The problem of estimating recent genetic connectivity in a changing world

Area of Science:

• Ecology
• Conservation Biology
• Population Genetics

Background:

• Understanding population connectivity is crucial for conservation efforts, influencing population dynamics and rescue.
• Genetic methods are increasingly favored over ecological methods for inferring connectivity due to technological advancements.
• Recent habitat loss has drastically reduced wildlife population connectivity, necessitating accurate assessment methods.

Purpose of the Study:

• To investigate the accuracy and biases of common genetic estimators of migration rate.
• To evaluate the impact of the timing and magnitude of connectivity changes on these estimates.
• To compare a coalescent-based method (Migrate-n) with a disequilibrium-based method (BayesAss) using simulated data.

Main Methods:

• Utilized simulated data to assess genetic connectivity estimators.
• Employed a coalescent-based method (Migrate-n) to estimate migration rates.
• Employed a disequilibrium-based method (BayesAss) to estimate migration rates.

Main Results:

• Both Migrate-n and BayesAss overestimated recent migration rates when historical connectivity had recently declined.
• The coalescent-based method (Migrate-n) yielded more accurate recent migration rate estimates than BayesAss.
• The coalescent-based method did not accurately reflect long-term genetic connectivity patterns.

Conclusions:

• Comparing coalescent and disequilibrium estimates can lead to inaccurate inferences about recent landscape change effects on genetic connectivity.
• Careful consideration of method limitations is essential when interpreting genetic connectivity data, especially concerning recent environmental changes.
• Further research is needed to refine genetic methods for accurately assessing contemporary population connectivity.