Summary1. Current national and international frameworks for assessing threats to species have not been developed in the context of climate change, and are not framed in a way that recognises new opportunities that arise from climate change. 2. The framework presented here separates the threats and benefits of climate change for individual species. Threat is assessed by the level of climate-related decline within a species' recently occupied (e.g. pre-1970s) historical distribution, based on observed (e.g. repeat census) and ⁄ or projected changes (e.g. modelled bioclimate space). Benefits are assessed in terms of observed and ⁄ or projected increases outside the recently occupied historical range. 3. Exacerbating factors (e.g. small population size, low dispersal capacity) that might increase levels of threat or limit expansion in response to climate change are taken into consideration within the framework. Protocols are also used to identify levels of confidence (and hence research and ⁄ or monitoring needs) in each species' assessment. 4. Observed and projected changes are combined into single measures of expected decline and increase, together with associated measures of confidence. We weight risk classifications towards information that is most certain. Each species is then placed in one of six categories (high risk, medium risk, limited impact, equivalent risks & benefits, medium benefit, high benefit) reflecting whether climate change is expected (or has been observed) to cause net declines or increases in the region considered, based on the balance of benefits and threats. 5. We illustrate the feasibility of using the framework by applying it to (i) all British butterflies (N = 58 species) and (ii) an additional sample of British species: 18 species of plants, bats, birds and beetles. 6. Synthesis. Our framework assesses net declines and increases associated with climate change, for individual species. It could be applied at any scale (regional, continental or global distributions of species), and complements existing conservation assessment protocols such as red-listing. Using observed and projected population and ⁄ or range data, it is feasible to carry out systematic conservation status assessments that inform the development of monitoring, adaptation measures and conservation management planning for species that are responding to climate change.

Summary 1.Mathematical methods for inferring time to extinction have been widely applied but poorly tested. Optimal linear estimation (also called the 'Weibull' or 'Weibull extreme value' model) infers time to extinction from a temporal distribution of species sightings. Previous studies have suggested optimal linear estimation provides accurate estimates of extinction time for some species; however, an in-depth test of the technique is lacking. 2. The use of data from wild populations to gauge the error associated with estimations is often limited by very approximate estimates of the actual extinction date and poor sighting records. Microcosms provide a system in which the accuracy of estimations can be tested against known extinction dates, whilst incorporating a variety of extinction rates created by changing environmental conditions, species identity and species richness. 3. We present the first use of experimental microcosm data to exhaustively test the accuracy of one sighting-based method of inferring time of extinction under a range of search efforts, search regimes, sighting frequencies and extinction rates. 4. Our results show that the accuracy of optimal linear estimation can be affected by both observer-controlled parameters, such as change in search effort, and inherent features of the system, such as species identity. Whilst optimal linear estimation provides generally accurate and precise estimates, the technique is susceptible to both overestimation and underestimation of extinction date. 5. Microcosm experiments provide a framework within which the accuracy of extinction predictors can be clearly gauged. Variables such as search effort, search regularity and species identity can significantly affect the accuracy of estimates and should be taken into account when testing extinction predictors in the future.

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The loss of a predator from an ecological community can cause large changes in community structure and ecosystem processes, or have very little consequence for the remaining species and ecosystem. Understanding when and why the loss of a predator causes large changes in community structure and ecosystem processes is critical for understanding the functional consequences of biodiversity loss. We used experimental microbial communities to investigate how the removal of a large generalist predator affected the extinction frequency, population abundance and total biomass of its prey. We removed this predator in the presence or absence of an alternative, more specialist, predator in order to determine whether the specialist predator affected the outcome of the initial species removal. Removal of the large generalist predator altered some species’ populations but many were unaffected and no secondary extinctions were observed. The specialist predator, though rare, altered the response of the prey community to the removal of the large generalist predator. In the absence of the specialist predator, the effects of the removal were only measurable at the level of individual species. However, when the specialist predator was present, the removal of the large generalist predator affected the total biomass of prey species. The results demonstrate that the effect of species loss from high trophic levels may be very context‐dependent, as rare species can have disproportionately large effects in food webs.