Ranking species based on sensitivity to perturbations under non-equilibrium community dynamics.

Area of Science:

• Ecology and ecological modeling
• Community dynamics
• Time-series analysis in ecology

Background:

• Effective ecological management necessitates rapid identification of species vulnerable to environmental changes.
• Traditional approaches focusing on equilibrium recovery limit understanding of species responses during fluctuating abundances.
• Current population monitoring often relies on abundance or rate of change, potentially missing key sensitive species.

Purpose of the Study:

• To introduce novel data-driven methods for ranking species sensitivity to perturbations using time-varying ecological data.
• To assess the utility of these methods in predicting species sensitivity order from time-series data.
• To identify characteristics of sensitive species beyond traditional monitoring metrics.

Main Methods:

• Development of two data-driven approaches: expected sensitivity and eigenvector rankings.
• Utilizing the time-varying Jacobian matrix to derive species sensitivity metrics.
• Application of methods to synthetic population dynamics models and two empirical time-series datasets.

Main Results:

• The proposed methods successfully infer and rank species sensitivities from time-series data.
• Species identified as most sensitive do not consistently exhibit the most rapid abundance changes or lowest population sizes.
• Sensitive species were found to be more challenging to forecast in empirical datasets.

Conclusions:

• Novel methods provide a more accurate way to identify species sensitivity in ecological communities.
• Rethinking species monitoring criteria beyond abundance and fluctuation is crucial for effective management.
• Integrating species interaction data through these new approaches can enhance the management of ecological communities, particularly those in non-equilibrium states.