Strength in Numbers: Combining Multi-Source Remotely Sensed Data to Model Plant Invasions in Coastal Dune Ecosystems

Marco Malavasi,Vojtěch Barták,Alicia Teresa Rosario Acosta,Tommaso Jucker,Manuele Bazzichetto,Maria Laura Carranza

doi:10.3390/rs11030275

Abstract

A common feature of most theories of invasion ecology is that the extent and intensity of invasions is driven by a combination of drivers, which can be grouped into three main factors: propagule pressure (P), abiotic drivers (A) and biotic interactions (B). However, teasing apart the relative contribution of P, A and B on Invasive Alien Species (IAS) distributions is typically hampered by a lack of data. We focused on Mediterranean coastal dunes as a model system to test the ability of a combination of multi-source Remote Sensing (RS) data to characterize the distribution of five IAS. Using generalized linear models, we explored and ranked correlates of P, A and B derived from high-resolution optical imagery and three-dimensional (3D) topographic models obtained from LiDAR, along two coastal systems in Central Italy (Lazio and Molise Regions). Predictors from all three factors contributed significantly to explaining the presence of IAS, but their relative importance varied among the two Regions, supporting previous studies suggesting that invasion is a context-dependent process. The use of RS data allowed us to characterize the distribution of IAS across broad, regional scales and to identify coastal sectors that are most likely to be invaded in the future.

Highlights

Invasive alien species (IAS) have become a global conservation issue, constituting a major threat to biodiversity and requiring costly management programs to control their spread [1,2,3]
We modeled the occurrence of the five IAS using a binomial generalized linear model (GLM) with logit link function using the PAB variables described above as model predictors
We modeled the occurrence of five invasive plant species in Mediterranean coastal dune ecosystems in relation to a set of predictors pertaining to PAB factors and derived from multi-source remotely sensed data

Summary

Introduction

Invasive alien species (IAS) have become a global conservation issue, constituting a major threat to biodiversity and requiring costly management programs to control their spread [1,2,3]. Successful invasions are relatively rare, with only around 1 in 10 introduced species becoming established and only 10% of these having a sizable impact on the communities they invade [4]. This suggests that in many cases the most effective solution to mitigating the impact of IAS lies in their early detection and rapid intervention [5]. Numerous hypotheses have been put forward to explain why a given species may (or may not) become invasive outside of its native range [6] These hypotheses largely converge on the fact that the outcome of an invasion event is determined by a combination of environmental drivers and biotic interactions that are typically context dependent [7,8]. Catford et al [7] proposed an integrative framework for predicting the extent and intensity of invasions based on a combination of drivers which can be grouped into three main factors: propagule pressure (P), abiotic drivers (A) and biotic interactions (B) (hereon PAB framework)

Objectives

Methods

Results

Discussion

Conclusion