Ecophysiological models are more data demanding and, consequently, less used than correlative ecological niche models to predict species’ distribution under climate change, especially for endotherms. Hybrid models that integrate both approaches are even less used, and several aspects about their predictions (e.g. accuracy, geographic extent and uncertainty) have been poorly explored. We developed a hybrid model for mammals using hours of activity and hours of heat stress as mechanistic variables, fitted using macroclimatic data and applied to conventional correlative modeling. We then compared the outputs from conventional correlative models with our hybrid model for 58 tropical mammals in term of accuracy, uncertainty, and predicted geographic distribution under climate change. We expected that hybrid models to have higher accuracy than correlative ones, with difference in predicted geographic distribution extent. We found no substantial differences between correlative and hybrid predictions for accuracy, uncertainty, and extent. Although the area predicted as suitable did not differ in extent, they differ in location, with lower congruence between models for future prediction. This result challenged the widespread assumption that hybrid models are more accurate. The ecophysiological model approach proposed here ease ecophysiological data requirements. We propose, therefore, choosing model approach based on study's objective, rather than on data requirements or the assumption that hybrid models have better predictions. The main advantage of the hybrid model is in providing a more complete view of the species response, as proximal (causal) and distal (environment) aspects are combined.

生态生理模型需要更多的数据，因此比相关的生态位模型更少用于预测气候变化下的物种分布，特别是对于吸热动物。集成这两种方法的混合模型使用得更少，关于它们预测的几个方面（例如准确性、地理范围和不确定性）的探索也很少。我们使用活动时间和热应激时间作为机械变量为哺乳动物开发了一个混合模型，使用大气候数据进行拟合并应用于传统的相关建模。然后，我们将传统相关模型的输出与我们的 58 种热带哺乳动物的混合模型在准确性、不确定性和气候变化下预测的地理分布方面进行了比较。我们期望混合模型比相关模型具有更高的准确性，预测的地理分布范围有所不同。我们发现相关预测和混合预测在准确性、不确定性和范围方面没有实质性差异。虽然预测为合适的区域在范围上没有差异，但它们的位置不同，未来预测模型之间的一致性较低。这一结果挑战了混合模型更准确的普遍假设。这里提出的生态生理学模型方法减轻了生态生理学数据的要求。因此，我们建议根据研究目标选择模型方法，而不是根据数据要求或混合模型具有更好预测的假设。