Power analysis for detecting trends in juvenile spotted seatrout abundance in Florida Bay

The spotted seatrout (Cynoscion nebulosus) is considered a key species relative to the implementation of the Comprehensive Everglades Restoration Plan (CERP). One of the goals of the CERP is to increase freshwater flows to Florida Bay. Increased freshwater flows can have potential positive and negative impacts on spotted seatrout populations. At low salinities, the planktonic eggs of spotted seatrout sink to the bottom and are not viable (Alshuth and Gilmore, 1994; Holt and Holt, 2002). On the other hand, increased freshwater flows can alleviate hypersaline conditions that could result in an expansion of the distribution of the early life stages of spotted seatrout (Thayer et al., 1999; Florida Department of Environmental Protection1). Thus it would be useful to develop a monitoring program that can detect changes in seatrout abundance on time scales short enough to be useful to resource managers. The NOAA Center for Coastal Fisheries and Habitat Research (NOAA) has made sporadic collections of juvenile seatrout using otter trawls since 1984 (see Powell et al, 2004). The results suggest that it might be useful to sample for seatrout in as many as eight different areas or basins (Figure 1): Bradley Key, Sandy Key, Johnson Key, Palm Key, Snake Bight, Central, Whipray and Crocodile Dragover. Unfortunately, logistical constraints are likely to limit the number of tows to about 40 per month over a period of six months each year. Inasmuch as few seatrout are caught in any given tow and the proportion of tows with zero seatrout is often high, it is important to determine how best to allocate this limited sampling effort among the various basins so that any trends in abundance may be detected with sufficient statistical confidence. (PDF contains 16 pages)

A catch-free stock assessment model with application to goliath grouper (Epinephelus itajara) off southern Florida

Many modern stock assessment methods provide the machinery for determining the status of a stock in relation to certain reference points and for estimating how quickly a stock can be rebuilt. However, these methods typically require catch data, which are not always available. We introduce a model-based framework for estimating reference points, stock status, and recovery times in situations where catch data and other measures of absolute abundance are unavailable. The specif ic estimator developed is essentially an age-structured production model recast in terms relative to pre-exploitation levels. A Bayesian estimation scheme is adopted to allow the incorporation of pertinent auxiliary information such as might be obtained from meta-analyses of similar stocks or anecdotal observations. The approach is applied to the population of goliath grouper (Epinephelus itajara) off southern Florida, for which there are three indices of relative abundance but no reliable catch data. The results confirm anecdotal accounts of a marked decline in abundance during the 1980s followed by a substantial increase after the harvest of goliath grouper was banned in 1990. The ban appears to have reduced fishing pressure to between 10% and 50% of the levels observed during the 1980s. Nevertheless, the predicted fishing mortality rate under the ban appears to remain substantial, perhaps owing to illegal harvest and depth-related release mortality. As a result, the base model predicts that there is less than a 40% chance that the spawning biomass will recover to a level that would produce a 50% spawning potential ratio.

A new growth model for red drum (Sciaenops ocellatus) that accommodates seasonal and ontogenic changes in growth rates

The red drum (Sciaenops ocellatus) is a popular gamefish found throughout the coastal waters of the Gulf of Mexico and along the eastern seaboard as far north as Massachusetts. Juvenile red drum grow extremely rapidly, especially during the warmer months, but adults grow very little. In fact, the change in growth with age is so abrupt that the standard von Bertalanffy curve has proven inadequate— the predicted lengths of younger fish are generally too large and the predicted lengths of older fish too small (see Beckman et al., 1988; Murphy and Taylor, 1990).