Gabriella Chen, University of Maryland College Park

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Class Year:

2023

Mentor:

Lorie Staver, Ph.D. Cindy Palinkas, Ph.D.

Project Title:

Comparison of Two Visual Estimation Methods for Coastal Vegetation Monitoring

Abstract:

Visual estimation is a technique used to measure vegetation cover in marshes and is crucial to perform effectively to monitor marsh health and the progression of restoration projects. However, there are two vastly different existing techniques that are both ‘visual estimation,’ and that is the ungridded and gridded quadrat. Both techniques require the user to place the quadrat over a plot and give total and species-specific cover. The ungridded quadrat covers a larger area (1.0 m2 vs. 0.25 m2), but the gridded quadrat has physical guidelines to help the user (25 cells of equal size). There is a lack of comparative studies of these two methods which can cause inconsistencies in data. In this study, we aimed to compare the two methods for estimating total cover and diversity across marsh types and observers. Multiple observers went to marshes of various zones (low/high) and ages (natural, 8 to 20 y/o, and >1 y/o) and gave their ungridded and gridded quadrat estimates. We also investigated observer biases between different observers and within the same observer by analyzing the same sites and on two different occasions. A regression analysis of the composite data produced an equation of conversion between the two methods. Our results revealed no significant difference between the ungridded and gridded quadrat total cover and diversity estimates between different marsh zones and ages. Significant observer bias was detected but within the same observer there were not significant differences between observations in the same locations. Further investigation is needed to confirm these results and to fine-tune the conversion factor. It is also strongly suggested to train observers prior to the field to avoid biases.  

Location:

Horn Point Laboratory

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