Non-destructive biomass estimation for eelgrass (Zostera marina): Allometric and percent cover-biomass relationships vary with environmental conditions
Bibliographic record
Abstract
Estimating plant biomass reliably over large areas while minimizing impacts on sampled habitats is an important goal in plant ecology. Often, this is accomplished by first using a small number of harvested plants to quantify the relationship between plant biomass and less destructive predictor variables (e.g., height, cover), and then applying this relationship across larger spatial scales. However, the influence of environmental conditions on these relationships is often poorly understood. Here, we assess the impact of environmental variability on two biomass estimation functions for the seagrass Zostera marina in Atlantic Canada: the allometric leaf length-weight relationship and the relationship between percent cover and above-ground biomass (AGBM). First, we determined allometric and cover-AGBM regression relationships at the regional level using data from all sites pooled. We then investigated whether these models could be improved by including a site group covariate based on principal component analysis of site-level environmental data. At the regional level, allometric and cover-biomass models were both strongly significant, although uncertainty was high in the cover-AGBM model. Both models improved markedly when environmental variability (i.e., site group) was included: in warm, shallow conditions, eelgrass leaves were lighter for a given length, and AGBM increased at a slower curvilinear rate with percent cover. This indicates that environmental effects on eelgrass morphological traits not typically included in biomass models (e.g., leaf thickness, rigidity) can be important. Our study suggests that environmental effects on eelgrass biomass models should be considered, particularly when highly accurate estimates with low uncertainty are required. • Non-destructive biomass estimates for plants rely on indirect estimation functions. • Allometric (leaf length-weight) and percent cover-biomass functions are often used. • Eelgrass leaf allometry and cover-biomass functions varied with environment. • Warm, shallow sites had lighter leaves per length, slower biomass gain per cover. • Environmental covariates can reduce uncertainty, bias in seagrass biomass 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".