From analysis of published global site biomass data ( n = 136) from primary forests, we discovered ( i ) the world's highest known total biomass carbon density (living plus dead) of 1,867 tonnes carbon per ha (average value from 13 sites) occurs in Australian temperate moist Eucalyptus regnans forests, and ( ii ) average values of the global site biomass data were higher for sampled temperate moist forests ( n = 44) than for sampled tropical ( n = 36) and boreal ( n = 52) forests ( n is number of sites per forest biome). Spatially averaged Intergovernmental Panel on Climate Change biome default values are lower than our average site values for temperate moist forests, because the temperate biome contains a diversity of forest ecosystem types that support a range of mature carbon stocks or have a long land-use history with reduced carbon stocks. We describe a framework for identifying forests important for carbon storage based on the factors that account for high biomass carbon densities, including ( i ) relatively cool temperatures and moderately high precipitation producing rates of fast growth but slow decomposition, and ( ii ) older forests that are often multiaged and multilayered and have experienced minimal human disturbance. Our results are relevant to negotiations under the United Nations Framework Convention on Climate Change regarding forest conservation, management, and restoration. Conserving forests with large stocks of biomass from deforestation and degradation avoids significant carbon emissions to the atmosphere, irrespective of the source country, and should be among allowable mitigation activities. Similarly, management that allows restoration of a forest's carbon sequestration potential also should be recognized.

Choice of variables, climate models and emissions scenarios all influence the results of species distribution models under future climatic conditions. However, an overview of applied studies suggests that the uncertainty associated with these factors is not always appropriately incorporated or even considered. We examine the effects of choice of variables, climate models and emissions scenarios can have on future species distribution models using two endangered species: one a short-lived invertebrate species (Ptunarra Brown Butterfly), and the other a long-lived paleo-endemic tree species (King Billy Pine). We show the range in projected distributions that result from different variable selection, climate models and emissions scenarios. The extent to which results are affected by these choices depends on the characteristics of the species modelled, but they all have the potential to substantially alter conclusions about the impacts of climate change. We discuss implications for conservation planning and management, and provide recommendations to conservation practitioners on variable selection and accommodating uncertainty when using future climate projections in species distribution models.

Calls for threat management actions to protect biodiversity and restore ecosystem function are rarely coupled with costed and prioritized sets of management actions for use in decision making. We present a cost‐effectiveness approach for prioritizing threat management to maximize the in situ protection of biodiversity per dollar spent. The approach draws on empirical data and expert knowledge of major threats to biodiversity, feasible threat management actions, and likely responses of biodiversity to a set of costed management scenarios. An application assessing 637 vertebrate wildlife species in the Kimberley region of north‐western Australia suggests that the likely functional loss of 45 mammals, birds, and reptiles over the next 20 years can be averted by effectively managing fire, grazing, and invasive species for approximately AU$40 million per year. Our approach is flexible and may be useful for delivering transparent guidance for conserving species and ecosystems in other regions, including those where data is limited.