Community disruption in small biogenic habitats: A coastal invader overcomes habitat complexity to alter community structure
Bibliographic record
Abstract
Non-indigenous species are often identified as threats to native species and communities. Yet, the mechanisms that enable many of these invaders to thrive and alter their newly invaded habitats are still not fully understood. This applies to habitats such as widespread sedimentary shorelines characterized by the presence of scattered biogenic clumps of blue mussels (Mytilus edulis) structurally more complex than bare sediments. In Atlantic Canada, some of these shorelines are numerically dominated by native mud crabs (Dyspanopeus sayi) but have been gradually invaded by the European green crab (Carcinus maenas). This study describes between-habitat (mussel clump vs. bare sediment) differences in density and diversity of invertebrates. It also tests the impact of juvenile green crabs in comparison to native mud crabs using two approaches: First, measuring habitat-related differences in these crabs' feeding rates on a common prey (soft-shell clams, Mya arenaria). Second, measuring their influence on invertebrate communities associated with mussel clumps. The results show that mussel clumps hold higher invertebrate density and diversity than surrounding sedimentary bottoms. In the laboratory, the feeding rates of native mud crabs were dependent on the type of habitat (sand flat > mussel clump), whereas those of green crabs were significantly higher and unrelated to the habitat in which predation occurred. In field experiments, juvenile green crabs were also the only predators that changed community structure in the mussel clump habitat. These results indicate that green crabs can cause a significant impact on native species and communities. Moreover, they suggest that the ability of this species to overcome the refuge provided by complex biogenic habitats for prey may represent an unexplored mechanism to explain this invader's expansion here and elsewhere.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".