Reconstructing ecological networks with noisy dynamics.

Area of Science:

• Ecology
• Network Science
• Computational Biology

Background:

• Ecosystem functioning relies on complex interactions, often difficult to observe directly, especially in diverse, small, or rare species.
• Inferring ecological network structure from time-series species abundance data is a common but imperfect approach.

Purpose of the Study:

• To evaluate how intrinsic noise and network topology affect the accuracy of inferring ecological network structure from time-series data.
• To understand the influence of stochasticity on ecological network reconstruction methods.

Main Methods:

• Analysis of seven distinct four-species ecological motifs using a stochastic model.
• Evaluation of different methods for inferring network structure from simulated time-series species abundance data.

Main Results:

• Star-shaped ecological motifs were differentially detected, while ring motifs were differentially constructed by the inference methods.
• The magnitude of stochasticity in population dynamics did not significantly impact the ability to reconstruct the network.
• The interplay between stochastic and deterministic system components critically influences the trajectory to equilibrium and shapes species covariance.

Conclusions:

• Network reconstruction accuracy is less dependent on the level of stochasticity and more on its interaction with deterministic processes.
• Long transient dynamics play a significant role in shaping ecological system trajectories.
• Development of more ecologically robust statistical inference techniques is needed, considering the impact of stochasticity and system dynamics.