52,000 years of woolly rhinoceros population dynamics reveal extinction mechanisms.

The extinction of the woolly rhinoceros (Coelodonta antiquitatis) at the onset of the Holocene remains an enigma, with conflicting evidence regarding its cause and spatiotemporal dynamics. This partly reflects challenges in determining demographic responses of late Quaternary megafauna to climatic and anthropogenic causal drivers with available genetic and paleontological techniques. Here, we show that elucidating mechanisms of ancient extinctions can benefit from a detailed understanding of fine-scale metapopulation dynamics, operating over many millennia. Using an abundant fossil record, ancient DNA, and high-resolution simulation models, we untangle the ecological mechanisms and causal drivers that are likely to have been integral in the decline and later extinction of the woolly rhinoceros. Our 52,000-y reconstruction of distribution-wide metapopulation dynamics supports a pathway to extinction that began long before the Holocene, when the combination of cooling temperatures and low but sustained hunting by humans trapped woolly rhinoceroses in suboptimal habitats along the southern edge of their range. Modeling indicates that this ecological trap intensified after the end of the last ice age, preventing colonization of newly formed suitable habitats, weakening stabilizing metapopulation processes, triggering the extinction of the woolly rhinoceros in the early Holocene. Our findings suggest that fragmentation and resultant metapopulation dynamics should be explicitly considered in explanations of late Quaternary megafauna extinctions, sending a clarion call to the fragility of the remaining large-bodied grazers restricted to disjunct fragments of poor-quality habitat due to anthropogenic environmental change.

Millennial processes of population decline, range contraction and near extinction of the European bison.

European bison (Bison bonasus) were widespread throughout Europe during the late Pleistocene. However, the contributions of environmental change and humans to their near extinction have never been resolved. Using process-explicit models, fossils and ancient DNA, we disentangle the combinations of threatening processes that drove population declines and regional extinctions of European bison through space and across time. We show that the population size of European bison declined abruptly at the termination of the Pleistocene in response to rapid environmental change, hunting by humans and their interaction. Human activities prevented populations of European bison from rebounding in the Holocene, despite improved environmental conditions. Hunting caused range loss in the north and east of its distribution, while land use change was responsible for losses in the west and south. Advances in hunting technologies from 1500 CE were needed to simulate low abundances observed in 1870 CE. While our findings show that humans were an important driver of the extinction of the European bison in the wild, vast areas of its range vanished during the Pleistocene-Holocene transition because of post-glacial environmental change. These areas of its former range have been climatically unsuitable for millennia and should not be considered in reintroduction efforts.

Process-explicit models reveal pathway to extinction for woolly mammoth using pattern-oriented validation.

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.

Faster ocean warming threatens richest areas of marine biodiversity.

The vulnerability of marine biodiversity to accelerated rates of climatic change is poorly understood. By developing a new method for identifying extreme oceanic warming events during Earth's most recent deglaciation, and comparing these to 21st century projections, we show that future rates of ocean warming will disproportionately affect the most speciose marine communities, potentially threatening biodiversity in more than 70% of current-day global hotspots of marine species richness. The persistence of these richest areas of marine biodiversity will require many species to move well beyond the biogeographic realm where they are endemic, at rates of redistribution not previously seen. Our approach for quantifying exposure of biodiversity to past and future rates of oceanic warming provides new context and scalable information for deriving and strengthening conservation actions to safeguard marine biodiversity under climate change.

Spatiotemporal influences of climate and humans on muskox range dynamics over multiple millennia.

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.

Models of spatiotemporal variation in rabbit abundance reveal management hot spots for an invasive species.

The European rabbit (Oryctolagus cuniculus) is a notorious economic and environmental pest species in its invasive range. To better understand the population and range dynamics of this species, 41 yr of abundance data have been collected from 116 unique sites across a broad range of climatic and environmental conditions in Australia. We analyzed this time series of abundance data to determine whether interannual variation in climatic conditions can be used to map historic, contemporary, and potential future fluctuations in rabbit abundance from regional to continental scales. We constructed a hierarchical Bayesian regression model of relative abundance that corrected for observation error and seasonal biases. The corrected abundances were regressed against environmental and disease variables in order to project high spatiotemporal resolution, continent-wide rabbit abundances. We show that rabbit abundance in Australia is highly variable in space and time, being driven primarily by internnual variation in temperature and precipitation in concert with the prevalence of a non-pathogenic virus. Moreover, we show that internnual variation in local spatial abundances can be mapped effectively at a continental scale using highly resolved spatiotemporal predictors, allowing "hot spots" of persistently high rabbit abundance to be identified. Importantly, cross-validated model performance was fair to excellent within and across distinct climate zones. Long-term monitoring data for invasive species can be used to map fine-scale spatiotemporal fluctuations in abundance patterns when accurately accounting for inherent sampling biases. Our analysis provides ecologists and pest managers with a clearer understanding of the determinants of rabbit abundance in Australia, offering an important new approach for predicting spatial abundance patterns of invasive species at the near-term temporal scales that are directly relevant to resource management.

Spatial resilience of the Great Barrier Reef under cumulative disturbance impacts.

In the face of increasing cumulative effects from human and natural disturbances, sustaining coral reefs will require a deeper understanding of the drivers of coral resilience in space and time. Here we develop a high-resolution, spatially explicit model of coral dynamics on Australia's Great Barrier Reef (GBR). Our model accounts for biological, ecological and environmental processes, as well as spatial variation in water quality and the cumulative effects of coral diseases, bleaching, outbreaks of crown-of-thorns starfish (Acanthaster cf. solaris), and tropical cyclones. Our projections reconstruct coral cover trajectories between 1996 and 2017 over a total reef area of 14,780 km2 , predicting a mean annual coral loss of -0.67%/year mostly due to the impact of cyclones, followed by starfish outbreaks and coral bleaching. Coral growth rate was the highest for outer shelf coral communities characterized by digitate and tabulate Acropora spp. and exposed to low seasonal variations in salinity and sea surface temperature, and the lowest for inner-shelf communities exposed to reduced water quality. We show that coral resilience (defined as the net effect of resistance and recovery following disturbance) was negatively related to the frequency of river plume conditions, and to reef accessibility to a lesser extent. Surprisingly, reef resilience was substantially lower within no-take marine protected areas, however this difference was mostly driven by the effect of water quality. Our model provides a new validated, spatially explicit platform for identifying the reefs that face the greatest risk of biodiversity loss, and those that have the highest chances to persist under increasing disturbance regimes.

Why decadal to century timescale palaeoclimate data are needed to explain present-day patterns of biological diversity and change.

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 palaeoclimate 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 timescales, showing that these details differ radically from what might be inferred from longer timescale 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 palaeoclimatic change and variability in shaping current macroecological patterns in many regions of the world.

How complex should models be? Comparing correlative and mechanistic range dynamics models.

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.

Predicting the likely response of data-poor ecosystems to climate change using space-for-time substitution across domains.

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.

Reconstructing colonization dynamics to establish how human activities transformed island biodiversity.

Drivers and dynamics of initial human migrations across individual islands and archipelagos are poorly understood, hampering assessments of subsequent modification of island biodiversity. We developed and tested a new statistical-simulation approach for reconstructing the pattern and pace of human migration across islands at high spatiotemporal resolutions. Using Polynesian colonisation of New Zealand as an example, we show that process-explicit models, informed by archaeological records and spatiotemporal reconstructions of past climates and environments, can provide new and important insights into the patterns and mechanisms of arrival and establishment of people on islands. We find that colonisation of New Zealand required there to have been a single founding population of approximately 500 people, arriving between 1233 and 1257 AD, settling multiple areas, and expanding rapidly over both North and South Islands. These verified spatiotemporal reconstructions of colonisation dynamics provide new opportunities to explore more extensively the potential ecological impacts of human colonisation on New Zealand's native biota and ecosystems.

Impact of Holocene environmental change on the evolutionary ecology of an Arctic top predator.

The Arctic is among the most climatically sensitive environments on Earth, and the disappearance of multiyear sea ice in the Arctic Ocean is predicted within decades. As apex predators, polar bears are sentinel species for addressing the impact of environmental variability on Arctic marine ecosystems. By integrating genomics, isotopic analysis, morphometrics, and ecological modeling, we investigate how Holocene environmental changes affected polar bears around Greenland. We uncover reductions in effective population size coinciding with increases in annual mean sea surface temperature, reduction in sea ice cover, declines in suitable habitat, and shifts in suitable habitat northward. Furthermore, we show that west and east Greenlandic polar bears are morphologically, and ecologically distinct, putatively driven by regional biotic and genetic differences. Together, we provide insights into the vulnerability of polar bears to environmental change and how the Arctic marine ecosystem plays a vital role in shaping the evolutionary and ecological trajectories of its inhabitants.

StableClim, continuous projections of climate stability from 21000 BP to 2100 CE at multiple spatial scales.

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.

Evolutionary history and past climate change shape the distribution of genetic diversity in terrestrial mammals.

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.

Identifying island safe havens to prevent the extinction of the World's largest lizard from global warming.

The Komodo dragon (Varanus komodoensis) is an endangered, island-endemic species with a naturally restricted distribution. Despite this, no previous studies have attempted to predict the effects of climate change on this iconic species. We used extensive Komodo dragon monitoring data, climate, and sea-level change projections to build spatially explicit demographic models for the Komodo dragon. These models project the species' future range and abundance under multiple climate change scenarios. We ran over one million model simulations with varying model parameters, enabling us to incorporate uncertainty introduced from three main sources: (a) structure of global climate models, (b) choice of greenhouse gas emission trajectories, and (c) estimates of Komodo dragon demographic parameters. Our models predict a reduction in range-wide Komodo dragon habitat of 8%-87% by 2050, leading to a decrease in habitat patch occupancy of 25%-97% and declines of 27%-99% in abundance across the species' range. We show that the risk of extirpation on the two largest protected islands in Komodo National Park (Rinca and Komodo) was lower than other island populations, providing important safe havens for Komodo dragons under global warming. Given the severity and rate of the predicted changes to Komodo dragon habitat patch occupancy (a proxy for area of occupancy) and abundance, urgent conservation actions are required to avoid risk of extinction. These should, as a priority, be focused on managing habitat on the islands of Komodo and Rinca, reflecting these islands' status as important refuges for the species in a warming world. Variability in our model projections highlights the importance of accounting for uncertainties in demographic and environmental parameters, structural assumptions of global climate models, and greenhouse gas emission scenarios when simulating species metapopulation dynamics under climate change.

Using paleo-archives to safeguard biodiversity under climate change.

Strategies for 21st-century environmental management and conservation under global change require a strong understanding of the biological mechanisms that mediate responses to climate- and human-driven change to successfully mitigate range contractions, extinctions, and the degradation of ecosystem services. Biodiversity responses to past rapid warming events can be followed in situ and over extended periods, using cross-disciplinary approaches that provide cost-effective and scalable information for species' conservation and the maintenance of resilient ecosystems in many bioregions. Beyond the intrinsic knowledge gain such integrative research will increasingly provide the context, tools, and relevant case studies to assist in mitigating climate-driven biodiversity losses in the 21st century and beyond.

Cradles of diversity are unlikely relics of regional climate stability.

The stability of regional climates on millennial timescales is theorised to be a primary determinant of nearby diversification [1-5]. 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 [1-5], which operate on multi-millennial time scales [7].

The Australian National Rabbit Database: 50 yr of population monitoring of an invasive species.

With ongoing introductions into Australia since the 1700s, the European rabbit (Oryctolagus cuniculus) has become one of the most widely distributed and abundant vertebrate pests, adversely impacting Australia's biodiversity and agroeconomy. To understand the population and range dynamics of the species and its impacts better, occurrence and abundance data have been collected by researchers and citizens from sites covering a broad spectrum of climatic and environmental conditions in Australia. The lack of a common and accessible repository for these data has, however, limited their use in determining important spatiotemporal drivers of the structure and dynamics of the geographical range of rabbits in Australia. To meet this need, we created the Australian National Rabbit Database, which combines more than 50 yr of historical and contemporary survey data collected from throughout the range of the species in Australia. The survey data, obtained from a suite of complementary monitoring methods, were combined with high-resolution weather, climate, and environmental information, and an assessment of data quality. The database provides records of rabbit occurrence (689,265 records) and abundance (51,241 records, >120 distinct sites) suitable for identifying the spatiotemporal drivers of the rabbit's distribution and for determining spatial patterns of variation in its key life-history traits, including maximum rates of population growth. Because all data are georeferenced and date stamped, they can be coupled with information from other databases and spatial layers to explore the potential effects of rabbit occurrence and abundance on Australia's native wildlife and agricultural production. The Australian National Rabbit Database is an important tool for understanding and managing the European rabbit in its invasive range and its effects on native biodiversity and agricultural production. It also provides a valuable resource for addressing questions related to the biology, success, and impacts of invasive species more generally. No copyright or proprietary restrictions are associated with the use of this data set other than citation of this Data Paper.

Hydrologic landscape regionalisation using deductive classification and random forests.

Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications which have required the use of an a priori spatial unit (e.g. a catchment) which necessarily results in the loss of variability that is known to exist within those units. The use of a simple statistical approach to identify an appropriate number of classes eliminated the need for large amounts of post-hoc testing with different number of groups, or the selection and justification of an arbitrary number. Using statistical clustering, we identified 23 distinct groups within our training dataset. The use of a hybrid classification employing random forests extended this statistical clustering to an area of approximately 228,000 km2 of south-eastern Australia without the need to rely on catchments, landscape units or stream sections. This extension resulted in a highly accurate regionalisation at both 30-m and 2.5-km resolution, and a less-accurate 10-km classification that would be more appropriate for use at a continental scale. A smaller case study, of an area covering 27,000 km2, demonstrated that the method preserved the intra- and inter-catchment variability that is known to exist in local hydrology, based on previous research. Preliminary analysis linking the regionalisation to streamflow indices is promising suggesting that the method could be used to predict streamflow behaviour in ungauged catchments. Our work therefore simplifies current classification frameworks that are becoming more popular in ecohydrology, while better retaining small-scale variability in hydrology, thus enabling future attempts to explain and visualise broad-scale hydrologic trends at the scale of catchments and continents.