Using poststratification to improve abundance estimates from multispecies surveys: a study of juvenile flatfishes

Population assessments seldom incorporate habitat information or use previously observed distributions of fish density. Because habitat affects the spatial distribution of fish density and overall abundance, the use of habitat information and previous estimates of fish density can produce more precise and less biased population estimates. In this study, we describe how poststratification can be applied as an unbiased estimator to data sets that were collected under a probability sampling design, typical of many multispecies trawl surveys. With data from a multispecies survey of juvenile flatfish, we show how poststratification can be applied to a data set that was not collected under a probability sampling design, where both the precision and the bias are unknown. For each of four species, three estimates of total abundance were compared: 1) unstratified; 2) poststratified by habitat; and 3) poststratified by habitat and fish density (high fish density and low fish density) in nearby years. Poststratification by habitat gave more precise and (or) less design-biased estimates than an unstratified estimator for all species in all years. Poststratification by habitat and fish density produced the most precise and representative estimates when the sample size in the high fish-density and low fish-density strata were sufficient (in this study, n≥20 in the high fish-density stratum, n≥9 in the low fish-density stratum). Because of the complexities of statistically testing the annual stratified data, we compared three indices of abundance for determining statistically significant changes in annual abundance. Each of the indices closely approximated the annual differences of the poststratified estimates. Selection of the most appropriate index was dependent upon the species’ density distribution within habitat and the sample size in the different habitat areas. The methods used in this study are particularly useful for estimating individual species abundance from multispecies surveys and for retrospective st

Fishery-independent Bottom Trawl Surveys for Deep-water Fishes and Invertebrates of the U.S. Gulf of Mexico, 2002–08

From 2002 through 2008, the Mississippi Laboratories of the NMFS Southeast Fisheries Science Center, NOAA, conducted fishery-independent bottom trawl surveys for continental shelf and outer-continental shelf deep-water fishes and invertebrates of the U.S. Gulf of Mexico (50–500 m bottom depths). Five-hundred and ninety species were captured at 797 bottom trawl locations. Standardized survey gear and randomly selected survey sites have facilitated development of a fishery-independent time series that characterizes species diversity, distributions, and catch per unit effort. The fishery-independent surveys provide synoptic descriptions of deep-water fauna potentially impacted by various anthropogenic factors.

History of Alaska Red King Crab, Paralithodes camtschaticus, Bottom Trawl Surveys, 1940–61

Thirteen bottom trawl surveys conducted in Alaska waters for red king crab, Paralithodes camtschaticus, during 1940–61 are largely forgotten today even though they helped define our current knowledge of this resource. Government publications on six exploratory surveys (1940–49, 1957) included sample locations and some catch composition data, but these documents are rarely referenced. Only brief summaries of the other seven annual (1955–61) grid-patterned trawl surveys from the eastern Bering Sea were published. Although there have been interruptions in sampling and some changes in the trawl survey methods, a version of this grid-patterned survey continues through the present day, making it one of the oldest bottom-trawl surveys in U.S. waters. Unfortunately, many of the specific findings made during these early efforts have been lost to the research community. Here, we report on the methods, results, and significance of these early surveys, which were collated from published reports and the unpublished original data sheets so that researchers might begin incorporating this information into stock assessments, ecosystem trend analyses, and perhaps even revise the baseline population distribution and abundance estimates.