The role of short‐term temperature variability and light in shaping the phenology and characteristics of seagrass beds
Bibliographic record
Abstract
Abstract Seagrass beds inhabit highly heterogeneous temperature regimes that characterize the marine nearshore. Temperature directly influences seagrasses and also provides indirect information on other ecologically relevant environmental variables. Multiple temperature processes operate on seasonal and sub‐seasonal timescales (i.e., hours to months) and include variation from seasonal air–sea heat fluxes, advective heat transport from upwelling and tidal circulation, and daily heating and cooling of shallow waters. Despite this, seagrass–temperature studies typically only examine a single isolated temperature process, often seasonal heating/cooling or marine heatwaves. Furthermore, elucidation of relationships between different short‐term temperature processes and seagrass metrics could provide insights into biologically relevant temperature metrics for seagrasses. Here, we examine the effects of multiple short‐term temperature processes on Zostera marina beds in Atlantic Canada, by describing the seasonal phenology of bed characteristics, plant morphology, and physiology across different temperature regimes and by identifying relationships between different temperature and seagrass metrics. We also include water depth as a proxy for light availability. Four distinct short‐term temperature processes (median temperature, growing degree day [heat accumulation], daily temperature range, and time in the optimal temperature range [5–23°C]) were used to categorize temperature regimes across our study sites as warm and highly variable, or cool and less variable. We found that both temperature regime and light availability were important for leaf area index (LAI) and shoot density, which both decreased with increasing depth yet had the strongest seasonal differences and maximum rates of increase (shoot density) or lowest values and maximum rates of decrease (LAI) in warm and highly variable temperature regimes. These temperature regimes were also associated with seasonal patterns in number of leaves, reduced leaf lengths and number of leaves per shoot, and thinner rhizomes with less carbohydrate storage. Generalized additive models (GAMs) for each seagrass metric using the four temperature processes indicated that while median temperature displayed expected relationships with almost every seagrass metric, inclusion of other temperature processes revealed additional insights not evident from median temperature alone. Our study shows that different sources of short‐term temperature variation influence seagrass properties and that accounting for these will improve our understanding of the temperature–seagrass interaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".