Predicting the occurrence of persistent hotspots in ecosystem variables
Bibliographic record
Abstract
Ecological resources and services (e.g. organisms, nutrient cycling) are distributed heterogeneously across landscapes. While spatial variation has been studied extensively, the pattern of hotspots and coolspots persisting over time – called persistent spatial variation (PSV) – has not. Yet this pattern imparts key information to managers about whether resources will be found consistently in certain locations or vary unpredictably. Anticipating whether an ecosystem variable will display PSV is thus a valuable prospect. We tested the ability of attributes of variables (e.g. niche breadth, abundance, temporal scale) to predict the occurrence of PSV. Using a new measure of PSV based on the F‐value of analysis of variance, we were able to 1) decompose the pattern of persistent hotspots into spatial and temporal components – ‘spatial variation’ of site mean values and ‘stability’ of time series at each site – and 2) identify predictors of these patterns in temperate lakes and tropical coastal rock pools. We found PSV to be highly predictable ( R2 = up to 0.80) from an estimate of stability taken at a single site, as well as from other factors related to stability. These factors included whether the variable was environmental (stable, slow) or was an aggregate of other variables (stabilized by statistical averaging). Species properties like niche position and abundance were modest predictors because they correlated with PSV components of site occupancy, spatial variation and stability. We conclude that PSV and the distribution of resources in space and time might be predicted from simple temporal indicators (e.g. stability at a single location) when data are scarce.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".