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

Area of Science:

• Ecology
• Biodiversity
• Conservation Biology

Background:

• Ecological niche modeling (ENM) estimates species distributions using environmental data but often omits crucial biotic and historical factors.
• Expert knowledge offers valuable insights into species' ecological and historical attributes, yet its integration into ENM remains underexplored.

Purpose of the Study:

• To enhance the accuracy of geographic distribution models for Mexican primates by integrating expert knowledge into the ENM process.
• To assess the impact of expert knowledge on the transition from potential to realized species distributions.

Main Methods:

• An iterative process involving expert elicitation, data review, ENM development (Maximum Entropy algorithm), and model evaluation was employed.
• Ecological niche models were constructed both with and without expert-derived occurrence data and insights.
• Model performance was validated using metrics such as specificity, sensitivity, kappa, and true skill statistic.

Main Results:

• ENMs incorporating expert knowledge yielded more accurate potential distribution predictions compared to models without expert input.
• A substantial improvement was observed in predicting realized distributions, with reduced overprediction when expert knowledge was integrated.
• The combined approach identified a sympatric area between Alouatta palliata mexicana and Alouatta pigra.

Conclusions:

• Integrating expert knowledge systematically at various stages of ENM construction is a recommended practice for improving realized species distribution representations.
• This approach enhances model accuracy and can reveal important ecological interactions like areas of sympatry.