Palaeontologists characterize mass extinctions as times when the Earth loses more than three-quarters of its species in a geologically short interval, as has happened only five times in the past 540 million years or so. Biologists now suggest that a sixth mass extinction may be under way, given the known species losses over the past few centuries and millennia. Here we review how differences between fossil and modern data and the addition of recently available palaeontological information influence our understanding of the current extinction crisis. Our results confirm that current extinction rates are higher than would be expected from the fossil record, highlighting the need for effective conservation measures.

There is an urgent need to understand species and community responses to climatic and ecological changes to predict biodiversity patterns given anticipated global change. The current distribution of species and the environment provide a limited perspective to study and predict ecological responses; therefore, biodiversity responses to past environmental changes must be examined. The rapid development of ecological niche models (ENMs) and their use in reconstructing past species distributions has facilitated inclusion of past observations into predictive models. Paleodata offer an opportunity to test the predictive ability of ENMs and their underlying assumptions. However, paleodata remain underutilized despite the rapidly growing field of paleoinformatics. New modeling methods that incorporate species associations, coupled with paleodata, provide more robust approaches to studying species and community responses, especially given the predicted emergence of no-analog climates and communities in the future.

Species distribution models (SDMs) assume species exist in isolation and do not influence one another's distributions, thus potentially limiting their ability to predict biodiversity patterns. Community-level models (CLMs) capitalize on species co-occurrences to fit shared environmental responses of species and communities, and therefore may result in more robust and transferable models. Here, we conduct a controlled comparison of five paired SDMs and CLMs across changing climates, using palaeoclimatic simulations and fossilpollen records of eastern North America for the past 21 000 years. Both SDMs and CLMs performed poorly when projected to time periods that are temporally distant and climatically dissimilar from those in which they were fit; however, CLMs generally outperformed SDMs in these instances, especially when models were fit with sparse calibration datasets. Additionally, CLMs did not over-fit training data, unlike SDMs. The expected emergence of novel climates presents a major forecasting challenge for all models, but CLMs may better rise to this challenge by borrowing information from co-occurring taxa.

Future climates are projected to be highly novel relative to recent climates. Climate novelty challenges models that correlate ecological patterns to climate variables and then use these relationships to forecast ecological responses to future climate change. Here, we quantify the magnitude and ecological significance of future climate novelty by comparing it to novel climates over the past 21,000 years in North America. We then use relationships between model performance and climate novelty derived from the fossil pollen record from eastern North America to estimate the expected decrease in predictive skill of ecological forecasting models as future climate novelty increases. We show that, in the high emissions scenario (RCP 8.5) and by late 21st century, future climate novelty is similar to or higher than peak levels of climate novelty over the last 21,000 years. The accuracy of ecological forecasting models is projected to decline steadily over the coming decades in response to increasing climate novelty, although models that incorporate co-occurrences among species may retain somewhat higher predictive skill. In addition to quantifying future climate novelty in the context of late Quaternary climate change, this work underscores the challenges of making reliable forecasts to an increasingly novel future, while highlighting the need to assess potential avenues for improvement, such as increased reliance on geological analogs for future novel climates and improving existing models by pooling data through time and incorporating assemblage-level information.