Harnessing Trait Evolution to Predict Economic Costs of Biological Invasions

Abstract

Biological invasions cause multi-trillion-dollar impacts worldwide. However, the development of approaches to predict drivers and magnitudes of economic costs remain limited. The use of fitness-relevant traits offers a promising, yet neglected, avenue to close this gap. Certain traits acquired during evolutionary history predispose species to succeed in non-native regions and determine variation in impact within and among invasive alien species. Invader’s performance can also rapidly be optimized via natural selection and phenotypic plasticity once exposed to the newly invaded environmental conditions. Given that invader impacts are increasingly viewed through an economic lens, this generates a trait-mediated component of economic impacts that can be quantified through individual traits and the synergistic effects across multiple traits. We discuss these new concepts and highlight emerging transdisciplinary avenues to quantify invasion costs from species traits, and the key roles that big data, museum collections, and machine learning approaches are expected to play.

Keywords: evolutionary background; invasion front; invasive alien species; monetary impact; phenotypic plasticity