Variation in propagule and colonization pressures following rapid human‐mediated transport: Implications for a universal assemblage‐based management model
Bibliographic record
Abstract
Recent studies have recognized the importance of propagule pressure (number of individuals) and colonization pressure (number of species) for explaining establishment success of nonindigenous species. However, the International Ballast Water Management Convention, when ratified, will require ships to satisfy a numeric discharge standard that focuses only on cumulative propagule pressure of all individuals released. Because of practical constraints, the standard does not differentiate between discharges of single vs. multiple species. The assemblage‐based approach, which uses rank‐abundance gradients to quantify and manage introduction risk, may compensate for this limitation (e.g., even gradient [relatively consistent propagule pressures among n transported species] or uneven gradient [uneven propagule pressures among n transported species]). Here we explore species abundance distributions of zooplankton during transportation in ballast water to assess variability in the structure of assemblages, with implications for the potential development of an assemblage‐based management model. Specifically, we explored species abundance distributions for voyages that lasted < 24 h, those from 24 to 48 h, and those from 48 to 72 h (i.e., three time scales). Species abundance distributions within and across transit time scales were highly variable. As transport time increased, we observed a shift from uneven to even rank‐abundance gradients. Owing to variation in assemblage structure, the number of organisms necessary to quantify colonization pressure exhibited similarly strong variation within and across time scales. Our study indicates that assemblage‐based approaches to estimate introduction risk are warranted, yet the variation inherent in transported assemblages will induce substantial uncertainty within management models.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".