Invasive species and their impacts on agri-ecosystems: issues and solutions for restoring ecosystem processes
Bibliographic record
Abstract
Humans are the most invasive of vertebrates and they have taken many plants and animals with them to colonise new environments. This has been particularly so in Australasia, where Laurasian and domesticated taxa have collided with ancient Gondwanan ecosystems isolated since the Eocene Epoch. Many plants and animals that humans introduced benefited from their pre-adaptation to their new environments and some became invasive, damaging the biodiversity and agricultural value of the invaded ecosystems. The invasion of non-native organisms is accelerating with human population growth and globalisation. Expansion of trade has seen increases in purposeful and accidental introductions, and their negative impacts are regarded as second only to activities associated with human population growth. Here, the theoretical processes, economic and environmental costs of invasive alien species (i.e. weeds and vertebrate pests) are outlined. However, defining the problem is only one side of the coin. We review some theoretical underpinnings of invasive species science and management, and discuss hypotheses to explain successful biological invasions. We consider desired restoration states and outline a practical working framework for managing invasive plants and animals to restore, regenerate and revegetate invaded Australasian ecosystems.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".