IUCN classification zones concord with, but underestimate, the population genetic structure of the zebra shark Stegostoma fasciatum in the Indo-West Pacific

The Indo-West Pacific (IWP), from South Africa in the western Indian Ocean to the western Pacific Ocean, contains some of the most biologically diverse marine habitats on earth, including the greatest biodiversity of chondrichthyan fishes. The region encompasses various densities of human habitation leading to contrasts in the levels of exploitation experienced by chondrichthyans, which are targeted for local consumption and export. The demersal chondrichthyan, the zebra shark, Stegostoma fasciatum, is endemic to the IWP and has two current regional International Union for the Conservation of Nature (IUCN) Red List classifications that reflect differing levels of exploitation: ‘Least Concern’ and ‘Vulnerable’. In this study, we employed mitochondrial ND4 sequence data and 13 microsatellite loci to investigate the population genetic structure of 180 zebra sharks from 13 locations throughout the IWP to test the concordance of IUCN zones with demographic units that have conservation value. Mitochondrial and microsatellite data sets from samples collected throughout northern Australia and Southeast Asia concord with the regional IUCN classifications. However, we found evidence of genetic subdivision within these regions, including subdivision between locations connected by habitat suitable for migration. Furthermore, parametric FST analyses and Bayesian clustering analyses indicated that the primary genetic break within the IWP is not represented by the IUCN classifications but rather is congruent with the Indonesian throughflow current. Our findings indicate that recruitment to areas of high exploitation from nearby healthy populations in zebra sharks is likely to be minimal, and that severe localized depletions are predicted to occur in zebra shark populations throughout the IWP region.

Variation in ecophysiology and carbon economy of invasive and native woody vines of riparian zones in south-eastern Queensland

Exotic and invasive woody vines are major environmental weeds of riparian areas, rainforest communities and remnant natural vegetation in coastal eastern Australia, where they smother standing vegetation, including large trees, and cause canopy collapse. We investigated, through glasshouse resource manipulative experiments, the ecophysiological traits that might facilitate faster growth, better resource acquisition and/or utilization and thus dominance of four exotic and invasive vines of South East Queensland, Australia, compared with their native counterparts. Relative growth rate was not significantly different between the two groups but water use efficiency (WUE) was higher in the native species while the converse was observed for light use efficiency (quantum efficiency, AQE) and maximum photosynthesis on a mass basis (Amax mass). The invasive species, as a group, also exhibited higher respiration load, higher light compensation point and higher specific leaf area. There were stronger correlations of leaf traits and greater structural (but not physiological) plasticity in invasive species than in their native counterparts. The scaling coefficients of resource use efficiencies (WUE, AQE and respiration efficiency) as well as those of fitness (biomass accumulated) versus many of the performance traits examined did not differ between the two species-origin groups, but there were indications of significant shifts in elevation (intercept values) and shifts along common slopes in many of these relationships – signalling differences in carbon economy (revenue returned per unit energy invested) and/or resource usage. Using ordination and based on 14 ecophysiological attributes, a fair level of separation between the two groups was achieved (51.5% explanatory power), with AQE, light compensation point, respiration load, WUE, specific leaf area and leaf area ratio, in decreasing order, being the main drivers. This study suggests similarity in trait plasticity, especially for physiological traits, but there appear to be fundamental differences in carbon economy and resource conservation between native and invasive vine species.

Rabbit haemorrhagic disease: are Australian rabbits (Oryctolagus cuniculus) evolving resistance to infection with Czech CAPM 351 RHDV?

Rabbit haemorrhagic disease is a major tool for the management of introduced, wild rabbits in Australia. However, new evidence suggests that rabbits may be developing resistance to the disease. Rabbits sourced from wild populations in central and southeastern Australia, and domestic rabbits for comparison, were experimentally challenged with a low 60 ID50 oral dose of commercially available Czech CAPM 351 virus - the original strain released in Australia. Levels of resistance to infection were generally higher than for unselected domestic rabbits and also differed (0-73% infection rates) between wild populations. Resistance was lower in populations from cooler, wetter regions and also low in arid regions with the highest resistance seen within zones of moderate rainfall. These findings suggest the external influences of non-pathogenic calicivirus in cooler, wetter areas and poor recruitment in arid populations may influence the development rate of resistance in Australia.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.