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Selecting the optimum plot size for a California design-based stream and wetland mapping program.

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  • 1Southern California Coastal Water Research Project, 3535 Harbor Blvd., Suite 110, Costa Mesa, CA, 92626, USA.

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Probabilistic wetland monitoring using random sampling is more cost-effective than comprehensive mapping. A 4 km(2) plot size balances statistical accuracy and the detection of rare wetland types for California's status and trends program.

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Area of Science:

  • Environmental science
  • Ecology
  • Conservation biology

Background:

  • Accurate wetland and stream mapping is crucial for environmental management and regulatory programs.
  • Comprehensive mapping is resource-intensive and often statistically unreliable for large areas.
  • Probabilistic, design-based approaches offer a cost-effective alternative for status and trends assessment.

Purpose of the Study:

  • To evaluate the impact of sample plot size on the efficiency and accuracy of probabilistic wetland monitoring.
  • To determine the optimal plot size for remote monitoring of stream and wetland extent in California.
  • To inform the selection of plot size for the California status and trends program.

Main Methods:

  • Utilized simulated sampling to assess four plot sizes (1, 4, 9, and 16 km(2)).
  • Evaluated performance across three distinct geographic regions in California.
  • Analyzed statistical accuracy, precision, cost-efficiency, and detection of rare wetland subtypes.

Main Results:

  • Smaller plot sizes (1 and 4 km(2)) demonstrated higher statistical efficiency for accuracy and precision.
  • Larger plot sizes (9 and 16 km(2)) were more effective at capturing rare and spatially limited wetland subtypes.
  • A 4 km(2) plot size was identified as the optimal balance between competing factors.

Conclusions:

  • The 4 km(2) plot size provides a robust and efficient solution for probabilistic wetland monitoring in California.
  • This finding supports the use of probabilistic sampling designs for cost-effective environmental monitoring.
  • The study provides a data-driven recommendation for optimizing sample plot selection in similar monitoring programs.