Analysis of variance and standardization of longline hook rates of bigeye (Thunnus obesus) and yellowfin (Thunnus albacares) tunas in the Eastern Pacific Ocean during 1975-1987

ENGLISH: Longline hook rates of bigeye and yellowfin tunas in the eastern Pacific Ocean were standardized by maximum depth of fishing, area, and season, using generalized linear models (GLM's). The annual trends of the standardized hook rates differ from the unstandardized, and are more likely to represent the changes in abundance of tunas in the age groups most vulnerable to longliners in the fishing grounds. For both species all of the interactions in the GLM's involving years, depths of fishing, areas, and seasons were significant. This means that the annual trends in hook rates depend on which depths, areas, and seasons are being considered. The overall average hook rates for each were estimated by weighting each 5-degree quadrangle equally and each season by the number of months in it. Since the annual trends in hook rates for each fishing depth category are roughly the same for bigeye, total average annual hook rate estimates are possible with the GLM. For yellowfin, the situation is less clear because of a preponderance of empty cells in the model. The full models explained 55% of the variation in bigeye hook rate and 33% of that of yellowfin. SPANISH: Se estandardizaron las tasas de captura con palangre de atunes patudo y aleta amarilla en el Océano Pacífico oriental por la profunidad máxima de pesca, área, y temporada, usando modelos lineales generalizados (MLG). Las tendencias anuales de las tasas de captura estandardizadas son diferentes a las de las tasas no estandardizadas, y es más que representen los cambios en la abundancia de los atunes en los grupos de edad más vulnerables a los palangreros en las áreas de pesca. Para ambas especies fueron significativas todas las interacciones en los MLG con año, profundidad de pesca, área, y temporada. Esto significa que las tendencias anuales de las tasas de captura dependen de cuál profundidad, área, y temporado se está considerando. Para la estimación de la tasa de captura general media para cada especie se ponderó cada cuadrángulo de 5 grados igualmente y cada temporada por el número de meses que contiene. Ya que las tendencias anuales en las tasas de captura para cada categoría de profundidad de pesca son aproximadamente iguales para el patudo, son posibles estimaciones de la tasa de captura anual media total con el MLG. En el caso del aleta amarilla, la situación es más confusa, debido a una preponderancia de celdas vacías en el modelo. Los modelos completos explican el 55% de la variación de la tasa de captura de patudo y 33% de la del aleta amarilla. (PDF contains 19 pages.)

Measurement errors in body size of sea scallops (Placopecten magellanicus) and their effect on stock assessment models

Body-size measurement errors are usually ignored in stock assessments, but may be important when body-size data (e.g., from visual sur veys) are imprecise. We used experiments and models to quantify measurement errors and their effects on assessment models for sea scallops (Placopecten magellanicus). Errors in size data obscured modes from strong year classes and increased frequency and size of the largest and smallest sizes, potentially biasing growth, mortality, and biomass estimates. Modeling techniques for errors in age data proved useful for errors in size data. In terms of a goodness of model fit to the assessment data, it was more important to accommodate variance than bias. Models that accommodated size errors fitted size data substantially better. We recommend experimental quantification of errors along with a modeling approach that accommodates measurement errors because a direct algebraic approach was not robust and because error parameters were diff icult to estimate in our assessment model. The importance of measurement errors depends on many factors and should be evaluated on a case by case basis.

Removing observational noise from fisheries-independent time series data using ARIMA models

Abundance indices derived from fishery-independent surveys typically exhibit much higher interannual variability than is consistent with the within-survey variance or the life history of a species. This extra variability is essentially observation noise (i.e. measurement error); it probably reflects environmentally driven factors that affect catchability over time. Unfortunately, high observation noise reduces the ability to detect important changes in the underlying population abundance. In our study, a noise-reduction technique for uncorrelated observation noise that is based on autoregressive integrated moving average (ARIMA) time series modeling is investigated. The approach is applied to 18 time series of finfish abundance, which were derived from trawl survey data from the U.S. northeast continental shelf. Although the a priori assumption of a random-walk-plus-uncorrelated-noise model generally yielded a smoothed result that is pleasing to the eye, we recommend that the most appropriate ARIMA model be identified for the observed time series if the smoothed time series will be used for further analysis of the population dynamics of a species.