# Generalized Mechanism Model for Ecosystem Hysteresis

## TL;DR

The paper introduces a new model to predict and understand sudden ecosystem changes caused by disturbances, using feedback mechanisms and critical tipping points.

## Contribution

A generalized mechanism model is proposed to quantify feedback strengths and predict irreversible ecosystem state transitions.

## Key findings

- A dimensionless critical constant was identified to determine if hysteresis occurs.
- The model captures forward and backward hysteresis trajectories across ecological scales.
- Tests on lakes, grasslands, and pitcher-plant leaves showed alignment with empirical data.

## Abstract

Ecosystem hysteresis, the occurrence of catastrophic transitions due to external disturbances, is a prevalent phenomenon in dynamic ecosystems. Understanding hysteresis is essential for predicting ecosystem responses and developing effective restoration strategies. However, the intrinsic dynamics quantifying the positive‐negative feedback in driving hysteresis and its intensity remain undisclosed. We introduce a quantitative framework to address hysteresis by assessing ecosystem states and feedback loops, which diverges from prior phenomenological theories of hysteresis. Employing this framework, a generalized mechanism model is proposed to estimate positive‐negative feedback strengths and defines the irreversible potential of hysteresis to determine its intensity. We identify a dimensionless critical constant that indicates whether hysteresis occurs. The model effectively captures both forward and backward trajectories of hysteresis across various ecological scales. The direction of state transitions may be predicted using unidirectional data. Our findings offer a universal framework for predicting and mitigating catastrophic state shifts of ecosystems.

A simplified model examines ecosystem transitions into alternative states after disturbances by leveraging positive and negative feedbacks. It identifies critical tipping points and quantifies hysteresis. Tests conducted on lakes, grasslands, and pitcher‐plant leaves align closely with empirical data and enable the prediction of recovery trajectories from unidirectional observational records.