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 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.物种分布模型（Species Distribution Models, SDMs）假设物种独立存在且互不影响对方的分布范围，因此可能限制其预测生物多样性格局的能力。群落级模型（Community-level Models, CLMs）则借助物种共现模式，拟合物种与群落共有的环境响应特征，因此可构建更稳健且可迁移的模型。本研究针对不同气候变化场景下的5组配对物种分布模型与群落级模型开展控制性对比实验，采用过去21000年北美东部的古气候模拟数据与化石花粉记录作为数据集。当将模型外推至与建模时段时间跨度较远、气候特征差异显著的时期时，两类模型的表现均较差；但在此类场景下，群落级模型的整体表现优于物种分布模型，尤其是当模型基于稀疏校准数据集构建时。此外，与物种分布模型不同，群落级模型不会对训练数据产生过拟合问题。预期将出现的新型气候情境为所有模型带来了重大的预测挑战，但群落级模型可通过借鉴共现类群的信息，更好地应对这一挑战。