Predicting the species abundance distribution using a model food web

Area of Science:

• Ecology
• Theoretical Ecology
• Ecological Modeling

Background:

• Numerous species abundance distribution (SAD) models exist, often resembling the log-normal distribution and proving difficult to distinguish with empirical data.
• Ecological datasets are incomplete and noisy, limiting our ability to accurately approximate true underlying distributions.
• The complexity of real-world ecosystems makes analytical study of SAD challenging.

Purpose of the Study:

• To investigate the predicted species abundance distribution (SAD) using the complex Webworld ecosystem model.
• To identify distinguishing features between SAD models that are not apparent in field data.
• To compare the performance of different SAD models, including log-normal, logit-normal, and power-law normal distributions.

Main Methods:

• Utilized simulation data from the Webworld ecosystem model, a complex system not amenable to analytical solutions.
• Employed an analytical approach similar to that used for empirical ecological data.
• Examined large, fully described datasets from simulations to identify subtle model distinctions.

Main Results:

• The power-law normal distribution demonstrated superior performance compared to both log-normal and logit-normal distributions for modeling SAD.
• Simulation data revealed features capable of distinguishing between different SAD models, which are often obscured in empirical datasets.
• The study identified potential improvements to the power-law normal distribution at the high-population cut-off.

Conclusions:

• The power-law normal distribution is a more effective model for species abundance distributions than previously assumed log-normal or logit-normal distributions.
• Complex ecosystem models, when analyzed via simulation, can uncover critical differences between ecological distribution models.
• Further refinement of SAD models, particularly at population cut-offs, is warranted based on simulation insights.