Calixto-Pérez Edith, Alarcón-Guerrero Jesús, Ramos-Fernández Gabriel, Dias Pedro Américo D, Rangel-Negrín Ariadna, Améndola-Pimenta Monica, Domingo Cristina, Arroyo-Rodríguez Víctor, Pozo-Montuy Gilberto, Pinacho-Guendulain Braulio, Urquiza-Haas Tania, Koleff Patricia, Martínez-Meyer Enrique
Instituto de Biología, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico.
Posgrado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico.
Primates. 2018 Sep;59(5):451-467. doi: 10.1007/s10329-018-0673-8. Epub 2018 Jul 9.
Ecological niche modeling is used to estimate species distributions based on occurrence records and environmental variables, but it seldom includes explicit biotic or historical factors that are important in determining the distribution of species. Expert knowledge can provide additional valuable information regarding ecological or historical attributes of species, but the influence of integrating this information in the modeling process has been poorly explored. Here, we integrated expert knowledge in different stages of the niche modeling process to improve the representation of the actual geographic distributions of Mexican primates (Ateles geoffroyi, Alouatta pigra, and A. palliata mexicana). We designed an elicitation process to acquire information from experts and such information was integrated by an iterative process that consisted of reviews of input data by experts, production of ecological niche models (ENMs), and evaluation of model outputs to provide feedback. We built ENMs using the maximum entropy algorithm along with a dataset of occurrence records gathered from a public source and records provided by the experts. Models without expert knowledge were also built for comparison, and both models, with and without expert knowledge, were evaluated using four validation metrics that provide a measure of accuracy for presence-absence predictions (specificity, sensitivity, kappa, true skill statistic). Integrating expert knowledge to build ENMs produced better results for potential distributions than models without expert knowledge, but a much greater improvement in the transition from potential to realized geographic distributions by reducing overprediction, resulting in better representations of the actual geographic distributions of species. Furthermore, with the combination of niche models and expert knowledge we were able to identify an area of sympatry between A. palliata mexicana and A. pigra. We argue that the inclusion of expert knowledge at different stages in the construction of niche models in an explicit and systematic fashion is a recommended practice as it produces overall positive results for representing realized species distributions.
生态位建模用于根据物种出现记录和环境变量来估计物种分布，但它很少纳入对决定物种分布至关重要的明确生物或历史因素。专家知识可以提供有关物种生态或历史属性的额外有价值信息，但在建模过程中整合这些信息的影响尚未得到充分探索。在这里，我们在生态位建模过程的不同阶段整合了专家知识，以更好地呈现墨西哥灵长类动物（ Geoffroy蜘蛛猴、中美绒毛蛛猴和墨西哥白额蛛猴）的实际地理分布。我们设计了一个启发过程来从专家那里获取信息，并且通过一个迭代过程来整合这些信息，该迭代过程包括专家对输入数据的审查、生态位模型（ENM）的生成以及对模型输出的评估以提供反馈。我们使用最大熵算法以及从公共来源收集的出现记录数据集和专家提供的记录构建了ENM。还构建了没有专家知识的模型用于比较，并且使用四个验证指标对有和没有专家知识的模型进行评估，这些指标提供了存在-缺失预测准确性的度量（特异性、敏感性、kappa、真实技能统计量）。与没有专家知识的模型相比，整合专家知识构建ENM在潜在分布方面产生了更好的结果，但在从潜在地理分布到实际地理分布的转变中通过减少过度预测有了更大的改进，从而更好地呈现了物种的实际地理分布。此外，通过生态位模型和专家知识的结合，我们能够确定墨西哥白额蛛猴和中美绒毛蛛猴之间的同域分布区域。我们认为，以明确和系统的方式在生态位模型构建的不同阶段纳入专家知识是一种推荐做法，因为它在表示实际物种分布方面产生了总体积极的结果。
