Data from: 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 paleoclimatic simulations and fossil pollen records of eastern North America for the last 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 overfit 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.

物种分布模型（Species Distribution Models, SDMs）假设物种独立存在且互不影响彼此的分布范围，因此其预测生物多样性格局的能力可能受限。群落水平模型（Community-level Models, CLMs）则利用物种共现关系拟合物种与群落共有的环境响应，故而可构建更稳健且可迁移的模型。本研究依托过去21000年北美东部的古气候模拟数据与化石花粉记录，针对5组配对的物种分布模型与群落水平模型在气候变化情境下的表现开展了控制性对比研究。当将模型投影至与建模时段气候差异显著且时间跨度较远的时期时，两类模型的表现均较差；但在此类场景下，群落水平模型总体优于物种分布模型，尤其当模型采用稀疏校准数据集进行训练时。此外，与物种分布模型不同，群落水平模型不会出现训练数据过拟合的问题。全新气候类型的预期出现对所有模型而言都是一项重大的预测挑战，但群落水平模型可通过借鉴共现类群的相关信息，更好地应对这一挑战。