Biological invasions as burdens to primary economic sectors.
Bibliographic record
Abstract
<title>Abstract</title> Many human-introduced alien species economically impact essential industries worldwide. Management prioritization and coordination efforts towards biological invasions are hampered by a lack of comprehensive quantification of costs to key economic activity sectors. Here, we quantify and predict global invasion costs to seven major sectors and unravel the introduction pathways of species causing these costs — focusing mainly on resource-based agriculture, fishery and forestry industries. From 1970 to 2020, costs reported in the InvaCost database as pertaining to <italic>Agriculture, Fisheries</italic>, and <italic>Forestry</italic> totaled $509 bn, $1.3 bn, and $134 bn, respectively (in 2017 United States dollars). Pathways of costly species were diverse, arising predominantly from cultural and agricultural activities, through unintentional contaminants with trade, and often impacted different sectors than those for which species were initially introduced. Costs to <italic>Agriculture</italic> were pervasive and greatest in at least 37% (n = 46/123) of the countries assessed, with the United States accumulating the greatest costs for resource-based industries ($365 bn), followed by China ($101 bn), and Australia ($36 bn). We further identified 19 countries highly economically reliant on <italic>Agriculture</italic>, <italic>Fisheries</italic>, and <italic>Forestry</italic> that are experiencing massive economic impacts from biological invasions, especially in the Global South. Based on an extrapolation to fill cost data gaps, we estimated total global costs ranging at least from $517 − 1,400 bn for <italic>Agriculture</italic>, $5.7–6.5 bn for <italic>Fisheries</italic>, and $142–768 bn for <italic>Forestry</italic>, evidencing substantial underreporting in the <italic>Forestry</italic> sector in particular. Burgeoning global invasion costs challenge sustainable development and urge for improved management action to reduce future impacts on industry.
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 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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.083 |
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; both teacher heads agree on what is shown here.
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".