Quantifying seagrass light requirements using an algorithm to spatially resolve depth of colonization.

Area of Science:

• Marine Ecology
• Seagrass Ecology
• Remote Sensing Applications

Background:

• Depth of colonization (Zc) and percent surface irradiance (PSI) are key seagrass metrics for light availability and requirements.
• Current methods for estimating Zc and PSI are highly variable, hindering comparative studies and consistent management.
• Standardized metrics are crucial for accurate seagrass status and condition assessments.

Purpose of the Study:

• To develop and present a novel algorithm for computing maps of median (Zc,med) and maximum (Zc,max) depth of colonization.
• To generate maps of PSI at Zc,med and Zc,max for four Florida coastal areas.
• To provide a standardized analytical approach for seagrass light requirement assessments.

Main Methods:

• Combined MODIS satellite imagery for light attenuation (Kd) with PAR profiles and Secchi depth measurements.
• Integrated seagrass growth estimates with light data to compute PSI maps.
• Applied a new algorithm to estimate Zc,med and Zc,max across specified Florida estuaries.

Main Results:

• Mean Zc,med varied significantly across estuaries, from 0.80±0.13 m in Old Tampa Bay to 2.33±0.26 m in Western Choctawhatchee Bay.
• PSI at Zc,med averaged 18% in Indian River Lagoon, 42% in Tampa Bay, and 58% in Choctawhatchee Bay.
• Indian River Lagoon showed significantly lower PSI estimates compared to other estuaries; Tampa Bay and Choctawhatchee Bay estimates exceeded the commonly cited 20%.

Conclusions:

• The new algorithm provides consistent and comparable maps of seagrass depth of colonization and light requirements.
• Significant spatial gradients in Zc and PSI were observed within and among Florida estuaries.
• This analytical approach is readily applicable to new data and can be routinely integrated into seagrass monitoring programs.