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Exploring intraspecific life history patterns in sharks.

Marine ecosystems compose the major source (85%) of world fisheries production (Garcia and Newton, 1997). Although only a few fish species tend to dominate fishery catches (Jennings et al., 2001), a large diversity of fishes representing varied taxonomic levels, ecological guilds, and life histories is commonly taken. Recently, 66% of global marine resources were determined to be either fully, heavily, or over-exploited (Botsford et al., 1997). Considering the current state of many fisheries, the large diversity of species taken globally, and the general lack of resources to adequately assess many stocks, it has become important to develop shortcuts that may provide methods fisheries scientists can use to determine which stocks are in danger of overexploitation and which recovery plans are appropriate when biological data are limited (Stobutzki et al., 2001).

Applications of life history theory have proven a potentially useful means to accomplish such tasks (Stearns, 1992; Reynolds et al., 2001). Life history traits such as maximum size and age, maturity, mortality, and growth are correlated among teleost fishes (Adams, 1980; Winemiller and Rose, 1992; Gunderson, 1997; Cortes, 2000) and the relationships among such traits can be used to infer some general life history patterns. These general patterns reveal that teleost fishes with higher maximum ages tend to be larger, mature later, grow more slowly, and have lower natural mortality rates (K-selected species, Adams, 1980), whereas teleost fishes with lower maximum ages tend to show the opposite relationships (r-selected species, Adams, 1980). Correlations among traits may also allow one to approximate difficult to measure life history traits from traits that are easier to measure and possibly anticipate response to exploitation rates where life history data are limited (Jennings et al., 1999).

Applying these patterns to fisheries trends reveals some consistent and useful explanations. Jennings et al. (1998) found that teleost fishes from the northeast Atlantic that have decreased in abundance are generally K-selected species. Jennings et al. (1999) demonstrated that tropical teleost fishes of greatest maximum sizes were most vulnerable to exploitation. And Rochet (2000) illustrated the limitations of life history plasticity to compensate for heavy fishing pressure among four orders of teleosts.

Elasmobranchs, and particularly sharks, have also shown life history patterns similar to those of teleosts (Cortes, 2000; Frisk et al., 2001). Cortes (2000) offered three general life history patterns for sharks: 1) large litters, moderate to high longevity, large size, small offspring, slow growth, 2) small litters, high longevity, large size, large offspring, slow growth and 3) small litters, low longevity, small size, small offspring, fast growth. Simplified applications of the life history patterns have also been applied to elasmobranch fisheries. Smith et al. (1998) demonstrated that larger, later-to-mature Pacific shark species have lower rebound potentials (i.e., abilities to recover from fishing pressure), whereas Frisk et al. (2001) showed a similar pattern in sharks and rays in the north Atlantic. These relationships have been recommended as particularly useful when managing data-poor elasmobranch species (Musick et al., 2000).

As indicated by the above studies, variation in life history traits and patterns among shark species is well established (Cortes, 2000) and such relationships may be useful for the management of these fishes, but it is not known how these relationships may change within a species. Specifically, if and how do intraspecific life history traits of cosmopolitan species vary in different areas of the world? The spiny dogfish (Squalus acanthias) provides an alluring preamble to the topic: northeast Pacific spiny dogfish have been aged to 80+ years, and females mature at around 35 years (Jones and Geen, 1977; Saunders and McFarlane, 1993), whereas spiny dogfish in the north Atlantic obtain a maximum age of about 40 years, maturing at 12 years (Rago et al., 1998).

In the present study, I used generalized linear models (GLMs) to investigate whether spatial differences in life history traits, such as those seen in the spiny dogfish, reveal consistent patterns when compared with other spatially resolved life history information from other shark species. I then, as demonstration of potential utility, applied these models to predict life history trait values for areas lacking information for two species of shark, spiny dogfish (S. acanthias) and blue shark (Prionace glauca).

Materials and methods

Information for five life history traits (age at maturity, longevity (maximum age), mean fecundity, maximum size, and size at maturity) from 17 shark species in six families (Appendix, Table 1) for seven general areas (North Pacific (NP); North Atlantic (NoA); Gulf of Mexico (GM); Indian Ocean (I); Central Pacific (CP); South Pacific (SP); South Atlantic (SA)) was extracted from three primary literature sources (Smith et al., 1998; Cortes, 2000, 2002). Area distinctions were based on those by Cortes (2000). Spatial resolution to the particular ocean basins in this study was defined by the information I was able to obtain, as were the choice of species and life history traits to analyze. Although other information for these and other species may be currently available, I limited my data to those found in primary peer-reviewed literature. When mean fecundity values were unavailable, the mean was assumed to be the middle value of the fecundity range given. Phylogenetic variance was controlled because I strictly evaluated intraspecific comparisons.

Initial data exploration was performed by visualizing intraspecific gender-based pairwise comparisons by area with dot plots (Fig. 1). Within each species and gender, the outcome of each comparison (i.e., the value for a particular trait in one area greater than, less than, or equal to that of another area) was evaluated. An overall relationship among areas for each life history trait was then constructed as a composite of each pairwise result. The purpose of this exercise was to visually explore the data and assess whether any intraspecific life history patterns by area were apparent.

[FIGURE 1 OMITTED]

A GLM framework was then used to construct simple models that quantitatively relate the effect of certain factors (e.g., area, gender, or taxonomic level) to a response variable--in this case to a particular life history trait. The flexibility of the GLM framework also allows one to consider non-normal response distributions while maintaining the advantages of linear regression (Venablee and Ripley, 2002) by means of a link function relating the response variable mean to the linear predictors. I had no a priori knowledge of the variance structure for each life history trait (some of which will not have variance, given that they are maximum recorded values), so both lognormal (with an identity link) and gamma (with a log link) distributions were considered because of their appropriateness to continuous and nonzero data. Akaike's information criterion (AIC; Burnham and Anderson, 2002) was used to select among models, with the lowest AIC value indicative of the most appropriate model among all considered. Models explored included all combinations of the following factors: area, gender, and the taxonomic levels of species, genus, or family. Models that included the interaction between gender and area and gender and taxonomic level were also considered. Area effects and predictions of life history values among models with the use of the lognormal and gamma distributions were similar, but models with gamma error structure resulted in the lowest standard errors for area effects; thus a gamma error structure was ultimately chosen for each model.

Resultant model effects were used to compare area effects and to predict species- and gender-specific life history trait values for each area. The predicting models were then applied to two species (S. acanthias and P. glauca) to demonstrate the calculation of life history trait values for areas with missing values. These species were chosen as examples because they are taxonomically different, are found in most of the area designations, and are represented by at least one pairwise comparison for each life history trait. Further investigation of the predictive capacity of these models to fecundity, size at maturity, and maximum size for the two species was performed by cross-validation: for each life history trait, S. acanthias or P. glauca data for one area were removed, the models were re-estimated, and predicted values for the newly missing area were calculated. Model fits to age at maturity, longevity, and male P. glauca size at maturity were not explored with cross validation because these data lacked the sufficient sample size needed to calculate the species effect once one area was removed (at least two remaining areas were needed).

Results

Model structure

For each life history trait that contained gender as a factor, the following final GLM model with a gamma distribution and a log link was selected (i.e., had the lowest AIC value):

ln [y.sub.A+spxg] = ln [[beta].sub.A] + ln[[beta].sub.g] + ln [[beta].sub.8p] + ln[[beta].sub.gxsp], (1)

where [[beta].sub.A] = area effect;

[[beta].sub.g] = gender effect;

[[beta].sub.sp] = species effect;

[[beta].sub.gxsp] = gender and species interaction effect.

[y.sub.A+gx8p] = value of the response variable (age, litter size, or length) for each area, species, and gender, accounting for the gender-species interaction.

This model is also biologically realistic because it includes the possibility that males in one species may be smaller than females and vice versa. For fecundity, the following model was chosen:

+ln [y.sub.A+sp] = ln[[beta].sub.A] + ln[[beta].sub.sp]. (2)

An assessment of Cook's statistic for all models revealed no evidence of any highly influential data points. A subsequent analysis of residuals by species indicated, in one case, a potential departure from the assumption that both genders of all species in all areas have the same variance. Highest residuals were reported for age at maturity in S. acanthias. Whether these high residual values are truly reflective of the species or an artifact of low sample size is unknown, so I carried forth with the analysis using the above models.

Area effects

Despite the low and unbalanced numbers of comparisons among coarse area designations, inter-regional variation and bias in sampling each life history trait, and the concomitant lack of power to resolve statistically significant relationships across all areas, a general and consistent trend emerged among the five life history traits. Intraspecifically, populations progressed from larger, longer-lived, later-to-mature populations in the northern-most latitudes to smaller, shorter-lived, and earlier-to-mature populations in the mid and southern latitudes (Fig. 2).

[FIGURE 2 OMITTED]

Predicting missing life history information by area

In addition to providing a comparison of the area effects on the response variables, the resultant predicting model offers a way to estimate missing life history values by area for each species and gender:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (3)

The factors [[beta].sub.g] and [[beta].sub.spxg] in the above equation are not present in the fecundity predictions.

For both S. acanthias and P. glauca (Fig. 3), the predicted values mimicked the area trends of the reported values within two standard errors in all but one case (North Pacific P. glauca size at maturity was overestimated for both genders), and provided a means to estimate values for each life history by species and gender for areas not yet reported. The outlying cases may indicate an area (the North Pacific) where sampling is not representative of the true population (in this case, of P. glauca) and is in need of further investigation.

[FIGURE 3 OMITTED]

Cross validating models produced response variables similar to those of the full models in all but two cases (Fig. 4). In both cases (S. acanthias fecundity minus the South Atlantic, and P. glauca size at female maturity minus the North Pacific), the observed values were in opposite magnitude to that predicted. This difference could reflect either true relationships or possibly indicate areas that are undersampled (i.e., North Pacific for the size at maturity for female P. glauca).

[FIGURE 4 OMITTED]

Discussion

Knowledge of large-scale intraspecific spatial patterning in life history traits may be important when considering the population dynamics of a species, but such large-scale patterning has seldom been formally explored. Winemiller and Rose (1992) included median and range latitude correlations in their consideration of several life history variables of North American fishes, but comparisons were only interspecific. Vila-Gispert et al. (2002) demonstrated that fishes from higher latitudes north of the equator matured latest and had the highest fecundity, whereas fishes from South America had the lowest fecundity and earliest maturation, although these comparisons were again made interspecifically. Myers et al. (2001) described the relationship between maximum reproductive rate and carrying capacity among 21 stocks of Atlantic cod (Gadus morhua) in the North Atlantic using mixed effects models, but their analysis was done for only one species in a limited region. Helser and Lai (2004) also performed a similar analysis for individual growth rates in North American largemouth bass (Micropterus salrnoides) populations and found latitudinal changes in growth rate.

Regarding elasmobranchs, Cortes (2000) considered trends in intraspecific reproductive traits for sharks but did not explicitly investigate the spatial patterning of those trends. Frisk et al. (2001) found regional differences across five areas for the spiny dogfish using three life history measures (maximum size, and size and age at maturity), but did not specify regional patterns. The authors also performed a similar analysis with several skate species, finding no difference among areas, but they considered only interspecific patterns. Cortes and Parsons (1996) compared the demography of two Floridian populations of the bonnethead shark (Sphyrna tiburo), which included several life history measures in the life table analyses, but the small spatial resolution was inadequate to indicate large-scale spatial life history correlations within this species. Lombardi-Carlson et al. (2003) extended the bonnethead shark investigation to a larger portion of the eastern Gulf of Mexico and found latitudinal variation in maturity and size, but again the scale of this study was relatively small. Additional small scale studies on intraspecific geographic variation in reproductive parameters of sharks have been presented by Horie and Tanaka (2002), Taniuchi et al. (1993), and Yamaguchi et al. (2000).

The results of the present study, specifically aimed at sharks as an example, indicate an emerging pattern for intraspecific life history variation, not unlike previously recognized interspecific patterns. Generally, there is a distinct difference in life history traits among areas--a pattern potentially useful when considering region-specific population dynamics. Across taxonomic designations, populations in the northern latitudes tended to be larger, to mature later in life, to have longer life spans, and to have greater fecundity compared to conspecifics in the central and southern latitudes. Populations in the North Pacific, in particular, seem to demonstrate dramatic departures in life history measures compared to conspecifics in other areas. Therefore, instead of assuming life history information from one region should be applied to another region, the trends and predictive methods offered in the present study provide a means to extrapolate life history traits of cosmopolitan species in specific areas when only information from other areas is available; this method may prove useful for developing informative priors for Bayesian analyses (Punt and Hilborn, 1997). Caveats to these results include area-specific biases (i.e., certain-size individuals susceptible to capture) and errors in sampling programs and migratory patterns of specific species (e.g., individuals may be found in multiple areas during different parts of their life history). Thus a proper knowledge of the biology of the species is recommended before interpreting the interpolated life history values.

Other factors, such as fishing pressure, may influence regional differences in life history traits, challenging the interpretation of such patterns. Truncation of size and age classes, and reduction in age at maturity are recognized byproducts of heavy fishing (Longhurst, 1998a; Rochet, 2000). Although all the populations used in this study are and have been fished--some more intensely than others--this study assumes there is no consistent pattern to such exploitation in shark populations that would explain the results. Specifically, there is no reason to believe that species in the southern hemisphere have been more heavily fished than conspecifics in the northern hemisphere.

The results offered in the present study are based on small sample sizes in most areas, but hopefully they will bring attention to the usefulness of collecting spatially varying intraspecific information with the idea of constructing more robust models. Most investigations describing the patterns of shark life history traits suffer from insufficient biological resolution (either temporally or spatially) of the very parameters and subsequent relationships they attempt to explain (Smith et al., 1998; Cortes, 2000). Limited biological knowledge and subsequent high uncertainty in the estimation of vital rates of many marine species, including elasmobranchs, is testimony to the fact that the accumulation of life history information should be a priority to biologists, fisheries scientists, and resource managers. Results from the models presented here could be used to hypothesize life history values in areas currently lacking information and thus be tested with further sampling in those areas. It is also hoped that the approach offered here may indicate areas where sampling may not be sufficient, as denoted by departures from the general model trend. Targeted sampling in that area would help resolve whether the departure is from a true area effect or species effect. As more data is gathered, it will be possible to explore other factors--such as temperature and guilds (e.g., coastal versus oceanic)--in the model structure.

Once steps are made to further resolve the species and area effects, one may start to ask questions regarding the cause of particular area effects. Potential mechanisms of true coarse-scale gradation of life history traits may be contained within the generalized characteristics of oceanic zoogeographic realms (Longhurst, 1998b), although a slightly less abstract mechanism could be found in the physical forcing events that characterize regions in the northern and southern hemisphere. Although both hemispheres demonstrate similar large-scale current and wind patterns, physical forcing events tend to be stronger in the northern hemisphere (Trenberth et al., 1998). Because this study offers coarse area designations to intraspecific life history variation, it is most likely a product of some macroscale characteristics of each region. Attention should therefore be directed towards large-scale characteristics of each region to explain these patterns, although small-scale dynamics are important for understanding each population's specific response to local environmental conditions (i.e., countergradient variation in growth rates [Conover, 1990; Conover and Present, 1990]).

It is becoming increasingly important to be able to assess fish stocks with minimal data. By combining genetic data revealing differing levels of intraspecific population substructure with the increasing number of studies demonstrating localized adaptations and plasticity in population parameters, it is apparent that intraspecific spatial differences must be considered in species management (Avise, 2000; Roff, 2002). Although the predictive power of this study may currently be weak because of low sample sizes, it offers a method to quantify potential spatial patterning in intraspecific life history traits that may allow responsible management of regionally data-poor species, and it may help frame future sampling protocols and studies of spatial patterns in life history traits.
Appendix
Life history traits and area assignments for species used in the
analyses. Sizes are total length (cm). Areas: NP = North Pacific;
NoA = North Atlantic; GM = Gulf of Mexico; I = Indian Ocean; CP =
Central Pacific; SP = South Pacific; SA = South Atlantic.

 Maximum size Size at maturity

Species Area F M F M

Carcharinus acronotus GM 130 122 110 103
Carcharinus acronotus NoA 154 164 120 110
Carcharinus amblyrhynchos CP 190 185 137 132.5
Carcharinus amblyrhynchos I 178 168 135 130
Carcharinus falciformis GM 308 314 235 217.5
Carcharinus falciformis NoA 330 234 218
Carcharinus falciformis I 283 244 248
Carcharinus falciformis SP 250 225 200 212.5
Carcharinus leucas GM 285 225 215
Carcharinus leucas I 300 239.5 239.5
Carcharinus limbatus GM 191 175 156 133
Carcharinus limbatus NoA 202 189 156 143.5
Carcharinus limbatus I 247 246 208.5 201.5
Carcharinus longimanus NoA 260 175
Carcharinus longimanus SA 250 235 185 185
Carcharinus longimanus I 270 245 185 198
Carcharinus longimanus SP 270 251 200
Carcharinus longimanus CP 272 240 182 182
Carcharinus obscurus NoA 371 360 284 279
Carcharinus obscurus I 389 324 300 280
Carcharinus plumbeus CP 190 172 144 131
Carcharinus plumbeus NoA 234 226 183 180
Carcharinus plumbeus I 199 190.5 169 167
Galeocerdo cuvier NoA 450 317.5 310
Galeocerdo cuvier GM 450
Galeocerdo cuvier I 410 370 340 290
Galeocerdo cuvier SP 428 350 287
Prionace glauca NP 310 150 145
Prionace glauca NoA 327 340 221 215
Prionace glauca SP 316 312 218
Prionace glauca I 321.5 214
Rhizoprionodon taylori SP 78 69 54 56
Rhizoprionodon taylori I 66 55 45 43
Sphyrnalewini GM 310 300 250 180
Sphyrna lewini NP 324 305 210 198
Sphyrna lewini I 346 301 200 150
Galeorhinus galeus NP 195 185 180 175
Galeorhinus galeus SP 174 175 140 135
Galeorhinus galeus SA 155 148 128 117
Isurus oxyrinchus NP 351 280
Isurus oxyrinchus SP 340 270 280 195
Isurus oxyrinchus NoA 375 298 280
Isurus oxyrinchus I 333 271 266 199.5
Alopias superciliosus NoA 444 410 341 276
Alopias superciliosus NP 422 357 341 288
Squalus acanthias NP 130 103 94 78.5
Squalus acanthias NA 110 90 80 59.5
Squalusacanthias SA 95.5 78 70 63
Squalus acanthias SP 111 90 71.5 58
Squalus mitsukurii NP 114 94 97.5 70
Squalus mitsukurii I 95 81 69 60
Squalus mitsukurii CP 91 82 69 48
Squalus mitsukurii SP 104 102 85

 Age at Longevity
 maturity
 Mean
Species F M fecundity F M

Carcharinus acronotus 3 2.5 5
Carcharinus acronotus 4 3 5
Carcharinus amblyrhynchos 5
Carcharinus amblyrhynchos 3
Carcharinus falciformis 11
Carcharinus falciformis 10
Carcharinus falciformis 11
Carcharinus falciformis 7
Carcharinus leucas 8
Carcharinus leucas 9
Carcharinus limbatus 7 4.5 4.9 10 9
Carcharinus limbatus 4
Carcharinus limbatus 7 6 6 11 10
Carcharinus longimanus 12.5
Carcharinus longimanus 17 14
Carcharinus longimanus
Carcharinus longimanus 6
Carcharinus longimanus 7 11 11
Carcharinus obscurus 21 19 11 39 39
Carcharinus obscurus 24 20.5 9.9 34
Carcharinus plumbeus 5.5
Carcharinus plumbeus 15 15 9 32 40
Carcharinus plumbeus 8
Galeocerdo cuvier 10 10 55 16 15
Galeocerdo cuvier 8 7 11 9
Galeocerdo cuvier 35
Galeocerdo cuvier 31
Prionace glauca 6 5 60 24 27
Prionace glauca 6 6 13 16
Prionace glauca 32
Prionace glauca 34
Rhizoprionodon taylori 4.5
Rhizoprionodon taylori 5
Sphyrnalewini 15 10 17 12
Sphyrna lewini 4.1 3.8 26 14 11
Sphyrna lewini 16.5
Galeorhinus galeus
Galeorhinus galeus 15 17 28.4 53 41
Galeorhinus galeus 17.5 13 23 36 36
Isurus oxyrinchus 16 34
Isurus oxyrinchus 9
Isurus oxyrinchus 14 13.5 23 9
Isurus oxyrinchus 11.5
Alopias superciliosus 2
Alopias superciliosus 13 10 2 20 19
Squalus acanthias 35.5 19 7.1 81 50
Squalus acanthias 12.1 6 6.6 40 35
Squalusacanthias 7
Squalus acanthias 5
Squalus mitsukurii 22 12 8.8
Squalus mitsukurii 6.4
Squalus mitsukurii 15 4 3.6 27 18
Squalus mitsukurii 3.5 16 14


Acknowledgments

I am grateful to Andre Punt, Joe Bizzarro, Gavin Fay, and Arni Magnusson for their many important comments regarding model structure and data use. I also thank Kristin Benshoof, Marilyn Cope, and three anonymous reviewers who contributed insightful comments that improved the presentation and clarity of this note.

Manuscript submitted 6 December 2004 to the Scientific Editor's Office.

Manuscript approved for publication 15 September 2005 by the Scientific Editor.

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Table 1
List of shark species used in the analyses. Species abbreviations are
used in tables and figures to simplify presentation.

Scientific name Common name Abbreviation

Carcharhinidae
 Carcharinus acronotus Blacknose shark Cac
 Carcharinus amblyrhynchos Grey reef shark Cal
 Carcharinus falciformis Silky shark Cfa
 Carcharinus leucas Bull shark Cle
 Carcharinus limbatus Blacktip shark Cli
 Carcharinus longimanus Oceanic whitetip shark Clo
 Carcharinus obscurus Dusky shark Cob
 Carcharinus plumbeus Sandbar shark Cpl
 Galeocerdo cuuier Tiger shark Gcu
 Prionace glauca Blue shark Pgl
 Rhizoprionodon taylori Australian sharpnose shark Rta
Sphyrnidae
 Sphyrna lewini Scalloped hammerhead shark Sle
Triakidae
 Galeorhinus galeus Soupfin shark Gga
Lamnidae
 Isurus oxyrinchus Mako shark Iox
Alophiidae
 Alopias superciliosus Bigeye thresher shark Asu
Squatinidae
 Squalus acanthias Spiny dogfish Sac
 Squaluss mitsukurii Shortspine spurdog Smi
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Author:Cope, Jason M.
Publication:Fishery Bulletin
Geographic Code:1USA
Date:Apr 1, 2006
Words:4974
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