Quantifying annual variation in catchability for commercial and research fishing

Biomass indices, from commercial catch per unit of effort (CPUE) or random trawl surveys, are commonly used in fisheries stock assessments. Uncertainty in such indices, often ex-pressed as a coefficient of variation (CV), has two components: observation error, and annual variation in catchability. Only the former can be estimated directly. As a result, the CVs used for these indices either ignore the annual-variation component or assume a value for it (often implicitly). Two types of data for New Zealand stocks were examined: 48 sets of residuals and catchability estimates from stock assessments using either CPUE or trawl survey indices; and biomass estimates from 17 time series of trawl surveys with between 4 and 25 species per time series. These data show clear evidence of significant annual variation in catchability. With the trawl survey data, catchability was detectably extreme for many species in about one year in six. The assessment data suggest that this annual variability typically has a CV of about 0.2. For commercial CPUE the variability is slightly less, and a typical total CV (including both components) of 0.15 to 0.2. This is much less than the values of 0.3 to 0.35 that have commonly been assumed in New Zealand. Some estimates of catchability are shown to be implausible.

Bycatch in the tuna purse-seine fisheries of the western Indian Ocean

Bycatch taken by the tuna purse-seine fishery from the Indian Ocean pelagic ecosystem was estimated from data collected by scientific observers aboard Soviet purse seiners in the western Indian Ocean (WIO) during 1986–92. A total of 494 sets on free-swimming schools, whale-shark-associated schools, whale-associated schools, and log-associated schools were analyzed. More than 40 fish species and other marine animals were recorded. Among them only two species, yellow-fin and skipjack tunas, were target species. Average levels of bycatch were 0.518 metric tons (t) per set, and 27.1 t per 1000 t of target species. The total annual purse-seine catch of yellowfin and skipjack tunas by principal fishing nations in the WIO during 1985–94 was 118,000–277,000 t. Nonrecorded annual bycatch for this period was estimated at 944–2270 t of pelagic oceanic sharks, 720–1877 t of rainbow runners, 705–1836 t of dolphinfishes, 507–1322 t of triggerfishes, 113–294 t of wahoo, 104–251 t of billfishes, 53–112 t of mobulas and mantas, 35–89 t of mackerel scad, 9–24 t of barracudas, and 67–174 t of other fishes. In addition, turtle bycatch and whale mortalities may have occurred. Because the bycatches were not recorded by some purse-seine vessels, it was not possible to assess the full impact of the fisheries on the pelagic ecosystem of the Indian Ocean. The first step to solving this problem is for the Indian Ocean Tuna Commission to establish a pro-gram in which scientific observers are placed on board tuna purse-seine and longline vessels fishing in the WIO.

The annual course of precipitation over much of the United States: observed versus GCM simulation

General Circulation Models (GCMs) may be useful in estimating the ecological impacts of global climatic change. We analyzed seasonal weather patterns over the conterminous United States and determined that regional patterns of rainfall seasonality appear to control the distributions of the Nation's major biomes. These regional patterns were compared to the output from three GCMs for validation. The models appear to simulate the appropriate seasonal climates in the northern tier of states. However, the spatial extent of these regions is distorted. None of the models accurately portrayed rainfall seasonalities in the southern tier of states, where biomes are primarily influenced by the Bermuda High.

Proceedings of the Tenth Annual Pacific Climate (PACLIM) Workshop, Asilomar, California - April 4-7, 1993 [appendices]

The appendices include the workshop agenda, a list of poster presentations, and a list of attendees.

Determining the role of linear and nonlinear interactions in records of climate change: possible solar influences in annual to century-scale records

Time series analysis methods have traditionally helped in identifying the role of various forcing mechanisms in influencing climate change. A challenge to understanding decadal and century-scale climate change has been that the linkages between climate changes and potential forcing mechanisms such as solar variability are often uncertain. However, most studies have focused on the role of climate forcing and climate response within a strictly linear framework. Nonlinear time series analysis procedures provide the opportunity to analyze the role of climate forcing and climate responses between different time scales of climate change. An example is provided by the possible nonlinear response of paleo-ENSO-scale climate changes as identified from coral records to forcing by the solar cycle at longer time scales.