Climate Dynamics, Invader Fitness, and Ecosystem Resistance in an Invasion-Factor Framework
Bibliographic record
Abstract
As researchers and land managers increasingly seek to understand plant invasions and the external (climate) and internal (plant genetics) conditions that govern the process, new insight is helping to answer the elusive question of what makes some invasions successful and others not. Plant invasion success or failure is based on a combination of evolutionary and ecological processes. Abiotic (e.g., climate) and biotic (e.g., plant competition) conditions in the environment and plant genetics (e.g., fitness) combine in either decreasing or increasing invasion, yet it has proven challenging to know exactly which of these conditions leads to success for a given species, even when a wealth of empirical data is available. Further, current regional distribution models for invasive plant species rarely consider biotic and fitness interactions, instead focusing primarily on abiotic conditions. The crucial role of all three factors (climate dynamics, invader fitness, and ecosystem resistance) must not be ignored. Here we construct a three-factor invasion framework from which we develop conceptual models using empirical studies for yellow starthistle, nonnative common reed, and musk thistle, three dissimilar but commonly occurring invasive plant species in North America. We identify how components of the invasion process—rapid population increase, established local dominance, and rapid range expansion—are influenced by ecosystem resistance, invader fitness, and/or climate dynamics, a set of broadly defined factors for each of the three invasive plant species. Our framework can be used to (1) establish research priorities, (2) address gaps in theoretical understanding, and (3) identify invasion process components that can be targeted to improve management. Building on previous models, our unifying framework, which can be used for assessing any invasive plant species having sufficient empirical data, simultaneously shows the influence of ecosystem resistance, invader fitness, and climate dynamics factors on the invasion process.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".