Aim  To assess whether eight factors thought to be involved in the extinction process can explain the pattern of recent decline in Australia's mammal fauna. Location  Australia. Methods  We compiled the first comprehensive lists of mammal species extant at the time of European settlement in each of Australia's 76 mainland regions, and assigned a current conservation status to each species in each region to derive an index of faunal attrition. We then sought to explain the observed region‐to‐region variation in attrition (the dependent variable) by building a series of models using variables representing the eight factors. Results  A strong geographically based pattern of attrition emerged, with faunal losses being greatest in arid regions and least in areas of high rainfall. The Akaike information criterion showed support for one model that explained 93% of the region‐to‐region variation in attrition. Its six variables all made independent contributions towards explaining the observed variation. Two were environmental variables, namely mean annual rainfall (a surrogate for regional productivity) and environmental change (a measure of post‐European disturbance). The other four were faunal variables, namely phylogenetic similarity, body‐weight distribution, area (as a surrogate for extent of occurrence), and proportion of species that usually shelter on the ground (rather than in rock piles, burrows or trees). Main conclusions  In combination with historical evidence, the analysis provides an explicit basis for setting priorities among regions and species. It also shows that the long‐term recovery of populations of many species of Australian mammals will require introduced predator suppression as well as extensive habitat management that includes controlling feral herbivores. Specifically, habitat management should restore aspects of productivity relevant to the types of species at risk and ensure the continual availability of suitable refuges from physiological stressors.

Phytophthora cinnamomi continues to cause devastating disease in Australian native vegetation and consequently the disease is listed by the Federal Government as a process that is threatening Australia’s biodiversity. Although several advances have been made in our understanding of how this soil-borne pathogen interacts with plants and of how we may tackle it in natural systems, our ability to control the disease is limited. The pathogen occurs widely across Australia but the severity of its impact is most evident within ecological communities of the south-west and south-east of the country. A regional impact summary for all states and territories shows the pathogen to be the cause of serious disease in numerous species, a significant number of which are rare and threatened. Many genera of endemic taxa have a high proportion of susceptible species including the iconic genera Banksia, Epacris and Xanthorrhoea. Long-term studies in Victoria have shown limited but probably unsustainable recovery of susceptible vegetation, given current management practices. Management of the disease in conservation reserves is reliant on hygiene, the use of chemicals and restriction of access, and has had only limited effectiveness and not provided complete control. The deleterious impacts of the disease on faunal habitat are reasonably well documented and demonstrate loss of individual animal species and changes in population structure and species abundance. Few plant species are known to be resistant to P. cinnamomi; however, investigations over several years have discovered the mechanisms by which some plants are able to survive infection, including the activation of defence-related genes and signalling pathways, the reinforcement of cell walls and accumulation of toxic metabolites. Manipulation of resistance and resistance-related mechanisms may provide avenues for protection against disease in otherwise susceptible species. Despite the advances made in Phytophthora research in Australia during the past 40 years, there is still much to be done to give land managers the resources to combat this disease. Recent State and Federal initiatives offer the prospect of a growing and broader awareness of the disease and its associated impacts. However, awareness must be translated into action as time is running out for the large number of susceptible, and potentially susceptible, species within vulnerable Australian ecological communities.

Summary 1.To develop a conservation management plan for a species, knowledge of its distribution and spatial arrangement of preferred habitat is essential. This is a difficult task, especially when the species of concern is in low abundance. In south-western Victoria, Australia, populations of the rare rufous bristlebird Dasyornis broadbenti are threatened by fragmentation of suitable habitat. In order to improve the conservation status of this species, critical habitat requirements must be identified and a system of corridors must be established to link known populations. A predictive spatial model of rufous bristlebird habitat was developed in order to identify critical areas requiring preservation, such as corridors for dispersal. 2. Habitat models generated using generalized linear modelling techniques can assist in delineating the specific habitat requirements of a species. Coupled with geographic information system (GIS) technology, these models can be extrapolated to produce maps displaying the spatial configuration of suitable habitat. 3. Models were generated using logistic regression, with bristlebird presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multispectral digital imagery, as the predictors. A multimodel inference approach based on Akaike's information criterion was used and the resulting model was applied in a GIS to extrapolate predicted likelihood of occurrence across the entire area of concern. The predictive performance of the selected model was evaluated using the receiver operating characteristic (ROC) technique. A hierarchical partitioning protocol was used to identify the predictor variables most likely to influence variation in the dependent variable. Probability of species presence was used as an index of habitat suitability. 4. Negative associations between rufous bristlebird presence and increasing elevation, 'distance to creek', 'distance to coast' and sun index were evident, suggesting a preference for areas relatively low in altitude, in close proximity to the coastal fringe and drainage lines, and receiving less direct sunlight. A positive association with increasing habitat complexity also suggested that this species prefers areas containing high vertical density of vegetation. 5. The predictive performance of the selected model was shown to be high (area under the curve 0·97), indicating a good fit of the model to the data. Hierarchical partitioning analysis showed that all the variables considered had significant independent contributions towards explaining the variation in the dependent variable. The proportion of the total study area that was predicted as suitable habitat for the rufous bristlebird (using probability of occurrence at a ≥ 0·5 level) was 16%. 6. Synthesis and applications. The spatial model clearly delineated areas predicted as highly suitable rufous bristlebird habitat, with evidence of potential corridors linking coastal and inland populations via gullies. Conservation of this species will depend...

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Background The challenges faced by the Global South during the coronavirus disease (COVID-19) pandemic are compounded by the presence of informal settlements, which are typically densely populated and lacking in formalized sanitation infrastructure. Social distancing measures in informal settlements may be difficult to implement due to the density and layout of settlements. This study measures the distance between dwellings in informal settlements in Cape Town to identify the risk of COVID-19 transmission. Objective The aim of this paper is to determine if social distancing measures are achievable in informal settlements in Cape Town, using two settlements as an example. We will first examine the distance between dwellings and their first, second, and third nearest neighbors and then identify clusters of dwellings in which residents would be unable to effectively practice social isolation due to the close proximity of their homes. Methods Dwellings in the settlements of Masiphumelele and Klipfontein Glebe were extracted from a geographic information system data set of outlines of all informal dwellings in Cape Town. The distance to each dwelling’s first, second, and third nearest neighbors was calculated for each settlement. A social distance measure of 2 m was used (buffer of 1 m, as dwellings less than 2 m apart are joined) to identify clusters of dwellings that are unable to effectively practice social distancing in each settlement. Results The distance to each dwelling’s first 3 nearest neighbors illustrates that the settlement of Masiphumelele is constructed in a denser fashion as compared to the Klipfontein Glebe settlement. This implies that implementing social distancing will likely be more challenging in Masiphumelele than in Klipfontein Glebe. However, using a 2-m social distancing measure, it was demonstrated that large portions of Klipfontein Glebe would also be unable to effectively implement social distancing. Conclusions Effectively implementing social distancing may be a challenge in informal settlements due to their density. This paper uses dwelling outlines for informal settlements in the city of Cape Town to demonstrate that with a 2 m measure, effective social distancing will be challenging.