Estimating Ecosystem Resilience From Noisy Observational Data

Area of Science:

• Ecology
• Environmental Science
• Data Science

Background:

• Ecosystem resilience, the capacity to recover from disturbances, is vital for persistence under global change.
• Temporal autocorrelation (TAC) of ecosystem states is a key indicator of declining resilience and proximity to tipping points.
• The impact of measurement noise on TAC-based resilience assessments is not well understood.

Purpose of the Study:

• To investigate how measurement noise affects the calculation of temporal autocorrelation (TAC) for ecosystem resilience.
• To determine the extent to which noise influences the accuracy of resilience decline detection.
• To provide guidance for more robust resilience assessments in the face of noisy data.

Main Methods:

• Mathematical derivations to theoretically assess noise effects on TAC.
• Idealized simulation experiments with controlled noise levels.
• Analysis of remote sensing datasets with varying degrees of measurement noise.

Main Results:

• TAC estimates are systematically lower with increasing measurement noise.
• The degree of underestimation depends on noise levels, observational frequency, and disturbance intensity.
• Higher temporal resolution and greater disturbance intensity improve TAC accuracy under noise.

Conclusions:

• Measurement noise can bias resilience trends and generate false early warning signals.
• Increasing observational temporal resolution and employing advanced data processing can mitigate noise impacts.
• Accurate assessment of global ecosystem resilience requires careful consideration and management of measurement noise.