Biometric conversion factors as a unifying platform for comparative assessment of invasive freshwater bivalves
Bibliographic record
Abstract
Abstract Invasive bivalves continue to spread and negatively impact freshwater ecosystems worldwide. As different metrics for body size and biomass are frequently used within the literature to standardise bivalve‐related ecological impacts (e.g. respiration and filtration rates), the lack of broadly applicable conversion equations currently hinders reliable comparison across bivalve populations. To facilitate improved comparative assessment among studies originating from disparate geographical locations, we report body size and biomass conversion equations for six invasive freshwater bivalves (or species complex members) worldwide: Corbicula fluminea, C. largillierti, Dreissena bugensis, D. polymorpha, Limnoperna fortunei and Sinanodonta woodiana, and tested the reliability (i.e. precision and accuracy) of these equations. Body size (length, width and height) and biomass metrics of living‐weight (LW), wet‐weight (WW), dry‐weight (DW), dry shell‐weight (SW), shell free dry‐weight (SFDW) and ash‐free dry‐weight (AFDW) were collected from a total of 44 bivalve populations located in Asia, the Americas and Europe. Relationships between body size and individual biomass metrics, as well as proportional weight‐to‐weight conversion factors, were determined. For most species, although inherent variation existed between sampled populations, body size directional measurements were found to be good predictors of all biomass metrics (e.g. length to LW, WW, SW or DW: R2 = 0.82–0.96), with moderate to high accuracy for mean absolute error (MAE): ±9.14%–24.19%. Similarly, narrow 95% confidence limits and low MAE were observed for most proportional biomass relationships, indicating high reliability for the calculated conversion factors (e.g. LW to AFDW; CI range: 0.7–2.0, MAE: ±0.7%–2.0%). Synthesis and applications. Our derived biomass prediction equations can be used to rapidly estimate the biologically active biomass of the assessed species, based on simpler biomass or body size measurements for a wide range of situations globally. This allows for the calculation of approximate average indicators that, when combined with density data, can be used to estimate biomass per geographical unit‐area and contribute to quantification of population‐level effects. These general equations will support meta‐analyses, and allow for comparative assessment of historic and contemporary data. Overall, these equations will enable conservation managers to better understand and predict ecological impacts of these bivalves.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".