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Processes leading to range contractions and population declines of Arctic megafauna during the late Pleistocene and early Holocene are uncertain, with intense debate on the roles of human hunting, climatic change, and their synergy. Obstacles to a resolution have included an overreliance on correlative rather than process-explicit approaches for inferring drivers of distributional and demographic change. Here, we disentangle the ecological mechanisms and threats that were integral in the decline and extinction of the muskox (Ovibos moschatus) in Eurasia and in its expansion in North America using process-explicit macroecological models. The approach integrates modern and fossil occurrence records, ancient DNA, spatiotemporal reconstructions of past climatic change, species-specific population ecology, and the growth and spread of anatomically modern humans. We show that accurately reconstructing inferences of past demographic changes for muskox over the last 21,000 years require high dispersal abilities, large maximum densities, and a small Allee effect. Analyses of validated process-explicit projections indicate that climatic change was the primary driver of muskox distribution shifts and demographic changes across its previously extensive (circumpolar) range, with populations responding negatively to rapid warming events. Regional analyses show that the range collapse and extinction of the muskox in Europe (~13,000 years ago) was likely caused by humans operating in synergy with climatic warming. In Canada and Greenland, climatic change and human activities probably combined to drive recent population sizes. The impact of past climatic change on the range and extinction dynamics of muskox during the Pleistocene–Holocene transition signals a vulnerability of this species to future increased warming. By better establishing the ecological processes that shaped the distribution of the muskox through space and time, we show that process-explicit macroecological models have important applications for the future conservation and management of this iconic species in a warming Arctic.

Fossil leaf anatomy has previously been used as a proxy for paleoclimate. However, the exposure of leaves to sun or shade during their growth can lead to morphotype differences that confound the interpretation of fossil leaf anatomy in relation to climate and prevent reliable paleoclimate reconstruction. This work aims to model the differences in leaf anatomy that are due to various climatic drivers and differences attributable to sun or shade positions, using Nothofagus cunninghamii as the model species. Leaves from the sun and shade parts of three trees have been sampled from each of 11 sites in Victoria and Tasmania, Australia. The gross morphological and cuticular features have been scored and modelled with climate data from the sites. Random forest models can accurately predict Nothofagus cunninghamii contemporary climatic conditions of the spring temperature and summer rainfall based on leaf anatomical measurements. Leaf area, stomatal density and epidermal cell density are the most accurate predictors of whether a leaf grew in the sun or shade. Leaf area is also the strongest predictor of the maximum and minimum spring temperatures and rainfall. The models have implications for the use of fossilised leaves in paleoclimate reconstruction. The models we have built can be used to effectively predict whether a fossil leaf was from a sun or shade position on the tree and thus enable more reliable inference of paleoclimate by removing the confounding issues of variable leaf anatomy due to sun exposure during growth. Finally, these models could conceivably be used to make predictions of past paleoclimatic conditions provided suitable training and validation data on climatic conditions are available.

Knowledge of global patterns of biodiversity, ranging from intraspecific genetic diversity (GD) to taxonomic and phylogenetic diversity, is essential for identifying and conserving the processes that shape the distribution of life. Yet, global patterns of GD and its drivers remain elusive. Here we assess existing biodiversity theories to explain and predict the global distribution of GD in terrestrial mammal assemblages. We find a strong positive covariation between GD and interspecific diversity, with evolutionary time, reflected in phylogenetic diversity, being the best predictor of GD. Moreover, we reveal the negative effect of past rapid climate change and the positive effect of inter-annual precipitation variability in shaping GD. Our models, explaining almost half of the variation in GD globally, uncover the importance of deep evolutionary history and past climate stability in accumulating and maintaining intraspecific diversity, and constitute a crucial step towards reducing the Wallacean shortfall for an important dimension of biodiversity., The drivers of genetic diversity (GD) are poorly understood at the global scale. Here the authors show, for terrestrial mammals, that within-species GD covaries with phylogenetic diversity and is higher in locations with more stable past climates. They also interpolate GD for data-poor locations such as the tropics.

Aim To determine the ecological processes and drivers of range collapse, population decline and eventual extinction of the steppe bison in Eurasia. Location Siberia. Time period Pleistocene and Holocene. Major taxa studied Steppe bison (Bison priscus). Methods We configured 110,000 spatially explicit population models (SEPMs) of climate-human-steppe bison interactions in Siberia, which we ran at generational time steps from 50,000 years before present. We used pattern-oriented modelling (POM) and fossil-based inferences of distribution and demographic change of steppe bison to identify which SEPMs adequately simulated important interactions between ecological processes and biological threats. These "best models" were then used to disentangle the mechanisms that were integral in the population decline and later extinction of the steppe bison in its last stronghold in Eurasia. Results Our continuous reconstructions of the range and extinction dynamics of steppe bison were able to reconcile inferences of spatio-temporal occurrence and the timing and location of extinction in Siberia based on hundreds of radiocarbon-dated steppe bison fossils. We showed that simulating the ecological pathway to extinction for steppe bison in Siberia in the early Holocene required very specific ecological niche constraints, demographic processes and a constrained synergy of climate and human hunting dynamics during the Pleistocene-Holocene transition. Main conclusions Ecological processes and drivers that caused ancient population declines of species can be reconstructed at high spatio-temporal resolutions using SEPMs and POM. Using this approach, we found that climatic change and hunting by humans are likely to have interacted with key ecological processes to cause the extinction of the steppe bison in its last refuge in Eurasia.

Pathways to extinction start long before the death of the last individual. However, causes of early stage population declines and the susceptibility of small residual populations to extirpation are typically studied in isolation. Using validated process-explicit models, we disentangle the ecological mechanisms and threats that were integral in the initial decline and later extinction of the woolly mammoth. We show that reconciling ancient DNA data on woolly mammoth population decline with fossil evidence of location and timing of extinction requires process-explicit models with specific demographic and niche constraints, and a constrained synergy of climatic change and human impacts. Validated models needed humans to hasten climate-driven population declines by many millennia, and to allow woolly mammoths to persist in mainland Arctic refugia until the mid-Holocene. Our results show that the role of humans in the extinction dynamics of woolly mammoth began well before the Holocene, exerting lasting effects on the spatial pattern and timing of its range-wide extinction.

Climate stability leads to high levels of speciation and reduced extinction rates, shaping species richness patterns1–3. Hotspots of species diversity often overlap with regions that experienced stable temperatures and, perhaps, variable rates of precipitation during the late Quaternary4,5. These hotspots potentially harbour many species with low vagility and small geographical ranges6, making them more vulnerable to future ecoclimatic change4,7,8. By comparing global and regional patterns of climate stability during short periods of unusually large and widespread climate changes since the Last Glacial Maximum with twenty-first-century patterns, we show that human-driven climate change will disproportionally affect biodiversity in late Quaternary climate refugia, ultimately affecting the species, communities and ecosystems that are most vulnerable to climate change. Moreover, future changes in absolute temperature will probably erode the mechanisms that are theorized to sustain biodiversity hotspots across time. These impending shifts from stable to unstable temperatures—projected for the majority of the world’s biodiversity regions—threaten to reduce the size and extent of important climatic safe havens for diversity. Where climate refugia are forecast to persist until the end of this century, temperatures in these refuges are likely to exceed the acclimation capacity of many species, making them short-term hospices for biodiversity at best7–9. The stability of climatic conditions since the Last Glacial Maximum has contributed to current global patterns of species richness. Changes in patterns of climate stability this century reveal areas where climate change could reduce biodiversity, with largest losses in past climatic safe havens.

Pathways to extinction start long before the death of the last individual. However, causes of early-stage population declines and the susceptibility of small residual populations to extirpation are typically studied in isolation. Using validated process-explicit models, we disentangle the ecological mechanisms and threats that were integral in the initial decline and later extinction of the woolly mammoth. We show that reconciling ancient DNA data on woolly mammoth population decline with fossil evidence of location and timing of extinction requires process-explicit models with specific demographic and niche constraints, and a constrained synergy of climatic change and human impacts. Validated models needed humans to hasten climate-driven population declines by many millennia, and to allow woolly mammoths to persist in mainland Arctic refugia until the mid-Holocene. Our results show that the role of humans in the extinction dynamics of woolly mammoth began well before the Holocene, exerting lasting effects on the spatial pattern and timing of its range-wide extinction.

Paleoclimatic data are used in eco-evolutionary models to improve knowledge of biogeographical processes that drive patterns of biodiversity through time, opening windows into past climate–biodiversity dynamics. Applying these models to harmonised simulations of past and future climatic change can strengthen forecasts of biodiversity change. StableClim provides continuous estimates of climate stability from 21,000 years ago to 2100 C.E. for ocean and terrestrial realms at spatial scales that include biogeographic regions and climate zones. Climate stability is quantified using annual trends and variabilities in air temperature and precipitation, and associated signal-to-noise ratios. Thresholds of natural variability in trends in regional- and global-mean temperature allow periods in Earth’s history when climatic conditions were warming and cooling rapidly (or slowly) to be identified and climate stability to be estimated locally (grid-cell) during these periods of accelerated change. Model simulations are validated against independent paleoclimate and observational data. Projections of climatic stability, accessed through StableClim, will improve understanding of the roles of climate in shaping past, present-day and future patterns of biodiversity., Measurement(s) climate change • climate • temperature of air • volume of hydrological precipitation Technology Type(s) computational modeling technique • digital curation Factor Type(s) timing of temperature and precipitation estimates Sample Characteristic - Environment climate system Sample Characteristic - Location Earth (planet) Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12831935

Using the past to inform the future The late Quaternary paleorecord, within the past ∼130,000 years, can help to inform present-day management of the Earth's ecosystems and biota under climate change. Fordham et al. review when and where rapid climate transitions can be found in the paleoclimate record. They show how such events in Earth's history can shape our understanding of the consequences of future global warming, including rates of biodiversity loss, changes in ecosystem structure and function, and degradation in the goods and services that these ecosystems provide to humanity. They also highlight how recent developments at the intersection of paleoecology, paleoclimatology, and macroecology can provide opportunities to anticipate and manage the responses of species and ecosystems to changing climates in the Anthropocene. Science , this issue p. eabc5654

Criticism has been levelled at climate-change-induced forecasts of species range shifts that do not account explicitly for complex population dynamics. The relative importance of such dynamics under climate change is, however, undetermined because direct tests comparing the performance of demographic models vs. simpler ecological niche models are still lacking owing to difficulties in evaluating forecasts using real-world data. We provide the first comparison of the skill of coupled ecological-niche-population models and ecological niche models in predicting documented shifts in the ranges of 20 British breeding bird species across a 40-year period. Forecasts from models calibrated with data centred on 1970 were evaluated using data centred on 2010. We found that more complex coupled ecological-niche-population models (that account for dispersal and metapopulation dynamics) tend to have higher predictive accuracy in forecasting species range shifts than structurally simpler models that only account for variation in climate. However, these better forecasts are achieved only if ecological responses to climate change are simulated without static snapshots of historic land use, taken at a single point in time. In contrast, including both static land use and dynamic climate variables in simpler ecological niche models improve forecasts of observed range shifts. Despite being less skilful at predicting range changes at the grid-cell level, ecological niche models do as well, or better, than more complex models at predicting the magnitude of relative change in range size. Therefore, ecological niche models can provide a reasonable first approximation of the magnitude of species' potential range shifts, especially when more detailed data are lacking on dispersal dynamics, demographic processes underpinning population performance, and change in land cover., The Australian Research Council supported D.A.F, C.B. and B.W.B (FT140101192, DP1096427, FT100100200, respectively). M.B.A and D.N. acknowledge support from the Foundation for Science and Technology (PTDC/AAG‐MAA/3764/2014).

The current distribution of species, environmental conditions and their interactions represent only one snapshot of a planet that is continuously changing, in part due to human influences. To distinguish human impacts from natural factors, the magnitude and pace of climate shifts since the Last Glacial Maximum are often used to determine whether patterns of diversity today are artefacts of past climate change. In the absence of high-temporal-resolution paleoclimate reconstructions, this is generally done by assuming that past climate change occurred at a linear pace between widely spaced (usually, ≥ 1,000 years) climate snapshots. We show here that this is a flawed assumption, because regional climates have changed significantly across decades and centuries during glacial/interglacial cycles, likely causing rapid regional replacement of biota. We demonstrate how recent atmosphere-ocean general circulation model (AOGCM) simulations of the climate of the past 21,000 years can provide credible estimates of the details of climate change on decadal to centennial time scales, showing that these details differ radically from what might be inferred from longer time scale information. High temporal resolution information can provide more meaningful estimates of the magnitude and pace of climate shifts, the location and timing of drivers of physiological stress, and the extent of novel climates. They also produce new opportunities to directly investigate whether short-term climate variability is more important in shaping biodiversity patterns, rather than gradual changes in long-term climatic means. Together these more accurate measures of past climate instability are likely to bring about a better understanding of the role of paleoclimatic change and variability in shaping current macro-ecological patterns in many regions of the world. This article is protected by copyright. All rights reserved.

Summary The stability of regional climates on millennial timescales is theorised to be a primary determinant of nearby diversification 1 , 2 , 3 , 4 , 5 . Using simulated patterns of past temperature change at monthly timescales [6] , we show that the locations of climatically stable regions are likely to have varied considerably across and within millennia during glacial–interglacial cycles of the Late Quaternary. This result has important implications for the role of regional climate stability in theories of speciation, because long-term climate refugia are typically presumed to be ‘cradles’ of diversity (areas of high speciation) only if they remain stable across Milankovitch climate oscillations 1 , 2 , 3 , 4 , 5 , which operate on multi-millennial time scales [7] .

Predicting ecological response to climate change is often limited by a lack of relevant local data from which directly applicable mechanistic models can be developed. This limits predictions to qualitative assessments or simplistic rules of thumb in data-poor regions, making management of the relevant systems difficult. We demonstrate a method for developing quantitative predictions of ecological response in data-poor ecosystems based on a space-for-time substitution, using distant, well-studied systems across an inherent climatic gradient to predict ecological response. Changes in biophysical data across the spatial gradient are used to generate quantitative hypotheses of temporal ecological responses that are then tested in a target region. Transferability of predictions among distant locations, the novel outcome of this method, is demonstrated via simple quantitative relationships that identify direct and indirect impacts of climate change on physical, chemical and ecological variables using commonly available data sources. Based on a limited subset of data, these relationships were demonstrably plausible in similar yet distant (>2000 km) ecosystems. Quantitative forecasts of ecological change based on climate-ecosystem relationships from distant regions provides a basis for research planning and informed management decisions, especially in the many ecosystems for which there are few data. This application of gradient studies across domains - to investigate ecological response to climate change - allows for the quantification of effects on potentially numerous, interacting and complex ecosystem components and how they may vary, especially over long time periods (e.g. decades). These quantitative and integrated long-term predictions will be of significant value to natural resource practitioners attempting to manage data-poor ecosystems to prevent or limit the loss of ecological value. The method is likely to be applicable to many ecosystem types, providing a robust scientific basis for estimating likely impacts of future climate change in ecosystems where no such method currently exists.