Ecological niche models (ENMs) are widely used tools for predicting species geographic distribution as a function of environmental variables. The inclusion of biotic factors in the predictor suite can significantly increase the predictive power of such models, leading to a model closer to the realized niche for the species under investigation. In this study, we provide evidence of niche overlap between gray woolly monkeys (Lagothrix cana) and black-faced black spider monkeys (Ateles chamek) based on locality and ecological data covering their complete geographic range in the Amazon forests of Brazil, Peru and Bolivia. We also estimate the potential distribution of L. cana using environmental predictors, and the distribution of A. chamek as a biotic factor. Finally, we quantified current and future habitat loss and areas under legal protection. We found that only 39% of the L. cana area of occupancy is under legal protection and that the species could potentially lose up to 58% of habitat in the next 30 years. We also show that the use of a closely-related species that has a more robust dataset can improve ENMs of poorly studied, rare and/or cryptic species. The framework developed here can be applied to a wide range of sympatric species if they share similar ecological requirements. Since our focal species are the most frugivorous primates in our study region and especially vulnerable to habitat loss, the identification of highly suitable areas for both taxa can help to protect other forest-dwelling species, reducing the rate of overall biodiversity loss.

生态位模型（ENMs）是广泛使用的工具，可根据环境变量预测物种的地理分布。在预测变量集中包含生物因子可以显着提高此类模型的预测能力，从而使模型更接近所研究物种的实际生态位。在这项研究中，我们基于覆盖巴西，秘鲁和玻利维亚亚马逊森林完整地理范围的位置和生态数据，提供了灰色羊毛猴（Lagothrix cana）和黑脸黑蜘蛛猴（Ateles chamek）之间的利基重叠证据。。我们还估计的电位分布L.塔卡纳使用环境的预测，以及分布A. chamek作为生物因子。最后，我们量化了当前和未来栖息地的丧失以及受到法律保护的区域。我们发现只有39％的加拿大乳杆菌居住面积受到法律保护，该物种可能在未来30年内丧失多达58％的栖息地。我们还表明，使用具有更强大数据集的密切相关物种可以改善研究欠佳，稀有和/或隐秘物种的ENM。如果它们具有相似的生态要求，那么这里开发的框架可以应用于广泛的同胞物种。由于我们的重点物种是研究区域中最节食的灵长类动物，尤其容易遭受生境丧失的影响，因此，为两个生物分类群确定高度合适的区域有助于保护其他森林物种，从而降低整体生物多样性丧失的速度。