Consistent and transient drivers of freshwater zooplankton communities
Bibliographic record
Abstract
Abstract Aim Inferences of the predominant processes that structure communities are commonly based on single ‘snapshots’ in time, which may miss temporally transient but important mechanisms. In this study, we compared key environmental and spatial drivers of zooplankton composition across multiple years to quantify shifts in their relative importance through time, and to identify any drivers of temporal change. Location Southern Ontario, Canada. Taxon Zooplankton. Methods Zooplankton were collected from 29 lakes in southern Ontario, Canada over four years (2013–2016). Variation partitioning was used to quantify the relative importance of independent and covarying components of local environmental conditions, regional east‐west and north‐south compositional trends, and inter‐lake geographic distance in each year. Measured environmental metrics included aspects of lake morphology and chemistry, and spatial relationships were quantified using lake latitude/longitude coordinates and Moran's Eigenvector Maps (MEMs). Redundancy analyses (RDAs) were also used to compare the influence of individual environmental and spatial variables across years. Results Most local‐scale and regional‐scale community processes were consistently important across all surveyed years, but some were less consistent. Specifically, geomorphology was always an important driver of local environmental and regional spatial community patterns. This occurred because local community composition was strongly affected by whether a lake was shallower versus deeper, and due to spatial clusters of shallow and deep lakes that produced negative spatial autocorrelation in community composition. Conversely, the individual influences of lake chemistry and spatial east‐west compositional trends were important in some years and not in others, potentially due to inter‐annual shifts in the predominant environmental variables and extreme weather events. Main conclusions A single‐year community snapshot can provide insight into consistent or slowly changing community structuring processes, such as those driven by geomorphology, but may not completely capture temporally transient mechanisms. Furthermore, snapshots collected during anomalous seasons or years may misrepresent which mechanisms are predominantly determining community composition. Future efforts to understand local and regional community drivers would therefore benefit from considering which processes are likely temporally ‘consistent’ versus ‘transient’, and studies with more variable components would benefit from considering or controlling for temporal shifts in their importance.
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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".