How to prioritize species recovery after a megafire

Due to climate change, megafires are increasingly common and have sudden, extensive impacts on many species over vast areas, leaving decision makers uncertain about how best to prioritize recovery. We devised a decision-support framework to prioritize conservation actions to improve species outcomes immediately after a megafire. Complementary locations are selected to extend recovery actions across all fire-affected species' habitats. We applied our method to areas burned in the 2019-2020 Australian megafires and assessed its conservation advantages by comparing our results with outcomes of a site-richness approach (i.e., identifying areas that cost-effectively recover the most species in any one location). We found that 290 threatened species were likely severely affected and will require immediate conservation action to prevent population declines and possible extirpation. We identified 179 subregions, mostly in southeastern Australia, that are key locations to extend actions that benefit multiple species. Cost savings were over AU$300 million to reduce 95% of threats across all species. Our complementarity-based prioritization also spread postfire management actions across a wider proportion of the study area compared with the site-richness method (43% vs. 37% of the landscape managed, respectively) and put more of each species' range under management (average 90% vs. 79% of every species' habitat managed). In addition to wildfire response, our framework can be used to prioritize conservation actions that will best mitigate threats affecting species following other extreme environmental events (e.g., floods and drought)

Restoring habitat for fire-impacted species' across degraded Australian landscapes

In the summer of 2019-2020, southern Australia experienced the largest fires on record, detrimentally impacting the habitat of native species, many of which were already threatened by past and current anthropogenic land use. A large-scale restoration effort to improve degraded species habitat would provide fire-affected species with the chance to recover and persist in burnt and unburnt habitat. To facilitate this, decision-makers require information on priority species needs for restoration intervention, the suite of potential restoration interventions, and the priority locations for applying these interventions. We prioritize actions in areas where restoration would most likely provide cost-effective benefits to priority species (defined by each species proportion of habitat burned, threat status, and vulnerability to fires), by integrating current and future species habitat suitability maps with spatially modelled costs of restoration interventions such as replanting, removing invasive species, and implementing ecologically appropriate fire management. We show that restoring the top similar to 69% (112 million hectares) of the study region (current and future distributions of priority species) accounts for, on average, 95% of current and future habitat for every priority species and costs similar to AUD$73 billion yr(-1) (AUD$650 hectare(-1) yr(-1)) annualized over 30 years. This effort would include restoration actions over 6 million hectares of fire-impacted habitat, costing similar to AUD$8.8 billion/year. Large scale restoration efforts are often costly but can have significant societal co-benefits beyond biodiversity conservation. We also show that up to 291 MtCO2 (similar to 150 Mt DM) of carbon could be sequestered by restoration efforts, resulting in approximately AUD$253 million yr(-1) in carbon market revenue if all carbon was remunerated. Our approach highlights the scale, costs, and benefits of targeted restoration activities both inside and outside of the immediate bushfire footprint over vast areas of different land tenures

Use of mechanical airway clearance devices in the home by people with neuromuscular disorders: effects on health service use and lifestyle benefits

Background; People with neuromuscular disorders (NMD) exhibit weak coughs and are susceptible to recurrent chest infections and acute respiratory complications, the most frequent reasons for their unplanned hospital admissions. Mechanical insufflation-exsufflation (MI-E) devices are a non-invasive method of increasing peak cough flow, improving cough efficacy, the clearance of secretion and overcoming atelectasis. There is limited published evidence on the impact of home use MI-E devices on health service utilisation. The aims of the study were: to assess the self-reported health and lifestyle benefits experienced as a result of home use of MI-E devices; and evaluate the effects of in-home use of MI-E devices on Emergency Department (ED) presentations, hospital admissions and inpatient length of stay (LOS). Methods: Individuals with NMD who were accessing a home MI-E device provided through Muscular Dystrophy Western Australia were invited to participate in a quantitative survey to obtain information on their experiences and self-assessed changes in respiratory health. An ad-hoc record linkage was performed to extract hospital, ED and mortality data from the Western Australian Department of Health (DOHWA). The main outcome measures were ED presentations, hospital separations and LOS, before and after commencement of home use of an MI-E device.Results: Thirty seven individuals with NMD using a MI-E device at home consented to participate in this study. The majority (73%) of participants reported using the MI-E device daily or weekly at home without medical assistance and 32% had used the machine to resolve a choking episode. The survey highlighted benefits to respiratory function maintenance and the ability to manage increased health care needs at home. Not using a home MI-E device was associated with an increased risk of ED presentations (RR = 1.76, 95% CI 1.1-2.84). The number of hospital separations and LOS reduced after the use of MI-E device, but not significantly. No deaths were observed in participants using the MI-E device at home. Conclusions: Home use of a MI-E device by people living with NMD may have a potential impact on reducing their health service utilisation and risk of death. Future research with greater subject numbers and longer follow-up periods is recommended to enhance this field of study

Quantifying extinction risk and forecasting the number of impending Australian bird and mammal extinctions

A critical step towards reducing the incidence of extinction is to identify and rank the species at highest risk, while implementing protective measures to reduce the risk of extinction to such species. Existing global processes provide a graded categorisation of extinction risk. Here we seek to extend and complement those processes to focus more narrowly on the likelihood of extinction of the most imperilled Australian birds and mammals. We considered an extension of existing IUCN and NatureServe criteria, and used expert elicitation to rank the extinction risk to the most imperilled species, assuming current management. On the basis of these assessments, and using two additional approaches, we estimated the number of extinctions likely to occur in the next 20 years. The estimates of extinction risk derived from our tighter IUCN categorisations, NatureServe assessments and expert elicitation were poorly correlated, with little agreement among methods for which species were most in danger – highlighting the importance of integrating multiple approaches when considering extinction risk. Mapped distributions of the 20 most imperilled birds reveal that most are endemic to islands or occur in southern Australia. The 20 most imperilled mammals occur mostly in northern and central Australia. While there were some differences in the forecasted number of extinctions in the next 20 years among methods, all three approaches predict further species loss. Overall, we estimate that another seven Australian mammals and 10 Australian birds will be extinct by 2038 unless management improves

Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis

Background: Global and regional prevalence estimates for blindness and vision impairment are important for the development of public health policies. We aimed to provide global estimates, trends, and projections of global blindness and vision impairment. Methods: We did a systematic review and meta-analysis of population-based datasets relevant to global vision impairment and blindness that were published between 1980 and 2015. We fitted hierarchical models to estimate the prevalence (by age, country, and sex), in 2015, of mild visual impairment (presenting visual acuity worse than 6/12 to 6/18 inclusive), moderate to severe visual impairment (presenting visual acuity worse than 6/18 to 3/60 inclusive), blindness (presenting visual acuity worse than 3/60), and functional presbyopia (defined as presenting near vision worse than N6 or N8 at 40 cm when best-corrected distance visual acuity was better than 6/12). Findings: Globally, of the 7·33 billion people alive in 2015, an estimated 36·0 million (80% uncertainty interval [UI] 12·9–65·4) were blind (crude prevalence 0·48%; 80% UI 0·17–0·87; 56% female), 216·6 million (80% UI 98·5–359·1) people had moderate to severe visual impairment (2·95%, 80% UI 1·34–4·89; 55% female), and 188·5 million (80% UI 64·5–350·2) had mild visual impairment (2·57%, 80% UI 0·88–4·77; 54% female). Functional presbyopia affected an estimated 1094·7 million (80% UI 581·1–1686·5) people aged 35 years and older, with 666·7 million (80% UI 364·9–997·6) being aged 50 years or older. The estimated number of blind people increased by 17·6%, from 30·6 million (80% UI 9·9–57·3) in 1990 to 36·0 million (80% UI 12·9–65·4) in 2015. This change was attributable to three factors, namely an increase because of population growth (38·4%), population ageing after accounting for population growth (34·6%), and reduction in age-specific prevalence (–36·7%). The number of people with moderate and severe visual impairment also increased, from 159·9 million (80% UI 68·3–270·0) in 1990 to 216·6 million (80% UI 98·5–359·1) in 2015. Interpretation: There is an ongoing reduction in the age-standardised prevalence of blindness and visual impairment, yet the growth and ageing of the world’s population is causing a substantial increase in number of people affected. These observations, plus a very large contribution from uncorrected presbyopia, highlight the need to scale up vision impairment alleviation efforts at all levels

Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis

Background: Contemporary data on causes of vision impairment and blindness form an important basis for recommendations in public health policies. Refreshment of the Global Vision Database with recently published data sources permitted modeling of cause of vision loss data from 1990 to 2015, further disaggregation by cause, and forecasts to 2020. Methods: Published and unpublished population-based data on the causes of vision impairment and blindness from 1980 to 2015 were systematically analysed. A series of regression models were fit to estimate the proportion of moderate and severe vision impairment (MSVI; defined as presenting visual acuity <6/18 but ≥3/60 in the better eye) and blindness (presenting visual acuity <3/60 in the better eye) by cause by age, region, and year. Findings: Among the projected global population with MSVI (216.6 million; 80% uncertainty intervals [UI] 98.5-359.1), in 2015 the leading causes thereof are uncorrected refractive error (116.3 million; UI 49.4-202.1), cataract (52.6 million; UI 18.2-109.6), age-related macular degeneration (AMD; 8.4 million; UI 0.9-29.5), glaucoma (4.0 million; UI 0.6-13.3) and diabetic retinopathy (2.6 million; UI 0.2-9.9). In 2015, the leading global causes of blindness were cataract (12.6 million; UI 3.4-28.7) followed by uncorrected refractive error (7.4 million; UI 2.4-14.8) and glaucoma (2.9 million; UI 0.4-9.9), while by 2020, these numbers affected are anticipated to rise to 13.4 million, 8.0 million and 3.2 million, respectively. Cataract and uncorrected refractive error combined contributed to 55% of blindness and 77% of MSVI in adults aged 50 years and older in 2015. World regions varied markedly in the causes of blindness, with a relatively low prevalence of cataract and a relatively high prevalence of AMD as causes for vision loss in the High-income subregions. Blindness due to cataract and diabetic retinopathy was more common among women, while blindness due to glaucoma and corneal opacity was more common among men, with no gender difference related to AMD. Conclusions: The numbers of people affected by the common causes of vision loss have increased substantially as the population increases and ages. Preventable vision loss due to cataract and refractive error (reversible with surgery and spectacle correction respectively), continue to cause the majority of blindness and MSVI in adults aged 50+ years. A massive scale up of eye care provision to cope with the increasing numbers is needed if one is to address avoidable vision loss

Protecting 30% of the planet for nature: costs, benefits and economic implications

A. Waldron, K. Nakamura, J. Sze, T. Vilela, A. Escobedo, P. Negret Torres, R. Button, K. Swinnerton, A. Toledo, P. Madgwick, N. Mukherjee were supported by National Geographic and the Resources Legacy Fund. V. Christensen was supported by NSERC Discovery Grant RGPIN-2019-04901. M. Coll and J. Steenbeek were supported by EU Horizon 2020 research and innovation programme under grant agreement No 817578 (TRIATLAS). D. Leclere was supported by TradeHub UKRI CGRF project. R. Heneghan was supported by Spanish Ministry of Science, Innovation and Universities, Acciones de Programacion Conjunta Internacional (PCIN-2017-115). M. di Marco was supported by MIUR Rita Levi Montalcini programme. A. Fernandez-Llamazares was supported by Academy of Finland (grant nr. 31176). S. Fujimori and T. Hawegawa were supported by The Environment Research and Technology Development Fund (2-2002) of the Environmental Restoration and Conservation Agency of Japan and the Sumitomo Foundation. V. Heikinheimo was supported by Kone Foundation, Social Media for Conservation project. K. Scherrer was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 682602. U. Rashid Sumaila acknowledges the OceanCanada Partnership, which funded by the Social Sciences and Humanities Research Council of Canada (SSHRC). T. Toivonen was supported by Osk. Huttunen Foundation & Clare Hall college, Cambridge. W. Wu was supported by The Environment Research and Technology Development Fund (2-2002) of the Environmental Restoration and Conservation Agency of Japan. Z. Yuchen was supported by a Ministry of Education of Singapore Research Scholarship Block (RSB) Research Fellowship

Working paper analysing the economic implications of the proposed 30% target for areal protection in the draft post-2020 Global Biodiversity Framewor

58 pages, 5 figures, 3 tables- The World Economic Forum now ranks biodiversity loss as a top-five risk to the global economy, and the draft post-2020 Global Biodiversity Framework proposes an expansion of conservation areas to 30% of the earth’s surface by 2030 (hereafter the “30% target”), using protected areas (PAs) and other effective area-based conservation measures (OECMs). - Two immediate concerns are how much a 30% target might cost and whether it will cause economic losses to the agriculture, forestry and fisheries sectors. - Conservation areas also generate economic benefits (e.g. revenue from nature tourism and ecosystem services), making PAs/Nature an economic sector in their own right. - If some economic sectors benefit but others experience a loss, high-level policy makers need to know the net impact on the wider economy, as well as on individual sectors. [...]A. Waldron, K. Nakamura, J. Sze, T. Vilela, A. Escobedo, P. Negret Torres, R. Button, K. Swinnerton, A. Toledo, P. Madgwick, N. Mukherjee were supported by National Geographic and the Resources Legacy Fund. V. Christensen was supported by NSERC Discovery Grant RGPIN-2019-04901. M. Coll and J. Steenbeek were supported by EU Horizon 2020 research and innovation programme under grant agreement No 817578 (TRIATLAS). D. Leclere was supported by TradeHub UKRI CGRF project. R. Heneghan was supported by Spanish Ministry of Science, Innovation and Universities, Acciones de Programacion Conjunta Internacional (PCIN-2017-115). M. di Marco was supported by MIUR Rita Levi Montalcini programme. A. Fernandez-Llamazares was supported by Academy of Finland (grant nr. 311176). S. Fujimori and T. Hawegawa were supported by The Environment Research and Technology Development Fund (2-2002) of the Environmental Restoration and Conservation Agency of Japan and the Sumitomo Foundation. V. Heikinheimo was supported by Kone Foundation, Social Media for Conservation project. K. Scherrer was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 682602. U. Rashid Sumaila acknowledges the OceanCanada Partnership, which funded by the Social Sciences and Humanities Research Council of Canada (SSHRC). T. Toivonen was supported by Osk. Huttunen Foundation & Clare Hall college, Cambridge. W. Wu was supported by The Environment Research and Technology Development Fund (2-2002) of the Environmental Restoration and Conservation Agency of Japan. Z. Yuchen was supported by a Ministry of Education of Singapore Research Scholarship Block (RSB) Research FellowshipPeer reviewe