Age- and length-at-maturity of female arrowtooth flounder (Atheresthes stomias) in the Gulf of Alaska

Arrowtooth flounder (Atheresthes stomias) has had the highest abundance of any groundfish species in the Gulf of Alaska since the 1970s (Matarese et al., 2003; Turnock et al., 2005; Blood et al., 2007); however, commercial catches have been restricted because Pacific halibut (Hippoglossus stenolepis) are caught as bycatch in the fishery. Arrowtooth flounder plays a key role in the ecosystem because it is a dominant organism within the food web, both as an apex predator of fish and invertebrates, as well as an important prey for walleye pollock (Theragra chalcogramma; Aydin et al., 2002). Walleye pollock is the dominant groundfish in the Bering Sea, a principal groundfish in the Gulf of Alaska, and the primary prey for marine mammals. The distribution of arrowtooth flounder extends from Cape Navarin and the eastern Sea of Okhotsk in Russia, across the Bering Sea, Aleutian Islands, Gulf of Alaska, and south to the coast of central California (Shuntov, 1964; Britt and Martin, 2001; Chetvergov, 2001; Weinberg et al., 2002; Zenger, 2004). Because of the importance of arrowtooth flounder in the marine ecosystem of A laska, a maturity study of this species was undertaken to determine age-at-maturity, which is essential for age-based stock management models. Before these results, management has had to rely upon a length-at-maturity-based estimate (Zimmermann, 1997) to manage stocks in the Gulf of Alaska (GOA), Bering Sea, and Aleutian Islands. The central GOA was selected as the location for this maturity study Age- and length-at-maturity of female arrowtooth flounder (Atheresthes stomias) in the Gulf of Alaska because it contains approximately 70% of the total Gulf of Alaska arrowtooth flounder biomass (1.9×106 t, age 3 and older)— the highest percentage in the world (Shuntov, 1964; Britt and Martin, 2001; Weinberg et al., 2002; Wilderbuer and Nichol, 2006).

Incorporating fishery observer data into an integrated catch-at-age and multiyear tagging model for estimating mortality rates and abundance

Tagging experiments are a useful tool in fisheries for estimating mortality rates and abundance of fish. Unfortunately, nonreporting of recovered tags is a common problem in commercial fisheries which, if unaccounted for, can render these estimates meaningless. Observers are often employed to monitor a portion of the catches as a means of estimating reporting rates. In our study, observer data were incorporated into an integrated model for multiyear tagging and catch data to provide joint estimates of mortality rates (natural and f ishing), abundance, and reporting rates. Simulations were used to explore model performance under a range of scenarios (e.g., different parameter values, parameter constraints, and numbers of release and recapture years). Overall, results indicated that all parameters can be estimated with reasonable accuracy, but that fishing mortality, reporting rates, and abundance can be estimated with much higher precision than natural mortality. An example of how the model can be applied to provide guidance on experimental design for a large-scale tagging study is presented. Such guidance can contribute to the successful and cost-effective management of tagging programs for commercial fisheries.

Age and growth of swordfish (Xiphias gladius) caught by the Hawaii-based pelagic longline fishery

We verified the age and growth of swordfish (Xiphias gla-dius) by comparing ages determined from annuli in fin ray sections with daily growth increments in otoliths. Growth of swordfish of exploitable sizes is described on the basis of annuli present in cross sections of the second ray of the first anal fins of 1292 specimens (60−260 cm eye-to-fork length, EFL) caught in the region of the Hawaii-based pelagic longline fishery. The position of the initial fin ray annulus of swordfish was verified for the first time with the use of scanning electron micrographs of presumed daily growth increments present in the otoliths of juveniles. Fish growth through age 7 was validated by marginal increment analysis. Faster growth of females was confirmed, and the standard von Bertalanffy growth model was identified as the most parsimonious for describing growth in length for fish greater than 60 cm EFL. The observed growth of three fish, a year-old in size when first caught and then recaptured from 364 to1490 days later, is consistent with modeled growth for fish of this size range. Our novel approach to verifying age and growth should increase confidence in conducting an age-structured stock assessment for swordfish in the North Pacific Ocean.

Age and growth of three species of Lake Victoria fish determined by means of otolith daily growth rings

The sagittal otoliths of Lates niloticus, Haplochromis obesus, and Oreochromis niloticus from Lake Victoria were examined for daily growth rings using scanning electron microscopy. In the three species the increments were clear and thick enough to allow future studies with light microscopy. The daily nature of the increments seems supported by the rhythmic growth that were found.

Age and growth estimates of the thorny skate (Amblyraja radiata) in the western Gulf of Maine

The northwest Atlantic population of thorny skates (Amblyraja radiata) inhabits an area that ranges from Greenland and Hudson Bay, Canada, to South Carolina. Despite such a wide range, very little is known about most aspects of the biology of this species. Recent stock assessment studies in the northeast United States indicate that the biomass of the thorny skate is below the threshold levels mandated by the Sustainable Fisheries Act. In order to gain insight into the life history of this skate, we estimated age and growth for thorny skates, using vertebral band counts from 224 individuals ranging in size from 29 to 105 cm total length (TL). Age bias plots and the coefficient of variation indicated that our aging method represents a nonbiased and precise approach for the age assessment of A. radiata. Marginal increments were significantly different between months (Kruskal-Wallis P<0.001); a distinct trend of increasing monthly increment growth began in August. Age-at-length data were used to determine the von Bertalanffy growth parameters for this population: L∞ = 127 cm (TL) and k= 0.11 for males; L∞ = 120 cm (TL) and k= 0.13 for females. The oldest age estimates obtained for the thorny skate were 16 years for both males and females, which corresponded to total lengths of 103 cm and 105 cm, respectively.

Maturity, age and growth of Oreochromis karongae (Teleostei: cichlidae) in Lake Malawi and Lake Malombe

Size-at-50% maturity, age and growth, of Oreochromis (Nyasalapia) karongae (‘chambo’) in Lakes Malawi and Malombe were studied. Similar size-at-50% maturity and growth patterns were found for populations in Lake Malawi, but differences were observed for Lake Malombe populations, suggesting that current chambo fisheries management regulations, based on findings from the southern part of Lake Malawi, may be applicable to the central and southern parts of that lake, but not to Lake Malombe.

Age and length composition of Columbia Basin chinook, sockeye, and coho salmon at Bonneville Dam in 2001

In 2001, representative samples of adult Columbia Basin chinook (Oncorhynchus tshawytscha), sockeye (O. nerka), and coho salmon (O. kisutch) populations at Bonneville Dam were collected. Fish were trapped, anesthetized, sampled for scales and biological data, revived, and then released adult migrating salmonids. Scales were examined to estimate age composition; the results contributed to an ongoing database for age class structure of Columbia Basin salmon populations. Based on scale analysis of chinook salmon, four-year-old fish (from brood year [BY] 1997) comprised 88% of the spring chinook, 67% of the summer chinook, and 42% of the Bright fall chinook salmon population. Five-year-old fish (BY 1996) comprised 9% of the spring chinook, 14% of the summer chinook, and 9% of the fall chinook salmon population. The sockeye salmon population at Bonneville was predominantly four-year-old fish (81%), with 18% returning as five-year-olds in 2001. The coho salmon population was 96% three-year-old fish (Age 1.1). Length analysis of the 2001 returns indicated that chinook salmon with a stream-type life history are larger (mean length) than the chinook salmon with an ocean-type life history. Trends in mean length over the sampling period for returning 2001 chinook salmon were analyzed. Chinook salmon of age classes 0.2 and 1.3 show a significant increase in mean length over time. Age classes 0.1, 0.3, 0.4, 1.1, 1.2, and 1.4 show no significant change over time. A year class regression over the past 12 years of data was used to predict spring, summer, and Bright fall chinook salmon population sizes for 2002. Based on three-year-old returns, the relationship predicts four-year-old returns of 132,600 (± 46,300, 90% predictive interval [PI]) spring chinook and 44,200 (± 11,700, 90% PI) summer chinook salmon for the 2002 runs. Based on four-year-old returns, the relationship predicts five-year-old returns of 87,800 (± 54,500, 90% PI) spring, 33,500 (± 11,500, 90% PI) summer, and 77,100 (± 25,800, 90% PI) Bright fall chinook salmon for the 2002 runs. The 2002 run size predictions should be used with caution; some of these predictions are well beyond the range of previously observed data.

Age and length composition of Columbia Basin chinook, sockeye, and coho salmon at Bonneville Dam in 2000

In 2000, representative samples of adult Columbia Basin chinook (Oncorhynchus tshawytscha), sockeye (O. nerka), and coho salmon (O. kisutch), populations were collected at Bonneville Dam. Fish were trapped, anesthetized, sampled for scales and biological data, allowed to revive, and then released. Scales were examined to estimate age composition and the results contribute to an ongoing database for age class structure of Columbia Basin salmon populations. Based on scale analysis, four-year-old fish (from brood year (BY) 1996) were estimated to comprise 83% of the spring chinook, 31% of the summer chinook, and 32% of the upriver bright fall chinook salmon population. Five-year-old fish (BY 1995) were estimated to comprise 2% of the spring chinook, 26% of the summer chinook, and 40% of the fall chinook salmon population. Three-year-old fish (BY 1997) were estimated to comprise 14% of the spring chinook, 42% of the summer chinook, and 17% of the fall chinook salmon population. Two-year-olds accounted for approximately 11% of the fall chinook population. The sockeye salmon population sampled at Bonneville was predominantly four-year-old fish (95%), and the coho salmon population was 99.9% three-year-old fish (Age 1.1). Length analysis of the 2000 returns indicated that chinook salmon with a stream-type life history are larger (mean length) than the chinook salmon with an ocean-type life history. Trends in mean length over the sampling period were also analysis for returning 2000 chinook salmon. Fish of age classes 0.2, 1.1, 1.2, and 1.3 have a significant increase in mean length over time. Age classes 0.3 and 0.4 have no significant change over time and age 0.1 chinook salmon had a significant decrease in mean length over time. A year class regression over the past 11 years of data was used to predict spring and summer chinook salmon population sizes for 2001. Based on three-year-old returns, the relationship predicts four-year-old returns of 325,000 (± 111,600, 90% Predictive Interval [PI]) spring chinook and 27,800 (± 29,750, 90% PI) summer chinook salmon. Based on four-year-old returns, the relationship predicts five-year-old returns of 54,300 (± 40,600, 90% PI) spring chinook and 11,000 (± 3,250, 90% PI) summer chinook salmon. The 2001 run size predictions used in this report should be used with caution, these predictions are well beyond the range of previously observed data.

Age and length composition of Columbia Basin chinook, sockeye, and coho salmon at Bonneville Dam in 2002

In 2002, representative samples of migrating Columbia Basin chinook (Oncorhynchus tshawytscha), sockeye (O. nerka), and coho salmon (O. kisutch) adult populations were collected at Bonneville Dam. Fish were trapped, anesthetized, sampled for scales and biological data, revived, and then released. Scales were examined to estimate age composition; the results contributed to an ongoing database for age class structure of Columbia Basin salmon populations. Based on scale analysis of chinook salmon, four-year-old fish (from brood year [BY] 1998) comprised 86% of the spring chinook, 51% of the summer chinook, and 51% of the bright fall chinook salmon population. Five-year-old fish (BY 1997) comprised 13% of the spring chinook, 43% of the summer chinook, and 11% of the bright fall chinook salmon population. The sockeye salmon population at Bonneville was predominantly five-year-old fish (55%), with 40% returning as four-year-olds in 2002. For the coho salmon population, 88% of the population was three-year-old fish of age class 1.1, while 12% were age class 1.0. Length analysis of the 2002 returns indicated that chinook salmon with a stream-type life history are larger (mean length) at age than the chinook salmon with an ocean-type life history. Trends in mean length over the sampling period for returning 2002 chinook salmon were analyzed. Chinook salmon of age classes 1.2 and 1.3 show a significant increase in mean length over the duration of the migration. A year class regression over the past 14 years of data was used to predict spring, summer, and bright fall chinook salmon population sizes for 2003. Based on three-year-old returns, the relationship predicts four-year-old returns of 54,200 (± 66,600, 90% predictive interval [PI]) spring chinook, 23,800 (± 19,100, 90% PI) summer, and 169,100 (± 139,500, 90% PI) bright fall chinook salmon for the 2003 runs. Based on four-year-old returns, the relationship predicts five-year-old returns of 36,300 (± 35,400, 90% PI) spring, 63,800 (± 10,300, 90% PI) summer, and 91,100 (± 69,400, 90% PI) bright fall chinook salmon for the 2003 runs. The 2003 run size predictions should be used with caution; some of these predictions are well beyond the range of previously observed data.