SPATIOTEMPORAL VARIATION IN ECOSYSTEMS
Bibliographic record
Abstract
Ecosystems and their components (e.g., organisms, physicochemical variables) are dynamic in space and time. This dynamism makes ecological change notoriously difficult to study and manage. This thesis therefore aims to develop new ways of using spatiotemporal information for inference and prediction. Applying theoretical and statistical concepts to patterns of spatiotemporal variation in aquatic ecosystems led to three discoveries that show promise as ecological applications. First, I show that temporal variability of an ecosystem process can be inferred from its spatial variability. This application may be the first quantitative form of the widely-used method, space-for-time substitution. Its use is supported by an analytical framework giving the conditions under which space is a good surrogate for time. Second, I demonstrate the use of spatiotemporal patterns to predict responses of variables when ecosystem fragments are connected. Connection leads to large shifts in spatiotemporal pattern and other response metrics (e.g., temporal variability) for variables showing asynchrony and concentration gradients among sites (e.g., populations). Meanwhile, these changes are minimal if variables exhibit synchrony and homogeneity across space (e.g., energetic variables). A final discovery is that temporal attributes like stability are strong predictors of persistent spatial variation – a pattern that reflects how reliably resource concentrations occur in the same locations. This finding suggests the potential of time-for-space substitution, where one or few well-resolved time series could be used to infer landscape patterns. All but one of the tested approaches were data efficient and broadly-applicable across ecosystems and ecological processes. They thus contribute new possibilities for prediction when data are scarce, as well as new perspectives on dynamics in multi-variable landscapes. Research here shows that work at the intersection of spatial and temporal pattern can strengthen the interpretation of ecosystem dynamics and, more generally, foster synthesis from populations to landscapes.
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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 | 0.001 |
| 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) | 0.222 | 0.001 |
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".