Differing climatic mechanisms control transient and accumulated vegetation novelty in Europe and eastern North America.

Understanding the mechanisms of climate that produce novel ecosystems is of joint interest to conservation biologists and palaeoecologists. Here, we define and differentiate transient from accumulated novelty and evaluate four climatic mechanisms proposed to cause species to reshuffle into novel assemblages: high climatic novelty, high spatial rates of change (displacement), high variance among displacement rates for individual climate variables, and divergence among displacement vector bearings. We use climate simulations to quantify climate novelty, displacement and divergence across Europe and eastern North America from the last glacial maximum to the present, and fossil pollen records to quantify vegetation novelty. Transient climate novelty is consistently the strongest predictor of transient vegetation novelty, while displacement rates (mean and variance) are equally important in Europe. However, transient vegetation novelty is lower in Europe and its relationship to climatic predictors is the opposite of expectation. For both continents, accumulated novelty is greater than transient novelty, and climate novelty is the strongest predictor of accumulated ecological novelty. These results suggest that controls on novel ecosystems vary with timescale and among continents, and that the twenty-first century emergence of novelty will be driven by both rapid rates of climate change and the emergence of novel climate states. This article is part of a discussion meeting issue 'The past is a foreign country: how much can the fossil record actually inform conservation?'

How will climate novelty influence ecological forecasts? Using the Quaternary to assess future reliability.

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.

Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD.

Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and downscaled before they can be used by ecological models. Downscaling methods and observational baselines vary among researchers, which produces confounding biases among downscaled climate simulations. We present unified datasets of debiased and downscaled climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950-2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This downscaling includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850-2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity.

Controlled comparison of species- and community-level models across novel climates and communities.

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 fossil-pollen 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.