Expert-based versus habitat-suitability models to develop resistance surfaces in landscape genetics

Area of Science:

• Ecology
• Genetics
• Conservation Biology

Background:

• Landscape genetics analyzes how landscape features influence gene flow among wild populations.
• Least-cost path (LCP) distances, derived from resistance surfaces, often better explain genetic distances than Euclidean distances.
• Resistance surfaces traditionally rely on expert knowledge due to limited movement data, introducing potential bias.

Purpose of the Study:

• To compare the effectiveness of least-cost path (LCP) distances derived from habitat suitability models versus expert knowledge in landscape genetics.
• To evaluate how different resistance surface methods explain genetic distances and functional connectivity.

Main Methods:

• Generated LCP distances using both expert-based and habitat suitability model-derived resistance surfaces.
• Related LCP distances to genetic distances using linear mixed-effect models.
• Utilized an empirical dataset of Italian wolves (Canis lupus).

Main Results:

• Both LCP methods significantly explained genetic distances (P ≤ 0.0001).
• LCP distances derived from habitat suitability models consistently showed higher explanatory power than those from expert knowledge.
• All LCP distances outperformed simple Euclidean distances in explaining genetic patterns.

Conclusions:

• Habitat suitability models offer a more objective and effective approach for estimating resistance surfaces in landscape genetics.
• Using habitat suitability-based resistance surfaces can improve the accuracy of assessing gene flow and functional connectivity.
• Researchers are encouraged to adopt habitat suitability models for more robust landscape genetics analyses.