Using genetic data to improve species distribution models.

Area of Science:

• Ecology
• Epidemiology
• Conservation Biology

Background:

• Tsetse flies transmit trypanosomiasis, impacting human health and livestock in Africa.
• Current distribution models often overlook landscape connectivity, crucial for understanding species dispersal and gene flow.

Purpose of the Study:

• To integrate landscape functional connectivity into species distribution models for tsetse flies.
• To identify isolated tsetse populations for targeted elimination programs and understand factors influencing their movement.

Main Methods:

• Developed a methodology to map landscape friction (resistance to movement) for tsetse flies in West Africa.
• Utilized Maxent modeling and corrected suitable habitat predictions with landscape functional connectivity data.
• Analyzed the intersection of biotic, abiotic, and movement (BAM) factors affecting tsetse distribution.

Main Results:

• Integrating landscape connectivity reduced the predicted distribution area for *Glossina palpalis gambiensis* without degrading model specificity (P=0.751).
• The approach successfully identified unconnected habitat patches, highlighting barriers to tsetse dispersal.
• The friction analysis provided a reproducible method for quantifying landscape connectivity (M) without expert knowledge.

Conclusions:

• Landscape functional connectivity is a critical, often neglected, factor in species distribution modeling.
• This integrated approach enhances the accuracy of distribution models and aids in identifying isolated populations for effective control strategies.
• The developed methodology is generalizable and recommended for improving distribution models across various species.