Applications of graph theory to landscape genetics

Area of Science:

• Ecology
• Population Genetics
• Conservation Biology

Background:

• Understanding gene flow is crucial for managing wildlife populations and predicting their response to landscape changes.
• Fishers (Martes pennanti) are a key furbearer species in Canada, but their population genetic structure and the factors influencing gene flow remain incompletely understood.

Purpose of the Study:

• To investigate the relationships among landscape quality, gene flow, and population genetic structure of fishers in Ontario, Canada.
• To apply graph theory to model landscape connectivity and genetic structure.
• To identify factors influencing fisher movement and habitat use.

Main Methods:

• Utilized graph theory, defining landscapes as network nodes connected by edges representing potential movement corridors.
• Analyzed network structure, including clustering, path length, and resiliency.
• Employed community detection algorithms to delineate genetic clusters.
• Assessed relationships between node centrality, immigration, and snow depth.

Main Results:

• The fisher network exhibited high clustering and short path lengths, indicating efficient gene flow.
• Network resilience suggests that allele spread is unlikely to be significantly impacted by extirpations or conservative harvest.
• Node centrality was negatively correlated with immigration and snow depth, suggesting central nodes are emigrant sources and high-quality habitats.
• Five distinct genetic clusters were identified, revealing cryptic population structure.

Conclusions:

• Network models provide valuable system-level insights into gene flow processes.
• Landscape alterations can impact fisher population fitness and evolutionary potential.
• Understanding habitat quality and connectivity is essential for effective fisher conservation and management.