A study of green crab (Carcinus maenas) interactions, cannibalism, and a first approach to model the effects of harvesting on its populations
Bibliographic record
Abstract
Green crabs (Carcinus maenas) have been extensively studied because of the negative impacts that they have on the ecosystems that they invade. However, there are still substantial gaps of knowledge about their interactions and population dynamics. As green crabs continue to invade new locations, it is important to gain a deeper understanding of these subject areas in order to prevent or mitigate further introductions or spread. This thesis aims to address these knowledge gaps by focusing on two main topics: (1) green crab predator-prey interactions with smaller conspecifics and a native counterpart, the mud crab (Dyspanopeus sayi), and (2) a first attempt using a model to predict green crab population dynamics and the potential effects of a removal program.\nFirst, I analysed long-term observational beach-seine data collected from the southern Gulf of St. Lawrence, and found that there was a negative association between native mud crabs and green crabs in this area. Then I used laboratory experiments to examine their predator-prey interactions and assessed the influence of habitat complexity on the outcome of these interactions. I found that green crab predators consumed almost twice as many mud crabs compared to juvenile green crabs in the two less structured habitats (no substrate or sandy substrate), but predation rates were statistically similar in oyster bed habitat. This study found that mud crab mortality was significantly affected by habitat type, whereas green crab mortality was not. I then focused on green crab cannibalism by adults on juveniles with similar laboratory experiments. In this study, I included habitat types that represented a wider range of structural complexity, and found that cannibalism rates declined with increasing habitat complexity. I also conducted field inclusion experiments that gathered similar results, though the differences were not significant.\nI identified knowledge gaps and areas of future research by gathering all the available information about green crab life stages. This population information was used to build a relatively simple stage-based population matrix model for green crabs. The outcome of the model estimated that a theoretical green crab population could increase by approximately 43%. The outcome of this model also suggested that even if adult survival is set to zero (representing intensive trapping and effective removal of adults), the population was still able to grow because numbers in the other life stages were abundant enough to feed and maintain the population growth rate. These results suggests that current removal programs that catch mostly adults have little-to-no effect on the population growth rate, indicating that harvesting alone is unlikely to result in a reduction of annual green crab abundance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".