north side (Valdimarsson, personal communication). Shrimp samples from three different areas of the fjord (Fig. 1) were collected from a shrimp survey conducted in February 2013. The tow length was 2 nautical miles, and the tow speed was 2.0–2.2 knots. Arnarfjörður is on the Westfjord peninsula in the northwest of Iceland. The fjord is a two-armed, threshold fjord. It is 40 km long and the width is approximately 7 km. The maximum depth is approximately 110 m. A 10–30 m high threshold cuts across the main fjord. Like in Ísafjarðardjúp, currents mainly flow into the fjord on the south side and out on the north side (Valdimarsson, personal communication). A single shrimp sample was gained from the catch of a commercial fishing boat in the end of November 2012 (Fig. 1). All samples, from Arnarfjörður and Ísafjarðardjúp, were collected with a standard commercial shrimp bottom trawl with 40 mm squared meshes in the cod-end. As morphometric differences may be related to local environmental conditions, the mean sea surface and bottom temperatures were calculated for each area and used as a proxy for
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Fig. 1. Shrimp samples were collected from one location in Arnarfjörður (area 1; N = 571) and three locations in Ísafjarðardjúp (areas 2–4, with N = 1787, 643 and 726, respectively). Depth contours at 100 m.
temperature differences between them. Sea surface and bottom temperature were recorded for each station during an annual shrimp survey, which is a standardized survey conducted in autumn (September/October) every year since 1988 (Jónsdóttir et al., 2012). Sea surface temperature was determined using a thermometer while the sea bottom temperature was determined using a pre-calibrated trawl sensor (Scanmar) attached to the headline. During the survey, 22 stations were sampled in Arnarfjörður and 28 stations in Ísafjarðardjúp. For this present study, 3–7 stations in the nearby surrounding of the sampling location were chosen each year, leading to a total of 114 temperature recordings. The mean sea surface and bottom temperature were calculated for each area in the period from 2007 to 2012. 2.2. Individual measurements on shrimps Shrimps from each area were length measured using sliding callipers and the individuals grouped in 0.5 mm carapace length intervals. In total 3727 shrimps were length measured. Sex and maturity of all individuals were determined as described in Rasmussen (1953) and McCrary (1971). For the analysis, the same year class from each area was chosen. This was done to try to eliminate the year class effect from the analysis. However, due to difficulties in age determination of shrimps, the length distribution in each area was used to estimate the length interval for 1 and 2 year old shrimps (Fig. 2; Table 1). The modes of 1 and 2 year old shrimps were not easily distinguishable in all areas. Length frequencies were analysed for each area and the modes were estimated visually. The mode around 12–13 mm and 17–19 mm were estimated as 1 and 2 year old shrimps, respectively (Fig. 2). Northern shrimp is a protandric hermaphrodite, i.e. each individual matures and functions first as a male, and after a transitional phase, becomes a female (Shumway et al., 1985). In the study areas, the majority of the shrimps have gone through sex change when they are 2 years old. The proportion of 1 year old shrimps was very low in area 2, which resulted in great numbers of measured shrimps. In total, 749 individuals were randomly chosen for the morphometric study (Table 1). Older shrimps were not used in the study, as determination of their age from the length distribution is hard. Hence, we recognize that there may be some mixing of cohorts in the analysis. Broken shrimps, e.g. with broken telson, were excluded from the study. Shrimps were photographed using a Canon EOS 10D single lens-Reflex digital camera (sensor size 22.7 mm × 15.1 mm) and
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Table 1 Overview of sampling dates, sampling depth, length groups of each age (see determination in Fig. 2), proportion of females within age groups, number of individuals measured in each age group (N ) and number of randomly chosen shrimps that were used in the morphometric analysis. For locations see Fig. 1. Area
Date
1
Nov 24 2012
Depth (m) 55
2
Feb 5 2013
75
3
Feb 5 2013
50
4
Feb 5 2013
110
Length group (mm)
Age estimation
N
Proportion females
N morphology
13–15.5 17–19 12–14 16–18 11.5–13.5 17–18.5 13–15 18–20.5
1 2 1 2 1 2 1 2
169 402 154 1633 381 262 183 543
7.1 96.8 0 85.2 0 81.3 2.7 96.9
66 111 69 100 100 102 100 101
2.3. Statistical analyses
Fig. 2. Length distribution of male (solid line) and female (dashed line) shrimps in Arnarfjörður (area 1) and Ísafjarðardjúp (areas 2–4).
a Canon lens with a focal length of 28–135 mm. Each shrimp was placed on a white board, orientated in a consistent manner, lying with the left side up and with the head facing to the left. Furthermore, a picture was taken of the dorsal part of the telson of each shrimp. The camera was attached to a tripod and the shrimp was symmetrically lit up by four led lamps attached to each corner of the board. The aperture, shutter speed and exposure parameters were adjusted manually for each size group in every session and the white balance was automatically set. A total of ten different morphometric characters of each shrimp were measured using the ImageJ software (Abramoff et al., 2004) (Fig. 3).
Sea surface and bottom temperature were tested for normality and homogeneity of variance and no transformation was necessary. All 10 morphometric variables were used in the discriminant analysis. All variables were tested for normality and homogeneity of variance, and transformed if necessary. Leg, abdominal somite height 1 and 2 and abdominal somite length 2 and 6 were standardized by natural-log transformation. Body measurements strongly correlate with body size. Analysis of covariance (ANCOVA) was used to determine the effect of shrimp carapace length on the magnitude of the body measurement variables, with length as a covariate, and area as a factor. Where the effect of shrimp length was significant, the product of the shrimp length and the common within-group slope (b) from the ANCOVA for a given variable and age group was subtracted from the variable to create a standardized variable. After standardization, no character measurements were observed to be correlated significantly with carapace length, indicating that the transformation had removed size effects successfully. Multivariate analysis of variance (MANOVA) for all variables combined was used to test for overall differences among groups. When differences were detected, mean differences between areas for individual variables were tested with ANOVA. Tukey HSD was then used to examine individual variables in order to explain any significant differences detected by the ANOVA. The level of statistical significance was defined at 0.05. Linear discriminant analysis of the standardized data was used to discriminate between the individuals from the four different areas. This technique is used to discriminate between two or more naturally occurring groups when the identity of the individuals is known. Furthermore, it is used to identify which variables are the most important in distinguishing between areas and how well the individuals are classified to their group (Duarte Silva and
Fig. 3. Morphometric characters measured on (a) the left side of each shrimp and (b) the telson. Length of carapace (CL2), abdominal somite (ASL2, ASL6), diagonal carapace (DCL), leg (L), spike (S) and telson (T1, T2) as well as height of abdominal somate (ASH1, ASH2).
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Table 2 The first two standardized function coefficients (I and II) from the discriminant analysis for 1 year old shrimps and 2 year old shrimps. Standardized coefficients with large absolute values correspond to parameters with greater discriminating ability. Parameter
Standardized canonical discriminant function coefficient Age 1 I
Carapace length 2 Leg Abdominal somite height 1 Abdominal somite height 2 Abdominal somite length 2 Abdominal somite length 6 Telson Telson 2 Spike Diagonal carapace length
Age 2 II
−1.99 −3.87 1.57 9.47 2.94 8.78 8.01 5.96 −2.63 18.34
−1.68 10.04 −6.19 0.09 −5.61 6.44 13.46 1.73 −7.92 9.12
I 1.70 −1.70 4.08 5.64 −3.68 13.25 8.69 0.19 −6.01 −16.61
II 13.32
−8.95 −0.25 −11.45 9.50 9.29 2.58 −2.47 0.61 9.12
Fig. 4. Mean sea surface (solid line) and bottom temperature (dashed line) in Arnarfjörður (area 1) and Ísafjarðardjúp (areas 2–4). The temperature measurements were obtained from the annual shrimp survey in September/October during 2007–2012. For locations see Fig. 1.
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Stam, 1995). It creates discriminant functions which discriminate between the groups. Once the discriminant functions had been derived, classification functions were used to determine to which area each case most likely belonged. The classification accuracy was estimated with ‘‘leave-one-out’’ cross validation, where each sample was removed from the data set and was classified by means of the classification rule estimated on the basis of the remaining n − 1 samples (Duarte Silva and Stam, 1995). Prior probabilities of each group (age and area) ranged between 20% and 30%, indicating that classification procedures worked well when groups were classified at a percentage higher than expected by chance. Discriminant function analyses were restricted to common cohorts for all groups. The discriminant analysis was performed using the MASS library in the R statistical computing environment (RCoreTeam, 2014). 3. Results The mean surface and bottom temperature have been decreasing since 2009, except the mean bottom temperature in area 1 (Fig. 4). The mean sea surface temperature varied between 7.3 and 10.3 °C but it did not vary significantly between areas (ANOVA, d.f = 3,109, P = 0.202). The mean bottom temperature was in general slightly lower (5.7 to 9.3 °C) than the mean sea surface temperature (Fig. 4), and did not vary significantly between areas 2 and 4, whereas the mean bottom temperature in area 1 was significantly lower than in the other areas (Tukey HSD, P < 0.001). Using all standardized variables (Fig. 5), the overall shape differed significantly between areas for both ages (MANOVA, d.f = 3,331 (1 year old), d.f = 3,410 (2 year old), P < 0.001 for both). All the standardized variables differed significantly among areas (ANOVA, d.f = 3,331 (1 year old) and 3,410 (2 year old), all P < 0.001), except the distance to the first spike (ANOVA 1 year old: d.f = 3,331, P = 0.132, 2 year old: d.f = 3,410, P = 0.05). The discrimination analysis provided further evidence for a separation between the areas (Fig. 6). Differences were more pronounced for the 1 year old compared to the 2 year old shrimps. For the 1 year old shrimps, the first discriminant function explained 88.7% and the discriminant scores differed significantly between all four groups (Tukey HSD, P < 0.001), indicating that 1 year old shrimps from these areas are likely to remain separate. The diagonal carapace length (DCL) explained most of the variation in the first discriminant function (Table 2). The second discriminant function of the 1 year old shrimps explained 9.1%. The telson and leg lengths explained most of the variation in the second discriminant function (Table 2). The second discriminant function
Fig. 5. Medians, the 25th and 75th percentiles, and whiskers (the upper and lower 1.5 × inter-quartile ranges) of the ten body morphometric parameters (length of carapace (CL2), abdominal somite (ASL2, ASL6), diagonal carapace (DCL), leg (L), spike (S) and telson (T1, T2) as well as height of abdominal somate (ASH1, ASH2)) for 1 and 2 year old shrimps from four different areas.
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a
b
Fig. 6. Discrimination of (a) 1 year old and (b) 2 year old sampled in 4 areas in Arnarfjörður and Ísafjarðardjúp using various body measurements. For each area, the large symbol denotes the mean, surrounded by 95% confidence ellipses. For locations see Fig. 1 (area 1 (squares), area 2 (circles), area 3 (up pointing triangles), area 4 (diamonds)).
did not distinguish area 4 from 1 or 3, but the discriminant scores differed significantly between all other areas (Tukey HSD, P < 0.01). For the 2 year old shrimps, the first discriminant function explained 78.7% and the discriminant scores varied significantly between all four areas (Tukey HSD, P < 0.001). The abdominal somite length 6 and the diagonal carapace length explained most of the variation in the first discriminant function (Table 2). The second discriminant function of the 2 year old shrimps explained 18.6%. The discriminant scores of the second function varied significantly between all four areas (Tukey HSD, P < 0.001). The abdominal somite height 2 and the carapace length 2 explained most of the variations of the second discriminant function (Table 2). The two year old shrimps are therefore also likely to remain separate but the worse discrimination of the 2 year old shrimps may indicate more mixing of individuals with increased age. For 1 year old shrimps, the classification ranged between 42% and 79% (Table 3). Shrimps from Arnarfjörður (area 1) were never classified to area 4 and vice versa. The incorrectly classified shrimps from area 3 were in most cases classified to areas 1 and 2, while only 8% of the individuals were classified to area 4 (Table 3). For 2 year old shrimps, the classification accuracy was lower than for 1 year old shrimps, and ranged between 41% and 57% (Table 3). In general, the incorrectly classified individuals in area 1 were rarely classified to area 4: fewer than 10% of these individuals were classified to area 4 (Table 3). Based on the discriminant analysis and the classification accuracy, body morphometrics proved to be a promising method to discriminate between northern shrimp from different areas. 4. Discussion The results of this present study showed differences in body measurements of northern shrimp sampled from four distinct areas in two adjacent Icelandic fjords. It was possible to discriminate between shrimps (both 1 and 2 year old) among the areas and therefore northern shrimp inhabiting these areas are likely to remain largely separate, at least during the first two years of their lifespan. The differences were more pronounced for the 1
Table 3 Classification success (%) from discriminant analysis between shrimps from four different areas at the age of 1 and 2. The rows and columns are the observed and predicted areas, respectively. Bold numbers indicate classification success for correctly classified shrimps from each area. All other numbers indicate the incorrectly classified shrimps. For areas see Fig. 1. Area
1 2 3 4
Age 1
Age 2
Predicted area
Predicted area
1
2
3
4
1
2
3
4
68 7 12 0
11 42 10 14
21 18 70 7
0 33 8 79
53 13 28 2
21 41 10 29
23 13 46 12
3 33 16 57
year old compared to the 2 year old shrimp which might indicate more mixing when the shrimps grow older. Even though this does not confirm the existence of genetically distinct populations, it does reflect life history and phenotypic differences among the groups. Variations in body morphometric have not been routinely applied for shrimp species and results from those studies are conflicting: no distinct difference in morphometric traits was observed between kuruma shrimp despite being genetically distinct (Tsoi et al., 2005), while a difference was detected in brown shrimp (Beaumont and Croucher, 2006), and morphological and genetic differences were observed in northern shrimp (Kartavtsev et al., 1993). Both (1) environmental factors and (2) genetic isolation on could be the origin of differences in body shape in groups from different areas. (1) Several studies have expressed that environmental factors influence differences in morphometrics of crustacean species (Brian et al., 2006; Chow and Sandifer, 1991; Debuse et al., 2001; Dimmock et al., 2004). The only environmental factor presented in the present study showed that the bottom temperature was lower in Arnarfjörður compared to Ísafjarðardjúp. The results of the present study do not give the opportunity to determine which factors influenced the differences in morphometrics of shrimps in the present study, and highly likely that it was influenced by the interaction of various factors. (2) The planktonic phase of shrimps lasts for approximately three months (Astthorsson and Gislason, 1991; Ouellet and Allard, 2006), thus
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shrimp larvae may be transported substantial distances during that time. Estimated transport of larvae in West Greenland waters was about 3.1 km d−1 (Pedersen et al., 2002) and they may travel distances up to 500 km (Storm and Pedersen, 2003). Ísafjarðardjúp does not have a sill near the mouth and the water exchange into the fjord is rather unrestricted. Due to the flow of currents in the fjord, shrimp larvae hatching in the outer part of the fjord may flow with currents away from the hatching site and settle in the inner part of the fjord. Shrimp larvae may even flow from Arnarfjörður to Ísafjarðardjúp. Genetic differences between the areas are therefore likely to be minimal. In fact, genetic differences were not observed between areas within Ísafjarðardjúp, and not even between Arnarfjörður and Ísafjarðardjúp in an earlier study (Jónsdóttir et al., 1998). The morphometric differences observed between the areas within Ísafjarðardjúp are therefore likely to reflect differences between the local environmental conditions rather than restricted gene flow. The fact that northern shrimps from different areas within the same fjord system remain largely separate at least during the first two years of their lives as both males and then females indicate that their population structure may be defined at the time of settlement. Even though it was possible to discriminate between the areas in this present study, it does not indicate that the shrimps are localized at the same area during the whole life cycle. After settlement, shrimps may migrate from the area of settlement and the lower discrimination between the 2 year old shrimps compared to the 1 year old may indicate that more mixing occurs with age. A tagging experiment in Ísafjarðardjúp in 1971 showed that the few recaptures were recovered near the tagging location (Skúladóttir, 1985). Shrimps may nevertheless migrate some distances and migration distance of shrimps in the Gulf of St. Lawrence, which is somewhat larger than the two fjords of the present study, was estimated to be a maximum 85 km yr−1 (Simard and Savard, 1990). Migration may furthermore be influenced by gadoid species, as northern shrimp may retrieve away from areas occupied by gadoids as it was shown in Arnarfjörður (Björnsson et al., 2011). Body morphometric analysis has the advantage over other phenotypic methods, like L50 and Lmax , that a single cohort can be studied at time, while measurements from all year classes are used when most other methods are applied. This gives a good opportunity to study early life stages of northern shrimp although little information is available about the reproductive patterns and the reproductive biology of northern shrimp. Better knowledge of juveniles is important for fisheries management as the year class strength regarding northern shrimp is probably largely driven by environmental changes during the early life stages (Parsons and Colbourne, 2000; Shumway et al., 1985). The population dynamics during the first years of marine species are often poorly known, and the processes causing the interannual variations in recruitment remain largely unexplained. Therefore, information on the early life history and the origin of juveniles is needed in order to understand the spatial stock structure and the mechanism responsible for variable recruitment. As noted above, the lower discrimination observed between the 2 year old shrimps compared to the 1 year old shrimps may indicate more mixing of older shrimps. However, two other reasons may explain the lower discrimination. First of all, it may be due to the occurrence of older shrimps in the samples. When different year classes are combined in a discriminant analysis it is possible that age and year-class effects explain a part of the discrimination among the groups. It has actually been noted in various marine species, such as the Atlantic mackerel (Castonguay et al., 1991) and coho salmon (Taylor and McPhail, 1985). Studies on body morphometric should preferable concern age-determined shrimps, a parameter that is possible to obtain, as shown in
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previous studies (Kilada et al., 2012). Second, the analyses were performed using both sexes, which may have some impact on the results. In fact, morphological variation was observed between sexes, both in white shrimp (Penaeus vannamei) (Chow and Sandifer, 1991) and other invertebrates, for instance the European lobsters (Homarus gammarus) (Debuse et al., 2001). However, in the present study, the majority of 1 year old shrimps were males while the majority of 2 year old shrimps were females and therefore, the influence is likely to be minimal. Information on stock structure is central to fisheries management and it has been argued that stocks may still be treated as separate management units if genetic differences cannot be detected and stock differentiation can be shown with alternative methods. The processes which generate stock structure are driven throughout the lifespan of the individual. Therefore, in order to understand how stock structure is maintained, information on dispersal, distribution and migration of both juveniles and adults is needed. Integration of results from various methods will help to clarify factors determining the population structure (Begg and Waldman, 1999). Due to larvae drift and lack of genetic differences in small systems (e.g. fjords) alternative methods such as morphometrics are useful to study the stock structure within the system. The results of this study illustrate the potential use of inexpensive and relatively short-time morphometrics methods to study stock structure of northern shrimp before more complex and more expensive methods are applied. Acknowledgements We would like to thank the captains and crews at the vessels used for sampling and researchers at the Marine Research Institute in Ísafjörður for sampling in Ísafjarðardjúp. Furthermore, we thank Jón Páll Jakobsson, captain of ‘Andri’ for sampling of shrimps in Arnarfjörður, Teresa Silva for assistance with the ImageJ software, Sverrir D. Halldórsson and Valerie Chosson for camera support and Dr. Guðmundur J. Óskarsson, Jónas P. Jónasson and two anonymous reviewers for valuable comments on an earlier draft of the manuscript. References Abramoff, M.D., Magalhaes, P.J., Ram, S.J., 2004. Image processing with image. J. Biophotonics Int. 11, 36–42. Astthorsson, O.S., Gislason, A., 1991. Seasonal abundance and distribution of Caridea larvae in Ísafjord-deep, north-west Iceland. J. Plankton Res. 13, 91–102. Beaumont, A.R., Croucher, T., 2006. Limited stock structure in UK populations of the brown shrimp, Crangon crangon, identified by morphology and genetics. J. Mar. Biol. Assoc. UK 86, 1107–1112. Begg, G.A., Hare, J.A., Sheehan, D.D., 1999. The role of life history parameters as indicators of stock structure. Fish. Res. 43, 141–163. Begg, G.A., Waldman, J.R., 1999. An holistic approach to fish stock identification. Fish. Res. 43, 35–44. Bergstrom, B.I., 2000. The biology of Pandalus. In: Advances in Marine Biology, Vol. 38. pp. 55–245. Björnsson, B., Reynisson, P., Solmundsson, J., Valdimarsson, H., 2011. Seasonal changes in migratory and predatory activity of two species of gadoid preying on inshore northern shrimp Pandalus borealis. J. Fish Biol. 78, 1110–1131. Booke, H.E., 1981. The conundrum of the stock concept — are nature and nurture definable in fishery science. Can. J. Fish. Aquat. Sci. 38, 1479–1480. Brian, J.V., Fernandes, T., Ladle, R.J., Todd, P.A., 2006. Patterns of morphological and genetic variability in UK populations of the shore crab, Carcinus maenas Linnaeus, 1758 (Crustacea: Decapoda: Brachyura). J. Exp. Mar. Biol. Ecol. 329, 47–54. Cadrin, S.X., Friedland, K.D., 1999. The utility of image processing techniques for morphometric analysis and stock identification. Fish. Res. 43, 129–139. Castonguay, M., Simard, P., Gagnon, P., 1991. Usefulness of Fourier analysis of otolith shape for Atlantic mackerel (Scomber scombrus) stock discrimination. Can. J. Fish. Aquat. Sci. 48, 296–302. Charnov, E.L., Anderson, P.J., 1989. Sex change and population fluctuations in pandalid shrimp. Am. Nat. 134, 824–827. Chow, S., Sandifer, P.A., 1991. Differences in growth, morphometric traits, and male sexual maturity among Pacific white shrimp, Penaeus vannamei, from different commercial hatcheries. Aquaculture 92, 165–178.
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