# Quantitative analysis of self-organized patterns in ombrotrophic peatlands

## TL;DR

This paper uses numerical modeling to analyze and classify self-organized vegetation patterns in peatlands, focusing on how nutrient levels influence pattern transitions and stability under climate stress.

## Contribution

It introduces a systematic quantitative method to classify peatland vegetation patterns based on statistical analysis of clusters, linking pattern transitions to nutrient availability.

## Key findings

- Identified transition from Sphagnum to vascular plant patterns with increased nutrients.
- Developed a quantitative framework for pattern stability analysis under drought conditions.
- Characterized pattern stability and transitions in peatlands under climate stress.

## Abstract

We numerically investigate a diffusion-reaction model of an ombrotrophic peatland implementing a Turing instability relying on nutrient accumulation. We propose a systematic and quantitative sorting of the vegetation patterns, based on the statistical analysis of the numbers and filling factor of clusters of both \textit{Sphagnum} mosses and vascular plants. In particular, we define the transition from \textit{Sphagnum}-percolating to vascular plant-percolating patterns as the nutrient availability is increased. Our pattern sorting allows us to characterize the peatland pattern stability under climate stress, including strong drought.