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South eastern Australia is a global warming hotspot, and is also home to ~70% of Australia's population and one of Australia's largest fisheries—the South East Scalefish and Shark Fishery. This fishery spans shelf to deep waters, subtropical to temperate waters, employs many gears, and interacts with over 100 species. Despite following best practice fisheries management principles, including taking an ecosystem perspective to overall fisheries interactions, management interventions have failed to arrest or recover the decline of some focal species. Using an Atlantis ecosystem model developed for the fishery over the past 20 years, this paper presents simulations that suggest climate change may be a major contributor to the trajectories seen for a number of species—such as jackass morwong (Nemadactylus macropterus), blue warehou (Seriolella brama), and gemfish (Rexea solandri). This kind of climate influence poses many challenges for fisheries management into the future, raising questions about what to do when climate change undermines or overrides fisheries management actions and objectives. [ABSTRACT FROM AUTHOR]

Studying range expansions is central for understanding genetic variation through space and time as well as for identifying refugia and biological invasions. Range expansions are characterized by serial founder events causing clines of decreasing genetic diversity away from the center of origin and asymmetries in the two-dimensional allele frequency spectra. These asymmetries, summarized by the directionality index (ψ), are sensitive to range expansions and persist for longer than clines in genetic diversity. In continuous and finite meta-populations, genetic drift tends to be stronger at the edges of the species distribution in equilibrium populations and populations undergoing range expansions alike. Such boundary effects are expected to affect geographic patterns in genetic diversity and ψ. Here we demonstrate that boundary effects cause high false positive rates in equilibrium meta-populations when testing for range expansions. In the simulations, the absolute value of ψ (| ψ |) in equilibrium data sets was proportional to the fixation index (F ST). By fitting signatures of range expansions as a function of ɛ | ψ |/ F ST and geographic clines in ψ , strong evidence for range expansions could be detected in data from a recent rapid invasion of the cane toad, Rhinella marina , in Australia, but not in 28 previously published empirical data sets from Australian scincid lizards that were significant for the standard range expansion tests. Thus, while clinal variation in ψ is still the most sensitive statistic to range expansions, to detect true signatures of range expansions in natural populations, its magnitude needs to be considered in relation to the overall levels of genetic structuring in the data. [ABSTRACT FROM AUTHOR]

Mapping the density contrast through the 3D gravity inversion can help detect goals under the subsurface. However, it is a challenge to accurately and efficiently solve the 3D gravity inversion. The Krylov subspace method is commonly used for large linear problems due to its high computational efficiency and low storage requirement. In this study, two classical algorithms of Krylov subspace method, namely the generalized minimum residual method and the conjugate gradient method, are applied to 3D gravity inversion. On the basis of the recovered models of the deep mineral and the shallow L-shaped tunnel models, it was found that the generalized minimum residual method provided similar density contrast results to the conjugate gradient method. The obtained inversion results of density contrast corresponded well to the position of the deep mineral resources model and the L-shaped tunnel model. The 3D distribution of Fe content underground was obtained by inverting the measured gravity data from the Olympic Dam in Australia. The recovered results correspond well with the distribution of Fe content in the geological profile collected. The accuracy of inversion using the generalized minimum residual method was similar to that of the conjugate gradient method under the same conditions. However, the generalized minimum residual method had a faster convergence speed and increased inversion efficiency by ∼90%, greatly reducing the inversion time and improves the inversion efficiency. [ABSTRACT FROM AUTHOR]