Conservation and management of species and their habitats depends in part on knowing where those species and habitats occur both today as well as into the future. In the face of rapid climate change, being able to forecast regional- to global-scale changes in species distributions, species assemblages, and patterns of biodiversity is more critical than ever. Ecologists typically use statistical models as tools to make these types of forecasts, which are then used to inform management strategies. One common type of modeling approach used for this purpose is species distribution models (SDMs). To forecasts changes in species assemblages, individual SDMs are fitted and projected for each species, which are then aggregated, or stacked, to infer potential changes in community-level patterns. SDMs can be limited because they assume that species exist in isolation and independently of one another, which is most often not the case in the natural world.
Conversely, community-level models (CLMs) employ an ‘assemble and predict together’ strategy, which involves combining data from multiple species to simultaneously analyze and map patterns of biodiversity at the community level. CLMs can capture any process driving co-occurrence patterns, including shared climatic requirements, responses to unmeasured environmental variables, and possibly biotic interactions. By simultaneously modelling all observed species within a region of interest and incorporating co-occurrence data, CLMs may have the potential to predict species distributions and changes in community composition better than SDMs, especially for large climatic shifts and novel climate regimes. The potential greater transferability of CLMs to novel climates may also be beneficial considering the predicted emergence of no-analogue climates in the near future. However, these ideas have been untested and the relative ability of SDMs and CLMs to simulate the past emergence of no-analogue communities is unknown.
This work is the first to compare model predictions with observed species assemblages using head-to-head evaluations of five SDM algorithms and their direct CLM counterparts across longer time periods, as well as across climate change similar in magnitude to that expected this century. Using fossil pollen from sediment cores in eastern North America spanning the past 21,000 years, we performed the first comprehensive SDM and CLM model comparison across the large and rapid climate changes of the Late Quaternary to evaluate how these models may perform in predicting species distributions and assemblages under climate change.
We used paleoclimatic simulations and fossil-pollen records from the past 21,000 years since the Last Glacial Maximum (LGM – 21 kyr BP) to conduct a controlled comparison of five paired SDMs and CLMs, covering a wide range of model-class types. We examined taxon occurrence data for fossil-pollen from sediment cores collected in eastern North America and used paleoclimate simulations from the Community Climate System Model Version 3 (CCSM3) SynTrace transient simulation with seasonally averaged model outputs saved at a decadal time step from LGM to present. We used multiple metrics to evaluate the ability of the models to predict taxa distributions in order to examine the extent to which models can be reliably projected to new climatic regimes and no-analogue communities.