Approximately 25% of mammals are currently threatened with extinction, a risk that is amplified under climate change. Species persistence under climate change is determined by the combined effects of climatic factors on multiple demographic rates (survival, development and reproduction), and hence, population dynamics. Thus, to quantify which species and regions on Earth are most vulnerable to climate‐driven extinction, a global understanding of how different demographic rates respond to climate is urgently needed. Here, we perform a systematic review of literature on demographic responses to climate, focusing on terrestrial mammals, for which extensive demographic data are available. To assess the full spectrum of responses, we synthesize information from studies that quantitatively link climate to multiple demographic rates. We find only 106 such studies, corresponding to 87 mammal species. These 87 species constitute <1% of all terrestrial mammals. Our synthesis reveals a strong mismatch between the locations of demographic studies and the regions and taxa currently recognized as most vulnerable to climate change. Surprisingly, for most mammals and regions sensitive to climate change, holistic demographic responses to climate remain unknown. At the same time, we reveal that filling this knowledge gap is critical as the effects of climate change will operate via complex demographic mechanisms: a vast majority of mammal populations display projected increases in some demographic rates but declines in others, often depending on the specific environmental context, complicating simple projections of population fates. Assessments of population viability under climate change are in critical need to gather data that account for multiple demographic responses, and coordinated actions to assess demography holistically should be prioritized for mammals and other taxa.

1. Matrix population models (MPMs) are an important tool for biologists seeking to understand the causes and consequences of variation in vital rates (e.g. survival, reproduction) across life cycles. Empirical MPMs describe the age-or stagestructured demography of organisms and usually represent the life history of a population during a particular time frame at a specific geographical location. 2. The COMPADRE Plant Matrix Database and COMADRE Animal Matrix Database are the most extensive resources for MPM data, collectively containing >12,000 individual projection matrices for >1,100 species globally. Although these databases represent an unparalleled resource for researchers, land managers and educators, the current computational tools available to answer questions with MPMs impose significant barriers to potential COM(P)ADRE database users by requiring advanced knowledge to handle diverse data structures and program custom analysis functions.3. To close this knowledge gap, we present two interrelated R packages designed to (a) facilitate the use of these databases by providing functions to acquire, quality control and manage both the MPM data contained in COMPADRE and COMADRE, and a user's own MPM data (Rcompadre) and (b) present a range of functions to calculate life-history traits from MPMs in support of ecological and evolutionary analyses (Rage). We provide examples to illustrate the use of both.4. Rcompadre and Rage will facilitate demographic analyses using MPM data and contribute to the improved replicability of studies using these data. We hope that this new functionality will allow researchers, land managers and educators | 771Methods in Ecology and Evoluঞon JONES Et al.

Article impact statement-Exploiting covariance among vital rates, phylogeny and traits to impute missing values holds promise for bridging demographic analysis gaps.

Approximately 25 % of mammals are threatened globally with extinction, a risk that is amplified under climate change1. Persistence under climate change is determined by the combined effects of climatic factors on multiple demographic rates (survival, development, reproduction), and hence, on population dynamics2. Thus, to quantify which species and places on Earth are most vulnerable to climate-driven extinction, a global understanding of how demographic rates respond to climate is needed3. We synthesise information on such responses in terrestrial mammals, where extensive demographic data are available4. Given the importance of assessing the full spectrum of responses, we focus on studies that quantitatively link climate to multiple demographic rates. We identify 106 such studies, corresponding to 86 mammal species. We reveal a strong mismatch between the locations of demographic studies and the regions and taxa currently recognised as most vulnerable to climate change5,6. Moreover, we show that the effects of climate change on mammals will operate via complex demographic mechanisms: a vast majority of mammal populations display projected increases in some demographic rates but declines in others. Assessments of population viability under climate change therefore need to account for multiple demographic responses. We advocate to prioritise coordinated actions to assess mammal demography holistically for effective conservation worldwide.

Africa is poised for a revolution in the quality and relevance of weather predictions, with potential for great benefits in terms of human and economic security. This revolution will be driven by recent international progress in nowcasting, numerical weather prediction, theoretical tropical dynamics and forecast communication, but will depend on suitable scientific investment being made. The commercial sector has recognized this opportunity and new forecast products are being made available to African stakeholders. At this time, it is vital that robust scientific methods are used to develop and evaluate the new generation of forecasts. The GCRF African SWIFT project represents an international effort to advance scientific solutions across the fields of nowcasting, synoptic and short-range severe weather prediction, subseasonal-to-seasonal (S2S) prediction, user engagement and forecast evaluation. This paper describes the opportunities facing African meteorology and the ways in which SWIFT is meeting those opportunities and identifying priority next steps.Delivery and maintenance of weather forecasting systems exploiting these new solutions requires a trained body of scientists with skills in research and training; modelling and operational prediction; communications and leadership. By supporting partnerships between academia and operational agencies in four African partner countries, the SWIFT project is helping to build capacity and capability in African forecasting science. A highlight of SWIFT is the coordination of three weather-forecasting “Testbeds” – the first of their kind in Africa – which have been used to bring new evaluation tools, research insights, user perspectives and communications pathways into a semi-operational forecasting environment.

SummaryMatrix population models (MPMs) are an important tool in the arsenal of biologists seeking to understand the causes and consequences of variation in vital rates (e.g. survival, reproduction) across life cycles. MPMs describe the age- or stage-structured demography of organisms and represent the life history of a population during a particular time frame at a specific geographic location.The COMPADRE Plant Matrix Database and COMADRE Animal Matrix Database are the most extensive resources for MPM data, collectively containing >12,000 MPMs for >1,100 species globally. Although these databases represent an unparalleled resource for researchers, land managers, and educators, the current computational tools available to answer questions with MPMs impose significant barriers to potential users by requiring advanced knowledge to handle diverse data structures and program custom analysis functions.To close this knowledge gap, we present two R packages designed to (i) facilitate the use of these databases by providing functions to acquire, check, and manage the MPM data contained in COMPADRE and COMADRE (Rcompadre), and (ii) expand the range of life history traits that can be calculated from MPMs in support of ecological and evolutionary analyses (Rage). We provide vignettes to illustrate the use of both Rcompadre and Rage.Rcompadre and Rage will facilitate demographic analyses using MPM data and contribute to the improved replicability of studies using these data. We hope that this new functionality will allow researchers, land managers, and educators to unlock the potential behind the thousands of MPMs and ancillary metadata stored in the COMPADRE and COMADRE matrix databases.

Testbeds have become integral to advancing the transfer of knowledge and capabilities from research to operational weather forecasting in many parts of the world. The first high-impact weather testbed in tropical Africa was recently carried out through the African SWIFT program, with participation from researchers and forecasters from Senegal, Ghana, Nigeria, Kenya, the United Kingdom, and international and pan-African organizations. The testbed aims were to trial new forecasting and nowcasting products with operational forecasters, to inform future research, and to act as a template for future testbeds in the tropics. The African SWIFT testbed integrated users and researchers throughout the process to facilitate development of impact-based forecasting methods and new research ideas driven both by operations and user input. The new products are primarily satellite-based nowcasting systems and ensemble forecasts at global and regional convection-permitting scales. Neither of these was used operationally in the participating African countries prior to the testbed. The testbed received constructive, positive feedback via intense user interaction including fishery, agriculture, aviation, and electricity sectors. After the testbed, a final set of recommended standard operating procedures for satellite-based nowcasting in tropical Africa have been produced. The testbed brought the attention of funding agencies and organizational directors to the immediate benefit of improved forecasts. Delivering the testbed strengthened the partnership between each country’s participating university and weather forecasting agency and internationally, which is key to ensuring the longevity of the testbed outcomes.

Adaptive landscapes are central to evolutionary theory, forming a conceptual bridge between micro- and macro-evolution. Evolution by natural selection across an adaptive landscape should drive lineages towards fitness peaks, shaping the distribution of phenotypic variation within and among clades over evolutionary timescales. Constant shifts in selection pressures mean the peaks themselves also evolve through time, thus a key challenge is to identify these "ghosts of selection past". Here, we characterise the global and local adaptive landscape for total length in cetaceans (whales and dolphins) across their approx. 53 million year evolutionary history, using 345 living and fossil taxa. We analyse shifts in long-term mean size and directional changes in average trait values using cutting-edge phylogenetic comparative methods. We demonstrate that the global macroevolutionary adaptive landscape of cetacean body size is relatively flat, with very few peak shifts after cetaceans colonised the oceans. Local peaks represent trends along branches linked to specific adaptations such as deep diving. These results contrast with previous studies using only extant taxa, highlighting the vital role of fossil data for understanding macroevolutionary dynamics. Our results indicate that adaptive peaks are constantly changing and are associated with subzones of local adaptations, resembling turbulent waters with waves and ripples, creating moving targets for species adaptation. In addition, we identify limits in our ability to detect some evolutionary patterns and processes, and suggest multiple approaches are required to characterise complex hierarchical patterns of adaptation in deep-time.