How to Identify Priority Sites for Invasive Alien Species Policy and Management
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".