Catches per unit of effort of bigeye tuna: a new analysis with regression trees and simulated annealing

ENGLISH: We analyzed catches per unit of effort (CPUE) from the Japanese longline fishery for bigeye tuna (Thunnus obesus) in the central and eastern Pacific Ocean (EPO) with regression tree methods. Regression trees have not previously been used to estimate time series of abundance indices fronl CPUE data. The "optimally sized" tree had 139 parameters; year, month, latitude, and longitude interacted to affect bigeye CPUE. The trend in tree-based abundance indices for the EPO was similar to trends estimated from a generalized linear model and fronl an empirical model that combines oceanographic data with information on the distribution of fish relative to environmental conditions. The regression tree was more parsimonious and would be easier to implement than the other two nl0dels, but the tree provided no information about the nlechanisms that caused bigeye CPUEs to vary in time and space. Bigeye CPUEs increased sharply during the mid-1980's and were more variable at the northern and southern edges of the fishing grounds. Both of these results can be explained by changes in actual abundance and changes in catchability. Results from a regression tree that was fitted to a subset of the data indicated that, in the EPO, bigeye are about equally catchable with regular and deep longlines. This is not consistent with observations that bigeye are more abundant at depth and indicates that classification by gear type (regular or deep longline) may not provide a good measure of capture depth. Asimulated annealing algorithm was used to summarize the tree-based results by partitioning the fishing grounds into regions where trends in bigeye CPUE were similar. Simulated annealing can be useful for designing spatial strata in future sampling programs. SPANISH: Analizamos la captura por unidad de esfuerzo (CPUE) de la pesquería palangrera japonesa de atún patudo (Thunnus obesus) en el Océano Pacifico oriental (OPO) y central con métodos de árbol de regresión. Hasta ahora no se han usado árboles de regresión para estimar series de tiempo de índices de abundancia a partir de datos de CPUE. EI árbol de "tamaño optimo" tuvo 139 parámetros; ano, mes, latitud, y longitud interactuaron para afectar la CPUE de patudo. La tendencia en los índices de abundancia basados en árboles para el OPO fue similar a las tendencias estimadas con un modelo lineal generalizado y con un modelo empírico que combina datos oceanográficos con información sobre la distribución de los peces en relación con las condiciones ambientales. EI árbol de regresión fue mas parsimonioso y seria mas fácil de utilizar que los dos otros modelos, pero no proporciono información sobre los mecanismos que causaron que las CPUE de patudo valiaran en el tiempo y en el espacio. Las CPUE de patudo aumentaron notablemente a mediados de los anos 80 y fueron mas variables en los extremos norte y sur de la zona de pesca. Estos dos resultados pueden ser explicados por cambios en la abundancia real y cambios en la capturabilidad. Los resultados de un arbal de regresión ajustado a un subconjunto de los datos indican que, en el OPO, el patudo es igualmente capturable con palangres regulares y profundos. Esto no es consistente con observaciones de que el patudo abunda mas a profundidad e indica que clasificación por tipo de arte (palangre regular 0 profundo) podría no ser una buena medida de la profundidad de captura. Se uso un algoritmo de templado simulado para resumir los resultados basados en el árbol clasificando las zonas de pesca en zonas con tendencias similares en la CPUE de patudo. El templado simulado podría ser útil para diseñar estratos espaciales en programas futuros de muestreo. (PDF contains 45 pages.)

Length-weight relationship of marine fishes from southern Brazil

The relationship between length (L) and weight (W) was estimated for 80 species belonging to 50 families of marine fishes from the shelf and upper slope of southern Brazil (lat. 28°S - 34°S). Sample sizes (n) for different species ranged from 11 to 14 741 specimens collected from commercial landings and research surveys. The fit of the equations (W=aLb) with a and b parameters estimated from regular and functional regression (of log-transformed weight and length data) as well as from a non-linear iterative process using the quasi-Newton algorithm were compared. The non-linear method gave the most accurate estimates in terms of residual sum of squares. Differences were less than 2.3% for n>500 compared with predictive regressions and 1.5% compared with functional regressions. No difference was observed between both predictive and functional regressions. Determination coefficients (r2) increased with sample size, and the highest r2 were obtained for 50

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.

Mapping reef fish and the seascape: using acoustics and spatial modeling to guide coastal management

Reef fish distributions are patchy in time and space with some coral reef habitats supporting higher densities (i.e., aggregations) of fish than others. Identifying and quantifying fish aggregations (particularly during spawning events) are often top priorities for coastal managers. However, the rapid mapping of these aggregations using conventional survey methods (e.g., non-technical SCUBA diving and remotely operated cameras) are limited by depth, visibility and time. Acoustic sensors (i.e., splitbeam and multibeam echosounders) are not constrained by these same limitations, and were used to concurrently map and quantify the location, density and size of reef fish along with seafloor structure in two, separate locations in the U.S. Virgin Islands. Reef fish aggregations were documented along the shelf edge, an ecologically important ecotone in the region. Fish were grouped into three classes according to body size, and relationships with the benthic seascape were modeled in one area using Boosted Regression Trees. These models were validated in a second area to test their predictive performance in locations where fish have not been mapped. Models predicting the density of large fish (≥29 cm) performed well (i.e., AUC = 0.77). Water depth and standard deviation of depth were the most influential predictors at two spatial scales (100 and 300 m). Models of small (≤11 cm) and medium (12–28 cm) fish performed poorly (i.e., AUC = 0.49 to 0.68) due to the high prevalence (45–79%) of smaller fish in both locations, and the unequal prevalence of smaller fish in the training and validation areas. Integrating acoustic sensors with spatial modeling offers a new and reliable approach to rapidly identify fish aggregations and to predict the density large fish in un-surveyed locations. This integrative approach will help coastal managers to prioritize sites, and focus their limited resources on areas that may be of higher conservation value.

Validation of a semi-Lagrangian, canonical regression model of climates in the southwestern United States

EXTRACT (SEE PDF FOR FULL ABSTRACT): A local climate model (LCM) has been developed to simulate the modern and 18 ka climate of the southwestern United States. ... LCM solutions indicate summers were about 1°C cooler and winters 11°C cooler at 18 ka. Annual PREC increased 68% at 18 ka, with large increases in spring and fall PREC and diminished summer monsoonal PREC. ... Validation of simulations of 18 ka climate indicate general agreement with proxy estimates of climate for that time. However, the LCM estimates of summer temperatures are about 5 to 10°C higher than estimates from proxy reconstructions.

Cumulative inbreeding rate in hatchery-reared indian major carps of Karnataka and Maharashtra states

The state fisheries department hatcheries are the major suppliers of seed to the farmers in Karnataka and Maharashtra. The brood stocks of these hatcheries are genetically closed units. In the present study, effective population size and cumulative inbreeding rates were estimated. The cumulative inbreeding rates ranged from 2.69 to 13.75, 8.63 to 15.21 and 3.02 to 5.88 per cent for catla, mrigal and rohu, respectively, in Karnataka state hatcheries. In Maharashtra, the cumulative inbreeding rates for catla ranged from 7.81 to 39.34 per cent and it was 5.84 to 14.09 and 2.46 to 10.20 per cent for mrigal and rohu, respectively. To estimate the inbreeding rates in future generations, predictive models were developed using linear regression, and polynomial and power equations separately for each hatchery. Their multiple correlation and standard errors suggested that simple linear regression can predict the future inbreeding rate efficiently.