Landscape resistance index aiming at functional forest connectivity

Area of Science:

• Ecology
• Conservation Biology
• Remote Sensing

Background:

• Understanding landscape resistance is crucial for species movement and habitat suitability, especially with increasing urban sprawl.
• The impact of urban sprawl on forest functional connectivity remains poorly understood.
• Existing resistance models often lack comprehensive parameterization for complex landscapes.

Purpose of the Study:

• To develop a landscape resistance index using structural equation modeling (SEM) to assess forest functional connectivity.
• To incorporate remote sensing-derived variables (heat emission, biomass, anthropogenic barriers) into the resistance index.
• To evaluate the index's performance in a biodiverse region like the Atlantic Forest in São Paulo's Green Belt Biosphere Reserve.

Main Methods:

• Employed structural equation modeling (SEM) to build the landscape resistance index.
• Utilized remote sensing data to derive observed variables: heat emission, biomass, and anthropogenic barriers.
• Applied the index to the Green Belt Biosphere Reserve, São Paulo, to model forest functional connectivity.

Main Results:

• The SEM demonstrated a significant adjustment, achieving a comparative fit index (CFI) of 1.00 and root mean square error of approximation (RMSEA) of 0.00.
• The developed landscape resistance index effectively reflects land use/land cover resistance levels.
• Highest resistance values were associated with anthropized uses and isolated forest patches, indicating barriers to movement.

Conclusions:

• The landscape resistance index, based on environmental attributes, accurately reflects forest functional connectivity.
• The index provides a valuable framework for designing effective forest corridors in landscapes impacted by human activities.
• This approach offers a method to quantify and visualize landscape resistance, aiding conservation planning.