A stochastic movement simulator improves estimates of landscape connectivity

Area of Science:

• Ecology
• Conservation Biology
• Genetics

Background:

• Landscape connectivity is crucial for effective conservation, guiding restoration and habitat creation for species movement.
• Traditional methods like least-cost paths (LCPs) and circuit theory have limitations in accurately predicting landscape connectivity.
• Estimating connectivity is vital for designing efficient conservation strategies.

Purpose of the Study:

• To compare the predictive performance of a stochastic movement simulator (SMS) against LCPs and circuit theory for genetic connectivity.
• To assess the efficacy of SMS in predicting dispersal patterns for species with different movement capabilities.
• To evaluate the robustness of SMS to landscape resolution and perceptual range uncertainty.

Main Methods:

• Developed and applied an individual-based stochastic movement simulator (SMS) using cost surfaces.
• Incorporated perceptual range and stochasticity in movement to relax LCP assumptions.
• Correlated model predictions with genetic connectivity data for a bird and an amphibian species.

Main Results:

• The stochastic movement simulator (SMS) showed substantially higher correlation with genetic connectivity data for both species compared to LCPs and circuit theory.
• SMS performance remained robust despite variations in landscape spatial resolution and perceptual range parameters.
• The study highlights the limitations of LCP and circuit theory in capturing complex movement behaviors.

Conclusions:

• The individual-based stochastic movement simulator (SMS) offers a more accurate approach to estimating landscape connectivity than traditional methods.
• SMS provides a valuable tool for improving the reliability of connectivity predictions in conservation planning.
• Integrating SMS with other methods like graph theory can advance connectivity research and land management recommendations.