Integrating expert knowledge and ecological niche models to estimate Mexican primates’ distribution

• Published in: Primates Vol. 59; no. 5; pp. 451 - 467
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Tokyo Springer Japan 01.09.2018; Springer Nature B.V
• Subjects: Animal Ecology; Animal models; Behavioral Sciences; Biomedical and Life Sciences; Ecological effects; Ecological niches; Entropy; Evolutionary Biology; Experts; Geographical distribution; Information processing; Iterative methods; Knowledge; Life Sciences; Maximum entropy; Modelling; Niches; Original Article; Primates; Representations; Species; Sympatry; Zoology; Species distribution models; Expert knowledge; Mexico; Maxent; Ecological niche modeling; Ateles geoffroyi; Alouatta pigra; Alouatta palliata mexicana
• ISSN: 0032-8332; 1610-7365
• DOI: 10.1007/s10329-018-0673-8

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.