The role of biotic and abiotic factors in exotic species replacement
Bibliographic record
Abstract
Biological invasions are a major component of global environmental change and are increasing in frequency. Most community-level impact studies of invasions are concerned with interactions between exotic and native species. However, interactions among exotic species are becoming increasingly common, potentially altering their respective impacts on invaded ecosystems. This research identifies one possible outcome of such interactions: the growing phenomenon of 'exotic species replacement', whereby a newly arriving exotic species surpasses the abundance of a functionally similar incumbent exotic species. This phenomenon is explored here using two Eurasian dreissenid bivalves that invaded the St. Lawrence River in the early 1990s, the zebra mussel (Dreissena polymorpha) and quagga mussel (D. bugensis). I review the general replacement pattern of the zebra mussel by its congener, and examine environmental factors and life history traits that mediate this replacement in a navigational canal connected to the river. Since the 1990s, quagga mussels have replaced the zebra mussel as the dominant dreissenid mussel, but the contemporary adult distribution and abundance is spatially structured: quagga mussels dominate the deep zone, while zebra mussels remain common in the shallow margins of the canal. Field surveys and in situ field experiments are used to examine the relative importance of pre-settlement, settlement and post-settlement processes, in the context of life history trade-offs and environmental gradients, in determining patterns of adult mussel distribution and abundance. Despite the potential importance of larval supply and behavior, including substrate selection at settlement, none of these processes determined adult dreissenid distribution and abundance. Rather, the main determinants were post-recruitment processes involving condition-specific competition. Adult zebra mussels generally had lower body condition, growth, shell length, and survivorship than quagga mussels, and these differences were most pronounced in the deep zone of the canal. This case highlights the importance of subtle differences in life history and abiotic tolerances among closely-related species in understanding changing patterns of distribution and abundance in an invaded community.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".