Spatio-temporal Functional Regression on Paleo-ecological Data

TL;DR

This paper introduces a spatio-temporal functional regression model to analyze how climate variables like temperature and precipitation influence genetic diversity in European beech forests, accounting for spatial dependence.

Contribution

It develops a novel linear functional regression approach with bilinear interactions and spatial dependence modeling for paleo-ecological climate-genetics data.

Findings

Model effectively captures climate-genetic relationships.

Accounts for spatial dependence in georeferenced data.

Provides a framework for ecological climate impact studies.

Abstract

The influence of climate on biodiversity is an important ecological question. Various theories try to link climate change to allelic richness and therefore to predict the impact of global warming on genetic diversity. We model the relationship between genetic diversity in the European beech forests and curves of temperature and precipitation reconstructed from pollen databases. Our model links the genetic measure to the climate curves through a linear functional regression. The interaction in climate variables is assumed to be bilinear. Since the data are georeferenced, our methodology accounts for the spatial dependence among the observations. The practical issues of these extensions are discussed.