Surprisingly high economic costs of biological invasions in protected areas
Bibliographic record
Abstract
Abstract Biological invasions are one of the main threats to biodiversity within protected areas (PAs) worldwide. Meanwhile, the resilience of PAs to invasions remains largely unknown. Consequently, providing a better understanding of how they are impacted by invasions is critical for informing policy responses and optimally allocating resources to prevention and control strategies. Here we use the InvaCost database to address this gap from three perspectives: (i) characterizing the total reported costs of invasive alien species (IAS) in PAs; (ii) comparing mean observed costs of IAS in PAs and non-PAs; and (iii) evaluating factors affecting mean observed costs of IAS in PAs. Our results first show that, overall, the reported economic costs of IAS in PAs amounted to US$ 22.24 billion between 1975 and 2020, of which US$ 930.61 million were observed costs (already incurred) and US$ 21.31 billion were potential costs (extrapolated or predicted). Expectedly, most of the observed costs were reported for management (73%) but damages were still much higher than expected for PAs (24%); in addition, the vast majority of management costs were reported for reactive, post-invasion actions (84% of management costs, focused on eradication and control). Second, differences between costs in PAs and non-PAs varied among continents and environments. We found significantly higher IAS costs in terrestrial PA environments compared to non-PAs, while regionally, Europe incurred higher costs in PAs and Africa and Temperate Asia incurred higher costs in non-PAs. Third, characterization of drivers of IAS costs within PAs showed an effect of environments (higher costs in terrestrial environments), continents (higher in Africa and South America), taxa (higher in invertebrates and vertebrates than plants) and Human Development Index (higher in more developed countries). Globally, our findings indicate that, counterintuitively, PAs are subject to very high costs from biological invasions. This highlights the need for more resources to be invested in the management of IAS to achieve the role of PAs in ensuring the long term conservation of nature. Accordingly, more spatially-balanced and integrative studies involving both scientists and stakeholders are required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".