ANOVATS: A subsampling-based test to detect differences among short time series in marine studies

TL;DR

The paper introduces ANOVATS, a novel subsampling-based statistical method designed to detect regional differences in short, small-sample marine time series data, overcoming limitations of classical methods and expert knowledge requirements.

Contribution

ANOVATS is a new method that detects differences among small-sample time series without spectral density estimation, suitable for marine ecological studies.

Findings

Successfully identified regional differences in zooplankton biomass data

Demonstrated effectiveness in small-sample, short-duration time series

Provides a post hoc grouping procedure for areas based on detected differences

Abstract

Assessing marine ecosystems is important for understanding the impacts of climate change and human activity, as well as for maintaining healthy oceans and ecosystems. In marine science, it is common for biologists and geologists to identify regional differences based on expert knowledge, frequently through data visualization. However, time series data collected through surveys in marine studies typically span only a few decades, limiting the applicability of classical time series methods. Additionally, without expert knowledge, detecting significant differences becomes challenging. To address these issues, we introduce ANOVATS (ANOVA for small-sample time series data), a subsampling-based method to detect regional differences in small-sample time series data with a fixed number of groups. This method bypasses the need for spectral density estimation, which requires a large number of time points in the data. Furthermore, after detecting differences in homogeneity across all areas using the ANOVATS procedure, we devised a simple ANOVATS post hoc procedure to group the areas. Finally, we demonstrate the effectiveness of our method by analyzing zooplankton biomass data collected in different strata of the North Sea, showing its ability to quantify differences in species between geographical areas without relying on prior biological or geographical knowledge.