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This study quantifies the amount of carbon stored and sequestered by vegetation in west Africa, carbon loss due to tree cover loss, and the potential natural sequestration relative to projected carbon emissions in 2030. GIS analysis of published data on vegetation biomass, tree cover, sequestration by vegetation and soil carbon indicate that vegetation and soil held 38,855 Mt of carbon in 2010, most of which was in above and below ground vegetation. The loss of 58,353 km2 (9%) of tree cover between 2010 and 2018 resulted in the loss of 672 Mt of 14,923 Mt carbon in vegetation. Key Biodiversity Areas, (sites of conservation importance), covered 7.4% of land but in 2010 contained 16% of carbon stored in vegetation. Trees sequestered between 23.6 and 53.6 Mt of carbon in 2018 (17% of which was in Key Biodiversity Areas). Restoration of the tree cover lost in west Africa between 2010 and 2018 could sequester an additional 27.5 Mt of carbon per annum during the first 30 years of growth. Our estimates indicate a combination of conservation and restoration of tree cover could sequester the equivalent of c.30% of projected 2030 regional emissions, contributing significantly towards mitigating climate change.

Climate change vulnerability assessments are commonly used to identify species at risk from global climate change, but the wide range of methodologies available makes it difficult for end users, such as conservation practitioners or policymakers, to decide which method to use as a basis for decision-making. In this study, we evaluate whether different assessments consistently assign species to the same risk categories and whether any of the existing methodologies perform well at identifying climate-threatened species. We compare the outputs of 12 climate change vulnerability assessment methodologies, using both real and simulated species, and validate the methods using historic data for British birds and butterflies (i.e. using historical data to assign risks and more recent data for validation). Our results show that the different vulnerability assessment methods are not consistent with one another; different risk categories are assigned for both the real and simulated sets of species. Validation of the different vulnerability assessments suggests that methods incorporating historic trend data into the assessment perform best at predicting distribution trends in subsequent time periods. This study demonstrates that climate change vulnerability assessments should not be used interchangeably due to the poor overall agreement between methods when considering the same species. The results of our validation provide more support for the use of trend-based rather than purely trait-based approaches, although further validation will be required as data become available.

It is important for conservationists to be able to assess the risks that climate change poses to species, in order to inform decision making. Using standardised and repeatable methods, we present a national-scale assessment of the risks of range loss and opportunities for range expansion that climate change could pose for over 3000 plants and animals. Species were selected by their occurrence in England, the primary focus of the study, but climate change impacts were assessed across Great Britain, widening their geographical relevance. A basic risk assessment that compared projected future changes in potential range with recently observed changes classified 21% of species as being at high risk and 6% at medium risk of range loss under a B1 climate change scenario. A greater number of species were classified as having a medium (16%) or high (38%) opportunity to potentially expand their distribution. A more comprehensive assessment, incorporating additional ecological information, including potentially confounding and exacerbating factors (e.g. dispersal, habitat availability and other constraints), was applied to 402 species, of which 35% were at risk of range loss and 42% may expand their range extent. This study covers a temperate region with a significant proportion of species at their poleward range limit; the balance of risks and opportunities from climate change may be different elsewhere. The outcome of both risk assessments varied between taxonomic groups, with bryophytes and vascular plants containing the greatest proportion of species at risk from climate change. Upland habitats contained more species at risk than other habitats. Whilst the overall pattern was clear, confidence was generally low for individual assessments, with the exception of well-studied taxa such as birds. In response to climate change, nature conservation needs to plan for changing species distributions and an uncertain future.

Climate change vulnerability assessments are commonly used to identify species at risk from global climate change, but the wide range of methodologies available makes it difficult for end users, such as conservation practitioners or policymakers, to decide which method to use as a basis for decision-making. In this study, we evaluate whether different assessments consistently assign species to the same risk categories and whether any of the existing methodologies perform well at identifying climate threatened species. We compare the outputs of 12 climate change vulnerability assessment methodologies, using both real and simulated species, and validate the methods using historic data for British birds and butterflies (i.e. using historical data to assign risks and more recent data for validation). Our results show that the different vulnerability assessment methods are not consistent with one another; different risk categories are assigned for both the real and simulated sets of species. Validation of the different vulnerability assessments suggests that methods incorporating historic trend data into the assessment perform best at predicting distribution trends in subsequent time periods. This study demonstrates that climate change vulnerability assessments should not be used interchangeably due to the poor overall agreement between methods when considering the same species. The results of our validation provide more support for the use of trend-based rather than purely trait-based approaches, although further validation will be required as data become available.