The dynamic adaptive landscape of cetacean body size

Adaptive landscapes are central to evolutionary theory, forming a conceptual bridge between micro- and macroevolution.1,2,3,4 Evolution by natural selection across an adaptive landscape should drive lineages toward fitness peaks, shaping the distribution of phenotypic variation within and among clades over evolutionary timescales.5 The location and breadth of these peaks in phenotypic space can also evolve,4 but whether phylogenetic comparative methods can detect such patterns has largely remained unexplored.6 Here, we characterize the global and local adaptive landscape for total body length in cetaceans (whales, dolphins, and relatives), a trait that spans an order of magnitude, across their ∼53-million-year evolutionary history. Using phylogenetic comparative methods, we analyze shifts in long-term mean body length7 and directional changes in average trait values8 for 345 living and fossil cetacean taxa. Remarkably, we find that the global macroevolutionary adaptive landscape of cetacean body length is relatively flat, with very few peak shifts occurring after cetaceans entered the oceans. Local peaks are more numerous and manifest as trends along branches linked to specific adaptations. These results contrast with previous studies using only extant taxa,9 highlighting the vital role of fossil data for understanding macroevolution.10,11,12 Our results indicate that adaptive peaks are dynamic and are associated with subzones of local adaptations, creating moving targets for species adaptation. In addition, we identify limits in our ability to detect some evolutionary patterns and processes and suggest that multiple approaches are required to characterize complex hierarchical patterns of adaptation in deep time.Copyright © 2023 The Author(s). Published by Elsevier Inc. 1787 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). The linked document is the published version of the article.NHM Repositor

Rcompadre and Rage—Two R packages to facilitate the use of the COMPADRE and COMADRE databases and calculation of life‐history traits from matrix population models

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 stage-structured demography of organisms and usually represent the life history of a population during a particular time frame at a specific geographical location. 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. 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. 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, and in their own MPM data

The myriad of complex demographic responses of terrestrial mammals to climate change and gaps of knowledge: A global analysis

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.\ud 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

The African SWIFT project: growing science capability to bring about a revolution in weather prediction

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