Protocols for conducting dolphin capture-release health assessment studies

Marine mammals, such as dolphins, can serve as key indicator species in coastal areas by reflecting the effects of natural and anthropogenic stressors. As such they are often considered sentinels of environmental and ecosystem health (Bossart 2006; Wells et al. 2004; Fair and Becker 2000). The bottlenose dolphin is an apex predator and a key component of many estuarine environments in the southeastern United States (Woodward-Clyde Consultants 1994; SCDNR 2005). Health assessments of dolphins are especially critical in areas where populations are depleted, show signs of epidemic disease and/or high mortality and/or where habitat is being altered or impacted by human activities. Recent assessments of environmental conditions in the Indian River Lagoon, Florida (IRL) and the estuarine waters surrounding Charleston, South Carolina (CHS) highlight the need for studies of the health of local bottlenose dolphins. While the condition of southeastern estuaries was rated as fair in the National Coastal Condition Report (U.S. EPA 2001), it was noted that the IRL was characterized by poorer than expected benthic communities, significant sediment toxicity and increased nutrient concentrations. Similarly, portions of the CHS estuary have sediment concentrations of aliphatic aromatic hydrocarbons, select inorganic metals, and some persistent pesticides far in excess of reported bioeffect levels (Hyland et al. 1998). Long-term trends in water quality monitoring and recent scientific research suggest that waste load assimilation, non-point source runoff impacts, contaminated sediments, and toxic pollutants are key issues in the CHS estuary system. Several ‘hot spots’ with high levels of heavy metals and organic compounds have been identified (Van Dolah et al. 2004). High concentrations of anthropogenic trace metals, polychlorinated biphenyls (PCB’s) and pesticides have been found in the sediments of Charleston Harbor, as well as the Ashley and Cooper Rivers (Long et al. 1998). Two superfund sites are located within the CHS estuary and the key contaminants of concern associated with these sites are: polycyclic aromatic hydrocarbons (PAH), lead, chromium, copper, arsenic, zinc and dioxin. Concerns related to the overall health of IRL dolphins and dermatologic disease observed in many dolphins in the area (Bossart et al. 2003) initiated an investigation of potential factors which may have impacted dolphin health. From May-August 2001, 35 bottlenose dolphins died in the IRL during an unusual mortality event (MMC 2003). Many of these dolphins were diagnosed with a variety of skin lesions including proliferative ulcerative dermatitis due to protozoa and fungi, dolphin pox and a vesicular dermatopathy of unknown etiology (Bossart et al. 2003). Multiple species from fish to dolphins in the IRL system have exhibited skin lesions of various known and unknown etiologies (Kane et al. 2000; Bossart et al. 2003; Reif et al. 2006). On-going photo-identification (photo-ID) studies have documented skin diseases in IRL dolphins (Mazzoil et al. 2005). In addition, up to 70% of green sea turtles in the IRL exhibit fibropapillomas, with the highest rates of occurrence being seen in turtles from the southern IRL (Hirama 2001).

Recruitment as an evolving random process of aggregation and mortality

The dynamics of the survival of recruiting fish are analyzed as evolving random processes of aggregation and mortality. The analyses draw on recent advances in the physics of complex networks and, in particular, the scale-free degree distribution arising from growing random networks with preferential attachment of links to nodes. In this study simulations were conducted in which recruiting fish 1) were subjected to mortality by using alternative mortality encounter models and 2) aggregated according to random encounters (two schools randomly encountering one another join into a single school) or preferential attachment (the probability of a successful aggregation of two schools is proportional to the school sizes). The simulations started from either a “disaggregated” (all schools comprised a single fish) or an aggregated initial condition. Results showed the transition of the school-size distribution with preferential attachment evolving toward a scale-free school size distribution, whereas random attachment evolved toward an exponential distribution. Preferential attachment strategies performed better than random attachment strategies in terms of recruitment survival at time when mortality encounters were weighted toward schools rather than to individual fish. Mathematical models were developed whose solutions (either analytic or numerical) mimicked the simulation results. The resulting models included both Beverton-Holt and Ricker-like recruitment, which predict recruitment as a function of initial mean school size as well as initial stock size. Results suggest that school-size distributions during recruitment may provide information on recruitment processes. The models also provide a template for expanding both theoretical and empirical recruitment research.

Length and age at maturity of female petrale sole (Eopsetta jordani) determined from samples collected prior to spawning aggregation

The problem of bias in female petrale sole age and length-at-maturity relationships caused by sampling from spawning aggregations was investigated. Samples were collected prior to aggregation, and histological methods were used to determine maturity status. Mature and immature fish were classified by inspecting oocytes for the presence of yolk in September, when substantial divergence in yolked and unyolked oocyte diameters had been observed. Comparison of macroscopic and microscopic assessment of maturity showed that maturity status cannot be determined accurately by using macroscopic inspection during the summer. Female petrale sole from the central Oregon coast were 50% mature at 33 cm and 5 years of age. Comparison of data from our study with data used in recent petrale sole stock assessments showed that both sampling bias and the use of samples from sea-sons when status cannot be accurately determined have likely caused errors in fitted maturity relationships.