To meet the ambitious objectives of biodiversity and climate conventions, countries and the international community require clarity on how these objectives can be operationalized spatially, and multiple targets be pursued concurrently 1 . To support governments and political conventions, spatial guidance is needed to identify which areas should be managed for conservation to generate the greatest synergies between biodiversity and nature's contribution to people (NCP). Here we present results from a joint optimization that maximizes improvements in species conservation status, carbon retention and water provisioning and rank terrestrial conservation priorities globally. We found that, selecting the top-ranked 30% (respectively 50%) of areas would conserve 62.4% (86.8%) of the estimated total carbon stock and 67.8% (90.7%) of all clean water provisioning, in addition to improving the conservation status for 69.7% (83.8%) of all species considered. If priority was given to biodiversity only, managing 30% of optimally located land area for conservation may be sufficient to improve the conservation status of 86.3% of plant and vertebrate species on Earth. Our results provide a global baseline on where land could be managed for conservation. We discuss how such a spatial prioritisation framework can support the implementation of the biodiversity and climate conventions.

Climate change vulnerability assessments are an important tool for understanding the threat that climate change poses to species and populations, but do not generally yield insight into the spatial variation in vulnerability throughout a species’ habitat. We demonstrate how to adapt the method of ecological‐niche factor analysis (ENFA) to objectively quantify aspects of species sensitivity to climate change. We then expand ENFA to quantify aspects of exposure and vulnerability to climate change as well, using future projections of global climate models. This approach provides spatially‐explicit insight into geographic patterns of vulnerability, relies only on readily‐available spatial data, is suitable for a wide range of species and habitats, and invites comparison between different species. We apply our methods to a case study of two species of montane mammals, the American pika Ochotona princeps and the yellow‐bellied marmot Marmota flaviventris.

Expanding the network of protected areas is a core strategy for conserving biodiversity in the face of climate change. Here, we explore the impacts on reserve network cost and configuration associated with planning for climate change in the USA using networks that prioritize areas projected to be climatically suitable for 1460 species both today and into the future, climatic refugia and areas likely to facilitate climate-driven species movements. For 14% of the species, networks of sites selected solely to protect areas currently climatically suitable failed to provide climatically suitable habitat in the future. Protecting sites climatically suitable for species today and in the future significantly changed the distribution of priority sites across the USA—increasing relative protection in the northeast, northwest and central USA. Protecting areas projected to retain their climatic suitability for species cost 59% more than solely protecting currently suitable areas. Including all climatic refugia and 20% of areas that facilitate climate-driven movements increased the cost by another 18%. Our results indicate that protecting some types of climatic refugia may be a relatively inexpensive adaptation strategy. Moreover, although addressing climate change in conservation plans will have significant implications for the configuration of networks, the increased cost of doing so may be relatively modest. This article is part of the theme issue ‘Climate change and ecosystems: threats, opportunities and solutions’.

paragraph 64 65To meet the ambitious objectives of biodiversity and climate conventions, countries and the 66 international community require clarity on how these objectives can be operationalized spatially, 67and multiple targets be pursued concurrently 1 . To support governments and political conventions, 68 spatial guidance is needed to identify which areas should be managed for conservation to generate 69 the greatest synergies between biodiversity and nature's contribution to people (NCP). Here we 70 present results from a joint optimization that maximizes improvements in species conservation 71 status, carbon retention and water provisioning and rank terrestrial conservation priorities globally. 72We found that, selecting the top-ranked 30% (respectively 50%) of areas would conserve 62.4% 73 (86.8%) of the estimated total carbon stock and 67.8% (90.7%) of all clean water provisioning, in 74 addition to improving the conservation status for 69.7% (83.8%) of all species considered. If 75 priority was given to biodiversity only, managing 30% of optimally located land area for 76 conservation may be sufficient to improve the conservation status of 86.3% of plant and vertebrate 77 species on Earth. Our results provide a global baseline on where land could be managed for 78conservation. We discuss how such a spatial prioritisation framework can support the 79 implementation of the biodiversity and climate conventions. 80 81 82(SDGs), the United Nations Framework Convention on Climate Change (UNFCCC) and the CBD 97 emphasize that habitat conservation and restoration should contribute simultaneously to 98 biodiversity conservation and climate change mitigation 4 . Recent analyses of conservation 99priorities for biodiversity and carbon have spatially overlaid areas of importance for both assets, 100effectively treating the two goals as to be pursued separately (e.g. 6,9 ). However, multi-criteria 101 spatial optimization approaches applied to conservation and restoration prioritisation have shown 102 that carbon sequestration could be doubled, and the number of extinctions prevented tripled, if 103 priority areas were jointly identified rather than independently 10,11 . Yet, no comparable 104 optimization analyses exist at a global scale. 105A number of recent studies have attempted to map spatial conservation priorities on land 12 , 106relying on spatial conservation prioritisation (SCP) methods . However, these approaches are 107 limited, in that: they (i) are limited by geographic extent 22 or focus on only a subset of global 108 biodiversity, notably ignoring either reptiles or plant species, which show considerable variation 109 in areas of importance compared to other taxa 18,19 ; (ii) focus on species representation only, rather 110 than reducing extinction risk, as per international biodiversity targets, and often ignore other 111 dimensions of biodiversity, e.g. evolutionary distinctiveness 20,21 ; (iii) do not investigate the extent 112 to which synergies between biodiversity and NCPs, such as carbon seq...

Summary Predicting and managing species’ responses to climate change is one of the most significant challenges of our time. Tools are needed to address problems associated with novel climatic conditions, biotic interactions and greater climate velocities. We present a spatially explicit moving‐habitat model (MHM) and demonstrate its versatility in tackling critical questions in climate change research, including dispersal in multiple spatial dimensions, population stage structure, interspecific interactions, asymmetric range shifts, Allee effects and the presence of infectious diseases. The model utilizes integrodifference equations to track changes in population density over time in a habitat that is moving. The model is quite flexible and can accommodate variation in demography, dispersal patterns, biotic interaction and stochasticity in the velocity of climate change. The methods provide a general mechanistic understanding of the underlying ecological processes that drive a system. Field data can be readily incorporated into the model to give insight into specific populations of interest and inform management decisions. Synthesis. Moving‐habitat models unite ecological theory, data‐centred modelling and conservation decision support under a single framework. Their ability to generate testable hypotheses, incorporate data and inform best management practices proves that these models provide a valuable framework for climate change biologists.

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