A perceived recent increase in global jellyfish abundance has been portrayed as a symptom of degraded oceans. This perception is based primarily on a few case studies and anecdotal evidence, but a formal analysis of global temporal trends in jellyfish populations has been missing. Here, we analyze all available long-term datasets on changes in jellyfish abundance across multiple coastal stations, using linear and logistic mixed models and effect-size analysis to show that there is no robust evidence for a global increase in jellyfish. Although there has been a small linear increase in jellyfish since the 1970s, this trend was unsubstantiated by effect-size analysis that showed no difference in the proportion of increasing vs. decreasing jellyfish populations over all time periods examined. Rather, the strongest nonrandom trend indicated jellyfish populations undergo larger, worldwide oscillations with an approximate 20-y periodicity, including a rising phase during the 1990s that contributed to the perception of a global increase in jellyfish abundance. Sustained monitoring is required over the next decade to elucidate with statistical confidence whether the weak increasing linear trend in jellyfish after 1970 is an actual shift in the baseline or part of an oscillation. Irrespective of the nature of increase, given the potential damage posed by jellyfish blooms to fisheries, tourism, and other human industries, our findings foretell recurrent phases of rise and fall in jellyfish populations that society should be prepared to face.

Various natural and anthropogenic processes influence heavy metal concentrations within estuaries. In situ, time-integrated DGT measurements made over concurrent tidal phases found significantly higher concentrations of Cu (probability p=0.017), Zn (p=0.003) and Ni (p=0.003) during the flood phase, because the incoming tide passes several point sources. DGT-reactive Cu concentrations significantly decreased with increased tidal-flushing and vice versa within a marina (correlation r=-0.788, p=0.02). DGT measurements also recorded significant increases in Cu (4 out of 4 sites, p<0.001) and Zn (3 out of 4 sites, p< or =0.015) after a 24 mm rainfall event. Finally, DGT-reactive Cu increased significantly (p<0.001) during peak boating times, due to increased numbers of Cu-antifouled boats. This study demonstrates that, with judicious selection of deployment times, DGT measurements enable changes in heavy metal concentrations to be related to various cycles and events within estuaries.

Stream fish assemblages were sampled by multiple-pass electrofishing and supplementary seine netting in 31 sites in the Johnstone River, north Queensland and 28 sites in the Mary River, southeastern Queensland to determine the sampling effort required to adequately describe the assemblages in terms of fish abundances, species composition and assemblage structure. A significantly greater proportion ofthe total number of fishes present at each site was collected by the first electrofishing pass in the Mary River (46%) than in the Johnstone P?iver (37%) and this difference was suggested to be due to higher water conductivity in the former river. The mean proportion of the total species richness detected by the first pass was also significantly higher in the Mary River than in the Johnstone River (89% and 82%, respectively). Multivariate comparisons offish assemblage structure revealed that data collected by the first electrofishing pass poorly estimated the actual assemblage structure within a site and that up to three passes were required for estimates of assemblage structure to stabilize. This effect was evident for comparisons based on both absolute abundance and relative abundance data and was particularly marked for comparisons based on presence/absence data. This latter result suggests that, even though most species were detected on the first pass, the addition of rare species by subsequent passes had an important effect on the resultant description of assemblage structure. Supplementary seine netting had a greater effect on the determination of assemblage structure in the Mary River than in the Johnstone River. The results are discussed with reference to sampling design in studies of stream fish assemblages and a sampling protocol is recommended that enables the accurate determination of abundance, richness and assemblage structure in small-to medium-sized streams.

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Mud crabs (Scylla spp.) are a high value commodity harvested in the Indo-West 15 Pacific. Scylla species support important artisanal fisheries in southeast Asia and 16 intensive commercial fisheries in Australia where the market demand and catch has 17 increased markedly over the last decade. Overfishing of Scylla spp. has been observed 18 at varying levels throughout its distribution. Fluctuations in catch rates and abundance 19 are thought to be driven by climate parameters. Here we analyse monthly, seasonal 20 and annual patterns in catch and effort data (from 1990 to 2008) for the commercial 21 giant mud crab (Scylla serrata) fishery in the Northern Territory, Australia, with 22 corresponding climatic data (rainfall, freshwater runoff, sea surface temperature) and 23 the Southern Oscillation Index (SOI) as an indicator of La Niña/El Niño events. 24 Between 30-40% of the variation in catch per unit effort can be explained by rainfall 25 and SOI alone. This result was supported by linear mixed models which identified 26 *Manuscript Click here to download Manuscript: SOI_and_mud crab catch_30122011_revised.doc Click here to view linked References SOI as the main contributor to the model. Spectral analyses showed that catch peaks 27 coincided with a four year La Niña cycle. One-and two-year time lags (consistent 28 with S. Serrata's life cycle) were also significantly correlated to SOI values and 29 rainfall. These outcomes may assist fishery managers in planning fishing exposure 30 period and duration. Furthermore, findings of this study provide information on the 31 vulnerability of S. serrata to fluctuations in environmental conditions and can help to 32 apply protective measures when and where necessary. 33 34