Prioritizing terrestrial invasive alien plant species for management in urban ecosystems
Bibliographic record
Abstract
Abstract Invasive alien plant species (IAPs) in urban areas can have detrimental effects on biodiversity, ecosystem services and human well‐being. Urban areas are complex social management mosaics with high land‐use diversity, complex land tenure patterns and many different stakeholder groups, some of which derive benefits from invading species. Urban conservation practitioners face complex decisions about which IAPs require management. Yet most IAPs prioritization frameworks have been designed for and implemented in natural or rural areas and are generally inadequate for guiding effective and sustainable interventions in urbanized areas. We modified an existing prioritization scheme to develop a framework for prioritizing terrestrial IAPs in urban areas which applies evidence‐based (data‐driven) and stakeholder‐based (local knowledge) assessments to score and rank alien plant species in terms of their priority for management using an objective set of criteria. The framework consists of 46 criteria, grouped into eight modules which assess invasion status, habitat requirements, biological characteristics, dispersal ability, distribution, impacts (positive and negative) and potential for control for each alien plant species under consideration. We use the city of Toronto, Canada as a case study to test our framework—a list of 50 IAPs were effectively scored and ranked in order of high to low priority for control. Species with the highest total prioritization scores were Vincetoxicum rossicum (Dog Strangling Vine), Convolvulus arvensis (Field Bindweed) and Taraxacum officinale (Common Dandelion, ranked 1, 2 and 3 respectively). Many of the identified high priority species align with those previously flagged as of management concern by conservation practitioners, but also include those that are not actively managed due to their perceived lower ecological impacts. These species still require high resource investment for other objectives such as aesthetics. This highlights the complexity of alien plant species management in urban areas. Synthesis and applications . Prioritizing invasive alien plants for management in urban areas is particularly challenging due to often conflicting ecological, economic and social objectives. We use available evidence and local stakeholder knowledge to develop an objective and systematic prioritization tool which can assist conservation practitioners in selecting priority species for management action in complex urban landscapes.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".