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Fast Colony Forming Unit Counting in 96-Well Plate Format Applied to the Drosophila Microbiome

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Dealing with varying detection probability, unequal sample sizes and clumped distributions in count data.

D Johan Kotze¹, Robert B O'Hara, Susanna Lehvävirta

¹Department of Environmental Sciences, University of Helsinki, Helsinki, Finland. johan.kotze@helsinki.fi

|August 23, 2012

Summary

Accounting for seasonal variation in species detectability is crucial for accurate abundance estimates. Failing to include seasonality in analyses with unequal sample sizes can lead to significant over or underestimation of population sizes.

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Area of Science:

Ecology
Wildlife population monitoring
Statistical modeling

Background:

Estimating species abundance accurately is vital for ecological research and conservation.
Temporal variations in species detectability, influenced by factors like seasonality, can bias abundance estimates.
Unequal sample sizes, often resulting from lost sampling equipment, further complicate accurate population assessments.

Purpose of the Study:

To demonstrate the critical importance of incorporating seasonality into ecological count data analysis.
To evaluate the performance of different statistical models when dealing with unequal sample sizes and temporal variation.
To provide recommendations for robustly analyzing wildlife population data affected by detectability changes.

Main Methods:

Simulated count data for a spring-active carabid beetle, incorporating random trap loss during periods of high and low activity.
Fitted five different statistical models to datasets with varying degrees of trap loss.
Assessed model performance based on the accuracy of estimated effect sizes, particularly when seasonality was included or omitted.

Main Results:

Models assuming a log-normal distribution severely underestimated or overestimated true effect sizes when seasonality was not included in analyses with unequal sample sizes.
Including seasonality and trapping days as offset terms did not improve results for log-normal models.
Negative binomial distribution models, with seasonality as a free factor or offset term, performed well, even with limited information on seasonal variation.

Conclusions:

Accurate abundance estimation requires accounting for temporal variation in detectability, especially seasonality.
Negative binomial or over-dispersed Poisson distributions are recommended for analyzing count data with unequal sample sizes and seasonality.
Incorporate sampling effort as an offset, and seasonality as an offset or free factor depending on available information for robust population estimates.