Reconstruction of Long-Term Time Series for Commercial Fisheries Abundance and Estuarine Pollution Loadings

Summers, J. Kevin; Polgar, Tibor T.; Tarr, Joel A.; Rose, Kenneth A.; Heimbuch, Douglas G.; McCurley, James; Cummins, Robert Alexander; Johnson, G. F.; Yetman, Kenneth T.; DiNardo, Gerard

doi:10.2307/1351861

Cited by 10 publications

(2 citation statements)

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“…Decadal-scale continuous records of biological data utilizing uniform sampling strategies are the exception rather than the rule for most estuarine and coastal realms [25]. Many of the estuarine studies that do take into account the spatial and temporal aspects of the physical environment often utilize commercial catch and effort records [26][27][28][29][30], and these catch per unit effort (CPUE) indices of abundance are not without problems. Technological advances and external economic factors which can directly affect actual effort are either poorly documented, or often entirely dismissed.…”

Section: Discussionmentioning

confidence: 99%

Estuarine Fisheries Community-Level Response to Freshwater Inflows

Tolan¹

2013

Water Resources Planning, Development and Management

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Section: Discussionmentioning

confidence: 99%

Estuarine Fisheries Community-Level Response to Freshwater Inflows

Tolan¹

2013

Water Resources Planning, Development and Management

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“…The northeastern estuaries of the United States support a large number of commercial and recreational fisheries, and many of these stocks have declined in recent years (NOAA, 1991), raising concerns over the roles played by exploitation, natural environmental variability, loss of spawning and nursery habitat, and pollution. The sources and derivation of the CPUE indices and environmental and pollution variables time series used in the analyses were documented by Summers et al (1985). The statistical analysis method we employed, categorical time series regression, has been described elsewhere (Rose et al, 1986).…”

Section: Introductionmentioning

confidence: 99%

Relationships among long'term fisheries abundances, hydrographic variables, and gross pollution indicators in northeastern U.S. estuaries

Rose

Summers

1992

Fisheries Oceanography

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Categorical time series regression was applied to 55 fish stocks in the Potomac, Hudson, Narragansett, Delaware, and Connecticut estuaries for the period 1929–1975. Interannual variability in catch per unit effort (CPUE) was related to CPUE, hydrographic variables, and pollution variables, lagged back in time to represent the conditions contributing to the multiple ages comprising each fishery. Hydrographic variables included water temperature and flow in the estuary– and, for offshore spawning stocks, wind direction and magnitude–during the months of spawning and early life stage development. Pollution variables included measures of dissolved oxygen conditions in the estuaries, volume of material dredged, and sewage loading (or human population). Lagged CPUE, hydrographic variables, and pollution variables all played important roles in explaining historical variability in CPUE. Lagged CPUE was significant in 45 of 55 stocks generally accounting for 5–35% of the variability. Lagged hydrographic variables were significant in 53 of 55 stocks, explaining an additional 5–40% of the variability unaccounted for by lagged CPUE. Lagged pollution variables were significant in 35 of 55 stocks, generally accounting for an additional 5–30% of the variability not explained by lagged CPUE and hydrographic variables. Results did not exhibit expected patterns of consistency in the importance of lagged CPUE for a species across estuaries or consistency in the importance of pollution variables across estuaries. Results did exhibit the expected north‐to‐south longitudinal pattern in the importance of timing of the hydrographic variables, the months of importance being one or two months later in more northerly estuaries. Higher‐order interaction effects were important in almost all stocks that were well‐modeled by categorical time series regression. Of the 30 stocks with final regression models having R2 > 0.55, 26 stocks involved significant interaction effects, five had only significant interaction effects (no significant main effects), and 20 stocks had significant interactions involving variables not significant as main effects. The difficulties involved in analyzing long‐term trends in fish populations and partitioning variability between natural and anthropogenic sources are discussed.

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