Bibliographic record
Abstract
Following the establishment of a non-native species, there is often speculation about the potential impacts to the native ecosystem. While these early predictions may be necessary for management, they are often based on a general understanding of invasion ecology rather than context-specific research. The unique nature of each introduction event means these generalizations are prone to over- or under-estimating invasive species impacts. This thesis predicts the impacts of invasive marine true crabs (infraorder Brachyura), with a focus on the invasive European green crab (Carcinus maenas), using both general ‘rules of thumb’ and context-specific research. In Chapter 2, I conduct a meta-analysis to demonstrate that while native and invasive crabs typically have a similar overall impact on prey species, some combinations of prey type and experimental design can favour invasive crabs. In Chapter 3, I examine the geographical variability of green crab impacts worldwide. Using green crabs collected from invasive (South Africa and Canada) and native (Northern Ireland) populations, I conduct a comparative functional response experiment to show how the foraging behaviour of an invasive species varies among regions. In Chapter 4, I use an enclosure experiment to determine how the impact of green crabs on eelgrass (Zostera marina) ecosystems changes with crab density, and conclude that there is the potential for extensive loss of habitat-forming eelgrass in the presence of high densities of green crabs. In Chapter 5, I explore the issue of site-level variability in the abundance, and therefore potential impact, of green crabs on the west coast of Vancouver Island, British Columbia. I develop a species distribution model to identify small-scale biotic and abiotic predictors of ‘hyper-abundant’ populations of green crab. The thesis as a whole explores the generalizations often used to predict invasive impacts and prioritize impact mitigation efforts. I find that, for green crabs, generalizations that rely on the origin or specific invasion history of an invasive species are prone to over-estimating impact. However, measures of density or abundance, paired with an understanding of context-specific behaviours, are more likely to produce reliable impact predictions for these invasive species.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".