How to Identify Priority Sites for Invasive Alien Species Policy and Management

Abstract

Aim

Identifying priority species and introduction pathways has long been a goal of national and international policy for reducing and mitigating the impacts of invasive alien species (IAS). Although identifying priority sites for invasion management is included within Target 6 of the Kunming–Montreal Global Biodiversity Framework, methods for doing so that capture both site sensitivity (i.e., the level of biodiversity value) and susceptibility to invasion have received little attention. Here we describe and implement a data-driven approach to priority site identification that integrates spatial conservation planning and biodiversity modelling techniques.

Location

Australia.

Methods

We use the modelled distributions of 5113 Australian native species and 12 invasive alien insect species as a case study for demonstrating a data-driven approach for identifying priority sites for the purposes of IAS surveillance and management. The approach consists of three components, namely the identification of sensitive, susceptible and subsequently their overlap (i.e., priority sites). We also compare our approach with a proposed alternative for use as priority sites, Australia's key biodiversity area (KBA) network.

Results

Numerous sensitive sites were identified across Australia using a large and taxonomically diverse set of native species and areas of known conservation importance. Most IAS distributions had a high degree of overlap with sensitive sites, with 10 out 12 species having median site sensitivities above 0.70. We also demonstrate that, by comparison, using KBA's as priority sites can underestimate the potential threat of environmentally invasive alien insects.

Main Conclusions

Given that sites most susceptible to invasion may not be the most sensitive, implementing site-based prioritisation approaches should account for both components of priority site identification to guide IAS management and most effectively mitigate their environmental impacts. The approach demonstrated here can be applied at multiple national and sub-national scales and improve the efficiency of interventions for IAS.