Why non-native grasses pose a critical emerging threat to biodiversity conservation, habitat connectivity and agricultural production in multifunctional rural landscapes
Bibliographic record
Abstract
(Uploaded by Plazi for the IPBES Invasive Alien Species Assessment) Methods We first establish a simple socio-ecological model that classifies different rural landscape elements within typical MRLs based on their joint conservation and agro-economic value. We then quantify the impacts of eight Australian NNGs (Andropogon gayanus, Cenchrus ciliaris, Eragrostis curvula, Hyparrhenia hirta, Nassella neesiana, Nassella trichotoma, Phalaris aquatica and Urochloa mutica) on different landscape elements and then classify and describe the socio-ecological transformations that result at the MRL scale. Results Our data indicate that two broad classes of NNGs exist. The first reduces both conservation and agro-economic value ('co-degrading' species) of invaded landscapes, while the second improves agroeconomic value at the expense of conservation value ('trade-off' species). Crucially, however, both classes cause hardening of the landscape matrix, agricultural intensification, reduced habitat connectivity, and the loss of multi-value land use types that are vital for landscape conservation. Conclusions NNGs drive socio-ecological transformations that pose a growing threat to landscape-scale connectivity and conservation initiatives in Australia and globally. There is an urgent need for further research into the impacts of NNGs on habitat connectivity and biodiversity within multifunctional landscapes, and the socio-ecological goals that can be achieved when landscape transformation and degradation by these species is unavoidable.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".