Functional zoning of biodiversity profiles

TL;DR

This paper introduces a novel method for spatially clustering biodiversity profiles using functional data analysis and penalized model-based clustering, providing a comprehensive view of biodiversity variation for conservation and management.

Contribution

It develops a functional zoning approach for biodiversity profiles, capturing their complexity and enabling spatial clustering beyond traditional single-index measures.

Findings

Effective spatial clustering of biodiversity profiles demonstrated on Harvard Forest data.

Reveals complex spatial patterns in biodiversity that single indices may miss.

Supports policy-making for conservation and resource management.

Abstract

Spatial mapping of biodiversity is crucial to investigate spatial variations in natural communities. Several indices have been proposed in the literature to represent biodiversity as a single statistic. However, these indices only provide information on individual dimensions of biodiversity, thus failing to grasp its complexity comprehensively. Consequently, relying solely on these single indices can lead to misleading conclusions about the actual state of biodiversity. In this work, we focus on biodiversity profiles, which provide a more flexible framework to express biodiversity through non-negative and convex curves, which can be analyzed by means of functional data analysis. By treating the whole curves as single entities, we propose to achieve a functional zoning of the region of interest by means of a penalized model-based clustering procedure. This provides a spatial clustering of the biodiversity profiles, which is useful for policy-makers both for conserving and managing natural resources and revealing patterns of interest. Our approach is discussed through the analysis of Harvard Forest Data, which provides information on the spatial distribution of woody stems within a plot of the Harvard Forest.